System, method, and apparatus for automated metagenome constituent detection

Abstract

A method for detecting metagenome constituents in a sample, and related system, are disclosed. The process involves the addition of a synthetic spike sample to a nucleic acid sample. The nucleic acid sample is sequenced using next-generation sequencing techniques, which produces sequences. These sequences are then preprocessed. The method proceeds by engaging with a database to extract various nucleic acid sequences that are characteristic of manufacturing processes. These specific sequences are then filtered out from the collection of sequences. In a similar vein, the database is utilized once more to obtain a series of environmental nucleic acid sequences, which are also removed from the sequences. A query to the database is conducted to procure a set of metagenome nucleic acid sequences. The detection of metagenome constituents is achieved through a comparison of these metagenome sequences against the sequences, facilitating the identification of the presence of metagenome constituents within the sample.

Description

SYSTEM, METHOD, AND APPARATUS FOR AUTOMATEDMETAGENOME CONSTITUENT DETECTIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. provisional patent application serial no. 63 / 634,009 filed April 15, 2024. The entire content of the identified provisional patent is hereby fully incorporated herein.BACKGROUNDRelevant Field

[0002] The present disclosure relates to test-sample constituent detection. More particularly, the present disclosure relates to a system, method and apparatus for automatic identification of the presence of metagenome constituents. Metagenome constituents may comprise (adventitious) agents including viruses, bacteria, and fungi. It can also cover additional taxa, like protozoa. Additionally, it can mean noncontaminating entities, like beneficial bacteria.Description of Related Art

[0003] Biosafety testing is a critical component in the development and manufacturing of biologic products, ensuring that these products are free from unwanted adventitious agents such as bacteria, viruses, and fungi. Traditional methods for detecting these agents have relied on broad-spectrum detection assays, which are designed to identify a wide range of potential contaminants that may inadvertently be present in the production process.

[0004] Historically, the primary methods for adventitious agent detection in biopharmaceuticals have been in-vitro and in-vivo assays. In-vitro tests typically involve the use of indicator cell lines that are inoculated with the biologic product. These cell lines are selected based on their susceptibility to a broad range of viruses. The presence of viral contaminants is inferred from the observation of cytopathic effects or through the use of other indirect markers of infection. While these tests have been foundational in contamination control, they have limitations in terms of the range of contaminants they can detect and the time required to observe a biological response.

[0005] In-vivo tests, on the other hand, involve the inoculation of live animals with the biological product and the subsequent observation for signs of infection. This method is labor-intensive, ethically contentious, and limited by the types of viruses that can infect the animal model used, which may not reflect the full spectrum of potential human pathogens.

[0006] Both in-vitro and in-vivo assays have significant limitations. They are time-consuming and can fail to detect non-cytopathic agents, which do not elicit visible changes in the host cells or animals. Additionally, these methods lack specificity, as they do not directly identify the viral agent but rather infer its presence through biological responses, which may lead to false positives or negatives.

[0007] The advent of polymerase chain reaction (PCR) technology provided a more specific approach to detect known viruses by amplifying sequences unique to the viral genome. However, PCR assays require prior knowledge of the potential viruses and are not suited for the discovery of unknown or unexpected pathogens. PCR assays also require several custom nucleic acid primers per virus. These primers must be manually designed, physically synthesized, then their efficacy for testing verified in the lab before the desired testing may begin.

[0008] Next-Generation Sequencing (NGS) technologies emerged as a promising alternative offering an unbiased approach to detect a wide array of known and unknown pathogens without the need for prior knowledge of their genetic makeup. Initially, NGS data analysis for adventitious agent testing (AAT) incorporated the use of Basic Local Alignment Search Tool (BLAST) for sequence identification. However, the scalability of BLAST did not match the rapidly increasing volumes of NGS data, resulting in prolonged analysis times or incomplete analyses.

[0009] Subsequent iterations of AAT assays employed the Burrows- Wheeler Aligner Maximal Exact Match (BWA-MEM) algorithm, an improvement over BLAST in terms of speed and memory usage, but BWA-MEM similarly struggled to keep pace with the expanding size of NGS datasets and reference databases. Moreover, the precision of detection was hindered by the presence of nucleic acid signatures from the biologies manufacturing process and environmental nucleic acid, which necessitated additional data preprocessing steps to enhance the specificity and sensitivity of the assays.

[0010] Furthermore, the determination of the Limit of Detection (LOD) for these assays was based on broad categories, rather than being sample-specific, which could result in less accurate assessments of assay sensitivity.

[0011] The manual processes involved in the drafting of analysis reports and preparation of regulatory documents were also highly time-consuming and demanded a high degree of accuracy to ensure compliance and reliability of results, highlighting a need for improved efficiency in these aspects of A AT workflows.

[0012] Although NGS has revolutionized the field of pathogen detection, the challenges of handling large datasets, ensuring specificity and sensitivity of detection, and efficiently processing regulatory documentation have underscored the need for an advanced solution in the realm of biosafety testing for biologic products.

[0013] In some embodiments, a computer-readable medium (e.g., non- transitory) may have instructions stored thereon that, when executed by a processor, cause the processor to perform a method for detecting metagenome constituents in a sample. The method may involve adding a synthetic spike sample to a nucleic acid sample, sequencing the nucleic acid sample to generate a plurality of sequences, preprocessing the plurality of sequences, querying a database to retrieve manufacturing process, environmental, and metagenomic nucleic acid sequences, filtering out manufacturing process and environmental nucleic acid sequences from the plurality of sequences, and comparing the plurality of sequences with metagenome nucleic acid sequences to detect the presence of metagenome constituents. The synthetic spike sample may act as a sensitivity control, while filtering out non-relevant sequences may improve detection specificity. Comparing remaining sequences to reference metagenome sequences enables identification of microbial contaminants.

[0014] In some embodiments, the computer-readable medium is configured to preprocess the plurality of sequences obtained from sequencing the nucleic acid sample. This preprocessing may include filtering out adapter sequences that were introduced during sample preparation. Additionally, base mismatches within the plurality of sequences may be corrected using respective quality scores associated with each base.

[0015] The adventitious agent detection embodiment may compare each of the adventitious agent nucleic acid sequences to the plurality of nucleic acid sequences to detect the presence of an adventitious agent. The comparing may involve utilizing exact match algorithms to identify matches between the adventitious agent nucleic acidsequences and the plurality of sequences. In some embodiments, the comparing may additionally utilize partial match algorithms that allow identifying partial matches. The use of partial match algorithms may enable detecting adventitious agents with possible genetic variations. The exact match and partial match algorithms can allow for identifying matches while accounting for genetic variations that can occur among adventitious agents such as viruses and bacteria.

[0016] In some embodiments, the method involves generating a report that summarizes the detected presence of any metagenome constituents, including the identity and quantity of the detected constituents. This report on identified metagenome constituents may detail the type of agents found as well as abundance levels or copies per sample. The reporting functionality facilitates compilation of detection outcomes into an organized format covering pertinent details on contamination events for convenient review. Additionally, the report format itself may be configured to comply with predefined file specification requirements suited for downstream usages.

[0017] The embodiments may optionally involve adding a synthetic spike sample to the nucleic acid sample prior to sequencing. The synthetic spike sample may contain a predetermined quantity of synthetic nucleic acid sequences that correspond to and serve as representative examples of metagenome constituents potentially detectable by the system. The relative abundance and representation of these predetermined synthetic spike sequences within the resulting plurality of sequences after sequencing can reveal useful signal strength parameters regarding the detection sensitivity and limits for endogenous metagenome constituents.

[0018] In some embodiments, the system includes functionality for presenting the results of metagenome constituent detection to users through an interactive graphical user interface (GUI). This GUI enables user-friendly analysis of detection outcomes without requiring extensive technical expertise. The presentation of results may involve generating HTML-formatted reports summarizing identified metagenome constituents. These interactive reports could incorporate dynamic visual representations of the contamination data, including charts, graphics, and genomic maps, which users may explore via built-in GUI controls.SUMMARY

[0019] In some embodiments, the method involves adding a synthetic spike sample containing known nucleic acid sequences to a nucleic acid sample obtained from a cell culture or biopharmaceutical product. The combined nucleic acid sample is then sequenced using next generation sequencing technology to generate sequence reads. These reads go through preprocessing, such as quality control and adapter trimming. Manufacturing process nucleic acid sequences and environmental nucleic acid sequences are retrieved from a database and filtered out of the sample sequences to eliminate confounding signatures. The refined sample sequences are queried against a database containing metagenome constituent sequences, such as adventitious viruses, bacteria and fungi. Significant matches detect the presence of microbial contaminants that may impact product safety and quality.

[0020] The method may involve filtering out manufacturing process nucleic acid sequences from the sample sequences by k-mer matching at least one manufacturing process sequence to the sample sequences and subsequently filtering the matched manufacturing process sequences out of the sample sequences. In some embodiments, filtering out the environmental nucleic acid sequences from the sample sequences comprises k-mer matching at least one of the retrieved environmental nucleic acid sequences and then filtering the matched environmental nucleic acid sequence(s) out of the sample sequences. More specifically, this filtering process may involve extracting all possible subsequence "words" of length k from the sample sequences and querying the environmental sequence database to find identical k-mer matches that indicate common environmental contaminants unrelated to true adventitious agents. Once these exactly matching k-mers are identified, the full sequencing reads containing those flagged k-mers can be selectively removed to filter out environmental signatures before downstream adventitious agent screening. This k-mer-based matching and filtering approach focuses comparisons on genetically informative regions to accurately eliminate confounding environmental nucleic acid from the sample dataset, refining the data for enhanced specificity in subsequent detection steps.

[0021] In some embodiments, the synthetic spike sample added to the nucleic acid sample may comprise a predetermined quantity of synthetic nucleic acid sequences. These synthetic sequences are designed to correspond to and represent potential metagenome constituents that could be present as adventitious contaminants.By spiking in these representative synthetic sequences at known concentrations, the downstream detection process can gauge assay sensitivity and use the synthetic spikes as internal controls. The synthetic spikes thereby act as standardized sensitivity benchmarks, with their predetermined quantities tailoring cutoff criteria to the samplespecific detection context.

[0022] The method may also involve adding a synthetic spike sample that contains a known amount of a synthetic nucleic acid sequence corresponding to a specific metagenome constituent of interest. This allows for a sensitivity control by targeting detection towards the nucleic acid sequence from that particular constituent. The synthetic spike sample provides a reference to calibrate detection sensitivity for the intended metagenome constituent.

[0023] In some embodiments, the method may further comprise determining a signal strength parameter of the synthetic spike sample within the plurality of sequences. A signal cutoff parameter defining the threshold of detection confidence may then be determined in accordance with the measured representation level of the spiked-in sample. By calibrating sensitivity based on the performance of these internal synthetic references, customized signal thresholds can be established in a sampledependent manner to delineate positives from background noise.

[0024] In some embodiments, preprocessing the nucleic acid sample sequences involves filtering out adapter sequences that may have been introduced during initial library preparation. More specifically, adapter trimming algorithms may be applied to remove ligated adapter constructs from the ends of sequence reads, preventing spurious alignments in downstream analysis. This adapter filtering constitutes one aspect of the quality control workflow enhancing data integrity prior to the contaminant screening process. The modular nature of the preprocessing component facilitates customizable inclusion of adapter trimming to accommodate diverse sample preparation methods across detection use cases.

[0025] In some embodiments, preprocessing the sequence data may also involve correcting base mismatches or sequencing errors based on quality scores assigned to each individual base call. By leveraging these quality metrics to fix low- confidence base reads, data accuracy can be enhanced prior to the contaminant screening process.

[0026] The method may include filtering out manufacturing process nucleic acid sequences by comparing the sample sequences to predetermined manufacturing process contaminants previously documented or stored as reference data. This approach can support the selective removal of manufacturing signatures unrelated to true adventitious agent contamination.

[0027] The method may involve filtering out the plurality of environmental nucleic acid sequences by applying a quality score threshold to exclude low-quality sequences from the plurality of sequences. In some embodiments, sequences that fall below a predetermined quality score threshold are removed to further refine the sequence dataset prior to scanning for matches against known adventitious agent signatures. By eliminating low-quality reads unlikely to represent true adventitious agents, the signal-to-noise ratio may be improved for enhanced detection specificity. The quality score thresholds applied during environmental contaminant filtration can be based on user-defined parameters or dynamically configured based on sample characteristics.

[0028] The querying of the database for the plurality of metagenome nucleic acid sequences may involve selecting k-mers based on their frequency of occurrence in a reference database of adventitious agents in some embodiments. By focusing on highly conserved genomic regions that commonly arise in adventitious organisms, the detection sensitivity and specificity for these hazardous contaminants can be enhanced. This targeted approach allows the analysis to concentrate computational resources on comparing key subsets of nucleic acid sequences particularly informative for identifying foreign microbial threats within the sample.

[0029] The method described may utilize minimizers from adventitious agent genomes to select k-mers based on their frequency of occurrence. This allows for improving the detection efficiency. Specifically, using minimizers, which are k-mers that lexicographically minimize sets of sequences sharing common subsequences, focuses the comparisons on genetically informative regions within the sequences being analyzed. By leveraging a custom database containing only adventitious agent genome sequences, rather than the entire reference sequence set, the k-mer selection can be further refined. Overall, the use of minimizers enables more precise and efficient matching during the process of detecting adventitious agents.

[0030] In some embodiments, the metagenome constituent detection process may utilize an exact string matching algorithm when comparing the metagenome nucleic acid sequences to the preprocessed sample sequences. This approach identifies matches between subsequences that are identical between the two datasets, allowing the system to pinpoint sample sequences harboring signatures matching known metagenome constituents. Using precise matching algorithms in this comparison step can enhance the specificity of detection, while retaining the sensitivity to identify conserved genomic regions between potentially mutated adventitious strains.

[0031] The method may involve comparing each of the metagenome nucleic acid sequences to the sample sequences in a manner that identifies partial matches between them. This allows for the detection system to account for possible genetic variations in the metagenome constituents being analyzed. By permitting partial sequence alignments during the comparison process, the detection approach can be more inclusive when screening for adventitious agents that may have mutations or strain variations relative to reference database signatures. Enabling partial matching provides flexibility to recognize divergent adventitious genomes while still meeting an overall similarity threshold for designating contamination matches. This comparision approach balancing detection sensitivity and specificity through custom similarity cutoffs aids in the identification of both conserved and more variable metagenome nucleic acid signatures that may indicate microbial contamination.

[0032] The method may also involve generating a report that summarizes the identity and quantity of any detected metagenome constituents, such as adventitious agents like viruses, bacteria, or fungi. The report provides details on the type of organisms identified as present and their measured concentrations within the analyzed nucleic acid sample. By compiling this information into an organized report, the detection results can be readily reviewed and interpreted to assess sample safety and quality.

[0033] In some embodiments, the nucleic acid sample that is sequenced and analyzed for metagenome constituents is derived from a biopharmaceutical product. For example, during biopharmaceutical manufacturing, samples may be taken and tested to screen for potential adventitious agent contamination. Nucleic acids isolated from the biopharmaceutical batch serve as the input material that undergoes sequencing and metagenome constituent detection according to the method, enabling sensitive andunbiased detection of microbial threats that may have been introduced during production.

[0034] The method may involve analyzing a nucleic acid sample derived from a cell culture used in the production of a biopharmaceutical product. More specifically, in some embodiments, the nucleic acid sample that is sequenced and analyzed for the presence of adventitious agents is obtained from cell cultures utilized in the manufacture of biopharmaceutical therapies. Traces of microbial contaminants originating from these production cell cultures can end up in the final drug product, so testing intermediates from the cell cultures enables early detection and correction of contamination events. By screening cell culture-derived samples, the method can identify adventitious agent nucleic acid that may ultimately taint the safety and purity of downstream biologies.

[0035] The nucleic acid sample may be sequenced using a next-generation sequencing device, which may operate as an automated standalone instrument that applies next-generation sequencing technology such as Illumina sequencing-by- synthesis approaches to generate nucleic acid sequences data. In particular, the sequencing may leverage an Illumina sequencing platform as the underlying technology. The Illumina platform enables sequencing of the nucleic acid sample and production of raw sequencing reads that capture the composition of bases (As, Ts, Cs and Gs) within the sample material.

[0036] In some embodiments, the metagenome nucleic acid sequences queried from the database may be of an optimized length to enable detection of a wide variety of potential metagenome constituents such as viruses, bacteria, fungi and other microorganisms. By tuning the nucleotide sequence length parameters, the system aims to balance sensitivity across genetically diverse pathogens with specificity to minimize false positives. The length criteria can be adjusted to scan for signature regions conserved across broader taxonomic groupings or target narrower phylogenic branches based on application needs. Optimized sequence lengths facilitate efficient screening for both known and novel adventitious agents.

[0037] In some embodiments, the metagenome constituents that are detected may include viruses, bacteria, and fungi. Specifically, the detection method is capable of identifying the presence of at least one metagenome constituent selected from the group consisting of viruses, bacteria, and fungi. The capability to detect this range ofpotential microbial contaminants enhances the versatility and usefulness of the method for adventitious agent screening in biomanufactured products.

[0038] In some embodiments, the plurality of metagenome nucleic acid sequences comprises a plurality of minimizers. Minimizers are short sequences that representatives a family of sequences. Taxa are assigned to each of the minimizers based on a lowest common ancestor (LCA) algorithm before the minimizers are compared to the plurality of sequences from the sample. The LCA algorithm determines the most recent common ancestor shared by the minimizer and assigns it a taxonomic rank. Using the phylogenetic context provided by the LCA algorithm allows for more precise identification during the comparison to the sample sequences.

[0039] In some embodiments, the method involves assigning taxonomic classifications (taxa) to the minimizers using a Lowest Common Ancestor (LCA) algorithm. The LCA algorithm determines the most specific taxonomic rank, such as species or genus, that can be reliably assigned to each minimizer based on its level of sequence conservation across related organisms. By leveraging the phylogenetic context provided by the LCA algorithm, the taxonomic lineage assigned to each minimizer sequence can facilitate more precise identification of the metagenome constituents detected in the sample. The specificity afforded by taxonomic labeling of minimizers may thereby enhance the accuracy of detecting adventitious agents or other microbial contaminants.

[0040] In some embodiments, the method involves assigning taxonomic lineages to the matched minimizer sequences using a lowest common ancestor (LCA) algorithm. The LCA analysis determines the most specific taxon that can be reliably assigned to each sequence match based on broader genomic variability across strains. Subsequently, during the comparison between minimizers and sample sequences, this phylogenetic context provides valuable information to interpret matches and reduce false positives from incidental environmental alignments unrelated to true microbial threats. By incorporating taxonomical details on the detected sequences, the overall specificity of contamination detection may be enhanced. The parameters for utilizing taxonomic data can be customized to balance detection precision versus sensitivity for individual processes or samples.

[0041] The method may further include generating a report file that summarizes the results of comparing the metagenome nucleic acid sequences to the samplesequences. This report file may detail the taxonomic classifications assigned to any detected metagenome constituents based on a Lowest Common Ancestor (LCA) algorithm. The LCA algorithm analyzes the phylogenetic relationships between related organisms to determine the most specific taxonomic group that can be reliably assigned to each sequence match. Using this approach, the system can precisely categorize detected adventitious sequences down to the strain or species level based on how well they match reference genomes from known microbial taxa. Incorporating this phylogenetic context into the final report file enhances interpretability by providing more resolution on the types of organisms present as contaminants within the sample. The report file may conform to canonical formats like Kraken-style reports which include sequence identifiers alongside matched regions and assigned taxonomic lineages in a standardized structure. By leveraging the classification capacities of the LCA algorithm, the system can generate report files that offer rich details on the evolutionary trajectories and likelihoods of microbial contaminant matches for downstream assessment.

[0042] The method may generate a report file that summarizes the results of comparing the metagenome nucleic acid sequences to the sample sequences. This report file includes taxonomic classifications for any detected metagenome constituents, which are assigned based on a Lowest Common Ancestor (LCA) algorithm. In some embodiments, the report file follows the canonical Kraken file format for representing metagenomic taxonomic classifications, enabling seamless integration with common analysis pipelines. By formatting the contamination results into a standardized Kraken report, downstream analysis tools can readily ingest the data for further study or archival records without needing additional data transformations. The Kraken report file provides an interoperable mechanism for communicating the metagenome detection outcomes to subsequent processes.

[0043] In some embodiments, the method may further involve presenting the results of the metagenome constituent detection through a graphical user interface (GUI). The GUI can be configured to enable user interaction for facilitating analysis of the detection results without requiring extensive technical knowledge. For example, the GUI may allow users to visualize, explore and customize views of the contamination data through interactive charts, graphs, genomic maps and other intuitive graphical formats. The GUI may also let users drill down into specifics on detected organisms,adjust analysis parameters and thresholds, and collaborate with other parties in realtime while maintaining data security. Overall, the inclusion of an interactive GUI aims to make the analysis process more accessible for a broad range of end-users across disciplines and technical skill levels.

[0044] In some embodiments, the method further comprises generating an interactive report that summarizes the detected presence of any metagenome constituents. The interactive report may be in HTML format and include interactive visual representations of the data. For example, the interactive report could contain charts, graphs, or genomic maps that allow users to explore the contamination results through intuitive interfaces and selection tools. The visual elements can link back to the underlying raw analysis data to provide full transparency. Enabling dynamic and customizable visualization of metagenome detection outcomes facilitates efficient review and interpretation without requiring extensive technical expertise.

[0045] The filtering process may further include distinguishing between true positive matches indicating potential adventitious agents, and false positive matches representing incidental non-hazardous sequences. This differentiation enhances detection accuracy by applying predetermined match qualification criteria to minimize false detections unrelated to real contamination threats. By leveraging statistical evaluations and pattern recognition logic, the specificity of sequence categorization into signal or noise can be refined beyond basic match identification. Additional techniques like concurrent phylogenetic lineage analysis provide supplementary context to further validate true positive matches when filtering sample datasets prior to final adventitious agent screening.

[0046] The method may include the use of an artificial intelligence model in the act of comparing the sample sequences to the database of metagenome sequences. This artificial intelligence model can be trained on the known structures and sequences of predetermined metagenome constituents to enable it to identify potentially novel or unknown agents that are not currently present in the database. By leveraging machine learning approaches, the detection capabilities of the system can be enhanced to flag previously unencountered adventitious sequences that may emerge due to mutations or other factors. This expands the scope of identifiable threats beyond what is directly referenceable in existing sequence databases.

[0047] The method may also involve securing and tracking the integrity of data and analysis results through the use of blockchain ledger technology. In particular, embodiments of the method can incorporate the use of a blockchain to record data transactions and analysis outcomes in a secure, immutable ledger. This ledger enables the tracking of information generated throughout the detection workflow to ensure transparency and prevent tampering. By leveraging blockchain infrastructure, data security and auditability may be enhanced as the metagenome constituent detection process unfolds.

[0048] In some embodiments, the method may further include utilizing a machine learning algorithm to dynamically adjust parameters related to the detection process, such as the length of k-mers and the selection criteria for minimizers. The machine learning algorithm can analyze data over time and optimize these parameters to improve the accuracy and efficiency of detecting metagenome constituents. By automatically tuning key variables like k-mer size and rules for choosing informative minimizers from reference genomes, the machine learning approach allows the detection methodology to adaptively enhance itself based on accumulated sample data. This provides a flexible way to balance sensitivity and specificity for each nucleic acid sample.

[0049] In some embodiments, the method further comprises reviewing any generated reports summarizing the detection results against current international biosafety standards. This review process flags any sections of the report that may require additional attention or revisions to meet full compliance with the relevant regulatory standards. This ensures that the reports generated by the method adhere to the latest guidance and best practices for biosafety testing documentation and reporting. The system facilitates compliance to promote safety, reliability, and accountability across the workflows and deliverables of the adventitious agent detection process.

[0050] In some embodiments, the method may utilize an automated regulatory compliance checker to review generated reports against current international biosafety standards. This compliance checker may leverage Large Language Models to systematically evaluate reports and flag any sections needing revision to satisfy regulatory requirements. Employing an automated compliance aid can help accelerate regulatory approval and ensure generated reports consistently adhere to evolving industry norms in a rigorous manner.

[0051] The method for detecting metagenome constituents may focus specifically on detecting adventitious agents rather than broader metagenome components. In some embodiments, the plurality of metagenome nucleic acid sequences queried from the database and used in the comparison analysis may be more narrowly curated to represent a subset of sequences from adventitious viral, bacterial, fungal, or other microbial agents commonly implicated as process or product contaminants in biomanufacturing environments. By tailoring the reference sequence database to these high-risk adventitious agents rather than general metagenomic contents, the specificity and sensitivity of detection may be enhanced. This targeted approach focusing computational resources on relevant adventitious agent genomes rather than all possible metagenome constituents allows the method to balance inclusiveness with precision for contaminants of greatest concern. Thus, the plurality of metagenome sequences serving as the reference database for matching and identification could comprise adventitious agent sequences in certain embodiments or workflows.

[0052] The present disclosure relates to methods for detecting metagenome constituents in a sample. The method includes estimating an abundance of the detected metagenome constituent by applying a Bayesian re-estimation algorithm to the plurality of sequences that match the metagenome nucleic acid sequences. The Bayesian reestimation algorithm may be implemented as a post-processing step to a k-mer matching act. The act of estimating the abundance includes reassigning ambiguously classified ones of the plurality of sequences that were initially assigned to higher taxonomic levels due to a predetermined limitation of a k-mer matching act.

[0053] Estimating the abundance may include accounting for biases in the database introduced by a genome size variance and a sequence length variation contained therein. The Bayesian re-estimation algorithm may produces abundance estimates with an accuracy within 0.04% of expected values. The abundance estimation act is performed by accessing k-mer data generated during the act of comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sequences to detect the presence of the metagenome constituent. Estimating the abundance includes improving species-level resolution by correcting taxonomic classification biases inherent in the lowest common ancestor approach used during initial k-mer matching.

[0054] The method further comprises characterizing low-abundance strains of the detected metagenome constituent using a strain-level genomic analysis. Additionally, the method includes determining viability of the detected metagenome constituent by analyzing transcriptomic data derived from the nucleic acid sample. The act of determining the viability of the detected metagenome constituent includes using a variable-length k-mer to thereby estimate a transcript abundance. The method further comprises utilizing a tree structure to identify at least one unique variable- length k-mer when using the variable-length k-mer associated with at least one transcript cluster having the transcript abundance estimate.

[0055] The method may also include generating proteomic data of a protein / peptide extract from a matrix having the nucleic acid sample and determining viability of the detected metagenome constituent by analyzing the proteomic data. The act of analyzing the proteomic data includes aligning at least one protein sequence corresponding to the proteomic data to a genomic sequence of the detected metagenome constituent.

[0056] The method further comprises adjusting for a genome size bias in the detection of the metagenome constituent. The act of comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sequences includes a postprocessing step to correct abundance underestimation caused by a lowest common ancestor algorithm.

[0057] The method includes analyzing the nucleic acid sample using a multi- omics approach integrating genomic, transcriptomic, and proteomic data to assess a presence and a viability of the metagenome constituent. The method further comprises detecting proteins produced by the metagenome constituent to confirm viability of the metagenome constituent and estimating a relative abundance of the detected metagenome constituent using a Bayesian statistical algorithm. The method includes determining whether the detected metagenome constituent is viable based on transcriptomic or proteomic analysis and characterizing at least one low-abundance strain in the nucleic acid sample at sequence coverages as low as 0. lx.

