Quality control templates to ensure validity of sequencing-based assays

Abstract

To provide a method and / or system to ensure the validity of sequencing-based assays.SOLUTION: Embodiments of the method and / or system can include: generating a set of quality control template (QCT) molecules; determining a set of QCT sequence read clusters based on the set of QCT molecules, such as based on variation regions of the set of QCT molecules; and, based on the set of QCT sequence read clusters, determining a sequencing-related parameter, such as a contamination parameter and / or molecule count parameter, associated with at least one of sequencing library preparation and sequencing.SELECTED DRAWING: Figure 1A

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. Provisional Patent Application No. 62 / 614,236, filed January 5, 2018. The benefit of the specification is claimed, and is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to the technical field of gene sequencing. [Background technology]

[0003] High-throughput sequencing (e.g., next-generation sequencing (NGS)) is a Both genome and exome sequencing are useful in diagnostic assays, as well as Non-invasive prenatal genetic testing (NIPT), liquid biopsy, and polymorphism detection These are increasingly being used for more specialized applications, such as similar assays for Put-to-Place Sequencing (e.g., NGS) requires multiple samples (e.g., up to 384 samples) ) can be processed in the same sequencing run, making cross-contamination clinically relevant. This is a major concern for the use of genomic DNA, especially when the mutation or polymorphism is rare and its allele frequency is a small fraction of the total. In assays where only a small percentage of the samples are cross-contaminated, This is especially true for NIPT and liquid biopsy. This is true for a number of cases where the difference between positive and negative outcomes is a few percentage points. The quantitative difference is less than .

[0004] In standard library preparation practice for high-throughput sequencing, the initial input DNA Amplification of the A sample may be necessary. Any amplification of the mutant allele in the laboratory R Contamination of subsequent samples and experiments, commonly known as carryover contamination These amplification steps exacerbate cross-contamination effects because To prevent this problem, some standard diagnostic assays, such as qPCR, use dUTP / UN The G carryover prevention system is used here instead of dTTP in PCR. dUTP is used, and after the assay, uracil-containing amplicons are converted to uracil by the enzyme uracil DNA glycosylation. However, high-throughput sequencing NGS is a more sensitive and high-throughput sequencing-based approach. Although this is even more urgently needed given that these assays measure minute quantitative changes, high-throughput sequencing-based assays (e.g., NGS-based assays) There is no analogous solution for Say et al.

[0005] Completely eliminate cross-contamination in high-throughput sequencing This is difficult due to the chemistry involved, but if it could be traced, it would be similarly In some instances, each sample may be spiked with a different distinguishable sequence. However, it is possible to track the contamination of other wells without having to reconstitute the sample. Examples where the sequence library is different for each sample and for each individual sample can be cumbersome. High-throughput sequencing-based assays (e.g., NGS-based When used to track cross-contamination between multiple assays (e.g., multiple 384 separate libraries (e.g., 384 separate libraries; processed in the same sequencing run) It may be necessary to manage a number of separate libraries (e.g., a number corresponding to the number of samples being processed). Moreover, in such an instance, the same library would be used for different experiments. Therefore, PCR carryover from previous experiments would not be traceable. Managing multiple separate libraries (e.g., 384 separate libraries) is difficult. Due to the nature of the identifier sequence, it may itself be subject to cross-contamination. While overcoming these limitations, it is also important to trace cross-contamination, etc. New and useful embodiments of the methods and / or systems are needed. [Brief description of the drawings]

[0006] [Figure 1A-1D] 1A-1D include flow chart diagrams of variations of certain method embodiments. [Diagram 2] FIG. 2 includes a flow chart diagram of a variation of a method embodiment. [Diagram 3] FIG. 3 includes a flow chart diagram of a variation of a method embodiment. [Figure 4A-4D] Figure 4A includes graphical representations of results from a validation portion of a method embodiment variation, particularly with respect to cross-contamination and index misassignment. Figure 4B includes graphical representations of results from a validation portion of a method embodiment variation, particularly with respect to cross-contamination and index misassignment. Figure 4C includes graphical representations of results from a validation portion of a method embodiment variation, particularly with respect to cross-contamination and index misassignment. Figure 4D includes graphical representations of results from a validation portion of a method embodiment variation, particularly with respect to cross-contamination and index misassignment. [Figure 5A-5B]Figure 5A includes an example of results from a validation experiment for the use of QCT molecules for molecule counting. Figure 5B includes an example of results from a validation experiment for the use of QCT molecules for molecule counting. [Figure 6] FIG. 6 contains examples of results related to quality aspects related to technician management and / or laboratory management. [Figure 7A-7C] Figure 7A includes a graphical representation of results from a validation portion of a method embodiment variation, particularly with respect to quantification of QCT molecules. Figure 7B includes a graphical representation of results from a validation portion of a method embodiment variation, particularly with respect to quantification of QCT molecules. Figure 7C includes a graphical representation of results from a validation portion of a method embodiment variation, particularly with respect to quantification of QCT molecules. [Figure 8A-8B] 8A and 8B include graphical representations of results from a validation portion of a method embodiment variation, particularly with respect to quantification of biological targets. [Figure 9] FIG. 9 includes an example of using QCT molecules to measure assayable genome equivalents. [Figure 10] FIG. 10 includes an example of determining the contamination parameters. [Figure 11] FIG. 11 includes an example of using QCT molecules on multiple samples to determine sample processing errors. [Figure 12] FIG. 12 includes examples of the use of QCT molecules at different stages. [Figure 13A-13B] Figure 13A includes an example of a characterization related to index hopping. Figure 13B includes an example of a characterization related to index hopping. [Figure 14] FIG. 14 includes an illustrative example that facilitates the determination of true contamination levels associated with the use of unique dual index primers. [Figure 15A-15D]Figure 15A includes examples relating to facilitating diagnosis of monogenic diseases. Figure 15B includes examples relating to facilitating diagnosis of monogenic diseases. Figure 15C includes examples relating to facilitating diagnosis of monogenic diseases. Figure 15D includes examples relating to facilitating diagnosis of monogenic diseases. [Figure 16A-16B] Figure 16A includes examples related to facilitating diagnosis of chromosomal abnormalities. Figure 16B includes examples related to facilitating diagnosis of chromosomal abnormalities. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] The following description of the embodiments is not intended to limit the scope of the present invention, but rather to provide a more detailed description of the embodiments that will be readily understood by those skilled in the art. The invention is intended to enable the creation and use of