[0058] The method further comprises determining viability of the detected metagenome constituent by analyzing protein sequences present in the nucleic acid sample. Determining viability includes distinguishing between a mere presence of nucleic acids and the active production of proteins by the metagenome constituent.Determining viability comprises correlating detected proteins with their corresponding nucleic acid sequences to confirm active protein expression by the metagenome constituent. Determining viability includes searching for at least one protein that is essential for the replication or pathogenicity of the detected metagenome constituent. Analyzing protein sequences comprises comparing detected proteins against a reference database of proteins known to be produced by viable metagenome constituents.

[0059] In the method, preprocessing the plurality of sequences comprises quality filtering and adapter trimming of the plurality of sequences. The method further comprises providing a first screening database and second screening databases each configured to minimize false positives in the plurality of sequences. The method includes mapping at least one of the plurality of sequences using the first screening database to classify the at least one of the plurality of sequences between a qualified sequence read and an unclassified sequence read. The method further comprises mapping the unclassified sequence read using the second screening database to reclassify the unclassified sequence read to between a second qualified sequence read and a second unclassified sequence read.

[0060] In the method, the database comprises a first screening database and a second screening database. The first screening database comprises host, UniVec, and RefSeq Virus sequences. The second screening database comprises RefSeq vims sequences, GenBank virus sequences, and Reference Viral Database (RVDB) sequences. The method further comprises assigning a taxonomic label to sequences from the plurality of sequences using the first and second screening databases.

[0061] A system comprises a sequencing component, a preprocessing component, a database interface component, a k-mer matching component, a filter component, and a comparison component for detecting metagenome constituents in a sample. The sequencing component receives a plurality of sequences of a nucleic acid sample having a synthetic spike. The preprocessing component preprocesses the plurality of sequences. The database interface component queries a database to retrieve manufacturing process nucleic acid sequences, environmental nucleic acid sequences, and / or metagenome nucleic acid sequences. The k-mer matching component finds a match of the plurality of sequences to any retrieved manufacturing process nucleic acid sequences, environmental nucleic acid sequences, and / or metagenome nucleic acid sequences. The filter component filters out any matches corresponding to themanufacturing process or environmental nucleic acid sequences. Finally, the comparison component compares each metagenome nucleic acid sequence to the remaining plurality of sequences, thereby detecting the presence of any metagenome constituents based on identified matches.

[0062] The system may have a k-mer matching component that is configured to specifically match the plurality of sequences only to manufacturing process nucleic acid sequences that are stored in a database. The purpose of only matching against sequences derived from manufacturing contaminants is to identify and filter out these particular signatures to improve the specificity of downstream adventitious agent detection. By removing confounding reads with matches to known manufacturing process genomes, the dataset can be refined to focus comparisons more directly on potential microbial contamination threats. The k-mer matching component interacts exclusively with the manufacturing process nucleic acid content from the database in this filtered targeted analysis workflow.

[0063] In some embodiments, the system includes a k-mer matching component that is configured to only match the plurality of sequences from the nucleic acid sample to the plurality of environmental nucleic acid sequences retrieved from the database. The k-mer matching component finds matches between the sample sequences and known environmental sequences, without comparing the sample to other sequence types like manufacturing process or adventitious agent sequences. By focusing its matching on environmental sequences, the k-mer component enables targeted identification of common environmental signatures. Subsequent filtering steps can then selectively exclude these matches to refine the data for enhanced specificity in detecting true microbial contamination threats.

[0064] In some embodiments, the k-mer matching component is configured specifically for matching the sample sequences only to the database containing the metagenome nucleic acid sequences. The component performs sequence comparisons between the sample sequences and these reference adventitious agent sequences to detect microbial contaminants, without matching against the manufacturing and environmental databases. This targeted approach focuses the analysis on identifying true hazards rather than incidental or common background contaminants unrelated to product safety.

[0065] In some embodiments, the system includes a synthetic spike sample added to the nucleic acid sample prior to sequencing. This synthetic spike sample contains known nucleic acid sequences that correspond to potential adventitious agents. The inclusion of this synthetic spike provides a sensitivity control that enables the downstream determination of signal strength parameters and cutoff levels distinguishing true contamination signals from background noise. By calibrating detection based on the measured representation of these added synthetic sequences, the system can set contamination thresholds in a sample-specific manner to reduce false positives and false negatives alike.

[0066] In some embodiments, the synthetic spike sample added to the nucleic acid sample may be a reference nucleic acid sequence of an adventitious agent at a predetermined concentration. The reference adventitious agent sequence enables sensitivity calibration by evaluating representation levels of the sequence through the detection workflow. By spiking in known adventitious agent genomes as a control mechanism, the performance of the analysis system in capturing true contamination threats amongst background noise can be assessed. Comparing endogenous target sequence recovery versus these characterized spike inputs helps optimize detection parameters and establish data-driven cutoffs customized to sample-specific conditions. The synthetic spikes thereby serve as internal sensitivity benchmarks gauging overall assay capability in identifying diverse microbial contaminants. Their inclusion as a control mechanism is an integral part of calibrating detection processes and minimizing false negatives or positives.

[0067] The system may further comprise a detection component configured to determine a signal strength parameter of the synthetic spike sample within the plurality of sequences generated through sequencing. The synthetic spike sample is included within the original nucleic acid sample and contains known reference sequences at predetermined concentrations. The signal strength parameter allows for characterization of the sequencing assay’s sensitivity based on measured representation levels of these spiked-in reference sequences relative to their known starting quantities. Subsequently, these signal strength determinations guide the establishment of a signal cutoff parameter used downstream to delineate likely true-positive contamination sequences from background noise for the given sample. The adaptive cutoff improvesdetection accuracy by calibrating sensitivity responses uniquely to the characteristics of each sequencing run.

[0068] The system may include a preprocessing component that is configured to prepare the sequences for analysis. In some embodiments, this preprocessing component may filter out adapter sequences that were introduced in the process of generating the sequence data. By removing these extraneous adapter sequences from the plurality of sequences that are fed into the analysis workflow, the preprocessing component helps refine the data to focus comparisons on the actual sample nucleic acid signatures.

[0069] In some embodiments, the preprocessing component may further be configured to correct base mismatches (e.g. sequencing errors) in the plurality of sequences. This can be done by using respective quality scores that are associated with each nucleic acid base in the sequences. Quality scores quantify the probability that a base was called correctly during sequencing, facilitating identification of likely errors for correction prior to downstream analysis.

[0070] The system's filter component may be configured to exclude nucleic acid sequences unrelated to adventitious agent contamination from the plurality of sequences generated by sequencing of the sample. This filtering workflow may improve the specificity of microbial contaminant detection by eliminating confounding signatures. In one implementation, the filter component screens nucleic acid sequences matching manufacturing process sequences accessed from the managed cloud storage and uses the k-mer matching component to find hits on these sequences within the plurality of sequences. The filter component then selectively removes the full sequence reads containing the flagged k-mers, thereby eliminating manufacturing signatures unlikely to represent adventitious contamination. This can facilitate downstream analysis.

[0071] In some embodiments, the database interface component may be configured to select k-mers based on their frequency of occurrence in a reference database containing known adventitious agent genome sequences. More specifically, the interface component can analyze the distributions of all possible k-length subsequence "words" across this reference database and selectively extract those k- mers that are highly recurrent, surpassing a minimum threshold frequency. These common k-mers can serve as signatures for broad detection of conserved regions withinadventitious agent genomes. By focusing computational comparisons on these informative k-mer subsets instead of full genomes, efficiency may be enhanced without sacrificing inclusiveness. The parameters and algorithms utilized for frequent k-mer selection from reference databases are customizable based on factors like desired sensitivity levels. Overall, this k-mer selection approach allows the system to hone in on genetically relevant regions to support rapid and accurate identification of microbial contamination threats.

[0072] The system may include a custom-curated database of minimizers, which are short sequences that represent regions within metagenome constituent genomes used for detection. These minimizers are pre-selected based on their informativeness and uniqueness to improve the efficiency of the detection process. By focusing analysis on these curated minimizer sets rather than complete reference genomes, the database interface component can accelerate identification of metagenome constituents by reducing the search space. The use of minimizer databases exemplifies the system’s options for optimizing and customizing the microbial detection workflow to balance accuracy and speed according to users' specific needs.

[0073] In some embodiments, the comparison component is configured to utilize an exact match algorithm to identify matches between the metagenome nucleic acid sequences retrieved from the database and the preprocessed sample sequences. Using an exact match algorithm allows the comparison component to accurately pinpoint identical subsequences that are shared between the known metagenome sequences and sample sequences, serving as indicators of potential contamination. This matching process compares the inherent nucleic acid composition patterns within the sample sequence reads to signatures derived from hazardous metagenomic organisms. Exact subsequence matches become candidate sequences for further review as possible matches to target adventitious sequences.

[0074] The system may include a comparison component that is configured to identify partial or inexact matches between the metagenome nucleic acid sequences from the database and the sequences from the nucleic acid sample. By allowing partial matches, the comparison component can account for possible genetic variations that may exist among different strains or variants of the metagenome constituents such as viruses, bacteria, or fungi. The ability to detect matches despite mutations or minor differences can enhance the sensitivity of detecting these metagenome constituents,whose genomes may naturally evolve and drift over time. Parameters such as alignment match thresholds can be customized to balance inclusiveness with accuracy when evaluating inexact sequence alignments indicative of adventitious agent presence.

[0075] In some embodiments, the system may include a reporting component configured to generate a report summarizing the presence of any detected metagenome constituents. The report may include information such as the identity and quantity of the metagenome constituents identified as present by the comparison component based on the analysis. By compiling the key details and findings regarding which metagenome constituents were detected and in what amounts, the reporting component facilitates review of the analysis outcomes and supports data-driven decision making. The reporting component works in coordination with other elements of the system, gathering inputs on the detected constituents from the comparison component to populate customizable report templates.

[0076] The system according to some embodiments has the capability to process nucleic acid samples derived from biopharmaceutical products. More specifically, in some embodiments, the sequencing component which receives the plurality of sequences of a nucleic acid sample is configured to accept input nucleic acid extracted directly from a biopharmaceutical batch or intermediate. This allows the detection workflow to screen biologies like vaccines, cell therapy products, plasma derivatives and others for adventitious agents like viruses, bacteria, or fungi during manufacturing. By sourcing nucleic acid test samples directly from biopharmaceutical production streams, the system can provide biosafety testing and contamination monitoring to support good manufacturing practices and regulatory compliance for these innovative therapies.

[0077] The system's sequencing component may be configured to receive the nucleic acid sample from a cell culture used in the production of a biopharmaceutical product. In some embodiments, the nucleic acid sample subjected to metagenome constituent detection originates from cell cultures involved in manufacturing biopharmaceuticals. The sequencing instrumentation can thus be tailored to process samples derived specifically from these biomanufacturing-associated cellular sources and cell types. This allows the system architecture and sample analysis pathways to align with common biopharmaceutical production environments and sample characteristics.

[0078] The system's sequencing component may be configured to receive nucleic acid sequence data that has been generated using an Illumina next-generation sequencing platform. In some embodiments, the Illumina sequencing technology is utilized to process the nucleic acid sample and produce the raw sequence data relating to sample's compositional nucleotides which serves as input data to the system's sequencing component. Therefore, the system demonstrates capabilities to integrate with and analyze datasets from Illumina sequencers through its flexible, modular architecture. The sequencing workflow thereby establishes compatibility with a predominant next-gen sequencing technology widely used in research and clinical genomics applications. However, it retains the adaptability to potentially incorporate sequence information from various other platforms as well depending on user needs.

[0079] In some embodiments, the database interface component is configured to handle nucleic acid sequences corresponding to metagenome constituents, such as adventitious agents. These sequences may be of a predetermined optimized length to enable detection of a diverse range of organisms. Using appropriately sized reference sequence lengths balances inclusivity across variable genomic structures with specificity. The modular database interface architecture allows flexible integration of additional sequence information on emerging threats to expand system capabilities over time.

[0080] The system's comparison component may be configured to detect various types of metagenome constituents, including but not limited to viruses, bacteria, and fungi. The modular and adaptable nature of the comparison component enables detection of these common adventitious agents which can contaminate manufacturing processes and impact end product safety. By screening for matches between sample nucleic acid sequences and reference genomes across multiple taxa, the system aims to provide broad inclusivity in its scans for microbial threats.

[0081] In some embodiments, the database interface component may utilize minimizers, which are short representative nucleotide sequences. Taxa can be assigned to each minimizer based on a lowest common ancestor (LCA) algorithm executed by an LCA component. The assigned taxa provides phylogenetic context to improve detection accuracy. After taxonomic lineage has been conferred to the minimizers, they are compared against the sample sequences by the comparison component to identify matches potentially indicating contamination.

[0082] In some embodiments, the system includes a lowest common ancestor (LCA) component configured to assign taxonomic lineages to matched sequences to improve detection specificity. The LCA component employs an algorithm to identify the most recent common ancestor shared across related organisms for each sequence match flagged during the comparison process. The taxonomic rank of this ancestor provides the most specific taxon that can be reliably assigned given broader genomic variability across strains. Utilizing this LCA approach enables precise classification of detected adventitious sequences based on genetic conservation and mutation patterns across taxonomic hierarchies. By determining a specific taxon such as family, genus or species for each matched sequence, the detection process focuses follow-up confirmation assays for enhanced resolution. The lineages assigned by the LCA component may also provide helpful context for interpreting sequence matches, which can reduce false positives from incidental environmental alignments unrelated to threats when integrated with detection outputs.

[0083] The system may include a comparison component configured to enhance the specificity of detecting metagenome constituents. More specifically, the system may utilize a lowest common ancestor (LCA) component to assign a specific taxon to each minimizer sequence. Minimizers refer to representative k-mers that minimize sets of sequences sharing common subsequences. The comparison component can then leverage the taxonomic lineage information assigned to each minimizer when matching sample sequences. By considering the phylogenetic context provided by the LCA component, the comparison process focuses matches on more conserved genomic regions, improving accuracy. The additional lineage specificity prevents false positives from incidental alignments unrelated to true threats. In this manner, the system employs taxonomic classification of minimizers to increase the precision of identifying metagenome constituents within the test sample.

[0084] The system may further include a reporting component that is configured to generate a report file summarizing the results of comparing the metagenome nucleic acid sequences to the sample sequences. In some embodiments, this report file includes taxonomic classifications for any detected metagenome constituents, which are assigned based on a Lowest Common Ancestor (LCA) algorithm. The reporting component leverages the comparison outcomes between reference and sample sequences to compile detection data into file outputs formatted topresent contamination matches along with their associated phylogenetic lineage labels as determined via the LCA taxonomic assignment process. The configurable nature of the reporting module allows flexible integration of adventitious agent screening outputs into customizable file-based reports meeting diverse user needs.

[0085] The system may include a reporting component that is configured to generate a report file summarizing the comparison results between the metagenome nucleic acid sequences and the sample sequences. This report file may include taxonomic classifications for any detected metagenome constituents, which are assigned based on a lowest common ancestor (LCA) algorithm. In order to align with standardized formats used in metagenomics analysis, the reporting component may be configured to format the taxonomy report file so that it complies with the Kraken file format. Using predetermined formatting specifications, the reporting component ensures the report file structure and contents meet Kraken file requirements, facilitating interoperability with data analysis pipelines designed for Kraken-formatted inputs. By supporting standardized file formats, the system enables seamless integration with downstream analysis tools commonly used in metagenomic workflows.

[0086] The system further provides a graphical user interface that presents the results of metagenome constituent detection in an interactive and user-friendly manner. This allows users to access and analyze the detection results without requiring extensive technical expertise. Specifically, the graphical interface is configured to visualize and convey the outcome of metagenome constituent screening in the test sample. It enables intuitive user interaction with the data, supporting examination of the detection findings by scientists, technicians, and other end-users alike without needing specialized computer skills or coding knowledge. The graphical interface thereby enhances accessibility and understanding of the system's contamination detection capabilities and outputs across various audiences.

[0087] In some embodiments, the system further comprises a reporting component configured to generate an interactive report that summarizes the detected presence of any metagenome constituents. The interactive report may be in HTML format and include interactive visual representations of the data, providing users with customizable and user-friendly visualization options for analyzing detection outcomes. By enabling dynamic and intuitive exploration of results, the interactive HTML report allows users to thoroughly investigate contamination details through integratedselection tools, graphical renderings tailored to different data perspectives, and underlying synchronized data accessible via visual elements. The reporting component taps into the accumulated data on identified organisms and transforms analytical outputs into an interactive format better conveying complex genomic interrelationships using web technologies. The modular nature of the reporting component allows adapting its implementation over time to leverage emerging visualization libraries and features benefiting end user interpretation. Overall, the interactive report generated by the reporting component aims to empower informed, user-guided assessments regarding sample safety and quality.

[0088] The system may further comprise a workflow adaptation component that is configured for compliance with Good Laboratory / Maufafcturing Practice (GxP) and non- GxP environments. This includes incorporating acts that are specific to the regulatory requirements of each respective environment. For example, for GxP environments, the system may implement additional documentation, traceability, and quality control measures to align with GxP expectations. Meanwhile, for non- GxP situations, the system remains configurable to relax certain stringencies while still delivering robust adventitious agent detection functionality. By integrating this workflow adaptation component, the system demonstrates flexibility to conform to variable regulatory contexts across different laboratories and use cases. Through modular adjustments, it can meet quality and reliability standards appropriate for both strictly controlled GxP facilities as well as more flexible non- GxP environments.

[0089] The system may optionally include additional logic in the comparison component to further refine the identification of true positive matches indicating potential adventitious agent contamination. More specifically, the comparison component may utilize a custom script that applies predetermined criteria to differentiate between true positive matches and false positive matches after the initial matching process. This additional script provides another layer of analysis that reviews the matches flagged during the sequence comparison and filtering steps, applying statistical evaluations of match distribution significance and other customizable rules to control for spurious or incidental alignments unrelated to true microbial threats. By leveraging this supplemental script to qualify matches based on criteria tailored to balance sensitivity and precision, the comparison component can enhance the overall accuracy of adventitious agent detection.

[0090] In some embodiments, the system may include a graphical user interface (GUI) component that allows for modular customization of various aspects of the analysis workflow based on user requirements. This customizability may enable users to tailor data preprocessing parameters, adjust criteria and thresholds for metagenome constituent detection, select desired report formats, and incorporate additional modules into the workflow in a plug-and-play manner through the interface without extensive recoding. The adaptability of the system architecture thereby supports modification and flexibly addresses diverse user needs.

[0091] The system may further include an artificial intelligence model that has been trained to identify potentially novel metagenome constituents that are not present in the database. This artificial intelligence model is trained on the structures and sequences of predetermined or known agents. The ability to identify unknown or emerging metagenome constituents enhances the system’s detection capabilities beyond solely searching for matches to constituents currently contained within the reference database. By analyzing intrinsic sequence properties and patterns, the artificial intelligence model provides an additional mechanism for flagging previously unencountered adventitious sequences that may represent threats. The integration of machine learning methodologies alongside established matching algorithms offers a robust, adaptable approach to screening for both recognized and novel microbial contaminants.

[0092] The system may further include a data security component configured to secure and track data integrity and analysis results using a blockchain ledger, wherein each act in the metagenome constituent detection process is recorded as a transaction on the blockchain ledger. In some embodiments, this allows for a complete audit trail showing each step taken during the analysis. The blockchain ledger provides a way to prevent tampering as well as verify the accuracy of results.

[0093] The system may have an additional feature in the k-mer matching component to further enhance its capabilities. Specifically, the k-mer matching component can incorporate a machine learning algorithm that is configured to dynamically adjust the length of k-mers (k) during sequence analysis. By training on datasets of sequences with known characteristics, the machine learning model can learn to optimize the k value over time in order to balance accuracy and performance. This allows the k length to be tailored to specific sample properties rather than fixed. As anexample, when analyzing a sample with potential viral contamination, the algorithm may focus on shorter k-mers base pairs to maximize detection sensitivity. In contrast, for a bacterial screening the k length could be increased improve specificity. By enabling automated and adaptive tuning of the k-mers in this manner, the accuracy of the overall sequence matching process is improved. This machine learning approach exemplifies the system's ability to integrate emerging techniques to augment capabilities.

[0094] In some embodiments, the system may include a graphical user interface (GUI) component configured for cloud-based operation. This would allow for collaborative analysis and real-time data sharing among multiple users or institutions, while maintaining confidentiality of sensitive data through security measures. The cloud-based GUI could enable authorized users to concurrently access detection results and related reports for review or further processing in a seamless and controlled manner.

[0095] Additionally, in some embodiments the system may include a reporting component with an automated regulatory compliance checker. This compliance checker is configured to review any reports generated by the system against current international biosafety standards. The purpose is to flag any sections of the reports that may require additional attention or edits in order to meet regulatory compliance. By including this automated compliance checking capability, the system can help streamline review processes and ensure that reports adhere to the latest global guidance and best practices around biosafety testing. The modular nature of the system allows for this type of regulatory compliance checker component to be incorporated as an optional feature where needed.

[0096] The system includes an automated regulatory compliance checker for reviewing reports against current international biosafety standards to flag sections requiring revisions for meeting compliance. In some embodiments, this compliance checker may be implemented as a Large Language Model ("LLM") capable of analyzing reports and identifying areas not conforming to needed requirements. The LLM checker ensures generated study reports adhere to necessary formats and contents before finalization.

[0097] In some embodiments, the plurality of metagenome nucleic acid sequences queried from the database and used in the comparison may be a plurality of adventitious-agent nucleic acid sequences. The system may leverage sequences fromknown adventitious agents such as viruses, bacteria, fungi, and other microorganisms that can contaminate manufacturing processes and impact product safety. By screening against these reference adventitious sequences, the presence of microbial threats within the sample data can be detected. Custom adventitious sequence databases curated from various trustworthy sources including past contamination events, genomic records, and scientific literature may serve to supply relevant signatures aiding reliable hazard identification. Overall, maintaining a broad set of potential adventitious agent genomes for matching enables the system to scan diverse samples for threats and risks that could inadvertently be introduced during bioproduction.BRIEF DESCRIPTION OF THE DRAWINGS

[0098] These and other aspects will become more apparent from the following detailed description of the various embodiments of the present disclosure with reference to the drawings wherein:

[0099] Fig. 1 shows a cloud-based system for metagenome constituent detection, such as adventitious agent detection, in accordance with an embodiment of the present disclosure;[000100] Fig. 2 shows a block diagram of a computer device for adventitious agent detection in accordance with an embodiment of the present disclosure;[000101] Fig. 3 shows a flow chart diagram of a method for adventitious agent detection in accordance with an embodiment of the present disclosure;[000102] Fig. 4 shows a flow chart diagram of a method for adventitious agent detection in accordance with an embodiment of the present disclosure;[000103] Fig. 5 shows a flow chart diagram of adventitious agent detection using an end-to-end analysis having abundance estimation calculations of test sample constituents in accordance with an embodiment of the present disclosure; and[000104] Fig. 6 is a schematic diagram illustrating a modular enhancement system for adventitious agent detection in accordance with an embodiment of the present disclosure; and[000105] Fig. 7 shows a schematic diagram of a two-stage mapping system to verify and qualify sequence read in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION[000106] Fig. 1 shows a system 100 for metagenome constituent detection, such as adventitious agent detection, in accordance with an embodiment of the present disclosure. The system 100 facilitates the identification and analysis of adventitious agents in a biological sample 101 through an integrated platform that can leverage cloud computing and next-generation sequencing technologies.[000107] The cloud service provider 102 provides the computational resources and services for the metagenome constituent detection component 112. In some specific embodiments, the metagenome constituent detection component 112 is an adventitious agent detection component. It hosts the managed cloud storage 132, which is a repository for containing various sequence data types such as environmental sequences 142, manufacturing process sequences 144, synthetic spike sequences 146, and adventitious agent sequences 148. These sequences 142, 144, 146, 148 are integral to the fdtering and detection processes as they provide reference points for the identification of potential contaminants within a nucleic acid sample 101, e.g., DNA, RNA, mRNA, a nucleic acid analogue, etc.[000108] The cloud service provider 102 enables collaborative interactions between the system 100 and various external elements. It receives nucleic acid sequences 105 over the network 108 from the sequencing instrumentation 103 located externally. The service provider's virtual server 122 and resource dispatcher 110 interface with the physical servers 1-n 121 to execute workflow tasks and share data as needed. The managed cloud storage 132 within the cloud exchanges information with various system components, providing reference data essential for contaminant filtering and detection. Overall, the cloud service provider 102 facilitates systematic coordination between the system 100 and devices external to it. In some embodiments, the cloud service provider 102 may utilize additional cloud capabilities to enhance functionality. These could include on-demand virtual resources like compute instances, object storage, and cache services to dynamically scale system elements. Cloud analytics tools could provide usage metrics to optimize workload distribution. Cloud data services like blockchain could augment data security and integrity tracking. The service provider 102 could also enable collaborative workflows via APIs, allowing transparent access to the system 100 by external organizations in a secure manner. Thecloud architecture allows the provider 102 to adapt to evolving technologies and customer needs.[000109] The server farm 119, encompassing servers l ...n 121, represents a scalable and dynamic array of computational resources. These servers can be physical machines or instantiated as virtual servers within the cloud service provider's infrastructure, providing flexibility in computational power and data storage capacity. [000110] A virtual server 122 is illustrated as part of the cloud service provider 102, encapsulating a virtual processor 124, virtual memory 126, and virtual disk space 128. These virtual components can be dynamically allocated to accommodate the varying demands of the sequencing and data analysis processes. The virtual server 122 exemplifies the elastic nature of the system's 100 computational resources, allowing for the scaling of processing power and storage in response to the needs of the metagenome constituent detection component 112.[000111] The resource dispatcher 110, positioned within the cloud service provider 102, can manage the distribution of computational tasks and resources among the servers l...n 121 and the virtual server 122. This dispatcher 110 ensures that the workflow is executed efficiently, balancing load and optimizing the use of the system's infrastructure.[000112] The data security component 111 is configured to safeguard sensitive information and ensure data integrity. It employs encryption technologies to protect data in transit and at rest, restricting access through authentication protocols. The data security component 111 establishes access controls, enforcing permission policies on resources. It also implements network security measures like firewalls and intrusion detection to monitor network traffic and mitigate threats.[000113] The data security component 111 interacts with the components that handle sensitive data. It works with the database interface component 118 to encrypt data queries and transmissions to and from the managed cloud storage 132. When sequencing results 105 flow into the cloud service provider 102, the data security component 111 encrypts and stores the data in the managed cloud storage 132 (or in memory or other location) while granting controlled access to authorized components, e.g., users authorized via user accounts 150 to view data viewable through a Graphical User Interface (“GUI”) component 162. The data security component 111 allows the reporting component 154 to retrieve results data (e.g., from the managed cloud storage132) in a secure manner for report generation. The data security component 111 also ensures the resource dispatcher 110 only permits workflow steps on compliant servers. If threats are detected, it can trigger alerts and automatically enact countermeasures while notifying system administrators.[000114] There are several optional features of the data security component 111. These include bolstered access controls with multi-factor authentication, advanced threat monitoring via heuristic-based analysis, automated penetration testing routines to probe for weaknesses, and blockchain-based data tracking to enable auditing. The data security component 111 could also utilize separate physical / virtual infrastructure solely for managing security functions like encryption / decryption, network monitoring, and access control. Additionally, machine learning algorithms can enable the component to fine-tune and adapt security measures to new threats based on updated datasets and activity patterns within the system.[000115] The metagenome constituent detection component 112 can perform analysis and identification of adventitious agents within the nucleic acid samples. This component may incorporate a variety of analytical algorithms and processes to compare sample sequences against known references stored in the managed cloud storage 132.[000116] The sequencing instrumentation 103, which may be a next- generation sequencing device, is tasked with processing the nucleic acid sample 101 to generate nucleic acid sequences data 105. The generated data 105 is then transmitted over a network 108, which can include public or private data communication networks, to the cloud service provider 102 for further processing.[000117] More specifically, the sequencing instrumentation 103 may be a nextgeneration sequencing device configured to process the nucleic acid sample 101 to generate nucleic acid sequences data 105. The nucleic acid sample 101 is prepared and loaded into the sequencing instrumentation 103, which utilizes sequencing technology such as Illumina sequencing to read the nucleic acid sequences of the sample 101 and produce raw sequencing output in the form of nucleic acid sequences data 105. The sample input may contain synthetic spike sample materials such as known sequences of adventitious agents which are added to the sample prior to sequencing to enable downstream signal strength and cutoff calculations. The sequencer 103 uses reagents and other sequencing components to process the nucleic acid sample in accordance with specific sequencing platform protocols. As an example, in Illumina sequencing thisinvolves bridge amplification of nucleic acid fragments on a flow cell and fluorescent labeling of bases during synthesis to ultimately produce reads containing As, Ts, Cs, and Gs representing the composition of the nucleic acid sample. The produced nucleic acid sequences data 105, generated through operations of the sequencing instrumentation 103, is transmitted over network 108 to the cloud service provider 102 for further processing to detect any potential adventitious agent contamination.[000118] The sequencing instrumentation 103 may operate as an automated standalone instrument that applies next-generation sequencing technology such as Illumina sequencing-by-synthesis approaches to the nucleic acid sample 101. The results are returned in the form of the nucleic acid sequences data 105 file, which represents the composition of raw nucleic acid bases within the sample. Metadata about the sequencing run parameters and sample type may also be embedded or associated with the data 105 file output. The sequencing instrumentation 103 executes all necessary biochemical processes internally to sequence the nucleic acid sample and does not require additional equipment for its core functionality. However, it may include communication interfaces and data ports allowing it to transmit the resulting nucleic acid nucleic acid sequences data 105 over network 108 to other components of the system 100. The sequencing instrumentation 103 is available from many commercial vendors and may vary in specific sequencing protocols, output file formats, and performance features. The system 100 can integrate various types of sequencing instrumentations through its modular design and comprehensive data parsing processes. [000119] The sequencing instrumentation 103 may be from other vendors besides Illumina, such as Oxford Nanopore, PacBio sequencing, etc. which can be readily integrated through the modular architecture of the system 100, enabling processing of sequencing data beyond Illumina technologies. Portable or miniaturized sequencers that are operational outside of a laboratory environment may also be utilized to generate the nucleic acid sequences data 105, affording flexibility in system usage, sequencing instrumentation 103 implementations may also incorporate automated preprocessing functionality, outputting sequences data that has been through quality trimming and filtering processes rather than raw sequences. These and other sequencer types bringing advantages in accessibility, cost, speed, output data formats, and sensitivity can be included in the system 100 workflow as alternative embodiments of the sequencing instrumentation 103.[000120] The data generated by the sequencing instrumentation 103, i.e., nucleic acid sequences data 105, can vary in format between different sequencing platform file types or for other reasons. Data preprocessing steps may differ depending on the file format. The nucleic acid sequences data 105 may vary in characteristics like read length and quality scores. Alternative data analysis approaches leveraging assembly methods rather than raw read mapping could also utilize the nucleic acid sequences data 105 as input. Furthermore, metadata attributes can be appended to the nucleic acid sequences data 105, providing sample 101 information for analysis context. The nucleic acid sequences data 105 is transmitted through a network 108 to the metagenome constituent detection component 112. The sequencing component 114 receives the nucleic acid sequences data 105 for the metagenome constituent detection component 112.[000121] Once the nucleic acid sequences data 105 is received by the metagenome constituent detection component 112 via the sequencing component 114, the preprocessing component 116 may begin processing the data. The preprocessing component 116 can prepare the raw nucleic acid sequences data 105 for downstream analysis and adventitious agent detection by implementing quality control and filtering steps to ensure that the data is optimized for the detection workflow.[000122] In the initial quality control phase, preprocessing component 116 screens the individual bases within each sequence read and removes or trims portions that fall below predetermined quality thresholds. Base call quality scores encoded in the FASTQ files may facilitate identification of low-quality bases which may represent sequencing errors. Adaptor sequences introduced during library preparation may also be trimmed, preventing spurious matches.[000123] In some implementations, the preprocessing component 116 leverages concurrent processing across many parallel threads to accelerate this sequence grooming. Batching can divide large portion of the inputs into chunks (e.g., FASTQ data chunks) for independent parallel quality control, which may be reassembled upon completion to reconstitute the dataset. Load balancing may be used to optimize throughput at scale.[000124] In addition to quality control, the component 116 can filter the sequence reads to remove signatures matching a predetermined sequences like those from common laboratory reagents documented to cause false positives in downstream processes. Here a bit-parallel sequence alignment algorithm may be used. Thepreprocessing component 116 may then add compression to the data and / or additional metadata annotation, e.g., to enhance searchability.[000125] In some embodiments, the preprocessing component 116 could utilize techniques like quality-based trimming algorithms, fixed length trimming, or some combination thereof. Machine learning approaches could also be integrated to optimize the preprocessing for characteristics of each sample. The component could be enhanced to support additional file formats beyond FASTQ, as well as data from multiple sequencing platforms beyond Illumina such as Oxford Nanopore.[000126] The database interface component 118 can query the managed cloud storage 132 to retrieve relevant sequence data, which includes environmental sequences 142, manufacturing process sequences 144, synthetic spike sequences 146, and adventitious agent sequences 148.[000127] The K-mer matching component 120 is involved in the identification and comparison of short nucleotide sequences, known as k-mers, within the nucleic acid sequences data 105. The K-mer matching component 120 may employ algorithms to match these k-mers against the reference sequences obtained from the managed cloud storage 132. In some embodiments, the K-mer matching component 120 may be a software module while in other embodiments it may be a specialized hardware component.[000128] In some specific embodiments, the K-mer matching component 120 may function by extracting all possible subsequence "words" of length k from the preprocessed nucleic acid sequences data 105 received from the preprocessing component 116. The value of k may be a predetermined parameter, reflecting the typical length of minimizers for efficient adventitious agent detection. In other embodiments, the value of k may be adeptly determined, may be a function of the particular sequence being searched, and / or may be determined by use of machine learning. The extracted k-mers serve as unique signatures that can be matched against k-mers derived from the reference database sequences (manufacturing process sequences 144, environmental sequences 142 and adventitious agent sequences 148) retrieved by the database interface component 118.[000129] To facilitate efficient lookup and matching, the K-mer matching component 120 may construct an index of k-mers, cataloguing the frequency and location of each distinct k-mer found within the nucleic acid sequences data 105. Thismay be achieved by sliding a window of size k along the sequence, capturing each subsequence of length k as a distinct k-mer. For example, consider a nucleotide sequence “ATGGCATGC”. If we set k to be 3, the resulting k-mers would be “ATG”, “TGG”, “GGC”, “GCA”, “CAT”, and “TGC”. The choice of k depends on the specific application and the desired balance between sensitivity and specificity. Smaller values of k provide higher sensitivity but may lead to increased noise and false positives, while larger values of k offer greater specificity but may miss shorter conserved regions. This k-mer index can be configured to allow rapid identification of sequence matches, without needing to scan the full dataset for perfect matches. The index may be implemented as a hash table, Bloom filter, Burrows-Wheeler transform or other specialized data structure optimized for fast sequence matching.[000130] More specifically, the matching process may entail querying the k-mer index to find instances where a k-mer exactly matches between the nucleic acid sequences data 105 and one of the reference database sequences. This may be done by using efficient string-matching algorithms, such as hash tables or suffix arrays, which enable rapid lookup and identification of matching k-mers. As mentioned, the reference sequence can also broken down into k-mers, and each k-mer from the input sequence is searched against the reference k-mers to find exact matches. These matches become candidate sequences that may correspond to manufacturing process contaminants, environmental contaminants, or adventitious agents, depending on the reference sequence type (detected via manufacturing process sequences 144, environmental sequences 142 and / or adventitious agent sequences 148).[000131] In some specific embodiments, a scoring scheme that assigns weights to the matches based on their significance or rarity. This allows for the prioritization of more informative and discriminative k-mers, reducing the impact of common or repetitive subsequences. Another optional technique is to consider the context of the matching k-mers by examining the neighboring bases or amino acids. This contextual information can help disambiguate matches and improve the specificity of the alignment.[000132] The K-mer matching component 120 may be implemented in coordination with the filter component 123 and comparison component 124 to exclude non-relevant matches and hone in on likely adventitious agent matches. It may also interact with the LCA component 158 which assigns taxonomic lineages to matchedsequences to improve detectionspecificity. The length k of k-mers may be dynamically adjusted over time by a machine learning model to optimize accuracy.[000133] In alternative embodiments, the K-mer matching component 120 may be upgraded to employ longer subsequences beyond typical k-mer lengths. Such ultralong words spanning hundreds of nucleotides may enable more precise sequence matching, at the expense of greater computational resource requirements, in some specific embodiments and / or configurations.[000134] In some embodiments, the K-mer matching component 120 may utilize variable length k-mers during the matching process. For example, k-mers of lengths 1, 2, 3, 4, 5, 10, 15, 20, 25, 30 etc. could be used to provide flexibility in detecting matches across adventitious agents (via adventitious agent sequences 148) with differing genomic structures. Shorter k-mer lengths may enable detection of more divergent or highly mutated sequences, while longer k-mer lengths can provide greater specificity. The k-mer length could be dynamically adjusted based on dataset characteristics to optimize sensitivity and specificity. The adjustment may be done manually or via the use of a trained machine learning model, such as a neural network, a deep network, a decision tree, a regression model, etc. Additionally or alternatively, parallel analysis of nucleic acid sequences data may be performed using different-length k-mers to capitalize on unique advantages of each k-mer length. The resulting datasets from each respective k-mer analysis can then be collated, compared, and the Intersection, Union, Difference, or Complement of these datasets can be taken as orthogonal evidence to more accurately determine which agents are present in starting material.[000135] Additionally, alternatively, or optionally, the selection of k-mers could be tailored based on the type of adventitious agent being analyzed. For viral detection, the K-mer matching component 120 may focus on highly conserved genomic regions by analyzing the frequency of k-mers across a curated viral sequence database and choosing those above a predetermined threshold. For bacteria, k-mers from genes related to pathogenicity, antibiotic resistance, or other clinically relevant markers could be prioritized. This targeted approach focusing on informative k-mer subsets may conserve computational resources.[000136] In another embodiment, the K-mer matching component 120 incorporates the use of minimizers, which are k-mers that lexicographically minimize sets of sequences that share common subsequences. Minimizer analysis concentratescomparisons on genetically informative k-mers within sequences, providing detection accuracy improvements over conventional k-mer matching. Further refinement of minimizer selection may be achieved, in some specific embodiments, by limiting matches only to those derived from a custom database of adventitious agent genomes rather than the entire reference sequence set.[000137] As previously mentioned, the K-mer matching process implemented in component 120 could also leverage Bloom filters, which are a probabilistic data structure designed for rapid look-ups of pattern matches. Use of Bloom filters enables parallelized analysis of sequencer output for the presence of adventitious agent signatures, accelerating the overall workflow. The parameters for Bloom filter implementation can be optimized to balance accuracy, speed and memory usage depending on dataset properties.[000138] In certain embodiments, the K-mer matching component 120 may employ locality-sensitive hashing (LSH) algorithms. These algorithms group similar K-mer sequences into buckets by transforming raw sequence into hashed values. K- mers landing in the same buckets are more likely to share significant sequence homology or overlap. This grouping narrows comparisons to within buckets rather than across entire sample sets, enhancing computational tractability. The hashed K-mer groupings also preserve sequence privacy and enable rapid adventitious agent signature lookups. Additional embodiments may include K-mer based machine learning models for detection of unknown adventitious sequences or integration of K-mer matching with other alignment methods in a hierarchical process, variations thereof, etc.[000139] The filter component 123 works in tandem with the K-mer matching component 120 to exclude sequences that match known manufacturing process sequences 144 and environmental sequences 142, thereby refining the dataset to include only relevant sequences that may indicate the presence of adventitious agents. The filter component 123 may be configured to exclude nucleic acid sequences unrelated to adventitious agent contamination from the nucleic acid sequences data 105 generated by sequencing of the sample 101. This filtering workflow may improve the specificity of microbial contaminant detection by eliminating confounding signatures.[000140] In one implementation, the filter component 123 screens nucleic acid sequences matching manufacturing process sequences 144 or environmental sequences 142 accessed from the managed cloud storage 132 and uses the k-mer matchingcomponent 120 to find hits on these sequences within the nucleic acid sequences data 105. The k-mer component 120 identifies sequence read subsets harboring identical or highly similar k-mer substrings to these known non-hazardous signatures. It subsequently passes these k-mer level matches back to the filter component 123.[000141] Equipped with this preliminary match data, the filter component 123 selectively removes the full sequence reads containing the flagged k-mers, thereby eliminating manufacturing and environmental signatures (from the environmental sequences 142 and / or the manufacturing process sequences 142) unlikely to represent adventitious contamination. This can facilitate downstream analysis. Various pattern recognition approaches help discern true positives from incidental partial alignments at this filtering stage.[000142] In some specific embodiments, the filter component 123 may integrate ancillary exclusion criteria such as quality scores or sequencing depth thresholds to further refine data filtering. For example, dynamically set quality cutoffs may be used to ensure bases with higher uncertainty are trimmed to enhance specificity. In some embodiments, the quality cutoffs are sample specific and / or based upon the metagenome constituent material being tested. These adjustments may be made by the user via the GUI component 162. The filter component 123 configurability thereby supports adaptation across variable sample types and detection requirements.[000143] Additional algorithms may optionally be applied alongside primary filtering to improve accuracy. For example, concurrent taxonomic classification of matches by the LCA component 158 can be used to provide phylogenetic context to inform filtering decisions. By considering this lineage information, the specificity of filtering may be enhanced. Meanwhile, statistical evaluations of match distribution significance help control for spurious alignments unrelated to true adventitious agents. [000144] The filter component 123 may output groomed FASTQ output that has the nucleic acid sequences from the sample 101 with production-process (e.g., those from the manufacturing process sequences 144) or environmental matches (e.g., those from the environmental sequences 142) removed. This sequence subset can reduce the search space for downstream matching against known adventitious references found in the adventitious agent sequences 148. The filter component 123 may thereby complement the initial k-mer identifications to support sequence-based contamination screening.[000145] In certain embodiments, the filter component 123 may perform filtration of sequences in multiple sequential steps. For example, an initial blacklist-based filtering of obvious environmental and process contaminants could be followed by a quality score filtering to further refine the sequence dataset.[000146] Additionally, more advanced filter algorithms relying on machine learning techniques may be implemented for contamination sequence removal. These techniques can analyze sequence composition, nucleotide distributions, and other intrinsic sequence features to classify reads as either high-value or unwanted sequences. Deep learning architectures, (e.g. neural networks) support vector machines, random forest classifiers, and other predictive models could be trained on sample datasets to automate the categorization and filtering of sequences for adventitious agent screening. [000147] For logging or regulatory purposes, the filter component 123 could generate a companion file detailing all sequences removed during the filtration process for later review. This log file would provide traceability regarding filtered sequences.[000148] The comparison component 124 further analyzes the nucleic acid sequence data 105 by comparing it against a curated list of adventitious agent sequences 148 by using the k-mer matching component 120. The comparison component 124 can therefore use the filtered nucleic acid sample sequences from the nucleic acid sequence data 105 against known adventitious agent sequences accessed from the managed cloud storage 132 to detect contamination.[000149] In one implementation, the comparison component 124 uses a k-mer based approach, dividing both the sample sequences and database adventitious sequences into short overlapping substrings of length k. These sample k-mers are then matched against the reference k-mers, with matches indicating potential contamination. The k-mer length may be optimized to balance detection sensitivity and accuracy.[000150] To account for genetic variability in pathogens, the comparison component 124 can utilize inexact matching algorithms. These identify gaps or mutations between k-mers but still meet an overall similarity threshold, delivering more inclusive detection across strain variations. The specific thresholds are customizable based on sample properties and desired assay performance factors.[000151] The comparison component 124 can match sample sequences against both entire microbial genomes as well as conserved genomic sub-regions or marker genes suited for detection of specific taxa. In certain versions, minimizerrepresentations of adventitious sequences are matched against to optimize speed without sacrificing accuracy. Meanwhile, concurrent lowest common ancestor analyses by the LCA component 158 enable precise taxonomic classification of detected organisms based on phylogenetic hierarchies.[000152] To further enhance sensitivity, the comparison component 124 leverages signal strength metadata from synthetic spike-in genomes generated by the detection component 152. This contextual data establishes sample-dependent cutoffs that minimize false negatives and positives alike. Combined exact and partial matching algorithms balance inclusiveness and precision, delivering configurable adventitious sequence detection tailored to scattered nicks and gaps in amplified Single Cell Long Reads while retaining specificity against near-neighbor environmental signatures from reagents and personnel in a cGMP production cleanroom.[000153] The comparison component 124 can utilize cloud-optimized architectures for scalable analytics leveraging both static and elastic infrastructure. Integrated automation can further enable hands-off regulatory documentation.[000154] In one specific embodiment, the comparison component 124 may allow for partial or inexact matches between sequences to account for genetic variations in adventitious agents over time. This can be achieved through implementing Smith- Waterman, Needleman-Wunsch or other dynamic programming algorithms to align sequences and identify regions of similarity. Match percentage thresholds can be adjusted based on the level of sensitivity required. The threshold may be adjusted via the GUI component 162 or via a machine learning model.[000155] The managed cloud storage 132 stores adventitious agent genome sequences 148 that may be broken (or can be pre-processed) into shorter subsections of length k. Sample sequence reads would also be split into k-mers and checked for matches among the stored adventitious agent k-mer sets, allowing faster search times. K-mer length can be optimized based on dataset specifics.[000156] The comparison component 124 itself may perform this hashing and in other embodiments, the k-mer matching component 120 may perform this hashing as described herein. That is, in certain embodiments, the comparison component 124 may implement hash-based matching for efficiency gains. MinHash and other locality sensitive hashing techniques that group similar sequences into the same "buckets" can facilitate faster identification of adventitious matches without having to exhaustivelysearch the entire database. Appropriate hash functions suited for genomic data would need to be adopted.[000157] Some embodiments could have the comparison component 124 leverage suffix trees or arrays as an alternative matching approach. Building a suffix index on the adventitious database sequences allows efficient searching for matches through tracing suffixes back to their origin sequences. Memory usage can be controlled by collapsing suffixes into a condensed subset.[000158] An additional embodiment may equip the comparison component 124 with machine learning and neural networks for sequence matching. Training classification models on labeled adventitious / non-adventitious sequence data can enable intelligent decision making on unidentified sample reads. For enhanced accuracy, unsupervised anomaly detection models may also be employed to recognize novel pathogen variants.[000159] Furthermore, in some embodiments the comparison component 124 may operate in a distributed, cloud-based architecture to harness network-wide computational resources for faster analysis. MapReduce and Spark frameworks could allow large sequence matching workloads to be parallelized across clusters of commodity machines. Appropriate data security mechanisms can be used as well, e.g., via the data security component 111.[000160] The comparison component 124 may also leverage specialized bioinformatics hardware like field-programmable gate arrays (FPGAs) or applicationspecific integrated circuits (ASICs) for accelerated sequence alignment, matching and discovery. These devices can offer faster processing versus general CPUs for suited workloads through dedicated circuits, in some embodiments.[000161] The detection component 152 may determine signal strength parameters, based on the synthetic spike sequences within the test sample, to establish a signal cutoff parameter that aids in the quantification and validation of the detection process. As mentioned previously, during the preparation of the test sample 101, physical synthetic spike sequences (e.g., which may include the synthetic spike sequences 146 in the managed cloud storage 132 for reference) are introduced in known proportions into the test sample 105 as a sensitivity control. The detection component 152 analyzes the sequencing data 105 to evaluate representation levels of these spikedin sequences relative to their inputs.[000162] The proportional representation of spikes in the sequence data 105 can reveal information about the limit of detection and general assay sensitivity for adventitious agent genomes. If spike-in genomes are detected at abundances close to their inputs, the assay demonstrates suitable sensitivity to detect low-levels of microbial contaminants. By contrast, poor spike recovery suggests suboptimal conditions that could increase false negatives for endogenous threats.[000163] The detection component 152 sets percentile cutoffs dynamically based on spike-in performance as part of establishing signal threshold parameters. For example, cutoff criteria may state that microbial genomes detected at over 1% of the representation level of spike sequences will constitute positives. Such cutoffs customize detection confidence to individual run characteristics, preventing inappropriate generalization across samples.[000164] In certain embodiments, the detection component 152 utilizes nonlinear regression models to predict optimal thresholds tuning spike quantity covariates to minimize false positives and negatives. Where multiple spikes of distinct genomes or concentrations allow multifactor modeling. Elsewhere, simpler single-predictor linear thresholds suffice for proportionate positivity designation.[000165] The signal parameters quantified by the detection component 152 further enable precise contamination quantification. By adjusting microbial genome abundance by the same scaling factors applied to cognate spike genomes, absolute adventitious agent copies per sequencing run can be derived. These precise quantities better inform contamination risk assessments.[000166] The detection component 152 may also characterize spike amplification biases like GC content skew during polymerase amplification or sequencing, if applicable or relevant. By evaluating representation evenness across the synthetic spike panel, subtraction of systemic technical biases improves accuracy when adapting sample 101 contamination loads. Storage of spike amplification profiles over time also facilitates troubleshooting.[000167] In one embodiment, the detection component 152 incorporates a machine learning algorithm that is trained on data from samples containing known quantities of synthetic spike sequences. The algorithm leams to distinguish true signals from background noise and can adjust signal cutoffs dynamically based on thecharacteristics of each sample. As more data is fed into the model, it continually updates and improves its accuracy.[000168] Another embodiment of the detection component 152 establishes initial signal cutoffs through a calibration process using samples with varying concentrations of synthetic spikes. The cutoff is set at the minimum level where the spikes are consistently detectable above background. This cutoff can then be adjusted in a samplespecific manner based on the measured signal strength of the spikes in that particular sample.[000169] The detection component 152 may also employ clustering techniques to identify groups of sequences that exhibit similarity above a configurable threshold. These sequence clusters can indicate potential adventitious agents even if some members fall below the standard signal cutoff. The clustering algorithm parameters such as similarity threshold and cluster size cutoff can be tuned to balance sensitivity and specificity.[000170] In certain embodiments, the detection component 152 performs consensus sequence generation of closely related sequences. Variants that differ slightly may actually originate from the same adventitious agent, so combining these sequences can raise their signal above the minimum cutoff. The criteria for grouping sequences into a consensus can be customized based on the level of sensitivity required.[000171] Some embodiments of the detection component 152 utilize advanced statistical models such as latent class analysis to incorporate results from multiple detection methods. By evaluating concordance across different techniques, the statistical model can assign a probability of true or false detection for each sequence that exceeds the standard cutoff levels. This enables more nuanced filtering instead of definitive binary flagging.[000172] The detection component 152 may also leverage external or proprietary databases that catalog common laboratory contaminants. By screening sequences against these contamination databases, detection accuracy can be enhanced by considering alternate explanations for sequence hits besides unknown adventitious agents. The contributory evidence from multiple analysis modules can be integrated through Bayesian statistics or neural networks within the detection component 152.[000173] In cloud-based implementations, the detection component 152 can take advantage of aggregated data from across client labs to identify global trends inbackground sequences versus true adventitious agents. As more labs feed data into a centralized database, the distinction becomes clearer, allowing for improved detection specificity. Federated learning is one technique allowing this aggregation without compromising proprietary data.[000174] In yet additional embodiments, further variations of the detection component 152 can utilize emerging techniques like generative adversarial networks or reinforced heuristic learning to enhance accuracy, sensitivity, and specificity.[000175] The reporting component 154 is configured to compile the results of the analysis into a comprehensive report, which may include information such as the identity and quantity of any detected adventitious agents. This report can be formatted in various ways, including compliance with regulatory documentation standards or as an interactive HTML document with visual representations of the data. The reporting component 154 can generate detailed reports that summarize the identity and quantity of any detected adventitious agents from the analyzed nucleic acid sample 101. These reports synthesize the pertinent details and findings in an organized manner to facilitate review, meet regulatory compliance standards, and enable data-driven decision making regarding the safety and quality of the sample.[000176] The reporting component 154 works in coordination with other system components to obtain the necessary inputs for report generation. It receives data on detected adventitious agents from the comparison component 124 and may also interface with the detection component 152 to incorporate signal strength parameters in assessing contamination levels. The reporting component 154 standardizes this multivariate data into user-friendly reports. The format can be interactive HTML with visualizations, text-based reports compliant with regulatory documentation standards, spreadsheet data outputs, and more. The reporting component 154 enables custom reporting tailored to end-user requirements. Collaborative cloud-based access as facilitated through the GUI component 162 allows real-time data sharing with security controls.[000177] The reporting component 154 can generate report contents and formats that can focus on different types of analysis results based on detection methodology variations. For machine learning approaches, model prediction probabilities and other algorithm outputs can be reported. With blockchain data integrity tracking, immutable hashes and time stamps within transactions can feature in the reports. Automatedregulatory compliance checking facilitated by a Large Language Model reviews reports against international standards, with rule-based algorithms flagging sections in need of revisions for standards alignment. The modular nature of the system 100 allows for the reporting component 154 workflow to adapt according to these varied approaches. The reporting component 154 may have several alternative configurations to generate reports summarizing the analysis results and detected adventitious agents.[000178] In one embodiment, the reporting component 154 utilizes a modular template system that allows users 150 to select report sections from a customizable library of content blocks. For example, users can choose to include or exclude the following sections: overview, methods, results, data visualizations, interpretations, and conclusions. Within each section, pre-formulated paragraphs and data representations catered to different analysis outcomes can be inserted based on relevance. This modular approach enables efficiency in report generation while retaining versatility to meet specific user needs.[000179] Additionally, the modular template library may offer alternate visualizations such as interactive charts, graphs, and genomic maps to represent the data. Some alternatives include heat maps showing quantity and location of sequence matches, circular genomic maps with detected agents annotated, and cladograms depicting taxonomic classifications. Users can select preferred visuals to embed within the customizable report.[000180] In another optional configuration, the reporting component 154 may integrate natural language generation technology to automatically compile analytically- driven narratives. Given the inputs of detected agents, sequence quantities, and other metadata, an Al text generator can produce cohesive summaries in multiple formats - from abstract-style summaries to complete analysis reports conforming to industry standards. This automates report drafting while allowing customization via user-tuned Al models.[000181] Some embodiments could also enable collaborative editing in which multiple authorized users 150 can simultaneously review and refine report drafts in realtime through cloud-based tools. Version histories can track changes for audit trail purposes with customizable user permissions at paragraph / sentence levels. Potential security measures include blockchain verification of edits and Al anomaly detection should suspicious changes arise.[000182] Additionally, functionality could be embedded to check reports against regulatory compliance rules or industry best practices, dynamically flagging problematic phrasing. Users then receive warnings to rectify flagged sections, supporting quality and safety standards vital to biomanufacturing domains.[000183] In certain alternatives, the reporting component 154 may integrate with digital electronic lab notebook (ELN) platforms, allowing two-way editing between automatically generated reports and experimental metadata captured in networked lab notebooks. This facilitates traceability back to raw data while preserving data integrity via access controls.