[0008] 1. Overview. As shown in FIGS. 1A-1D and 2-3, an embodiment of a method 100 (e.g., -Features related to at least one of sequencing library preparation and sequencing (e.g., for tagging; etc.) are a set of quality control template (QCT) molecules (e.g., each QCT molecule is a target S110: Generate a set of QCT molecules based on the QCT molecule set (including the relevant regions, variable regions, etc.) For example, QCT sequence read clusters based on the variation regions of the QCT molecule set; A set (e.g., corresponding to a QCT molecule set; etc.) is determined (e.g., computationally; etc.). and / or determining whether the QCT sequence read cluster set is a sequence read cluster set; Sequencing library preparation and / or sequencing Sing-related parameters (e.g., contamination parameters, molecular count parameters) The method may include determining S130 the number of pieces of information (e.g., the number of pieces of information).

[0009] Additionally or alternatively, embodiments of method 100 include preparing one or more sequence libraries. Sequencing one or more sequence libraries (S114); Facilitate (e.g., assist) in the diagnosis of one or more of the following conditions (e.g., genetic disorders, etc.) and determining, providing, etc.) S140 (e.g., determining, providing, etc., one or more sequencing-related based on parameters; etc.); sequencing-related parameters, diagnostics, and / or other suitable To promote treatment for one or more conditions, for example, based on a set of components (e.g., (assisting, determining, providing, administering, etc.) S150; and / or any or other suitable processes.

[0010] In a specific example, the method 100 (e.g., detecting a genetic disorder from a maternal sample associated with a pregnant woman) (e.g., to facilitate prenatal diagnosis) to identify QCT molecule sets associated with genetic disorders in maternal samples In addition, the set of QCT molecules may be a set of endogenous target molecules (e.g., Target-associated regions that have sequence similarity to target sequence regions of endogenous target molecules (e.g., regions associated with the target molecule) and sequences of endogenous target molecules. Variable regions with sequence dissimilarity to the sequence region (e.g., embedded regions containing variable "N" base sets) Embedded molecular identifier (EMI) region, where each "N" base is an "A" base, a "G" base, a "T" base, and a "C" base. a set of QCT molecules and a target sequence region; A co-amplification mixture based on co-amplifying a nucleic acid molecule (e.g., a nucleic acid; a nucleic acid fragment; etc.) comprising Generating products; Sequencing the co-amplified mixture; QC from sequencing Based on the number of distinct variable regions detected in the T molecule sequence reads, the QCT molecule set and computationally determining the number of unique QCT molecular sequence reads. Corresponding to a set of molecules; the number of QCT molecular sequence reads is the number of unique QCT molecules Calculate the average QCT sequencing depth based on dividing by 0.01 for endogenous target molecules. Endogenous targeting was determined based on the total read counts per sample divided by the average QCT sequencing depth. Determine the absolute counts of the target molecule; the total read counts for the endogenous reference molecule The number of endogenous reference molecules was calculated based on the number of counts divided by the average QCT sequencing depth. Determining absolute counts; and comparing the absolute counts of the endogenous target sequence with the endogenous reference sequence. US20130233633A1 - Facilitating prenatal diagnosis of genetic disorders based on comparison between absolute counts of gene sequences - Google Patents may include.