[000184] Other potential alternatives include virus-specific reporting formats with customizable data fields tailored to individual adventitious agents based on user- defined templates. This enhances relevance for viruses of interest while achieving compliance with norms in biomanufacturing safety testing.[000185] The LCA component 158 is responsible for assigning taxonomic classifications to the nucleic acid sequences identified as potential adventitious agent matches. By leveraging phylogenetic hierarchies, this component enhances the precision of contaminant detection.[000186] The LCA component employs an algorithm known as Lowest Common Ancestor (LCA). For each sequence match flagged during the comparison process with database adventitious sequences 148, the LCA identifies the most recent common ancestor shared across genomes of related organisms. The taxonomic rank of this ancestor provides the most specific taxon that can be reliably assigned given broader genomic variability across strains.[000187] Utilizing this LCA approach enables precise classification of detected adventitious sequences based on genetic conservation and mutation patterns across hierarchies like family, genus and species. Rather than broadly categorizing a match as viral, incorporation of phylogenetic context allows more specific identification. This level of resolution focuses follow-up confirmation assays.[000188] The lineages assigned by the LCA component 156 may provide context for interpreting sequence matches. Incorporating this taxonomic information with detection outputs reduces false positives from incidental environmental alignments unrelated to the threat. Customizable tree parameters prevent overspecificity, which may be adjusted via the GUI component 162.[000189] In certain implementations, the LCA component 156 integrates LCA- labeled sequence match data with downstream reporting component 154. Formatting follows canonical standards like Kraken-style reports. Alongside sequence identifiers and matched regions, the reports include taxonomic lineage information down to particular strains. Interactive visualizations give users quick overviews of phylogenies implicated across the contamination profile.[000190] In one alternative embodiment, the LCA component 158 employs a naive Bayes classifier to predict taxa based on sequence composition. This statistical model can be trained on known reference genomes to determine probabilistic assignments of taxonomic lineages. The naive Bayes approach has the benefit of not requiring extensive sequence alignments, thereby improving computational efficiency. [000191] In another embodiment, the LCA component 158 may implement a phylogenetic placement algorithm, locating query sequences on an existing phylogenetic reference tree to infer taxonomy. Methods such as pplacer can rapidly classify sequences by maximum likelihood calculations without reconstructing full phylogenies for each analysis. This phylogenetic placement approach provides a balance of accuracy and speed.[000192] Additionally, the LCA component 158 could employ a k-nearest neighbor algorithm, comparing new sequences to the most homologous training sequences in a reference database like NCBI NT or RefSeq. The taxa of the closest matching references define the taxonomic classification. The simplicity of k-nearest neighbor classifiers allows them to scale efficiently.[000193] In certain embodiments, the LCA component 158 integrates a combinatorial approach, harnessing both composition-based and homology-based methodologies for taxonomic assignments. For example, log-odds ratios determining sequence composition biases could provide a preliminary taxonomic hypothesis, followed by fast phylogenetic placement against NCBI taxonomy for further specification.[000194] To enhance accuracy, downstream filtering steps may be applied in which taxonomic outliers are detected via statistical approaches like interquartile range filters or binomial tests. This additional logic within the LCA component 158 minimizes false classifications.[000195] Some embodiments of the LCA component 158 may implement advanced neural network architectures for taxonomic classification. Convolutional neural networks can systematically learn sequence features and taxonomic relationships from genomic training data.[000196] Some embodiments of the LCA component 158 may allow user-guided selection between multiple algorithms to balance precision, speed, and customization preferences. The modularity of the LCA component 158 facilitates regular integration of new metagenomic classification techniques into the workflow.[000197] In some embodiments, the parallel processing capacity of the cloud service provider 102 may permit running diverse LCA methods concurrently. Ensemble approaches could rapidly classify sequences by consensus voting of multiple algorithms. Hyperparameter optimization routines would tune model configurations to maximize adventitious agent detection accuracy.[000198] In some embodiments, the integration component 158 may facilitate the incorporation of data from various sequencing platforms, expanding the system's compatibility and versatility. The workflow adaption component 160 ensures that the system's processes align with regulatory standards such as Good Laboratory Practice (GLP) and non-GLP environments. By integrating data from different sources, the detection capabilities of the system can be expanded.[000199] The integration component 158 interacts with both the sequencing component 114 that generates the raw nucleic acid sequences, as well as the downstream analysis components like the preprocessing component 116, the comparison component 124, and the reporting component 154. It acts as an intermediate layer, processing the incoming data from the sequencers and formatting it appropriately so that it can be properly analyzed by the other components. The integration component 158 may use techniques like data normalization, compression, and metadata tagging to ensure smooth data integration. In specific embodiments, the integration component may optionally provide APIs and adapters to connect the external data sources with the internal workflow.[000200] There can be many variations of the integration component 158 in terms of the sequencing platforms it supports and the techniques it utilizes for multi -platform data integration. Some embodiments may focus only on next-gen sequencers from Illumina and Oxford Nanopore, while others may incorporate a wider range likePacBio, Roche 454, and more. The component can also leverage technologies like ontologies and semantic mapping to enable integration at the data level. Alternative approaches could rely more on analytics and statistics rather than semantic integration. The integration process itself could be tailored to specific user requirements in a modular fashion rather than as a one-size-fits-all solution.[000201] The GUI component 162 provides a user-friendly interface that enables users to interact with the system, customize the workflow, and analyze the results without the need for extensive technical expertise. This component may be designed to function within a cloud-based operation, allowing for collaborative analysis and data sharing while maintaining data security. Access to the GUI component 162 may be controlled by the user accounts 150 in the database.[000202] In various embodiments, the system 100 may also include additional elements and steps, such as utilizing machine learning algorithms for dynamic adjustment of detection parameters, employing blockchain technology for data integrity tracking, and integrating regulatory compliance checkers to ensure adherence to international biosafety standards. These variations and additions exemplify the system's adaptability and its ability to cater to a wide range of detection and analysis requirements in the field of adventitious agent detection.[000203] Fig. 2 show a block diagram illustration of a computing device for adventitious agent detection in accordance with an embodiment of the present disclosure. The computing device 200 of Fig. 2 may be the computer 104 or mobile device 106 of Fig. 1. The computing device 200 includes an I / O interface 210 to communicate therewithin. The computing device 200 includes a data store 204, a processor 206, a network interface 208, a memory 225, and user I / O devices 226. The data store 204 stores data and may be a hard drive, flash drive, thumb drive, volatile memory, non-volatile memory, semi-volatile memory etc. The processor 206 can execute one or more processor-executable instructions 212, which may be stored in the data store 204 and / or the memory 225. For example, the processor 206 can execute processor-executable instructions 212 stored in memory 225 that was retrieved from the data store 204. The memory 225 also includes program data 214 that may include information related to the processor-executable instructions 212. The computing device 200 may include user I / O devices 226, such as a cursor device 230 (e.g., touchscreen or mouse), a keyboard 232 (virtual or physical), and / or a monitor 228 (which may be atouchscreen). The computing device 200 communicates with the network 202 via a network interface 208.[000204] Although the computing device 200 of Fig. 2 may be used as part of the system 100 of Fig. 1, in some embodiments, the adventitious agent detection may reside wholly within the computing device 200 of Fig. 2. For example, the metagenome constituent detection component 112 of Fig. 1 may reside within the processorexecutable instructions 212 of Fig. 2 as the metagenome constituent detection component 242. The database 244 may be similar to the managed cloud storage 132 of Fig. 1. The database 244 may, for example, be an SQLite 3 database embedded on the computing device 200.[000205] Thus, in some embodiments the metagenome constituent detection component 242 may reside wholly on a local device (such as on the computers 104, the mobile device 106, etc.) may be partially within a cloud service provider 102, and / or may be organized in a hybrid local and cloud configuration. In some embodiments, the metagenome constituent detection component 242 may be an application, may be executed on the computers 104, the mobile device 106, the cloud service provider 102, the computing device 200, etc. or some combination thereof.[000206] Fig. 3 shows a flow chart diagram of a method 300 for adventitious agent detection in accordance with an embodiment of the present disclosure. The method 300 can identify adventitious agents in a nucleic acid sample, e.g., sample 101 of Fig. 1. The method 300 starts with input files containing the nucleic acid sample sequences to analyze as well as relevant reference database sequences. It then progresses through several phases encompassing quality control 306, contaminant filtering 310-318, agent detection 324, quantification 326-332, and finally reporting generation 334-338. The overall goal of method 300 is to screen the nucleic acid sample for microbial contaminants utilizing next-generation sequencing data coupled with specialized algorithms and curated sequence databases.[000207] As on overview of the method 300, each step interacts with other components to execute the detection workflow. The quality control 306 takes the sequence data file and applies trimming and grooming techniques to optimize read quality. Its preprocessed output gets passed to the initial filtering step 308. The k-mer based filters 310 and 314 leverage shingle matches to reference databases 312 and 318 facilitated by the database interface. These filtered results feed into the comparisonmodule 324 which scans for signatures of adventitious organisms utilizing another reference database 322. The synthetic spike analysis 326 shapes threshold cutoffs 332 that determine final detections. Reporting modules 334 tap into the results to compile visualizations 336 and draft documents 338 constituting the final outputs. Overall, method 300 connects discrete data processing units into an integrated end-to-end analysis pipeline.[000208] In terms of implementation, the servers 1-n 121 and virtual server 122 within the cloud infrastructure depicted in Fig. 1 could execute the various components outlined in method 300. Load balancing facilitated by the resource dispatcher 110 would allocate tasks to available servers. Database queries would utilize the database interface 118 to fetch relevant reference data for the managed cloud storage 132. Data transfers between components would leverage high-speed internal networking 108. Output reports and visualizations could ultimately get routed to end user devices 104 or 106 via external networking 108.[000209] Many variations of the method 300 could enhance functionality. Additional quality checks beyond read trimming could identify and mask low complexity repeats. The filtering steps could implement cascade approaches that iteratively screen datasets. The k-mer matching could utilize variable length k values for broad detection. The LCA taxonomy assigner 158 from Figure 1 could add lineage labels. Machine learning model, e.g., in detection component 152, could classify unknown variants. The GUI 162 could allow user-guided customization of parameters and modules. The workflow could integrate emerging data integrity schemes like blockchain.[000210] Act 302 refers to the input of various files that serve as the starting point for the adventitious agent detection workflow. These input files may include a directory containing sequencing data in FASTQ format, data files containing sequencing run metadata, and supplemental report template files. Together these inputs provide the necessary information, including raw nucleic acid sequences, sample attributes, and report formatting guidance, to initiate the analysis pipeline.[000211] Act 302 directly feeds into the next step in the workflow, Act 304, which is a decision point regarding whether to proceed with sequencing read quality control. The input files from Act 302 contain the data that will be processed and filtered through the sequence analysis steps that follow. The FASTQ files with raw sequences undergoquality trimming or other preprocessing work before matching against contamination databases later on. Meanwhile, the supplemental templates input at Act 302 enable automated report drafting once sequences have been scanned for adventitious matches. So Act 302 provides key information resources leveraged in downstream workflow stages.[000212] The input directory, FASTQ files, XML files, sample sheet and supplemental report templates entered in Act 302 may originate from the sequencing instrumentation 103 shown in Fig. 1. As described above, the sequencing instrumentation 103 processes sample nucleic acids to generate raw sequence data in formats like FASTQ while capturing relevant metadata like sample attributes and run parameters. These sequencing outputs are relayed over a network to the cloud-based adventitious detection system, equating to the files fed into Act 302.[000213] In Act 302, in alternative embodiments, the file types could include additional formats beyond FASTQ, XML, and templates, such as BAM sequence alignments or proprietary formats. Input data may also stem from multiple sequencing instruments in a multi-platform workflow integration. Sources beyond sequencing instrumentation are also possible - input could come from public sequence databases or previously filtered datasets in some use cases. The input mechanism can range from a simple upload interface to programmatic pull via developer APIs. Encryption protocols will secure sensitive inputs in transit and at rest. Act 302 flexibly accommodates diverse inputs to spark robust adventitious agent detection through customization meeting user needs.[000214] Act 304 is a decision diamond in the workflow diagram of Fig. 3. The text in the decision diamond reads "Perform Sequencing read QC?". This act involves evaluating if quality control (“QC”) should be performed on the sequencing reads generated from the nucleic acid sample input in Act 302. Quality control processing aims to remove low quality bases and improve read accuracy for downstream analysis. [000215] Act 304 interacts directly with Act 302, receiving the initial FASTQ files, instrument metadata files, SampleSheet, and other inputs. Its output branches lead to two alternative paths: Act 306 if sequencing quality control is to be pursued or Act 308 if quality control is to be skipped. The choice impacts the downstream processes applied to the data. Quality controlling the reads via Act 306 provides cleaned data buttakes additional time, while progressing directly to Act 308 saves time but retains the original raw reads.[000216] The decision in Act 304 of whether or not to conduct quality control could be made programmatically within the sequencing read QC component residing on the virtual server 122 and / or executed by the processor 206. Alternately it could be a manual decision made by the user 150 via the GUI component 162, with user inputs leading to automated branching. Quality parameters and thresholds prompting QC could be predefined or set dynamically based on sample attributes and detection sensitivity requirements.[000217] There are several variations for implementing Act 304 in the workflow. It could be an automated decision point applying machine learning to predict the optimal path. The component could also walk the user through quality report visuals to justify QC needs. Act 304 may be omitted for expedited processing if read accuracy is less crucial. Multi-step workflows are possible where initial QC is followed later by deeper QC. The output branches could also lead to QC processes beyond just Act 306, offering additional quality improvement alternatives before reaching Act 308. Further embodiments may allow sampled QC processing if full analysis is too time intensive.[000218] Act 306 shows a block with the text "Sequencing read QC (e.g. FastP)” that has an arrow pointing to block 308. This act represents a step of performing quality control on the raw sequencing reads generated from the nucleic acid sample using a tool called FastP. The purpose is to clean and filter the reads before downstream analysis.[000219] Act 306 interacts directly with the previous act 304, which is a decision point of whether to perform sequencing read QC. If yes, then act 306 is executed using the initial raw reads as input. The cleaned reads outputted from act 306 then flow directly to the next decision point at act 308.[000220] As shown in Fig. 1, act 306 may be performed by the preprocessing component 116 of Fig. 1. This component can leverage FastP algorithms to trim low quality bases, remove adapter sequences, filter reads, etc. to ensure the integrity of data flowing to subsequent analysis steps. The preprocessing component 116 runs on the virtual server 122 which provides the necessary compute resources.[000221] There are several alternatives and variations on how act 306 could be implemented. Different quality control tools besides FastP may be used, such asTrimmomatic or BBDuk. The parameters and filters applied during QC could also be adjustable based on sample type and sequencing technology. In addition, QC algorithms could be chained together into a pipeline approach if needed. Machine learning models could potentially automate and optimize the QC process over time as more sample data is accumulated. Act 306 could also be performed in a distributed manner leveraging containerization and microservice architectures to enhance scalability. Quality- controlled output read formats may vary beyond FASTQ as needed.[000222] Act 308 is a decision diamond in the workflow with the text "Screen out manufacturing process DNA?" This act determines if manufacturing process nucleic acid should be filtered out from the sample sequences. It has a 'yes' arrow that points to step 310, where manufacturing process filter database matching is performed if manufacturing process nucleic acid is to be screened out. It also has a 'no' arrow that points to step 314 to skip the manufacturing process filter step if manufacturing process nucleic acid does not need to be screened out.[000223] Act 308 interacts with the upstream data preprocessing steps and the downstream filtering steps. The sample sequences that have gone through quality control and initial filtering in steps 302-306 serve as inputs to Act 308. Depending on the decision made in Act 308, the sample sequences will either progress to the manufacturing process filter at step 310 or bypass this filter and go directly to the environmental nucleic acid filter check at 314. The outputs of Act 308 determine what subsequent filtering logic is applied.[000224] The decision in Act 308 could be performed computationally by the adventitious agent detection software component 112 residing on the servers 121 within the cloud platform shown in Fig. 1. The software may have predetermined user-defined parameters set via the GUI component 162 or configuration files that specify whether manufacturing process nucleic acid filtering should be performed. The software can then automatically route the sequences to step 310 or 314 accordingly.[000225] There are several variations on the logic and implementation of Act 308. The manufacturing process nucleic acid filtering criteria could be manually specified by users designed via the user accounts 150 for each sample through the GUI component 162 rather than set as a fixed parameter. The decision could also be made dynamically at runtime based on characteristics of the input sample sequences, using heuristics or machine learning classifiers to determine if manufacturing process nucleicacid screening is needed. Additional logic could be built into Act 308 to selectively filter only certain types of manufacturing sequences rather than all. On the implementation side, Act 308 could be executed on a dedicated module specialized for contamination identification rather than the main software workflow. The output paths could also split the sequence data into parallel tracks both with and without manufacturing filtering rather than a single path. Further alternatives include policybased automated guidelines that set the Act 308 decision based on the sample source. [000226] Act 310, titled "K-mer matching (kraken2)", involves using the k-mer matching algorithm kraken2 to match k-mers between the preprocessed sample sequences and sequences in the manufacturing process filter database. K-mer matching is done to identify sequences in the sample that match known manufacturing process contaminant sequences. Identifying and removing these manufacturing process sequences enhances detection specificity by eliminating confounding signatures unrelated to true adventitious agents.[000227] Act 310 interacts directly with database 312, the manufacturing process filter database 312, by sharing incoming and outgoing arrows. The kraken2 algorithm extracts k-mers from the preprocessed sample sequences and queries database 312 to find matching k-mers that indicate manufacturing process contaminants. These matching k-mers are passed from the filter database 312 back to the kraken2 algorithm in act 310. Act 310 subsequently flags the full reads containing those matching k-mers for removal by downstream filtering steps prior to adventitious agent screening, thereby refining the data or it can filter out the matches at act 310.[000228] Act 310 may be performed by the K-mer matching component 120 and / or the filter component 123 described herein, which implements efficient string matching algorithms to rapidly look up and identify k-mers between the sample data and manufacturing process sequences accessed from the managed cloud storage 132. This hardware or software component analyzes k-mer contents and distributions to categorize sequences as either high-value or unwanted prior to adventitious agent detection. The length of k may be preset or dynamically optimized via machine learning to balance accuracy and performance.[000229] Possible variations of Act 310 include using longer key subsequences beyond typical k-mer lengths to enable more precise sequence matching. The k-mer selection could also be tailored based on manufacturing process contaminantcharacteristics to focus comparisons only on relevant regions. Act 310 could leverage locality-sensitive hashing, minimizer analysis, or Bloom filters for accelerated detection. The algorithm could be upgraded to allow partial matching between k-mers to account for mutations. Cloud computing could parallelize Act 310’s k-mer matching workload across distributed infrastructure to enhance scalability. Act 310 may also incorporate machine learning techniques, like neural networks, to leam patterns in manufacturing sequences over time, improving detection accuracy.[000230] The database 312 is the manufacturing process filter in the automated contamination detection system workflow shown in Fig. 3. This managed cloud storage 132 contains nucleic acid sequences corresponding to common manufacturing process contaminants that may be introduced during production of biopharmaceuticals and vaccines. The database has incoming and outgoing arrows connecting it to Block 310, indicating it provides reference sequence data to and receives query data from the K- mer matching component in that block.[000231] As shown in Fig. 1, the manufacturing process filter database 312 may reside within the overall managed cloud storage 132 (as manufacturing process sequences 144) hosted in the cloud environment 102. It exchanges sequence data with Block 310, which may represent a software module on virtual server 122. The comparison of sample k-mers against manufacturing reference k-mers enables the identification of sequencing reads likely containing manufacturing contaminants unrelated to adventitious agents. Communicating these match results supports later filtering by the filter component 123.[000232] There are several variations for the manufacturing process filter database 312. The sequences it contains could cover vector elements, viral seed stock nucleic acid, host cell genomic signatures, and other production-related contaminants. Its scope and specificity may be tuned to particular manufacturing systems and processes. The database could integrate public repositories of common contaminants or leverage proprietary data on in-house contamination events. It may be updated continuously with additional manufacturing runs to expand coverage. Advanced implementations could utilize machine learning to model manufacturing contaminant profiles, just-in-time database generation parallelizing k-mer extraction, and blockchain infrastructure enabling complete origin tracing of database entries. The database 312 contents, format,access controls and architecture allow significant customization across users and use cases while serving its core purpose of enabling manufacturing contaminant filtering.[000233] Act 314 is a decision diamond in the workflow diagram of Fig. 3. The text in the decision diamond reads "Screen out environmental DNA?". This step determines if environmental nucleic acid sequences will be filtered out from the sample sequences before proceeding to the next step. Environmental nucleic acid refers to nucleic acid sequences from microbes or viruses commonly found in the surrounding environment rather than a true contamination. Removing these sequences can improve detection specificity.[000234] Act 314 interacts directly with act 316 and act 324. If environmental nucleic acid is to be screened out, the "yes" arrow points to act 316, which performs k- mer matching against an environmental filter database 318 to identify and remove sequences matching common environmental organisms. If environmental screening is not needed, the "no" arrow points to act 324 to proceed directly to adventitious agent detection & reporting portion of the method 400.[000235] The filtering described in act 314 may be performed by the filter component 123 shown in Fig. 1. The filter component receives input from the k-mer matching component 120, which identifies matches to environmental sequences stored in the managed cloud storage 132. The matches are passed to the filter component 123, which selectively removes matching sequences to filter out environmental nucleic acid[000236] There are several alternatives and variations for implementing the environmental nucleic acid screening step of act 314. The criteria for filtering could be customized based on user requirements, with adjustable parameters like k-mer length for matching and thresholds for exclusion. Environmental filtering may utilize additional metadata like quality scores or sequence depth to inform decisions. The reference database could focus on particular geographies or sterility categories to narrow environmental matches. Individual users could also opt to bypass or alter this filtering step if environmental background characterization is not needed for their purposes. Machine learning approaches might eventually supplement rule-based filtering to identify unwanted environmental sequences.[000237] Act 316, titled "K-mer matching (kraken2)," involves utilizing the k-mer matching algorithm kraken2 to identify sequences in the sample data that match knownenvironmental contaminant sequences stored in the environmental filter database 318. By matching short overlapping subsequences of length k between the sample data and database, signature sequences derived from common environmental microbes can be flagged for exclusion to improve detection specificity.[000238] Act 316 interacts bidirectionally with the environmental filter database 318, exchanging k-mers to enable sequence matching and identification of environmental contaminants. The kraken2 software extracts all possible k- length words from the preprocessed sample sequences and checks for identical k-mer matches against those derived from database reference sequences. These matches represent potential environmental contaminants.[000239] As shown in Fig. 1, Act 316 may be executed by the K-mer matching component 120, which implements efficient string matching algorithms to rapidly lookup k-mer matches between sample sequences and environmental filter sequences stored in managed cloud storage 132. The matches can then be passed to the filter component 123, which selectively removes full reads containing those flagged k-mers. This refines the data upstream of adventitious agent detection, improving accuracy.[000240] There are several variations for implementing Act 316's k-mer matching approach. The k length could be fixed or dynamically optimized over time via machine learning to balance specificity vs. sensitivity. Matching could allow some mutations between k-mers to account for sequence variability. Cloud computing could enable distributed matching for enhanced scalability. Additional logic may support multi-step cascading filters or taxonomic labeling of matches using LCA component 158. Further embodiments could integrate emerging techniques like locality-sensitive hashing or minimizer representations to focus comparisons.[000241] Act 318 refers to a database named "Environmental filter database" that shares incoming and outgoing arrows with Act 316, "K-mer matching (kraken2)". This database contains nucleic acid sequences from common environmental microbes (like ones found in sequencing reagents) and other adventitious nucleic acid sources unrelated to true contamination events. By providing reference sequence data on environmental contaminants, the database facilitates the identification and filtering of environmental signatures in the sample data to enhance detection specificity.[000242] The environmental filter database interacts directly with Act 316 by exchanging k-mer data used for sequence matching. Act 316 extracts all possiblesubsequence "words" of length k from the sample data and checks these k-mers against those derived from the database entries to find identical strings. These matching k-mers are passed back from the database to Act 316 to enable flagging of reads contaminated by environmental agents. Removing these non-relevant reads refines the data for enhanced adventitious agent screening in downstream steps.[000243] As depicted in Fig. 1, the environmental filter database may reside within the larger managed cloud storage 132 hosted on the cloud platform 102 (e.g., the Environmental Sequences 142), contained alongside other reference sequence groupings. It exchanges k-mer information with the K-mer matching component 120 located on the virtual server 122. By providing sequences known to represent environmental noise, the filter database aids Act 120 and Act 316 in eliminating confounding nucleic acid from sample datasets to improve signal detection. Machine learning algorithms and specialized data structures within the database can optimize storage and lookup speeds to smoothly supply environmental signatures at scale.[000244] There are several variations for the environmental filter database implementation, configuration and integration. The database scope could be expanded beyond microbes to encompass sequences from common lab reagents, personnel, dust, etc. It may incorporate public repositories like UniVec in addition to custom contaminants. Entries could use compressed representations to conserve space. Query mechanisms might leverage locality sensitive hashing for accelerated sequence matching. The database may be frequently updated to capture new environmental agents. Further embodiments could employ neural networks, just-in-time database generation, and on-demand scaling to meet computational demands. Overall, the environmental filter database plays a key role in providing contaminant signatures to remove environmental noise and enhance adventitious agent detection accuracy.[000245] Act 324 "K-mer matching (kraken2)" involves using the k-mer matching algorithm kraken2 to match k-mers between the preprocessed sample sequences and adventitious agent genome sequences stored in the adventitious agent database 322. The k-mer matching process enables efficient identification of short sequences shared between the sample data and known adventitious references, serving as indicators of potential contamination. This matching process compares the inherent nucleic acid composition patterns within sequences reads to signatures derived from hazardousorganisms. Exact k-mer matches become candidate sequences for further review as possible matches to the target adventitious sequences from database 322.[000246] Act 324 leverages incoming sequence data that has undergone the preceding fdtration methods removing confounding manufacturing and environmental matches. This refined dataset enhances the specificity of Act 324's adventitious agent k-mer matching. Meanwhile, k-mer matches identified by Act 324 may be passed to subsequent method components like 326 / 332 to inform contamination quantification cutoff calculations based on signal strength. Detection outputs may also integrate with downstream taxonomic classifiers to enable precise phylogenetic assignments. Reporting steps like 334 further compile Act 324 results into safety assessment documentation and visualizations. So Act 324 interlinks to various upstream and downstream data transformations shaping the detection workflow.[000247] As a software module, Act 324 could reside on the virtual server 122 within the cloud architecture, as shown in Fig. 1. It would execute via the processor 124, accessing the adventitious database 322 sequences by querying the database interface 118. Match outputs get stored in cloud storage like the disk space 128 for downstream access. Act 324 may interface other cloud elements like the data security 111 to enable encrypted lookups and authenticated output access. Distributed Act 324 instances spread matching workloads across server farm 119 resources based on parallelization protocols from the dispatcher 110 balancing loads for scalability. Alternative non-cloud environments could implement Act 324 locally on endpoint devices like desktop computers 104. Standalone hardware configurations are also viable, like a portable detector device with integrated matching logic and reference sequence storage.[000248] In act 324, matching could assess longer k-mer lengths beyond typical values, enhancing specificity at the cost of more computation. Intelligent k-mer size selection may help optimize accuracy. Act 324 could enable partial matching between k-mers to account for mutations using alignments with mismatch tolerance levels set by users 150 or machine learning. Cloud computing enables Act 324 to leverage distributed resources for accelerated matching, while dedicated matching hardware like FPGAs provides another specialty processing option. Match data itself could be transformed into proprietary formats adding metadata like quality grades facilitating custom interpretation. Act 324 results may feed machine learning algorithmsidentifying contamination markers to supplement rule-based matching. While retaining core k-mer matching capabilities, Act 324 offers integration points for emerging methods improving flexibility.[000249] The Microbial database 322 is a specialized database containing nucleic acid sequences from a comprehensive set of potential adventitious microbial agents, including viruses, bacteria, fungi, protozoa, and other microorganisms known to contaminate biomanufacturing processes and impact product safety. It serves as a reference database to enable detection of these hazardous organisms within the sample nucleic acid sequence data.[000250] The Microbial database 322 directly interacts bidirectionally with the K- mer matching block 324. The database provides reference adventitious agent sequences to the k-mer matching algorithms in block 324 to facilitate comparison with sample sequence reads, allowing identification of microbial contaminant genome signatures. Subsequently, the taxonomic assignments and quantified adventitious sequence matches flow back into the Microbial database 322 from block 324 for centralized storage and visualization.[000251] The Database 322 is populated by accumulating genome sequences from culture collection records, previous contamination events, the scientific literature, proprietary safety test datasets across industry, and other trustworthy sources. These reference sequences may be stored and processed directly on infrastructure within the cloud platform 102 depicted in Fig. 1, including the database servers l...n 121, virtual servers 122, and associated high-speed networking 108. Retrieval and updates leverage SQL queries via the database interface component 118 and advanced data analytics capabilities.[000252] The microbial sequences stored in database 322 can span complete genomes or target informative subregions based on adventitious agent characteristics. The database scope covers common viral contaminant families like retroviruses and orthomyxoviruses, bacterial threats like mycoplasmas and spirochetes, and fungal and protozoan organisms problematic for bioprocess purity. Coverage expands continuously with emerging threats like prions or atypical bacteria assessed by machine learning watchdogs. Signatures of related near-neighbor species help balance detection inclusiveness with precision. Overall the microbial database 322 provides an reference for screening the sample sequence data.[000253] Act 328 involves a decision diamond in the workflow diagram with the text "Synthetic control spike?" and a 'yes’ arrow pointing to step 326 titled "Separate signal from noise cutoff derived from spike" and a 'no' arrow pointing to step 330 titled "Separate signal from noise set manually”. The cutoff could be set manually or derived from analytics.[000254] This act checks whether to use a synthetic control spike that has been introduced into the test sample that is being analyzed for adventitious agents. A synthetic spike contains known nucleic acid sequences that are spiked into the sample at predetermined concentrations to serve as sensitivity controls during the analysis. If a synthetic spike is used in Act 328, the 'yes' path leads to Act 326. Here, signal strength parameters derived from the representation of the spiked-in sequences in the sequencing data are used to set a data-driven cutoff distinguishing true positives from background noise. This customized cutoff minimizes false negatives and false positives alike.[000255] If no synthetic spike is identified, the 'no' path from Act 328 leads to Act 330. In this case, user-defined criteria set the signal cutoff levels differentiating adventitious agents from noise. While less tailored than spike-based cutoffs, user inputs still enable configurable contaminant detection.[000256] Act 328 may represent logic within a software module that analyzes sample metadata and sequencing reads to check for telltale signs of a synthetic spike such as atypical GC content or may be set by the GUI component 162 of Fig. 1. Preset identifiers embedded in read headers during library preparation can also indicate a spike. The paths then connect components that derive signal cutoffs based on spike recovery data vs. static user thresholds.[000257] In some embodiments, Act 328 could include machine learning models predicting optimal cutoff parameters instead of predefined rules. Cutoff criteria could also be manually reviewed and adjusted via an interface before finalizing detections. Additional paths splitting the data flow could apply multiple cutoff calculation techniques in parallel. Further embodiments may integrate related spike data like amplification efficiency profiles to select appropriate thresholds.[000258] Act 326 separates the true signal, indicating potential adventitious agent contamination, from background noise in the sequence data. It does this by deriving a cutoff value based on the representation of added synthetic spike-in genomes within the sequenced sample. These spikes act as internal controls that demonstrate the assay'sability to detect low levels of adventitious agents. Their measured abundance compared to the input quantity can gauge the sequencing run’s sensitivity. By scaling microbial matches to spike performance, sample-specific signal cutoffs are created dynamically to separate legitimate threats from noise.[000259] Act 326 interacts directly with the synthetic spike decision branch at Act 328, which checks if spikes were added to the workflow. If yes, Act 326 executes to leverage spike data for signal cutoff calculations using a script. The derived cutoffs get passed directly to Act 332, where final adventitious genomes are determined for reporting. Act 326 also provides key inputs for the quantification enabled in Act 332 by conveying relative sensitivity based on spike recovery.[000260] Referring to Fig. 1, Act 326 may be executed by the detection component 152, which analyzes representation levels of spiked-in sequences provided by the sequencing component 114. The detection component 152 runs quantification algorithms that derive sample-specific cutoffs, calibrating sensitivity based on synthetic spike performance. These scripts parsing spike data to set thresholds for positivity designation could operate on the virtual server 122, which provides computing resources from the underlying physical servers 121. Act 326 integrates spike metadata to uniquely tailor contamination detection responses to individual run characteristics.[000261] In optional embodiment, Act 326 could expand its by using statistical approaches like latent class analysis to help further differentiate noise from true threats. The process could also account for spikes with different known concentrations to enable multi-factor modeling. Additional quality checks could identify and control for amplification biases skewing spike quantification. Heuristic rules could provide fallback signal cutoffs when spikes are unavailable. To enhance workflow integration, Act 326 could support added file formats, sequencing platforms, and spike delivery mechanisms. It could also leverage automation, e.g. via machine learning, to "self-tune" signal filtering over many experiments. And cloud infrastructure could parallelize Act 326's computations for accelerated turnaround times.[000262] Act 330 shows a block with text "Separate signal from noise - cutoff set manually" with an arrow pointing to block 332. This act represents the step of separating true signal, indicating potential adventitious agent contamination, from background noise in the sequence data. A signal cutoff is derived from user-definedparameters to delineate meaningful signals. This focuses downstream analysis on sequences likely representing real threats rather than noise.[000263] Act 330 interacts directly with block 332 by having a directional arrow pointing to it. The signal and noise separation performed in Act 330 refines the sequence dataset passed to block 332, which generates a final taxonomy report of detected adventitious organisms. Appropriately tuned cutoffs in Act 330 ensure minimal false negatives and positives in the final output.[000264] As shown in Fig. 1, Act 330 may be executed by the detection component 152 and utilizes scripts to filter the sequence data. The detection component 152 leverages user-defined metrics captured via the GUI component 162 to categorize sequences as signal or noise based on relative abundance, prevalence, clustering, and other statistical approaches. These parameters are customizable to balance sensitivity and specificity for each run. Act 330 may also derive supplemental signal data like spike- in performance from the quantification component 152 to contextualize cutoff decisions.[000265] There are several alternatives and optional additions to Act 330's implementation. The scripts could employ machine learning algorithms trained on labeled contamination datasets to intelligently delineate signals. Act 330 may interact with downstream reporting component 154 to embed interactive cutoff rationales for user evaluation. When paired with Act 326 for synthetic spike signal analysis, Act 330 could implement multivariate models integrating both endogenous target and spike covariate inputs. Additional logic may qualify or override standard cutoffs if sequence clusters reveal local signal consistency. Further embellishments include outlier detection algorithms to eliminate aberrant noise and negative signal tracking for troubleshooting. The modularity of Act 330 allows its signal parsing approach to adapt to emerging techniques.[000266] Act 332, titled "Generate final taxonomy report" refers to a step in the adventitious agent detection workflow where a final taxonomy report summarizing the identified adventitious agents is generated using a script. This act takes the filtered and matched sequences from previous steps that indicate potential microbial contaminants and compiles them into an organized report detailing the types of organisms detected and their quantities.[000267] Act 332 receives input from the preceding act 330, which establishes cutoff thresholds to separate true signal from background noise. Any sequences exceeding these cutoffs are passed to act 332 as likely positives. The script in act 332 aggregates this data, leveraging taxonomic context provided by the LCA component 158, to generate a final taxonomy summary. The output report from act 332 is then provided to subsequent act 334 titled "Automated Reporting Modules" for further processing and review. So act 332 gathers relevant inputs and structures them into a usable report to enable downstream analysis.[000268] Act 332 may be performed by the reporting component 154, as shown in Fig. 1. This component can compile the filtered data containing suspected microbial sequences and transform it into a readable report. The component 154 may utilize dynamic templates, allowing users 150 to customize report formats for their specific needs. The underlying script facilitates rapid parsing and analysis of sequence identifiers, abundance levels, and attributes to build the taxonomy summary. As the filtered data is retrieved from the comparison component 124, the script assembles the pertinent details on detected microbes. The virtual server 122 provides computing resources needed to generate reports for user access via accounts 150 and interfaces 162.[000269] In Act 332, the script could be upgraded to include more detailed visualizations like interactive phylogenetic trees depicting evolutionary relationships between reported organisms. Automated mapping to regulatory standards may flag sections needing revision. The script could integrate third-party language models to generate free-form interpretable summaries suited for both technical and non-technical audiences. Containerized microservice architectures would allow scaling across distributed cloud infrastructure. Additional modules could enable collaboration via workflows tracking edit histories with blockchain-verified changes. The taxonomy report contents and format may be adapted to focus on specific biological risks associated with detected adventitious sequences tailored to individual process or product needs. The modularity of act 332 allows its implementation details to vary while preserving its core purpose of aggregating classified sequence matches into an informative contamination report.[000270] Act 334 refers to the "Automated Reporting Modules" block. This encompasses the components involved in compiling the results from the adventitiousagent detection process into final output deliverables including visualizations and report documents. The automated reporting modules take the identified sequences matching adventitious agents, along with associated metadata like quantities and taxonomic classifications, and transform these analytical outputs into consumable formats for end users.[000271] The Automated Reporting Modules box contains two elements - the Interactive Data Visualization module (336) and the Draft COA document module (338). The interactive visualizations could include charts, graphs, sequence maps, and other graphics that allow users to explore the contamination results through intuitive interfaces. Meanwhile, the Draft COA (Certificate of Analysis) module auto-generates a standardized document summarizing the key details and conclusions regarding sample safety and adventitious agent status for regulatory compliance. Both modules tap into the centralized results repository that has accumulated data on detected organisms from the comparison component pipeline.[000272] The reporting modules may interface with other components shown in Fig. 1 to compile inputs for visualization and document generation. The comparison component 124 and LCA taxonomy classifier 158 provide core data on identified sequences, quantity metrics, and assigned lineages. The detection component 152 contributes signal cutoff thresholds that inform contamination quantity assessments. The managed cloud storage 132 manages storage of results data that reporting modules query to populate their outputs. The data security component 111 enables encrypted access to sensitive data. The computational resources of the virtual server 122 and physical servers 121 facilitate the reporting workload. So the automated reporting modules leverage other elements of the system while transforming raw analytical outcomes into consumable formats.[000273] Visualizations could be enriched with interactive lineage trees showing phylogenetic hierarchies. Natural language algorithms might auto-generate written analysis text for reports. Further custom report sections could be added to the modular templates based on user needs. Report access controls and edit histories would bolster information security. External ELN platforms could sync experimental metadata with reports. The modules could check documents against regulatory standards, flagging non-compliant sections for revision. Cloud analytics might reveal contamination trends across organizations to improve detections. Overall, the automated reporting modulesenable significant customization, integration, security and collaborative capabilities via the system's modular architecture.[000274] The visualization 336 may be a graphical interface that allows users to dynamically visualize and interact with the data on detected adventitious agents from the analysis workflow. This visualization enables intuitive exploration of the contamination results through interactive charts, graphs, genomic maps, and other data representations.[000275] The "Interactive Data Visualization" module may interact with the other components of the system to obtain necessary inputs for visualization. It interfaces with the comparison component to access adventitious agent detection results and quantification data. It may also connect with the reporting component to reuse visualized data assets within analysis reports. The visualization pulls relevant data extracts to power dynamic graphical renderings customizable via user inputs. Integrated selection tools allow drilling down into contamination specifics. Underlying data remains synchronized during user exploration.[000276] Referring to Fig. 1 , the "Interactive Data Visualization" module referred to in 336 may be a software component residing on the servers 121 within the cloud platform 102. It can generate visualizations server-side then relay them to end user devices 104, 106 for display via browsers or mobile applications. Common web visualization libraries like D3.js may be employed. Visual elements can link back to raw analysis data accessible from cloud storage 128. The module 336 may also leverage GPU computing for accelerated rendering. It could further enable collaborative features where multiple remote users 150 can simultaneously explore contamination data online via shared sessions.[000277] The visualizations could cover orthogonal types like cladograms, circular genomes, and network graphs showing sequence cluster interconnectivity. Layouts may be customizable via drag-and-drop without coding. Datatips could reveal deeper elements like contamination loads and gene markers upon clicking. Machine learning techniques might auto-recommend relevant visualizations based on data trends. Animated transitions could highlight temporal changes across reports. Further interactivity options are possible via VR / AR for multi-sensory engagement. The modular nature of element 336 allows integrating new visualization libraries, features, and technologies over time per user needs.[000278] A document icon 338 labeled "Draft COA" represents the generate document. That is, this represents a draft certificate of analysis (COA) document that is generated as part of the automated reporting modules block 334. The purpose of the COA is to certify and document various quality attributes of a manufactured batch of biopharmaceutical product. The draft COA provides a template report covering details like product specifications, test methods, acceptance criteria, and results demonstrating conformance. The COA gives end users confidence in the safety, identity, purity, strength, and quality of the batch by presenting verification data aligned to predetermined standards. The modular nature of block 334 allows interchangeable report outputs including the draft COA to suit different regulatory documentation needs.[000279] The draft COA symbolized in 338 may be programmatically compiled by the reporting component 154, which can pull detection results and metadata to populate relevant fields and sections. The component 154 could access the comparison component 124 to incorporate adventitious agent screening outcomes and filter component 123 for listing of excluded environmental contaminant reads. The draft would reflect related latest regulatory guidance. A large language model reviewing body text against domain corpus may ensure terminology accuracy. The reporting component 154 workflows allow the modular incorporation of acts 336 and 338 analytics visualizations and draft COA respectively into customized report generation per user 150 preferences set via the GUI component 162. Collaborative editing could enable rapid remote reviews for production readiness.[000280] The draft COA represented by act 338 may be generated by executing reporting workflows on the virtual server 122 within the cloud environment. Template selection and population routines access detection outputs queried from the managed cloud storage 132 via the database interface component 118. Programmatic text generation models customize prose explanations using unstructured metadata. Containerized microservices scale generation loads across server farm 121 resources with intermediate data staging. Final human review before issuing formal COA ensures quality. Failover policies sustain continuity during primary cloud resource downtimes before formal redundancy implementation. Secured blockchain transactions record draft evolution, while access controls limit exposure prior to release. The draft COAact 338 constitutes an integral modular element within the automated compliance documentation architecture.[000281] There are several alternatives and additional embodiments for act 338 beyond a basic COA draft. The document could cover ancillary reports like process validation summaries or stability assessments. It may integrate with digital systems for request / approval routing to accelerate change control versioning. Variable syntax templates allow formatting for distinct end uses and jurisdictions. Hyperlinks could tether representations to underlying raw data for transparency. Cloud collaboration could reduce review turnaround through crowdsourced inputs. External environments enable remote subject matter expert contributions. Augmented text summarization reduces drafting time for accelerated delivery schedules. Underlying neural networks continuously refine written content from multiple sources to enhance cohesion. Cryptographic signing prevents tampering while permitting authenticated revisions prior to finalization. Act 338 demonstrates system adaptability through modular report customization without compromising security or accountability.[000282] Fig. 4 illustrates an exemplary method 400 for detecting adventitious agents in a sample, encompassing a sequence of steps designed to screen for and identify potential microbial contamination.[000283] In some embodiments, step 402 involves adding a synthetic spike to a test sample. The synthetic spike sample can include known quantities of synthetic nucleic acid sequences that simulate potential adventitious agents, providing a control mechanism for subsequent detection processes. In other embodiments, the synthetic spike may constitute sequences from actual adventitious agents. The addition of the synthetic spike sample allows for the calibration of detection sensitivity and the establishment of a threshold for distinguishing true signals from background noise. This step may leverage a range of techniques for spike addition, ensuring consistent integration with the sample nucleic acid .[000284] Step 404 involves sequencing the nucleic acid sample, potentially using next-generation sequencing (NGS) platforms such as Illumina, Oxford Nanopore, PacBio, among others, as reflected in various claims. This step generates a plurality of sample sequences that may include both target and non-target nucleic acid . In some embodiments, the sequencing step can be tailored to accommodate different sequencing technologies and protocols, offering flexibility in processing diverse sample types.[000285] In step 406, preprocessing of the sequences is carried out to prepare the data for more accurate analysis. This may include filtering adapter sequences, correcting base mismatches using quality scores, and other quality control measures. Different tools and algorithms, including but not limited to FastP, Trimmomatic, or BBDuk, may be utilized to optimize the quality of the sequencing reads. This step ensures that the data used in downstream processes is of high integrity, reducing the likelihood of false-positive or false-negative results.[000286] Step 408 encompasses retrieving manufacturing process sequences from a database and filtering these out from the sample sequences. This step may involve k- mer matching, where sequences from the sample are compared to a database of known manufacturing contaminants. Various string-matching algorithms and data structures may be employed to efficiently identify and exclude these sequences, thereby refining the dataset for subsequent steps. This filtering step may be dynamically adjusted or tailored to target specific types of manufacturing sequences.[000287] Similar to step 408, step 410 involves retrieving environmental sequences from a database and filtering these out from the sample sequences. This step removes sequences derived from common environmental microbes that are not indicative of true contamination events. The filtering may involve quality score thresholds and other criteria to ensure that only relevant sequences are retained for further analysis. In some embodiments, advanced machine learning techniques or heuristic rules may be applied to enhance the specificity of environmental nucleic acid screening.[000288] In step 412, adventitious-agent nucleic acid sequences are retrieved from a database for comparison with the sample sequences. This database may include a comprehensive set of sequences from various potential adventitious agents, such as viruses, bacteria, and fungi. The database may be curated to include complete genomes or specific subregions that are informative for detection purposes. Custom-curated databases of minimizers may also be employed to improve detection efficiency.[000289] Step 414 involves comparing the retrieved adventitious-agent nucleic acid sequences with the sample sequences. This comparison may use exact match algorithms or allow for partial matches to account for genetic variations among adventitious agents. In some embodiments, an artificial intelligence model may betrained to identify novel agents not present in the database, enhancing the system's ability to detect emerging threats.[000290] Step 416 sets a cutoff threshold based upon the performance of the synthetic spike sample within the sequenced nucleic acid sample. This step determines signal strength parameters and establishes signal cutoffs dynamically, informed by the representation levels of the spiked-in sequences. Nonlinear regression models or machine learning algorithms may be employed to predict optimal thresholds, ensuring that the detection sensitivity is sample-specific and calibrated.[000291] Step 418, generates a comprehensive report that summarizes the results of the adventitious agent detection process. This report may include the identity and quantity of any detected adventitious agents and may be formatted to comply with regulatory documentation standards. The report may be an interactive HTML document with visual representations of the data, providing a user-friendly interface for analysis. In some embodiments, the system may integrate with digital “ELN”s or other platforms for streamlined documentation and traceability. The report in step 418 may be any report described herein.[000292] Fig. 5 shows a flow chart diagram of adventitious agent detection using an end-to-end analysis having abundance estimation calculations of test sample constituents in accordance with an embodiment of the present disclosure.[000293] Stage 502 of Fig. 5 depicts the "Genomics" component, which encompasses the sequencing and analysis of nucleic acids (NA) from both viable and non viable sources present in a test sample. In various embodiments, this stage represents the genomic analysis phase of the multi-omics approach for metagenome constituent detection. The genomic analysis begins with extraction of nucleic acids from the test sample, which may be derived from a biopharmaceutical product, cell culture used in production of a biopharmaceutical product, or other biological material potentially containing metagenome constituents.[000294] In some embodiments, stage 502 may involve the addition of a synthetic spike sample to the nucleic acid sample prior to sequencing. The synthetic spike sample may comprise a predetermined quantity of synthetic nucleic acid sequences that correspond to representative metagenome constituents or a predetermined quantity of synthetic nucleic acid sequence of a specific predetermined metagenome constituent. This synthetic spike can serve as an internal control for calibrating detection sensitivityand establishing signal thresholds for distinguishing true signals from background noise.[000295] The nucleic acid sample in stage 502 may be sequenced using various next- generation sequencing technologies. In some embodiments, an Illumina sequencing platform may be utilized to generate the plurality of sequences. Alternative sequencing platforms such as Oxford Nanopore, PacBio, or other emerging technologies may also be employed in certain implementations. The sequencing process generates a plurality of nucleic acid sequences representing the genomic content of the sample, which may include sequences from both viable and nonviable organisms.[000296] Following sequencing, stage 502 includes preprocessing of the plurality of sequences to enhance data quality. This preprocessing may include filtering adapter sequences that were introduced during library preparation. In certain embodiments, the preprocessing may further include correcting base mismatches in the plurality of sequences using quality scores associated with each base. Additional quality control measures may be applied, such as trimming low-quality regions or filtering out sequences below a predetermined quality threshold.[000297] In various embodiments, stage 502 involves querying a database to retrieve a plurality of manufacturing process nucleic acid sequences and filtering these sequences out from the plurality of sample sequences. This filtering may be accomplished through k-mer matching, where at least one of the plurality of manufacturing process nucleic acid sequences is matched and subsequently filtered out of the plurality of sequences. Similarly, the process may include querying the database to retrieve a plurality of environmental nucleic acid sequences and filtering these out from the plurality of sequences, also potentially using k-mer matching techniques.[000298] After filtering out manufacturing process and environmental sequences, stage 502 may include querying the database for a plurality of metagenome nucleic acid sequences. These metagenome nucleic acid sequences may represent potential adventitious agents such as viruses, bacteria, and fungi. In some embodiments, the metagenome nucleic acid sequences may be of a length optimized for the detection of a broad range of metagenome constituents.[000299] The comparison process in stage 502 may involve comparing each of the plurality of metagenome nucleic acid sequences to the remaining plurality of sample sequences to detect the presence of metagenome constituents. This comparison mayutilize exact match algorithms to identify matches between the metagenome nucleic acid sequences and the sample sequences. In alternative embodiments, the comparison may include identifying partial matches to account for possible genetic variations of the metagenome constituents.[000300] In some implementations, stage 502 may utilize minimizers from adventitious agent genomes to improve detection efficiency when selecting k-mers based on their frequency of occurrence in a reference database. The plurality of metagenome nucleic acid sequences may comprise a plurality of minimizers, and taxa may be assigned to each of the minimizers based on a lowest common ancestor (LCA) algorithm before comparing the minimizers to the plurality of sequences.[000301] Stage 502 may also include estimating an abundance of the detected metagenome constituent by applying a Bayesian re-estimation algorithm to the plurality of sequences that match the metagenome nucleic acid sequences. This Bayesian reestimation algorithm may be implemented as a post-processing step to the k-mer matching act. The abundance estimation may include reassigning ambiguously classified sequences that were initially assigned to higher taxonomic levels due to limitations of the k-mer matching process. In certain embodiments, the abundance estimation may account for biases in the database introduced by genome size variance and sequence length variation. The Bayesian re-estimation algorithm may produce abundance estimates with an accuracy within 0.04% of expected values in some implementations.[000302] In various embodiments, stage 502 may include characterizing low- abundance strains of the detected metagenome constituent using strain-level genomic analysis. This analysis may be capable of characterizing strains at sequence coverages as low as 0. lx, enabling detection of rare or underrepresented metagenome constituents in the sample.[000303] The genomic analysis in stage 502 may include adjusting for genome size bias in the detection of metagenome constituents. In some embodiments, the comparison of metagenome nucleic acid sequences to sample sequences may include a post-processing step to correct abundance underestimation caused by the lowest common ancestor algorithm. This correction may improve species-level resolution by addressing taxonomic classification biases inherent in the LCA approach used during initial k-mer matching.[000304] Stage 502 may serve as the initial phase in the multi-omics approach depicted in Fig. 5, providing data for subsequent transcriptomic and proteomic analyses. The genomic data generated and analyzed in this stage can be integrated with data from stages 506 and 508 to provide a comprehensive assessment of the presence and viability of metagenome constituents in the sample.[000305] In some embodiments, the results from stage 502 may be compiled into a report summarizing the detected presence of any metagenome constituents, including their identity and quantity. This report may be formatted to comply with regulatory documentation standards or may be presented in an interactive format with visual representations of the data.[000306] Stage 504 of Fig. 5 illustrates the "Quantitation of NA Sample Constituents" component, which represents an analytical phase focused on determining the relative abundance of nucleic acid (NA) constituents identified within the test sample. This stage builds upon the initial genomic sequencing and analysis performed in stage 502, providing enhanced quantitative assessment of the metagenome constituents detected in the sample.[000307] In various embodiments, stage 504 may involve applying a Bayesian reestimation algorithm to the plurality of sequences that match the metagenome nucleic acid sequences, thereby producing more accurate abundance estimates of the detected metagenome constituents. As previously mentioned, the Bayesian re-estimation approach can function as a post-processing step following the k-mer matching performed in stage 502. This statistical framework may allow for refinement of the initial taxonomic classifications and abundance estimates generated during the genomic analysis phase.[000308] The quantitation process in stage 504 may address limitations in some embodiments of an initial k-mer matching approach. In some embodiments, stage 504 can include reassigning ambiguously classified sequences that were initially assigned to higher taxonomic levels due to the predetermined limitations of the k-mer matching act. This reassignment process may enable more precise taxonomic resolution and improved abundance estimation at lower taxonomic ranks, such as genus and species levels.[000309] Stage 504 may also account for biases in the reference database introduced by genome size variance and sequence length variation contained therein.Larger genomes typically generate more k-mer matches than smaller genomes, potentially leading to overrepresentation of organisms with larger genomes in the initial analysis. The quantitation algorithms applied in stage 504 can adjust for these biases, providing more balanced abundance estimates across organisms with varying genome sizes.[000310] In some implementations, the Bayesian re-estimation algorithm utilized in stage 504 may produce abundance estimates with an accuracy within 0.04% of expected values. This high level of accuracy can be particularly valuable when analyzing samples containing closely related species or strains, where precise quantitation may be essential for distinguishing between normal microbiota and potential pathogens or contaminants.[000311] The quantitation process in stage 504 may access k-mer data generated during the act of comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sample sequences performed in stage 502. By leveraging this existing k-mer data, stage 504 can efficiently perform abundance estimation without requiring additional sequence comparisons, potentially reducing computational overhead and processing time.[000312] In certain embodiments, stage 504 may improve species-level resolution by correcting taxonomic classification biases that are part of some embodiments of the lowest common ancestor (LCA) approach used during an initial k-mer matching. The LCA algorithm, while effective for taxonomic assignment, may underestimate abundance at lower taxonomic levels due to its conservative approach to classification. Stage 504 can address this by redistributing reads assigned to higher taxonomic nodes to their likely species of origin based on statistical models and reference database composition.[000313] Stage 504 may include characterizing low-abundance strains of the detected metagenome constituents using strain-level genomic analysis. This capability can be used for detecting rare but potentially significant organisms present in the sample, such as low-level contaminants or emerging pathogens. In some embodiments, this strain- level analysis may be effective at sequence coverages as low as O.lx, enabling detection and quantitation of organisms represented by relatively few sequencing reads.[000314] The quantitation methods employed in stage 504 may vary depending on the specific requirements of the analysis. In some embodiments, tools such as Bracken (Bayesian Re-estimation of Abundance with Kraken) may be utilized to perform the abundance estimation. Alternative approaches may include custom algorithms designed to address specific challenges in metagenome constituent quantitation, such as handling closely related species or dealing with highly variable genome regions.[000315] Stage 504 may incorporate machine learning algorithms to dynamically adjust parameters related to the quantitation process, such as the length of k-mers and the selection criteria for minimizers. These machine learning approaches can analyze data over time and optimize these parameters to improve the accuracy and efficiency of quantifying metagenome constituents. By automatically tuning key variables, the machine learning algorithms may allow the quantitation methodology to adaptively enhance itself based on accumulated sample data.[000316] In some implementations, stage 504 may include generating visualizations of the quantitation results to facilitate interpretation. These visualizations may include relative abundance charts, heatmaps, or other graphical representations that illustrate the composition of the sample in terms of the detected metagenome constituents. Such visualizations can aid in identifying patterns or anomalies in the sample composition that may be relevant for biosafety assessment or quality control purposes.[000317] The results from stage 504 may be incorporated into a comprehensive report that includes both the identity and quantity of detected metagenome constituents. This report may be formatted to comply with regulatory documentation standards or may be presented as an interactive document that allows users to explore the quantitation data in more detail. In some embodiments, the report may include confidence intervals or other statistical measures to indicate the reliability of the abundance estimates.[000318] Stage 504 may serve as a bridge between the initial genomic analysis in stage 502 and the subsequent transcriptomic and proteomic analyses in stages 506 and 508, respectively. The quantitative data generated in stage 504 may provide context for interpreting the results of these other analyses, potentially enabling morecomprehensive assessment of the biological significance of the detected metagenome constituents.[000319] In various embodiments, the quantitation results from stage 504 may be used to inform decision-making processes related to biosafety testing, quality control, or regulatory compliance. For example, the abundance estimates may help determine whether detected adventitious agents are present at levels that pose a risk to product safety or efficacy. Additionally, the quantitation data may be valuable for monitoring trends over time, such as changes in the composition of manufacturing environments or the effectiveness of contamination control measures.[000320] Stage 506 of Fig. 5 depicts the "Transcriptomics" component, specifically focused on the quantitation of transcriptomic data derived from the test sample. This stage represents the second phase in the multi-omics approach for comprehensive metagenome constituent detection and characterization. Transcriptomics analysis examines RNA molecules present in the sample, which can provide valuable insights into the expression patterns and potential viability of detected metagenome constituents.[000321] In various embodiments, stage 506 may involve the analysis of RNA sequencing (RNA-seq) data generated from the same test sample used for genomic analysis in stage 502. The RNA extraction and sequencing process may capture various RNA types, including messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and other non-coding RNA molecules. These RNA molecules can serve as indicators of active gene expression and, by extension, the viability of the organisms present in the sample.[000322] Stage 506 may include determining the viability of detected metagenome constituents by analyzing transcriptomic data derived from the nucleic acid sample. The presence of RNA transcripts corresponding to genomic sequences identified in stage 502 can suggest that the associated organisms are metabolically active and potentially viable, rather than merely represented by residual DNA from non- viable sources. This distinction can be valuable in biosafety testing contexts, where the viability of detected adventitious agents may have significant implications for product safety and quality.[000323] In some implementations, stage 506 may utilize variable-length k-mers to estimate transcript abundance with enhanced accuracy. Unlike the fixed-length k-mers often used in genomic analysis, variable-length k-mers (sometimes referred to as "sig-mers") can provide more flexible and representative sequence matching, potentially improving the precision of transcript quantification. These variable-length k-mers may be particularly effective for handling the complexity and diversity of transcriptomic data.[000324] The transcriptomic analysis in stage 506 may employ a tree structure, such as a suffix tree, to efficiently identify unique variable-length k-mers associated with specific transcript clusters. This approach can enhance computational efficiency while maintaining high sensitivity for transcript detection and quantification. The tree structure may allow for rapid searching and matching of sequence patterns, facilitating the processing of large transcriptomic datasets.[000325] In certain embodiments, stage 506 may incorporate tools such as Fleximer or similar algorithms specifically designed for RNA-seq quantification. These specialized tools can address challenges unique to transcriptomic data, such as alternative splicing, variable expression levels, and the presence of isoforms. By employing algorithms tailored to RNA-seq analysis, stage 506 can provide more accurate and comprehensive assessment of transcriptomic activity within the sample.[000326] Stage 506 may include correlation analyses between the transcriptomic data and the genomic data obtained in stage 502. By comparing the presence and abundance of RNA transcripts with their corresponding DNA sequences, the analysis can provide insights into which genomic regions are actively transcribed. This correlation can help distinguish between actively replicating organisms and those represented only by residual DNA, contributing to the assessment of viability for detected metagenome constituents.[000327] In some implementations, stage 506 may involve differential expression analysis to identify patterns of gene expression that may be indicative of specific biological activities or responses. For example, the expression of genes related to replication, metabolism, or virulence factors may suggest not only the viability of detected organisms but also their potential biological impact. This information can be valuable for assessing the significance of detected metagenome constituents in terms of biosafety or product quality.[000328] The transcriptomic analysis in stage 506 may include normalization procedures to account for variations in sequencing depth, RNA quality, or othertechnical factors that could affect the quantification of transcripts. These normalization methods may include techniques such as RPKM (Reads Per Kilobase of transcript per Million mapped reads), TPM (Transcripts Per Million), or more sophisticated approaches that consider the unique characteristics of metagenomic transcriptomic data.[000329] In various embodiments, stage 506 may employ machine learning algorithms to improve the accuracy of transcript abundance estimation and viability assessment. These algorithms may learn from patterns in the data to better distinguish between true biological signals and technical artifacts, potentially enhancing the reliability of the transcriptomic analysis. The machine learning approaches may be particularly valuable for handling complex or ambiguous cases where traditional analytical methods may be less effective.[000330] Stage 506 may include the generation of visualizations specific to transcriptomic data, such as heatmaps of gene expression, pathway enrichment diagrams, or network analyses showing relationships between expressed genes. These visualizations can facilitate the interpretation of the transcriptomic data and its implications for the viability and activity of detected metagenome constituents. In some implementations, interactive visualizations may allow users to explore the data at different levels of detail or from different analytical perspectives.[000331] The transcriptomic analysis in stage 506 may be tailored to specific types of metagenome constituents, such as viruses, bacteria, or fungi. Different organisms may have distinct transcriptomic signatures or patterns of gene expression that can inform the assessment of their viability and potential impact. By considering the biological characteristics of different organism types, the analysis can provide more nuanced and relevant insights into the composition and activity of the metagenome constituents present in the sample.[000332] In some embodiments, stage 506 may include time-series analysis of transcriptomic data to track changes in gene expression over time. This approach can be used for monitoring the dynamics of microbial communities or the progression of potential contamination events. By analyzing how transcriptomic profiles evolve over time, the analysis may provide insights into the growth, adaptation, or response of metagenome constituents under different conditions.[000333] Stage 506 may integrate with stage 504 to provide a more comprehensive assessment of the abundance and activity of metagenome constituents. While stage 504 focuses on the quantitation of nucleic acid sequences, stage 506 adds the dimension of gene expression, offering insights into which genomic elements are actively transcribed. This integration can enhance the overall understanding of the biological significance of detected metagenome constituents.[000334] The results from stage 506 may be incorporated into a comprehensive report that includes information on both the presence and viability of detected metagenome constituents. This report may present the transcriptomic data alongside the genomic data, highlighting correlations or discrepancies that may be relevant for interpreting the overall results. In some implementations, the report may include recommendations or conclusions based on the integrated analysis of genomic and transcriptomic data.[000335] Stage 506 can serve as a bridge between the genomic analysis in stages 502-504 and the proteomic analysis in stage 508, providing an intermediate layer of biological information that connects DNA sequences to protein expression. By analyzing the transcriptome, stage 506 can offer insights into the functional activity of detected metagenome constituents, complementing the structural information provided by genomic analysis and setting the stage for the proteomic investigation of viability and activity.[000336] Stage 508 of Fig. 5 illustrates the "Proteomics" component, which focuses on the determination of viability via proteomic data analysis. This stage represents the third phase in the multi-omics approach for comprehensive metagenome constituent detection and characterization. Proteomic analysis examines the proteins present in the test sample, providing direct evidence of active protein synthesis and, consequently, offering robust insights into the viability status of detected metagenome constituents.[000337] In various embodiments, stage 508 may involve generating proteomic data from the nucleic acid sample through protein sequencing technologies. These technologies can include mass spectrometry-based approaches, such as liquid chromatography-tandem mass spectrometry (LC-MS / MS), or emerging protein sequencing methods that allow for direct determination of amino acid sequences. The proteomic data generated through these methods can complement the genomic andtranscriptomic data obtained in stages 502 and 506, respectively, providing a more comprehensive view of the biological activity within the sample.[000338] Stage 508 may include determining the viability of detected metagenome constituents by analyzing the proteomic data. The presence of proteins corresponding to genomic sequences identified in stage 502 can provide compelling evidence that the associated entities (e.g., organisms, viruses, etc.) are not only present but also metabolically active and viable. This protein-level confirmation can be particularly valuable in distinguishing between viable entities and residual nucleic acids from non-viable sources, which may be detected in genomic analysis but do not represent active biological threats.[000339] In some implementations, stage 508 may involve aligning protein sequences corresponding to the proteomic data to genomic sequences of the detected metagenome constituents. This alignment process can establish direct connections between the proteins detected in the sample and the genetic material identified in earlier stages. Tools such as Miniprot may be employed for this purpose, utilizing advanced techniques like k-mer sketching and vectorized dynamic programming to efficiently perform protein-to-genome alignment with high accuracy and sensitivity.[000340] The proteomic analysis in stage 508 may include distinguishing between the mere presence of nucleic acids and the active production of proteins by the metagenome constituents. While the detection of DNA or RNA can indicate the presence of an organism, the detection of its proteins provides stronger evidence of viability, as protein synthesis typically requires active cellular machinery. This distinction can be crucial in biosafety testing contexts, where the viability of detected adventitious agents may have significant implications for product safety and quality.[000341] In certain embodiments, stage 508 may involve correlating detected proteins with their corresponding nucleic acid sequences to confirm active protein expression by the metagenome constituents. This correlation analysis can provide a comprehensive view of the flow of genetic information from DNA to protein, offering insights into which genomic regions are not only transcribed (as indicated by transcriptomic data) but also translated into functional proteins. Such multi-level confirmation can enhance confidence in viability assessments.[000342] Stage 508 may include searching for specific proteins that are essential for the replication or pathogenicity of detected metagenome constituents. These mayinclude enzymes involved in DNA replication, structural proteins required for viral assembly, or toxins and virulence factors associated with bacterial pathogens. The detection of such proteins can not only confirm viability but also provide insights into the potential biological impact of the detected organisms.[000343] In some implementations, stage 508 may involve comparing detected proteins against a reference database of proteins known to be produced by viable metagenome constituents. This comparison can help identify and characterize the proteins present in the sample, facilitating the assessment of viability and potential biological significance. Tools such as MMSeqs2 or DIAMOND may be utilized for this purpose, enabling rapid and sensitive searching of protein sequences against comprehensive reference databases.[000344] The proteomic analysis in stage 508 may employ advanced computational approaches to enhance protein identification and characterization. For instance, MMSeqs2 may be used to implement a three-stage search process involving initial k-mer matching, vectorized ungapped alignment, and gapped alignment. This approach can significantly improve the speed and accuracy of protein identification, particularly for low-abundance proteins that may be indicative of viable but rare metagenome constituents.[000345] In various embodiments, stage 508 may incorporate stable isotope labeling techniques, such as SILAC (Stable Isotope Labeling by Amino acids in Cell culture), to enhance the detection and quantification of proteins. These techniques can provide additional layers of information about protein synthesis and turnover, further supporting the assessment of viability for detected metagenome constituents. The use of isotope labeling can also facilitate the distinction between newly synthesized proteins (indicating active metabolism) and pre-existing proteins.[000346] Stage 508 may include the analysis of post-translational modifications (PTMs) of proteins detected in the sample. PTMs, such as phosphorylation, glycosylation, or ubiquitination, can provide insights into the regulatory state and functional activity of proteins. The presence of specific PTMs may indicate not only the viability of the source organisms but also their physiological state or response to environmental conditions, offering a more nuanced understanding of the biological context.[000347] In some implementations, stage 508 may involve the use of protein interaction network analysis to identify functional relationships between detected proteins. By mapping proteins to known interaction networks, the analysis can provide insights into the biological processes and pathways that are active within the sample. This functional context can complement the viability assessment, offering a more comprehensive understanding of the biological significance of detected metagenome constituents.[000348] The proteomic analysis in stage 508 may include quantitative approaches to estimate the abundance of detected proteins. These approaches can range from simple spectral counting methods to more sophisticated techniques like isobaric tagging for relative and absolute quantitation (iTRAQ) or sequential window acquisition of all theoretical mass spectra (SWATH-MS). Quantitative proteomic data can provide insights into the expression levels of different proteins, potentially indicating the relative activity or dominance of different metagenome constituents within the sample.[000349] Stage 508 may incorporate machine learning algorithms to improve the accuracy and sensitivity of protein identification and viability assessment. These algorithms can learn from patterns in the data to better distinguish between true protein signals and technical artifacts, potentially enhancing the reliability of the proteomic analysis.[000350] In certain embodiments, stage 508 may include the generation of visualizations specific to proteomic data, such as protein abundance heatmaps, pathway enrichment diagrams, or network visualizations showing protein interactions. These visualizations can facilitate the interpretation of the proteomic data and its implications for the viability and activity of detected metagenome constituents. Interactive visualizations may allow users to explore the data at different levels of detail or from different analytical perspectives.[000351] Stage 508 may integrate with stages 502, 504, and 506 to provide a comprehensive multi-omics assessment of the presence, abundance, and viability of metagenome constituents. By combining genomic, transcriptomic, and proteomic data, the analysis can offer a more complete and nuanced understanding of the biological composition and activity within the sample. This integrated approach can enhanceconfidence in the conclusions drawn from the analysis, particularly regarding the viability status of detected organisms.[000352] The results from stage 508 may be incorporated into a comprehensive report that includes information on the presence, abundance, and viability of detected metagenome constituents. This report may present the proteomic data alongside the genomic and transcriptomic data, highlighting correlations or discrepancies that may be relevant for interpreting the overall results. The report may include recommendations or conclusions based on the integrated analysis of all three data types, providing a holistic view of the biological significance of the findings.[000353] In some implementations, stage 508 may include regulatory compliance checks to ensure that the proteomic analysis and its conclusions meet relevant standards and guidelines for biosafety testing or quality control. These checks may involve comparing the results against established criteria or thresholds, documenting the analytical methods and their validation, and ensuring traceability of the data and conclusions. Such compliance measures can be essential for applications in regulated industries, such as biopharmaceutical manufacturing or clinical diagnostics.[000354] Stage 508 represents the culmination of the multi-omics approach depicted in Fig. 5, providing the most direct evidence of viability for detected metagenome constituents. By analyzing proteins, which are the functional products of gene expression, stage 508 can offer insights that go beyond the presence of genetic material to address the biological activity and potential impact of the organisms present in the sample. This comprehensive assessment can be used for making informed decisions regarding biosafety, quality control, or regulatory compliance.[000355] Fig. 6 is a schematic diagram 600 illustrating a modular enhancement system for adventitious agent detection in accordance with an embodiment of the present disclosure. The system 600 provides a framework for not only detecting metagenome constituents in biological samples but also for estimating their abundance and determining their viability through a multi-phase approach that integrates genomic, transcriptomic, and proteomic analyses.[000356] The overall system 600 depicted in Fig. 6 illustrates a modular and phased approach to enhancing adventitious agent detection capabilities. The NGS AAT Enhanced system provides the foundation for identifying metagenome constituents in biological samples, while the three expansion phases shown in Fig. 6 offercomplementary analyses that address specific aspects of abundance estimation and viability determination. In some embodiments, these modules may be implemented as independent components that can be selectively applied based on the specific requirements of the analysis. The modular design may allow for flexibility in workflow configuration, enabling users to choose which analyses to perform based on factors such as sample type, testing context, or regulatory requirements. In various implementations, the system 600 may include data integration mechanisms to combine results from multiple modules into comprehensive reports that provide a holistic view of the biological composition and activity within the sample.[000357] In some embodiments, the system 600 may be implemented within a cloud computing environment, allowing for scalable processing of large datasets and facilitating access to the analysis capabilities from various locations. The computational components may be containerized using technologies such as Docker to ensure reproducibility and ease of deployment across different computing environments. In various implementations, the system may include a graphical user interface (GUI) that allows users to configure analysis parameters, monitor workflow progress, and explore results through interactive visualizations. The GUI may be accessible through standard web browsers, enabling use by semi- or non-technical users without requiring specialized software installation.[000358] The system 600 may be designed to comply with regulatory requirements for biosafety testing in different contexts, such as biopharmaceutical manufacturing, vaccine production, or clinical diagnostics. In some embodiments, the system 600 may include validation protocols, audit trails, and other features to support regulatory compliance. The modular nature of the system 600 may allow for customization to meet specific regulatory frameworks or industry standards. In various implementations, the system 600 may incorporate machine learning algorithms that can learn from accumulated data over time to improve detection accuracy, optimize analysis parameters, or identify emerging patterns in contamination events.[000359] The connections between components in Fig. 6, represented by arrows, may indicate data flow pathways through which information is transferred between modules. In some embodiments, these connections may be implemented as standardized data interfaces that ensure compatibility between components while allowing for independent development and updating of individual modules. The dashedlines may represent optional or conditional data flows that may be activated based on specific analysis requirements or user configurations. In various implementations, the system may include data validation checks at interface points to ensure the integrity and compatibility of information as it flows between components.[000360] The system 600 depicted in Fig. 6 represents a approach to adventitious agent detection that goes beyond simple presence / absence determination to address questions of abundance and viability. By integrating multiple analytical modalities — genomics, transcriptomics, and proteomics — the system 600 may provide a more complete and nuanced understanding of the biological composition and activity within tested samples. This multi-omics approach may enhance confidence in safety assessments by providing multiple lines of evidence regarding the presence and biological significance of detected metagenome constituents. The modular design of the system 600 may allow for ongoing enhancement and expansion as new technologies and analytical methods emerge, ensuring that the adventitious agent detection capabilities can evolve to meet future challenges and requirements.[000361] The laboratory environment depicted on the left side of Fig. 6 represents the physical infrastructure where biological sample processing and initial preparation may occur. In some embodiments, the laboratory environment may be divided into two distinct areas: a wet lab area 602 and a dry lab area 603, separated by a dashed vertical line. The wet lab area 602 may be equipped with instrumentation for sample preparation, nucleic acid extraction, library preparation, sequencing, and other experimental procedures requiring physical handling of biological materials. The image within the wet lab section 602 may depict typical laboratory equipment such as sequencers, PCR machines, centrifuges, and other instrumentation necessary for processing biological samples. In various embodiments, the wet lab 602 may include specialized equipment for different types of sample processing, including DNA extraction, RNA isolation, and protein purification, depending on the specific analysis requirements. The dry lab area 603 may house computational resources, data storage systems, and workstations for bioinformatics analysis, where the digital processing of sequencing and other analytical data can take place. In some implementations, the dry lab 603 may include high-performance computing clusters, data visualization stations, and collaborative workspaces for data interpretation and reporting.[000362] The element 604 of Fig. 6 depicts the "NGS AAT Enhanced" system, which represents the adventitious agent testing workflow encircled by an oval. In some embodiments, the NGS AAT Enhanced system may include several key components as illustrated within the green oval. The "Input Directory: FASTQ, XML, SampleSheet, template files" component represents the entry point for raw sequencing data and associated metadata files. These input files may originate from various next-generation sequencing platforms, such as Illumina, Oxford Nanopore, PacBio, or other emerging technologies. The FASTQ files may contain the raw nucleic acid sequences generated from the sample, while XML files and SampleSheet may provide metadata about the sequencing run and sample attributes. Template files may include standardized formats for result reporting and visualization.[000363] The "Screening DB" component within the NGS AAT Enhanced system 604 may represent a database containing reference sequences used for comparison with the sample sequences. In various embodiments, this database may include sequences from manufacturing process contaminants, environmental organisms, and known adventitious agents. The database may be regularly updated to incorporate newly identified sequences and may be customized based on specific manufacturing processes or testing requirements. In some implementations, the database may employ specialized data structures to optimize search efficiency and accuracy.[000364] The "Read Classification" component within the NGS AAT Enhanced system 604 may represent the algorithmic process of comparing sample sequences to reference databases to identify their origin. In some embodiments, this classification may utilize k-mer matching approaches, such as those implemented in tools like Kraken2. The classification process may involve extracting k-mers from sample sequences and comparing them to pre-computed k-mers from reference genomes. In various implementations, the read classification may employ a lowest common ancestor (LCA) algorithm to assign taxonomic classifications to sequences with matches to multiple reference genomes. The classification may be performed at different taxonomic levels, from kingdom down to species or strain, depending on the specificity of the matching results.[000365] The "Results: Identification of Sample Content" component within the NGS AAT Enhanced system 604 may represent the output of the adventitious agent testing workflow. In some embodiments, these results may include the identity ofdetected metagenome constituents, their taxonomic classification, and preliminary abundance information. The results may be formatted according to standardized templates to facilitate interpretation and reporting. In various implementations, the results may include quality metrics, confidence scores, or other indicators of the reliability of the identifications. The output from this component may serve as input to the modular enhancements described in the subsequent phases.[000366] The "Abundance Estimation Expansion" module 606 is a modular enhancement to the NGS AAT Enhanced system. This may focus on improving the accuracy of abundance estimates for detected metagenome constituents, in some specific embodiments. In some embodiments, the module 606 may receive "Classified Reads, Screening DB" as input, which may include the read classification results from the core system along with the reference database used for classification. The input data may flow from the core NGS AAT Enhanced system to the abundance estimation module through a data transfer process that preserves the integrity and context of the classification results.[000367] The "Abundance Estimator" module 606 may be a computational module responsible for refining abundance estimates of detected metagenome constituents. In various embodiments, this component may implement a Bayesian reestimation algorithm, such as that used in tools like Bracken. The abundance estimator may address limitations in the initial k-mer matching approach by reassigning ambiguously classified sequences that were initially assigned to higher taxonomic levels. In some implementations, the abundance estimator may account for biases in the reference database introduced by genome size variance and sequence length variation. The algorithm may produce abundance estimates with high accuracy, potentially within 0.04% of expected values in certain scenarios. The abundance estimator may access k- mer data generated during the read classification process and may employ statistical models to redistribute reads across taxonomic nodes based on the composition of the reference database and the pattern of k-mer matches.[000368] The "Results: Relative Abundance Calculation of Sample Content" component within represents the output of the abundance estimation process. In some embodiments, these results may include refined estimates of the relative abundance of each detected metagenome constituent, expressed as percentages, read counts, or other quantitative metrics. The results may be presented at multiple taxonomic levels,allowing for analysis of community composition from broad categories down to specific species or strains. In various implementations, the abundance results may include confidence intervals or other statistical measures to indicate the reliability of the estimates. The output from this component may be integrated with the core identification results to provide a more comprehensive assessment of the sample composition.[000369] The "Transcriptomic-Based Viability Determination Expansion" module 608 represents another modular enhancement to the system. This module may focus on determining the viability of detected metagenome constituents through analysis of RNA expression. In some embodiments, the module 608 may receive "Classified RNA-Seq Data" as input, which may include sequencing data derived from RNA extracted from the same sample used for DNA analysis. The RNA-Seq data may be classified using similar methods to those applied to DNA sequences, with adaptations specific to RNA analysis. The input data may flow from a separate RNA sequencing process or may be generated alongside the DNA sequencing within the same workflow.[000370] The "RNA-Seq Quantitation" component within module 608 may represent the computational module responsible for analyzing RNA expression levels to assess the viability of detected metagenome constituents. In various embodiments, this component may implement specialized algorithms for RNA-Seq quantification, such as those used in tools like Fleximer. The RNA-Seq quantitation may utilize variable-length k-mers, sometimes referred to as "sig-mers," to improve transcript abundance estimation. In some implementations, the quantitation process may employ a tree structure, such as a suffix tree, to efficiently identify unique variable-length k- mers associated with specific transcript clusters. The algorithm may normalize expression levels to account for variations in sequencing depth, RNA quality, or other technical factors. The RNA-Seq quantitation may correlate expression data with genomic identifications to determine which detected organisms show evidence of active transcription, suggesting viability.[000371] The "Results: Transcript Abundance" component may represent the output of the RNA-Seq quantitation process. In some embodiments, these results may include measures of transcript abundance for genes associated with detected metagenome constituents, providing evidence of active gene expression and, byextension, viability. The results may highlight transcripts related to essential cellular functions, replication, metabolism, or virulence factors, which may be particularly informative for viability assessment. In various implementations, the transcript abundance results may include differential expression analyses, pathway enrichment analyses, or other interpretive frameworks to contextualize the expression data. The output from this component may be integrated with the genomic identification and abundance results to provide a more comprehensive assessment of the biological activity within the sample.[000372] The "Proteomic-Based Viability Determination Expansion" module 610 represents another modular to the system in some embodiments. This module 610 may focus on determining the viability of detected metagenome constituents through analysis of protein expression, providing the most direct evidence of metabolic activity. In some embodiments, the module 610 may receive "Proteomic Sequencing Data" as input, which may include data generated through protein sequencing technologies such as mass spectrometry or emerging protein sequencing methods. The proteomic data may be generated from the same sample used for nucleic acid analysis or from a parallel sample processed specifically for protein analysis. The input data may flow from a separate proteomics workflow or may be integrated within a comprehensive multi- omics analysis pipeline.[000373] The "Protein Identification" component may be the computational module responsible for identifying proteins present in the sample and correlating them with genomic identifications. In various embodiments, this component may implement specialized algorithms for protein sequence analysis, such as those used in tools like MMSeqs2, Miniprot, or DIAMOND. The protein identification process may involve aligning detected protein sequences to reference protein databases or directly to genomic sequences of identified metagenome constituents. In some implementations, the identification may employ advanced techniques such as k-mer sketching, vectorized dynamic programming, or three-stage search processes involving initial k-mer matching, vectorized ungapped alignment, and gapped alignment. The algorithm may distinguish between proteins indicative of viable organisms and those that may persist after cell death, focusing particularly on proteins associated with active metabolism, replication, or pathogenicity.[000374] The "Results: Protein Identity" component represent the output of the protein identification process. In some embodiments, these results may include the identity and abundance of proteins associated with detected metagenome constituents, providing direct evidence of protein synthesis and, consequently, viability. The results may highlight proteins essential for replication, structural proteins required for viral assembly, or toxins and virulence factors associated with bacterial pathogens. In various implementations, the protein identification results may include information on post- translational modifications, protein interactions, or functional annotations to provide context for interpreting the biological significance of the detected proteins. The output from this component may be integrated with the genomic and transcriptomic results to provide a comprehensive multi-omics assessment of the presence, abundance, and viability of metagenome constituents in the sample.[000375] Fig. 7 illustrates a schematic diagram 700 of the NGS Data Qualification for AAT (NGS-DQA) Application, which provides a system for qualifying and verifying adventitious agent detection results. The NGS-DQA application 700 may increase the reliability of adventitious agent detection by implementing two-stage mapping designed to minimize false positives while maximizing true positive detection. This application 700 can serve as a complementary component to the NGS AAT Enhanced workflow described previously, providing additional verification and confidence in the detection results.[000376] The overall system 700 depicted in Fig. 7 shows a workflow that begins with input sequencing reads and progresses through multiple screening stages to produce qualified sequencing reads with taxonomic labels. In various embodiments, the NGS-DQA application 700 may be implemented within the cloud computing environment described in Fig. 1 , leveraging the computational resources of the server farm 121 and virtual server 122. The application may be containerized using Docker or similar technologies to ensure reproducibility and consistent performance across different computing environments. In some implementations, the NGS-DQA application 700 may be integrated directly with the reporting component 154 of Fig. 1 to streamline the generation of comprehensive reports that include both primary detection results and their qualification status.[000377] Element 702 of Fig. 7 represents the "Input Sequencing Reads" component, which serves as the entry point for the NGS-DQA application 700workflow. In various embodiments, these input sequencing reads may be derived from the output of the NGS AAT Enhanced workflow described in previous figures, particularly the plurality of sequences generated during the sequencing step 404 of Fig. 4 and potentially processed through the preprocessing step 406. The input sequencing reads may be provided in FASTQ format, which is a standard file format for storing both nucleotide sequence data and its corresponding quality scores. In some embodiments, the input may specifically consist of paired-end FASTQ files, where each DNA fragment is sequenced from both ends, providing two reads with known relative orientation and approximate distance.[000378] The input sequencing reads 702 may contain a mixture of different sequence types, including reads with viral classification labels assigned during the primary analysis, unclassified reads that did not match any reference sequences during the primary analysis, and potentially reads with false positive assignments that incorrectly matched to adventitious agent sequences. In various implementations, the input sequencing reads may have already undergone quality filtering and adapter trimming where preprocessing the plurality of sequences comprises quality filtering and adapter trimming. This preprocessing may involve the removal of low-quality base calls, adapter sequences introduced during library preparation, and other technical artifacts that could interfere with accurate mapping.[000379] In some embodiments, the input sequencing reads 702 may be accompanied by metadata that provides additional context about the sample, such as the sample type (e.g., biopharmaceutical product, cell culture, etc.), the sequencing platform used (e.g., Illumina, Oxford Nanopore, etc.), and any relevant quality control metrics from the primary analysis. This metadata may be used to customize the subsequent analysis steps or to provide context for interpreting the qualification results. The input sequencing reads component may also include functionality for validating the input files to ensure they meet the required format and quality standards before proceeding with the qualification process.[000380] Element 704 of Fig. 7 depicts the "Screening Database 1" component, which represents the first reference database used in the initial screening stage of the NGS-DQA application. As illustrated in the figure and, this database may comprise host sequences, UniVec sequences, and RefSeq Virus sequences, forming a comprehensive collection of reference sequences for the first round of mapping. Thedatabase is represented visually as a cylindrical structure, indicating its role as a structured data repository within the system.[000381] The host sequences within Screening Database 1 (704) may include genomic sequences from the host organism used in the production of biopharmaceutical products, such as Chinese Hamster Ovary (CHO) cells, Human Embryonic Kidney (HEK) cells, or other common cell lines used in biomanufacturing. In various embodiments, these host sequences may be complete genomes, selected chromosomes, or specific regions known to cause false positive matches in adventitious agent detection. Including host sequences in the first screening database allows the system to identify and filter out reads that originate from the host organism rather than from potential adventitious agents.[000382] The UniVec sequences within Screening Database 1 (704) may comprise a collection of vector sequences, adapters, linkers, and other synthetic DNA commonly used in molecular biology procedures. These sequences, maintained in the UniVec database by the National Center for Biotechnology Information (NCBI), represent common sources of contamination in sequencing data that can lead to false positive matches in adventitious agent detection. In some embodiments, the UniVec sequences may be filtered to include only those most relevant to the specific manufacturing processes being analyzed, while in other embodiments, the complete UniVec database may be incorporated to provide comprehensive coverage.[000383] The RefSeq Virus sequences within Screening Database 1 (704) may include high-quality, manually curated viral genome sequences from the NCBI Reference Sequence Database (RefSeq). These sequences represent well-characterized viral species with complete and accurate genome annotations, providing a reliable reference for identifying viral sequences in the input data. In various implementations, the RefSeq Virus sequences may be selected based on relevance to biopharmaceutical manufacturing, known adventitious agents in specific production systems, or regulatory requirements for specific product types.[000384] In some embodiments, Screening Database 1 (704) may be customizable, allowing users to add or remove specific sequences based on their particular needs or the characteristics of the samples being analyzed. The database may be stored in various formats, such as FASTA for the sequences themselves, accompanied by additional files containing taxonomic information, annotations, orother metadata to enhance the mapping and classification process. The database may also be indexed using methods compatible with the mapping software employed in the subsequent steps, such as Bowtie2 indices, to optimize search efficiency and accuracy. [000385] Element 706 of Fig. 7 illustrates the "Sequencing read screening 1" component, which represents the first stage of the two-stage mapping strategy employed by the NGS-DQA application 700. This component 706 is an active processing module within the system 700. This component 706 step involves mapping at least one of the plurality of sequences using the first screening database 704 to classify the at least one of the plurality of sequences between a qualified sequence read and an unclassified sequence read.[000386] In various embodiments, the sequencing read screening 1 component 706 may utilize Bowtie2, a fast and memory-efficient tool for aligning sequencing reads to reference sequences. Bowtie2 employs a Burrows-Wheeler transform and FM-index approach to compress the reference genome and enable rapid searching. The alignment process may be configured with different sensitivity settings, allowing for exact matches or various degrees of mismatch tolerance to account for sequencing errors or genetic variations. In some implementations, the mapping parameters may be optimized for the specific characteristics of the input data, such as read length, expected error rates, or the presence of particular sequence features.[000387] The sequencing read screening 1 component 706 may process the input reads in batches or streams, depending on the volume of data and available computational resources. For paired-end reads, the component may perform concordant alignment, where both reads in a pair are mapped to the reference sequences with the expected orientation and distance. This approach can improve mapping accuracy by leveraging the additional constraints provided by the paired-end information. In some embodiments, the component may also track mapping quality scores, which indicate the confidence level of each alignment and can be used in downstream filtering or analysis steps.[000388] The output of the sequencing read screening 1 component 706 includes two main categories of reads: those that successfully map to sequences in Screening Database 1 (704) (qualified sequence reads) and those that fail to map (unclassified sequence reads). The qualified sequence reads are passed to the qualified sequencing reads component 708, while the unclassified sequence reads are directed to the database1 unclassified sequencing reads component 710 for further processing in the second screening stage. In some implementations, the component may also generate intermediate files containing detailed alignment information, such as SAM (Sequence Alignment / Map) or BAM (Binary Alignment / Map) files, which can be used for debugging, quality control, or more detailed analysis of the mapping results.[000389] In various embodiments, the sequencing read screening 1 component 706 may include additional functionalities beyond basic mapping, such as filtering based on alignment quality, removing duplicate reads, or performing local realignment around potential insertion / deletion events. These additional processing steps may enhance the accuracy and reliability of the mapping results, reducing the likelihood of false positives or false negatives in the subsequent analysis stages. The component 706 may also include logging and monitoring capabilities to track the progress of the mapping process and identify any potential issues or anomalies in the data.[000390] Element 708 of Fig. 7 represents the "Qualified sequencing reads" component resulting from the first screening stage. This component 708 is depicted as a parallelogram, indicating its role as a data output from the sequencing read screening 1 process. These qualified sequencing reads are sequences that have successfully mapped to reference sequences in Screening Database 1, indicating potential matches to host sequences, UniVec sequences, or RefSeq Virus sequences.[000391] In various embodiments, each qualified sequencing read may be assigned a taxonomic label based on the reference sequence it matched to in Screening Database 1 (704). This taxonomic labeling process may utilize the lowest common ancestor (LCA) algorithm or similar approaches to assign the most specific taxonomic classification possible based on the available evidence. For reads that match multiple reference sequences, the system may assign a higher-level taxonomic classification that encompasses all potential matches, or it may use additional criteria such as alignment quality or coverage to select the most likely match. The taxonomic labels may include standard taxonomic ranks such as kingdom, phylum, class, order, family, genus, and species, as well as strain-level classifications where applicable.[000392] The qualified sequencing reads 708 may retain their original sequence data along with quality scores, but now may be enhanced with additional metadata such as the matched reference sequence identifier, alignment position, alignment quality metrics, and taxonomic classification information. In some implementations, thequalified sequencing reads 708 may be organized or indexed based on their taxonomic classifications to facilitate downstream analysis and reporting. For example, reads matching viral sequences may be grouped separately from those matching host or vector sequences to enable focused analysis of potential adventitious agents.[000393] In some embodiments, the qualified sequencing reads 708 may include information related to functionality for filtering or prioritizing reads based on various criteria, such as alignment quality, coverage depth, or taxonomic specificity. This filtering can help focus subsequent analysis on the most reliable and relevant matches, reducing noise and improving the overall quality of the results. The qualified sequencing reads 708 may also include mechanisms for flagging potential false positives, such as reads that match both host and viral sequences with similar confidence levels, which may require additional scrutiny or validation.[000394] Element 710 of Fig. 7 illustrates the "Database 1 Unclassified Sequencing Reads" 710, which represents the collection of reads that did not successfully map to any reference sequences in Screening Database 1 (704) during the first screening stage. The reads 710 has a role as an intermediate data output that will undergo further processing in the second screening stage. That is, these unclassified sequence reads 710 will be subjected to a second mapping process using a different reference database. The retention of the unclassified sequence reads 710 also allows for traceability and auditability of the qualification process, which can be important for regulatory compliance and quality assurance in biopharmaceutical manufacturing contexts. The retained data may be stored in standardized formats such as BAM files, which efficiently store both the sequence data and its alignment information, or in custom formats designed to capture the specific metadata and taxonomic information generated during the qualification process.[000395] In various embodiments, the database 1 unclassified sequencing reads 710 may retain their original sequence data and quality scores, but may be tagged or flagged to indicate that they have already been processed through the first screening stage without finding matches. This tagging can help prevent redundant processing and maintain the provenance of the data throughout the workflow. The unclassified reads 710 may be stored in standard formats such as FASTQ to preserve both the sequence data and quality scores for the subsequent mapping stage.[000396] The database 1 unclassified sequencing reads 710 may represent a diverse collection of sequences that could include reads from novel or divergent adventitious agents not present in Screening Database 1 (704), reads with sequencing errors or quality issues that prevented successful mapping, or reads from sources not covered by the reference sequences in the first database 704. In some implementations, the system 700 may perform additional quality assessment on these unclassified reads before proceeding to the second screening stage, potentially filtering out reads with extremely low quality or other problematic characteristics that could interfere with accurate mapping.[000397] The database 1 unclassified sequencing reads 710 are directed to the sequencing read screening 2 component 714 for further processing in the second screening stage. This sequential approach allows the system to first identify and classify reads that match well-characterized reference sequences in Screening Database 1 (704), and then to attempt to classify the remaining reads using a broader or different set of reference sequences in Screening Database 2. This strategy can help balance specificity and sensitivity in the qualification process, potentially reducing false positives while still enabling the detection of novel or unusual adventitious agents.[000398] Element 712 of Fig. 7 depicts the "Screening Database 2" component, which represents the second reference database 712 used in the subsequent screening stage of the NGS-DQA application. As illustrated in the figure, this database 712 may comprise RefSeq virus sequences, GenBank virus sequences, and Reference Viral Database (RVDB) sequences, forming a comprehensive collection of viral reference sequences for the second round of mapping. In some embodiments, the databases 712, 704 may reside within the same system, software, and / or data controls.[000399] The RefSeq virus sequences within Screening Database 2 (712) may have some overlap with those in Screening Database 1 (704), providing continuity and consistency between the two screening stages. However, in some embodiments, different subsets of RefSeq virus sequences may be used in each database, with Screening Database 1 (704) perhaps focusing on the most common or well- characterized viral sequences and Screening Database 2 (712) including a broader range of viral species. The inclusion of RefSeq virus sequences in both databases ensures that high-quality, manually curated viral genome sequences are available throughout the qualification process.[000400] The GenBank virus sequences within Screening Database 2 (712) may include a much larger collection of viral genome sequences from the NCBI GenBank database, which contains publicly available DNA sequences submitted by individual laboratories and large-scale sequencing projects worldwide. Unlike RefSeq, which focuses on high-quality, non-redundant sequences, GenBank may contain multiple entries for the same viral species, including partial sequences, variants, and sequences with varying levels of annotation quality. This broader coverage can enable the detection of more diverse viral sequences, including variants or strains not represented in RefSeq, but may also introduce more potential for false positive matches due to the less stringent curation of the database.[000401] The Reference Viral Database (RVDB) sequences within Screening Database 2 may comprise a specialized database designed specifically for virus detection in next-generation sequencing data. RVDB integrates sequences from various sources, including RefSeq, GenBank, and other repositories, with a focus on comprehensive coverage of viral diversity while minimizing false positives through careful curation and annotation. The inclusion of RVDB can enhance the system's ability to detect novel or divergent viral sequences that might not be well-represented in standard reference databases.[000402] In some embodiments, Screening Database 2 (712) may be larger and more diverse than Screening Database 1 , reflecting its role in capturing sequences that were not matched in the first screening stage. The database may be optimized for sensitivity rather than specificity, aiming to classify as many of the remaining unclassified reads as possible, even if this means accepting a higher rate of potential false positives that would need to be evaluated in subsequent analysis steps. In other implementations, Screening Database 2 (712) may be more focused on specific types of viral sequences relevant to the particular biopharmaceutical manufacturing processes or products being analyzed.[000403] The Screening Database 2 (712) may be customizable and may be stored in various formats with appropriate indexing to optimize search efficiency and accuracy. The database 712 may also include additional metadata such as taxonomic information, genome annotations, or sequence quality metrics that can enhance the mapping and classification process. In some embodiments, the database 712 may be regularly updated to incorporate newly sequenced viral genomes or improvedannotations, ensuring that the qualification process remains current with the latest knowledge in viral genomics.[000404] Element 714 of Fig. 7 illustrates the "Sequencing read screening 2" component, which represents the second stage of the two-stage mapping strategy employed by the NGS-DQA application 700. This component 714 has a role as an active processing module within the system 700. The component 714 may map the unclassified sequence reads 710 from the first screening stage using the second screening database 712 to re-classify these reads between a second qualified sequence read and a second unclassified sequence read.[000405] In various embodiments, the sequencing read screening 2 component 714 may utilize the same mapping software as the first screening stage, such as Bowtie2, but with potentially different parameters optimized for the characteristics of Screening Database 2 (712) and the remaining unclassified reads 710. For example, the mapping settings may be adjusted to allow for more mismatches or gaps, enabling the detection of more divergent viral sequences that might have mutations or variations compared to the reference sequences. In some implementations, the component 714 may employ alternative mapping algorithms or approaches specifically designed for detecting distant homologies or novel viral sequences, such as DIAMOND, which uses reduced alphabet amino acid sequences to enhance sensitivity for remote homology detection.[000406] The sequencing read screening 2 component 714 processes the database 1 unclassified sequencing reads 710, attempting to map them to the reference sequences in Screening Database 2 (712). Like the first screening stage, this mapping process may involve various techniques to optimize accuracy and efficiency, such as indexing the reference sequences, employing heuristic search strategies, or leveraging paired-end information when available. In some embodiments, the component may also incorporate additional context from the first screening stage, such as the mapping results of other reads from the same DNA fragment or region, to improve the accuracy of the second-stage mapping.[000407] The output of the sequencing read screening 2 component 714 includes two main categories of reads: those that successfully map to sequences in Screening Database 2 (second qualified sequence reads) and those that fail to map to any reference sequences in either database (second unclassified sequence reads). The second qualifiedsequence reads are passed to the qualified sequencing reads 718, while the second unclassified sequence reads are directed to the database 2 unclassified sequencing reads 716. In some implementations, the component 714 may generate detailed alignment files and logs similar to those produced in the first screening stage, providing comprehensive documentation of the mapping process and results.[000408] In various embodiments, the sequencing read screening 2 component may include additional functionalities beyond basic mapping, such as assembly of contiguous sequences (contigs) from reads that map to the same region of a reference sequence, calculation of coverage statistics across reference genomes, or identification of potential structural variants or recombination events. These advanced analyses can provide deeper insights into the nature and characteristics of the viral sequences detected in the sample, enhancing the overall value of the qualification process. The component may also include mechanisms for flagging potential false positives or ambiguous matches that may require further validation or interpretation.[000409] Element 716 of Fig. 7 represents the "Database 2 Unclassified Sequencing Reads" 716, which comprises the collection of reads that did not successfully map to any reference sequences in either Screening Database 1 or Screening Database 2. In various embodiments, the database 2 unclassified sequencing reads 716 may be retained for further analysis or investigation, as they could potentially represent novel adventitious agents, highly divergent variants of known agents, or other sequences of interest not covered by the reference databases. These unclassified reads may be subjected to additional analyses outside the core NGS-DQA workflow, such as de novo assembly to create longer contiguous sequences, more sensitive homology searches against broader databases, or specialized analyses for particular types of genetic elements or structures. In some implementations, the system may flag samples with unusually high proportions of unclassified reads for additional scrutiny, as this could indicate the presence of unexpected or novel contaminants.[000410] The database 2 unclassified sequencing reads 716 may retain their original sequence data and quality scores, along with metadata indicating that they have been processed through both screening stages without finding matches. This comprehensive tracking of read provenance can be valuable for regulatory compliance and quality assurance purposes, providing a complete record of the qualification process for each read in the input data. In some embodiments, the unclassified reads716 may be stored in standard formats such as FASTQ or in custom formats designed to capture the specific processing history and metadata associated with each read.[000411] In some implementations, the database 2 unclassified sequencing reads 716 may include information related to functionality for categorizing or clustering the unclassified reads based on sequence similarity or other characteristics, even if they cannot be assigned specific taxonomic classifications. This clustering can help identify potential patterns or groups within the unclassified reads that might represent coherent biological entities, such as novel viral species or strains. The may also include mechanisms for estimating the potential significance or relevance of the unclassified reads based on their abundance, quality, or other features, helping prioritize further investigation efforts.[000412] While the database 2 unclassified sequencing reads 716 are not directly incorporated into the main qualification results, they may be included in the reporting outputs of the NGS-DQA application 700 as supplementary information. This inclusion can provide a complete picture of the sequencing data analysis, including not only the classified reads but also those that remain unclassified after the full qualification process. In some embodiments, summary statistics or visualizations of the unclassified reads may be generated to help users understand the proportion and characteristics of the data that could not be classified using the available reference databases.[000413] Element 718 of Fig. 7 depicts the "Qualified sequencing reads" component resulting from the second screening stage. These qualified sequencing reads 718 indicates its role as a data output from the sequencing read screening 2 process. These qualified sequencing reads 718 are sequences that have successfully mapped to reference sequences in Screening Database 2 (712), indicating potential matches to RefSeq virus sequences, GenBank virus sequences, or Reference Viral Database (RVDB) sequences.[000414] In various embodiments, each qualified sequencing read 718 from the second screening stage may be assigned a taxonomic label based on the reference sequence it matched to in Screening Database 2 (712), similar to the taxonomic labeling process in the first screening stage. The taxonomic labeling may utilize the lowest common ancestor (LCA) algorithm or similar approaches to assign the most specific taxonomic classification possible based on the available evidence. For reads that match multiple reference sequences, the system may assign a higher-level taxonomicclassification that encompasses all potential matches, or it may use additional criteria such as alignment quality or coverage to select the most likely match.[000415] The qualified sequencing reads 718 from the second screening stage may include sequences that match to viral species or strains not represented in Screening Database 1 (704), potentially including more divergent or less well- characterized viral sequences. These reads may provide valuable information about the presence of less common or newly identified adventitious agents in the sample. In some implementations, the qualified sequencing reads from both screening stages may be combined or compared to provide a comprehensive view of the viral sequences detected in the sample, with the second-stage results potentially complementing or extending the findings from the first stage.[000416] The qualified sequencing reads 718 during this second stage may retain their original sequence data along with quality scores, but now enhanced with additional metadata such as the matched reference sequence identifier, alignment position, alignment quality metrics, and taxonomic classification information. In some embodiments, the qualified sequencing reads 718 may be organized or indexed based on their taxonomic classifications to facilitate downstream analysis and reporting. The reads may also be filtered or prioritized based on various criteria, such as alignment quality, coverage depth, or taxonomic specificity, to focus subsequent analysis on the most reliable and relevant matches.[000417] In some implementations, the qualified sequencing reads 718 from the second screening stage may include information related to functionality for comparing the taxonomic classifications with those from the first screening stage, identifying consistencies or discrepancies that might provide insights into the reliability or specificity of the classifications. For example, if a viral species is detected in both screening stages, this might increase confidence in its presence in the sample, while discrepancies between the stages might indicate potential ambiguities or complexities in the classification process that warrant further investigation. The component may also include mechanisms for resolving conflicts or integrating information from both screening stages to produce a unified set of qualification results.[000418] Element 720 of Fig. 7 represents the "Automated Reporting Modules" component, which encompasses the various reporting and visualization functionalities of the NGS-DQA application. This component is depicted as a collection of report andvisualization icons, indicating its role in transforming the qualification results into accessible and informative outputs for users. The automated reporting modules receive input from both sets of qualified sequencing reads (from the first and second screening stages) and generate various types of reports and visualizations to communicate the qualification results effectively.[000419] In various embodiments, the automated reporting modules may include several sub-components, as illustrated in the figure: Interactive Data Visualization, Summary Data Tables, and Consensus Sequences. These sub-components represent different approaches to presenting and exploring the qualification results, catering to different user needs and use cases. The modular design of the reporting component allows for flexibility in generating outputs tailored to specific requirements or preferences, while maintaining consistency and coherence in the overall reporting framework.[000420] The Interactive Data Visualization sub-component may provide graphical representations of the qualification results, such as taxonomic distribution charts, coverage maps across reference genomes, alignment visualizations, or other visual formats that help users understand and interpret the data. In some implementations, these visualizations may be interactive, allowing users to explore the data at different levels of detail, filter or highlight specific aspects of interest, or drill down into particular findings for more in-depth examination. The visualizations may be generated using web-based technologies such as D3.js, Plotly, or similar libraries, enabling rich and responsive user interactions within standard web browsers.[000421] The Summary Data Tables sub-component may present the qualification results in tabular format, providing structured and organized access to the key findings and supporting data. These tables may include information such as the taxonomic classifications assigned to the reads, the number or proportion of reads matching each classification, quality metrics for the alignments, and other relevant statistics or metadata. In some embodiments, the tables may be sortable, filterable, or searchable, allowing users to focus on specific aspects of the results or to identify patterns or trends in the data. The tables may be generated in various formats, such as HTML, Excel, CSV, or others, depending on user preferences or system requirements.[000422] The Consensus Sequences sub-component may generate consensus sequences derived from the reads that map to specific reference sequences, providing arepresentation of the actual viral sequences present in the sample rather than just the reference sequences they match to. These consensus sequences can capture samplespecific variations or mutations compared to the reference genomes, offering a more accurate picture of the viral agents present. In some implementations, the consensus sequences may be accompanied by annotations indicating coverage depth, confidence levels, or other quality metrics at each position, helping users assess the reliability of the consensus. The sequences may be provided in standard formats such as FASTA, potentially with additional files containing annotations or quality information.[000423] In various embodiments, the automated reporting modules may include functionality for customizing the reports based on user preferences, regulatory requirements, or specific analysis goals. This customization could involve selecting which types of visualizations or tables to include, specifying the level of detail or granularity in the reports, or defining thresholds or criteria for highlighting or flagging particular findings. The modules may also include mechanisms for exporting the reports in various formats, such as PDF, HTML, or specialized laboratory information management system (LIMS) formats, to facilitate integration with existing workflows or systems.[000424] The automated reporting modules may also include features for tracking and documenting the qualification process itself, such as logs of the parameters used, versions of the reference databases, or other methodological details that may be important for reproducibility or regulatory compliance. This documentation can provide a transparent and traceable record of how the qualification results were generated, enhancing confidence in their validity and reliability. In some implementations, the modules may also include mechanisms for comparing results across different samples or runs, enabling trend analysis or the identification of common patterns or issues.[000425] In some embodiments, the automated reporting modules may leverage machine learning or other advanced analytical techniques to enhance the interpretation and presentation of the qualification results. For example, the modules might employ algorithms to identify potentially significant patterns in the data, flag unusual or unexpected findings for further investigation, or provide automated suggestions or recommendations based on the qualification outcomes. These advanced features canhelp users navigate and make sense of complex qualification results, particularly in cases involving large numbers of samples or diverse adventitious agent profiles.[000426] In various embodiments, the NGS-DQA application represented in Fig. 7 may be implemented using a combination of open-source software tools. These tools may include Docker for containerization and reproducible execution, Bowtie2 for efficient read mapping, samtools for processing and manipulating alignment files, and bcftools for variant calling and analysis. The use of established open-source tools can provide a foundation for the application 700, in some specific embodiments.[000427] The NGS-DQA application may be designed to operate in both GxP (Good Practices, including Good Laboratory Practice, Good Manufacturing Practice, etc.) and non-GxP environments, with appropriate controls and documentation to meet regulatory requirements where applicable. In GxP contexts, the application may include additional features for validation, audit trails, access controls, and other compliance- related functionalities. These features can help ensure that the qualification process meets the stringent standards required for safety testing in biopharmaceutical manufacturing and other regulated applications.[000428] Various alternatives and modifications can be devised by those skilled in the art without departing from the disclosure. Accordingly, the present disclosure is intended to embrace all such alternatives, modifications and variances. Additionally, while several embodiments of the present disclosure have been shown in the drawings and / or discussed herein, it is not intended that the disclosure be limited thereto, as it is intended that the disclosure be as broad in scope as the art will allow and that the specification be read likewise. Therefore, the above description should not be construed as limiting, but merely as exemplifications of particular embodiments. And, those skilled in the art will envision other modifications within the scope and spirit of the claims appended hereto. Other elements, steps, methods and techniques that are insubstantially different from those described above and / or in the appended claims are also intended to be within the scope of the disclosure.[000429] The embodiments shown in the drawings are presented only to demonstrate certain examples of the disclosure. And, the drawings described are only illustrative and are non-limiting. In the drawings, for illustrative purposes, the size of some of the elements may be exaggerated and not drawn to a particular scale.Additionally, elements shown within the drawings that have the same numbers may be identical elements or may be similar elements, depending on the context.[000430] Where the term "comprising" is used in the present description and claims, it does not exclude other elements or steps. Where an indefinite or definite article is used when referring to a singular noun, e.g., "a," "an," or "the,” this includes a plural of that noun unless something otherwise is specifically stated. Hence, the term "comprising" should not be interpreted as being restricted to the items listed thereafter; it does not exclude other elements or steps, and so the scope of the expression "a device comprising items A and B" should not be limited to devices consisting only of components A and B. This expression signifies that, with respect to the present disclosure, the only relevant components of the device are A and B.[000431] Furthermore, the terms "first," "second," "third," and the like, whether used in the description or in the claims, are provided for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances (unless clearly disclosed otherwise) and that the embodiments of the disclosure described herein are capable of operation in other sequences and / or arrangements than are described or illustrated herein.[000432] Each of the characteristics and examples described above, and combinations thereof, may be said to be encompassed by the present disclosure. The present disclosure is thus drawn to the following non-limiting aspects:[000433] (1) A method for detecting metagenome constituents in a sample, the method comprising: adding a synthetic spike sample to a nucleic acid sample; sequencing the nucleic acid sample using next-generation sequencing to generate a plurality of sequences; preprocessing the plurality of sequences; querying a database to retrieve a plurality of manufacturing process nucleic acid sequences; filtering out the plurality of manufacturing process nucleic acid sequences from the plurality of sequences; querying the database to retrieve a plurality of environmental nucleic acid sequences; filtering out the plurality of environmental nucleic acid sequences from the plurality of sequences; querying the database for a plurality of metagenome nucleic acid sequences; and comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sequences to detect a presence of a metagenome constituent.[000434] (2) The method according to aspect 1, wherein the act of filtering out the plurality of manufacturing process nucleic acid sequences from the plurality of sequences comprises k-mer matching at least one of the plurality of manufacturing process nucleic acid sequences and filtering the at least one of the plurality of manufacturing process nucleic acid sequences out of the plurality of sequences.[000435] (3) The method according to aspect 1, wherein the act filtering out the plurality of environmental nucleic acid sequences from the plurality of sequences comprises performing k-mer matching at least one of the plurality of environmental nucleic acid sequences and filtering the at least one of the plurality of environmental nucleic acid sequences out of the plurality of sequences.[000436] (4) The method according to aspect 1, wherein the synthetic spike sample comprises a predetermined quantity of synthetic nucleic acid sequences that correspond to representative metagenome constituents.[000437] (5) The method according to aspect 1, wherein the synthetic spike sample comprises a predetermined quantity of a synthetic nucleic acid sequence of a predetermined metagenome constituent.[000438] (6) The method according to aspects 4 or 5, further comprising determining a signal strength parameter of the synthetic spike sample within the plurality of sequences, wherein a signal cutoff parameter is determined in accordance with the signal strength parameter.[000439] (7) The method according to aspect 1, wherein preprocessing the plurality of sequences includes filtering at least one adapter sequence from the plurality of sequences.[000440] (8) The method according to aspect 1, wherein the act of preprocessing the plurality of sequences further includes correcting base mismatches in the plurality of sequences using quality scores associated with each base.[000441] (9) The method according to aspect 1, wherein the act of filtering out the plurality of manufacturing process nucleic acid sequences includes comparing the plurality of manufacturing process nucleic acid sequences to at least one predetermined manufacturing process contaminant.[000442] (10) The method according to aspect 1, wherein the act of filtering out the plurality of environmental nucleic acid sequences includes comparing the pluralityof environmental nucleic acid sequences to at least one predetermined environmental process contaminant.[000443] (11) The method according to aspect 1, wherein the act of filtering out the plurality of manufacturing process nucleic acid sequences includes applying a quality score threshold to exclude low-quality sequences from the plurality of sequences.[000444] (12) The method according to aspect 1, wherein the act of filtering out the plurality of environmental nucleic acid sequences includes applying a quality score threshold to exclude low-quality sequences from the plurality of sequences.[000445] (13) The method according to aspect 1, wherein the act of querying the database for the plurality of metagenome nucleic acid sequences involves selecting k- mers based on their frequency of occurrence in a reference database of adventitious agents.[000446] (14) The method according to aspect 13, wherein the act of selecting the k-mers based on their frequency of occurrence utilizes minimizers from adventitious agent genomes to thereby improve detection efficiency.[000447] (15) The method according to aspect 1, wherein the act of comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sequences includes using an exact match algorithm to identify matches.[000448] (16) The method according to aspect 1, wherein the act of comparing includes identifying partial matches between the metagenome nucleic acid sequences and the plurality of sequences to account for possible genetic variations of the metagenome constituents.[000449] (17) The method according to aspect 1, further comprising the act of generating a report summarizing the detected presence of any metagenome constituents, the report including an identity and a quantity of the detected metagenome constituents. [000450] (18) The method according to aspect 1, wherein the nucleic acid sample is derived from a biopharmaceutical product.[000451] (19) The method according to aspect 1, wherein the nucleic acid sample is derived from a cell culture used in a production of a biopharmaceutical product.[000452] (20) The method according to aspect 1, wherein the next- generation sequencing is performed using an Illumina sequencing platform.[000453] (21) The method according to aspect 1 , wherein the metagenome nucleic acid sequences are of a length optimized for the detection of a broad range of metagenome constituents.[000454] (22) The method according to aspect 1, wherein the metagenome constituents include at least one selected from the group consisting of viruses, bacteria, and fungi.[000455] (23) The method according to aspect 1, wherein the plurality of metagenome nucleic acid sequences comprises a plurality of minimizers, and the method further comprises the act of assigning taxa to each of the minimizers based on a lowest common ancestor (“LCA”) algorithm before comparing the minimizers to the plurality of sequences.[000456] (24) The method according to aspect 23, wherein the act of assigning the taxa includes using the LCA algorithm to determine a most specific taxon for each of the minimizers.[000457] (25) The method according to aspect 24, wherein the act of comparing the minimizers to the plurality of sequences utilizes taxonomic information assigned to each minimizer to thereby enhance a specificity of detecting the metagenome constituent.[000458] (26) The method according to aspect 1, further comprising the act of generating a report file that summarizes comparison results between the plurality of metagenome nucleic acid sequences and the plurality of sequences, wherein the report file includes taxonomic classifications for the detected metagenome constituents based on an LCA algorithm.[000459] (27) The method according to aspect 26, wherein the report file is formatted to comply with a Kraken file format.[000460] (28) The method according to aspect 1, further comprising the act of presenting results of the detection of the metagenome constituent through a graphical user interface (GUI) configured to enable user interaction for analysis.[000461] (29) The method according to aspect 1, further comprising the act of generating an interactive report summarizing a detected presence of the metagenome constituents, the interactive report being in HTML format and includes interactive visual representations.[000462] (30) The method according to aspect 1, further comprising the act of assessing the viability of the metagenomic constituent.[000463] (31) The method according to aspect 1, wherein the act of filtering further comprises the act of differentiating between a true positive match and a false positive match based on predetermined criteria.[000464] (32) The method according to aspect 1, wherein the comparing act includes an artificial intelligence model trained to identify novel metagenome constituents not present in the database trained on structures and sequences of predetermined agents.[000465] (33) The method according to aspect 1, further comprising the act of securing and tracking data integrity and analysis results using a blockchain ledger.[000466] (34) The method according to aspect 1, further comprising utilizing a machine learning algorithm to dynamically adjust a k-mer length and a minimizer selection criterion.[000467] (35) The method according to aspect 1, further comprising reviewing a report against current international biosafety standards to flag sections requiring attention to meet compliance.[000468] (36) The method according to aspect 35, wherein an automated regulatory compliance checker is a Large Language Model (“LLM”) checker configured to perform the act of reviewing the report.[000469] (37) The method according to any one of aspects 1 to 36, wherein the plurality of metagenome nucleic acid sequences is a plurality of adventitious-agent nucleic acid sequences.[000470] (38) The method according to aspect 1, further comprising estimating an abundance of the detected metagenome constituent by applying a Bayesian reestimation algorithm to the plurality of sequences that match the metagenome nucleic acid sequences.[000471] (39) The method according to aspect 38, wherein the Bayesian reestimation algorithm is implemented as a post-processing step to a k-mer matching act. [000472] (40) The method according to aspect 38, wherein the act of estimating the abundance includes reassigning ambiguously classified ones of the plurality of sequences that were initially assigned to higher taxonomic levels due to a predetermined limitation of a k-mer matching act.[000473] (41) The method according to aspect 38, wherein estimating the abundance includes accounting for biases in the database introduced by a genome size variance and a sequence length variation contained therein.[000474] (42) The method according to aspect 38, wherein the Bayesian reestimation algorithm produces abundance estimates with an accuracy within 0.04% of expected values.[000475] (43) The method according to aspect 38, wherein the abundance estimation act is performed by accessing k-mer data generated during the act of comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sequences to detect the presence of the metagenome constituent.[000476] (44) The method according to aspect 38, wherein estimating the abundance includes improving species-level resolution by correcting taxonomic classification biases inherent in the lowest common ancestor approach used during initial k-mer matching.[000477] (45) The method according to aspect 1, further comprising characterizing low-abundance strains of the detected metagenome constituent using a strain-level genomic analysis.[000478] (46) The method according to aspect 1, further comprising determining viability of the detected metagenome constituent by analyzing transcriptomic data derived from the nucleic acid sample.[000479] (47) The method according to aspect 46, wherein the act of determining the viability of the detected metagenome constituent includes using a variable-length k-mer to thereby estimate a transcript abundance.[000480] (48) The method according to aspect 47, further comprising utilizing a tree structure to identify at least one unique variable-length k-mer when using the variable-length k-mer associated with at least one transcript cluster having the transcript abundance estimate.[000481] (49) The method according to aspect 1, further comprising generating proteomic data of a protein / peptide extract from a matrix having the nucleic acid sample.[000482] (50) The method according to aspect 49, further comprising determining viability of the detected metagenome constituent by analyzing the proteomic data.[000483] (51) The method according to aspect 50, wherein the act of analyzing the proteomic data includes aligning at least one protein sequence corresponding to the proteomic data to a genomic sequence of the detected metagenome constituent.[000484] (52) The method according to aspect 1, further comprising adjusting for a genome size bias in the detection of the metagenome constituent.[000485] (53) The method according to aspect 1, wherein the act of comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sequences includes a post-processing step to correct abundance underestimation caused by a lowest common ancestor algorithm.[000486] (54) The method according to aspect 1 , further comprising analyzing the nucleic acid sample using a multi-omics approach integrating genomic, transcriptomic, and proteomic data to assess a presence and a viability of the metagenome constituent. [000487] (55) The method according to aspect 1, further comprising detecting proteins produced by the metagenome constituent to confirm viability of the metagenome constituent.[000488] (56) The method according to aspect 55, further comprising estimating a relative abundance of the detected metagenome constituent using a Bayesian statistical algorithm.[000489] (57) The method according to aspect 55, further comprising determining whether the detected metagenome constituent is viable based on transcriptomic or proteomic analysis.[000490] (58) The method according to aspect 55, further comprising characterizing at least one low-abundance strain in the nucleic acid sample at sequence coverages as low as 0. lx.[000491] (59) The method according to aspect 1, further comprising determining viability of the detected metagenome constituent by analyzing protein sequences present in the nucleic acid sample.[000492] (60) The method according to aspect 59, wherein determining viability includes distinguishing between a mere presence of nucleic acids and the active production of proteins by the metagenome constituent.[000493] (61) The method according to aspect 59, wherein determining viability comprises correlating detected proteins with their corresponding nucleic acid sequences to confirm active protein expression by the metagenome constituent.[000494] (62) The method according to aspect 59, wherein determining viability includes searching for at least one protein that is essential for the replication or pathogenicity of the detected metagenome constituent.[000495] (63) The method according to aspect 62, wherein analyzing protein sequences comprises comparing detected proteins against a reference database of proteins known to be produced by viable metagenome constituents.[000496] 64) The method according to aspect 1, wherein preprocessing the plurality of sequences comprises quality filtering and adapter trimming of the plurality of sequences.[000497] (65) The method according to aspect 1, further comprising providing a first screening database and second screening databases each configured to minimize false positives in the plurality of sequencies.[000498] (66) The method according to aspect 65, mapping at least one of the plurality of sequences using the first screening database to classify the at least one of the plurality of sequences between a qualified sequence read and an unclassified sequence read.[000499] (67) The method according to aspect 66, further comprising mapping the unclassified sequence read using the second screening database to re-classify the unclassified sequence read to between a second qualified sequence read and a second unclassified sequence read.[000500] (68) The method according to aspect 1, wherein the database comprises a first screening database and a second screening database.[000501] (69) The method according to aspect 68, wherein the first screening database comprises host, UniVec, and RefSeq Virus sequences.[000502] (70) The method according to aspect 68, wherein the second screening database comprises RefSeq virus sequences, GenBank virus sequences, and Reference Viral Database (RVDB) sequences.[000503] (71) The method according to aspect 68, further comprising assigning a taxonomic label to sequences from the plurality of sequences using the first and second screening databases.[000504] (72) A system for detecting metagenome constituents in a sample, the system comprising: a sequencing component configured to receive a plurality of sequences of a nucleic acid sample having a synthetic spike; a preprocessing componentconfigured to preprocess the plurality of sequences; a database interface component configured to query a database to retrieve at least one of a plurality of manufacturing process nucleic acid sequences, a plurality of environmental nucleic acid sequences, and a plurality of metagenome nucleic acid sequences; a k-mer matching component configured to find a match of the plurality of sequences to at least one of the plurality of manufacturing process nucleic acid sequences, the plurality of environmental nucleic acid sequences, and the plurality of metagenome nucleic acid sequences; a filter component in operative communication with the k-mer matching component, the filter component configured to filter out the match when it corresponds to one of the plurality of manufacturing process nucleic acid sequences and the plurality of environmental nucleic acid sequences; and a comparison component in operative communication with the k-mer matching component, wherein the comparison component is configured to compare each of the plurality of metagenome nucleic acid sequences to the plurality of sequences wherein the match thereby detects a presence of a metagenome constituent. [000505] (73) The system according to aspect 72, wherein the k-mer matching component is configured to only match the plurality of sequences to the plurality of manufacturing process nucleic acid sequences.[000506] (74) The system according to aspect 72, wherein the k-mer matching component is configured to only match the plurality of sequences to the plurality of environmental nucleic acid sequences.[000507] (75) The system according to aspect 72, wherein the k-mer matching component is configured to only match the plurality of sequences to the plurality of metagenome nucleic acid sequences.[000508] (76) The system according to aspect 72, wherein the synthetic spike includes a nucleic acid sequence that correspond to potential adventitious agents.[000509] (77) The system according to aspect 72, wherein the synthetic spike is a reference nucleic acid sequence of an adventitious agent.[000510] (78) The system according to aspects 72 or 77, further comprising a detection component configured to determine a signal strength parameter of the synthetic spike within the plurality of sequences, wherein a signal cutoff parameter is determined in accordance with the signal strength parameter.[000511] (79) The system according to aspect 72, wherein the preprocessing component is configured to filter adapter sequences from the plurality of sequences.[000512] (80) The system according to aspect 72, wherein the preprocessing component is further configured to correct base mismatches in the plurality of sequences using a respective quality score associated with each base of the plurality of sequences.[000513] (81) The system according to aspect 72, wherein the filter component is configured to filter out the plurality of manufacturing process nucleic acid sequences by comparing the plurality of sequences to the plurality of manufacturing process nucleic acid sequences.[000514] (82) The system according to aspect 72, wherein the database interface component is configured to select k-mers based on their frequency of occurrence in a reference database of adventitious agents.[000515] (83) The system according to aspect 82, wherein the database interface component includes a custom-curated database of minimizers from metagenome constituent genomes for improved detection efficiency.[000516] (84) The system according to aspect 72, wherein the comparison component is configured to use an exact match algorithm to identify matches between the plurality of metagenome nucleic acid sequences and the plurality of sequences.[000517] (85) The system according to aspect 72, wherein the comparison component is configured to identify partial matches between the metagenome nucleic acid sequences and the plurality of sequences to account for possible genetic variations of the metagenome constituents.[000518] (86) The system according to aspect 72, further comprising a reporting component configured to generate a report summarizing the detected presence of any metagenome constituents, including an identity and a quantity of the detected metagenome constituents.[000519] (87) The system according to aspect 72, wherein the nucleic acid sample is derived from a biopharmaceutical product.[000520] (88) The system according to aspect 72, wherein the sequencing component is configured to receive the nucleic acid sample from a cell culture used in a production of a biopharmaceutical product.[000521] (89) The system according to aspect 72, wherein the sequencing component is configured to receive data from next-generation sequencing using an Illumina sequencing platform.[000522] (90) The system according to aspect 72, wherein the database interface component is configured to handle metagenome nucleic acid sequences of a predetermined length optimized for the detection of a broad range of metagenome constituents.[000523] (91) The system according to aspect 72, wherein the comparison component is configured to detect metagenome constituents that include at least one selected from the group consisting of viruses, bacteria, and fungi.[000524] (92) The system according to aspect 72, wherein the database interface component comprises a plurality of minimizers and is configured to assign taxa to each of the minimizers based on a lowest common ancestor (LCA) algorithm performed by a LCA component before comparing the minimizers to the plurality of sequences.[000525] (93) The system according to aspect 92, wherein the LCA component is configured to determine a specific taxon for each of the minimizers.[000526] (94) The system according to aspect 93, wherein the comparison component is configured to use the specific taxon assigned to each minimizer to thereby enhance a specificity of detecting the metagenome constituent.[000527] (95) The system according to aspect 72, further comprising a reporting component configured to generate a report file that summarizes comparison results between the plurality of metagenome nucleic acid sequences and the plurality of sequences, wherein the report file includes taxonomic classifications for the detected metagenome constituents based on an LCA algorithm.[000528] (96) The system according to aspect 95, wherein the reporting component is configured to format the report file to comply with a Kraken file format. [000529] (97) The system according to aspect 72, further comprising a graphical user interface configured to present results of the detection of the metagenome constituent and enable user interaction for analysis without requiring extensive technical knowledge.[000530] (98) The system according to aspect 72, further comprising a reporting component configured to generate an interactive report summarizing the detected presence of the metagenome constituents, the interactive report being in HTML format and including interactive visual representations.[000531] (99) The system according to aspect 72, further comprising a workflow adaptation component configured for compliance with Good Laboratory Practice (GLP)and non-GLP environments, including acts specific to regulatory requirements of each environment.[000532] (100) The system according to aspect 72, wherein the comparison component utilizes a script to differentiate between true and false positive matches based on predetermined criteria.[000533] (101) The system according to aspect 72, further comprising a GUI interface allowing for modular customization of data preprocessing, metagenome constituent detection, and reporting based on user requirements.[000534] (102) The system according to aspect 72, further comprising an artificial intelligence model trained to identify potentially novel metagenome constituents not present in the database trained on structures and sequences of predetermined agents.[000535] (103) The system according to aspect 72, further comprising a data security component configured to secure and track data integrity and analysis results using a blockchain ledger, wherein each act in the detection is recorded as a transaction on the blockchain ledger.[000536] (104) The system according to aspect 72, wherein the k-mer matching component further comprises a machine learning algorithm configured to dynamically adjust a k-mer length.[000537] (105) The system according to aspect 72, wherein a GUI component is configured for cloud-based operation to allow for collaborative analysis and real-time data sharing among multiple users or institutions while maintaining data confidentiality. [000538] (106) The system according to aspect 72, further comprising a reporting component having an automated regulatory compliance checker that is configured to review reports against current international biosafety standards and flags sections requiring attention to meet compliance.[000539] (107) The system according to aspect 106, wherein the automated regulatory compliance checker is a Large Language Model (“LLM”) checker.[000540] (108) The system according to any one of aspects 72-107, wherein the plurality of metagenome nucleic acid sequences is a plurality of adventitious-agent nucleic acid sequences.[000541] (109) The system according to aspect 1, wherein the nucleic acid sample includes nucleic acid, proteins, peptides, and a medium.[000542] (HO) A non-transitory computer-readable medium having instructions stored thereon, which when executed by a processor, cause the processor to perform a method for detecting metagenome constituents in a sample, the method comprising: adding a synthetic spike sample to a nucleic acid sample; sequencing the nucleic acid sample to generate a plurality of sequences; preprocessing the plurality of sequences; querying a database to retrieve a plurality of nucleic acid sequences related to manufacturing processes, environmental sources, and metagenomes; filtering out manufacturing process and environmental nucleic acid sequences from the plurality of sequences; and comparing the plurality of sequences with metagenome nucleic acid sequences to detect the presence of metagenome constituents.[000543] (H l) The non-transitory computer-readable medium of aspect 110, wherein the preprocessing of the plurality of sequences includes filtering adapter sequences and correcting base mismatches in the plurality of sequences using quality scores.[000544] (112) The non-transitory computer-readable medium of aspect 110, wherein the comparing step includes using exact match and partial match algorithms to account for genetic variations of the metagenome constituents and to identify matches between the metagenome nucleic acid sequences and the plurality of sequences.[000545] (113) The non-transitory computer-readable medium of aspect 110, wherein the method further comprises generating a report summarizing the detected presence of any metagenome constituents, including identity and quantity of the detected metagenome constituents, and formatting the report to comply with a predefined file format.[000546] (H4) The non-transitory computer- readable medium of aspect 110, wherein the synthetic spike sample comprises a predetermined quantity of synthetic nucleic acid sequences that correspond to representative metagenome constituents, and the method further comprises determining a signal strength parameter of the synthetic spike sample within the plurality of sequences.[000547] (115) The non-transitory computer-readable medium of aspect 110, wherein the method further comprises presenting results of the detection of the metagenome constituent through a graphical user interface (GUI) configured to enable user interaction for analysis, including generation of interactive reports in HTML format with interactive visual representations.

Claims

What is claimed is:

1. A method for detecting metagenome constituents in a sample, the method comprising: adding a synthetic spike sample to a nucleic acid sample; sequencing the nucleic acid sample using next-generation sequencing to generate a plurality of sequences; preprocessing the plurality of sequences; querying a database to retrieve a plurality of manufacturing process nucleic acid sequences; filtering out the plurality of manufacturing process nucleic acid sequences from the plurality of sequences; querying the database to retrieve a plurality of environmental nucleic acid sequences; filtering out the plurality of environmental nucleic acid sequences from the plurality of sequences; querying the database for a plurality of metagenome nucleic acid sequences; and comparing each of the plurality of metagenome nucleic acid sequences to the plurality of sequences to detect a presence of a metagenome constituent.

2. The method according to claim 1 , wherein the act of filtering out the plurality of manufacturing process nucleic acid sequences from the plurality of sequences comprises k-mer matching at least one of the plurality of manufacturing process nucleic acid sequences and filtering the at least one of the plurality of manufacturing process nucleic acid sequences out of the plurality of sequences.

3. The method according to claim 1, wherein the act filtering out the plurality of environmental nucleic acid sequences from the plurality of sequences comprises performing k-mer matching at least one of the plurality of environmental nucleic acid sequences and filtering the at least one of the plurality of environmental nucleic acid sequences out of the plurality of sequences.

4. The method according to claim 1 , wherein the synthetic spike sample comprises a predetermined quantity of synthetic nucleic acid sequences that correspond to representative metagenome constituents.

5. The method according to claim 1 , wherein the synthetic spike sample comprises a predetermined quantity of a synthetic nucleic acid sequence of a predetermined metagenome constituent.

6. The method according to claims 4 or 5, further comprising determining a signal strength parameter of the synthetic spike sample within the plurality of sequences, wherein a signal cutoff parameter is determined in accordance with the signal strength parameter.

7. The method according to claim 1, wherein preprocessing the plurality of sequences includes filtering at least one adapter sequence from the plurality of sequences.

8. The method according to claim 1, wherein the act of preprocessing the plurality of sequences further includes correcting base mismatches in the plurality of sequences using quality scores associated with each base.

9. The method according to claim 1 , wherein the act of filtering out the plurality of manufacturing process nucleic acid sequences includes comparing the plurality of manufacturing process nucleic acid sequences to at least one predetermined manufacturing process contaminant.

10. The method according to claim 1, wherein the act of filtering out the plurality of environmental nucleic acid sequences includes comparing the plurality of environmental nucleic acid sequences to at least one predetermined environmental process contaminant.

11. The method according to claim 1 , wherein the act of filtering out the plurality of manufacturing process nucleic acid sequences includes applying a quality score threshold to exclude low-quality sequences from the plurality of sequences.

12. The method according to claim 1, wherein the act of filtering out the plurality of environmental nucleic acid sequences includes applying a quality score threshold to exclude low-quality sequences from the plurality of sequences.

13. The method according to claim 1, wherein the act of querying the database for the plurality of metagenome nucleic acid sequences involves selecting k-mers based on their frequency of occurrence in a reference database of adventitious agents.

14. The method according to claim 13, wherein the act of selecting the k-mers based on their frequency of occurrence utilizes minimizers from adventitious agent genomes to thereby improve detection efficiency.

15. The method according to claim 1, further comprising generating proteomic data of a protein / peptide extract from a matrix having the nucleic acid sample.

16. The method according to claim 15, further comprising determining viability of the detected metagenome constituent by analyzing the proteomic data.

17. The method according to claim 16, wherein the act of analyzing the proteomic data includes aligning at least one protein sequence corresponding to the proteomic data to a genomic sequence of the detected metagenome constituent.

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