[0011] In a specific example, the method 100 (e.g., sequencing library preparation and sequencing Characterize, including identify, any contaminants associated with at least one of the (e.g., for generating a set of QCT molecules, each QCT molecule having a variation region domain (e.g., including one or more EMI domains) and / or target-associated domain (e.g., biological the QCT molecule set contains a variable region that has sequence similarity to the target sequence region of the target; and computationally determining a set of QCT sequence read clusters based on the region, For example, here we compare the QCT sequence read cluster set with the QCT molecule set and the biological A sample containing a target (e.g., a sample containing an endogenous target molecule corresponding to a biological target; etc.) QCT molecules derived from sequencing corresponding to the QCT mixture generated on the base The sequencing library preparation includes sequence reads, e.g., and a nucleic acid molecule that contains the biological target (e.g., a target associated region and a target sequence of the biological target). and QCT sequence read classification. Based on the star set, a small number of sequencing library preparations and sequencing runs were performed. Determining sequencing-related parameters associated with at least one of the Sequencing library preparation and / or high-throughput sequencing (e.g., determining contamination parameters that describe the associated contamination) may include.

[0012] In a specific example, as shown in FIG. 2, a method 100 (e.g., a system based on QCT molecules) is (e.g., to ensure the validity of sequencing-based assays) Generating a library or QCT library mixture (e.g., QCT DNA is The primers shown as black arrows in Figure 2 were used to allow coamplification. QCT DNA contains target-associated regions with high similarity to the gene of interest; For example, the variable region may be a random "A" EMI regions that contain "N" bases that can take "C", "T", or "G" bases. For example, here, up to 4^4 unique EMI sequences are generated with "NNNN". For example, the probability that two QCT molecules have the same EMI is given by the hash The collision probability can be found by using the solution of the birthday problem, e.g., here QC A subsection of the sequence differences between T and HBB can be seen in FIG. -QCT to distinguish QCT libraries from gene sequences of interest in sequencing reads QCT libraries are spiked into human DNA. QCT molecules and biological targets (e.g., HBB, shown in Figure 2) can be identified by preparing a sequencing library based on one or more samples comprising: Apply computational approaches to cluster QCT molecular sequencing reads (e.g. , based on EMI sequence similarity; here the number of EMI clusters is determined by the number of EMI clusters spiked into the sample. corresponding to the absolute number of QCT molecules detected; etc.) and to classify the clusters into distinct sample identifiers (e.g., different corresponding to different samples; corresponding to different sample compartments used for sequencing and use such data to identify cross-contamination, infection, index misassignment, user error (e.g., in running the assay), Incompatibility with assay parameters (e.g., amount of input DNA, available genome equivalents in sample) Evaluate quality control metrics such as whether the assay is usable (e.g., too few samples; too few samples, etc.) and / or whether the assay is usable. The method may include quantifying the amount of an input biological target available for use.

[0013] Embodiments of the method 100 and / or system 200 provide a method for accurately determining the abundance of a biological target. Quantification and contamination (e.g., cross-contamination between different samples or different experiments) Nation; True contamination associated with the use of unique dual index primers and accurately track and / or quantify the extent of the sequencing base. Identify user errors in the execution of the assays and identify sequencing index mistakes Monitor sample assignments, determine incompatibilities with assay parameters, and identify contamination Identifying and / or removing primers that cause index hopping and / or index hopping and / or for improving diagnosis and / or treatment, etc. It may serve to improve any suitable aspect related to preparation and / or sequencing.

[0014] In one example, a single reagent (e.g., including a set of QCT molecules) is added to the sample set. A single reagent may be added to all samples to eliminate cross contamination. and other user errors can be tracked and are disclosed herein. Adding the QCT numerator gives us a custom algorithm based on solving the birthday problem for hash collisions. When mathematical and computational analysis pipelines are involved, it is often difficult to communicate across different users, different experiments, and Multiple examples can be used to simultaneously track cross-contamination between different samples. For user-friendliness and convenience, all samples (e.g., A single QCT library is added to the In a specific example, separate QCT libraries (e.g., different QCT identifier regions) may be used. , e.g. corresponding to a QCT ID, etc.) may be added at different stages of sample preparation, Any user error or loss of input samples can be tracked. Automated Fingerprint-on-Dispense A QCT molecule is then used to measure the amount of each sample. (e.g., based on a variable region of the QCT molecule; based on a QCT identifier region of the QCT molecule; etc.) In some instances, contamination due to PCR carryover has been Such contamination may be measured in clinical and / or other contexts. In a specific example, the QCT molecule is used to aliquot each PCR tube. A molecular fingerprint can be assigned to each test, and the results can be used to identify the tests performed at a given testing site or laboratory. All variable regions (e.g., EMI distribution in the EMI region) associated with any PCR tubes used Detect and quantify PCR carryover by maintaining a database of all PCR products (e.g., columns) Carry-over PCR in subsequent assays can then be added to the historical database. Computationally calculate the variation domain fingerprint (e.g., EMI fingerprint similarity) It can be identified by searching for:

[0015] Embodiments may additionally or alternatively include high throughput sequencing (e.g., NGS). There are important concerns about "index switching" or index mismatch. In some cases, it can be used to provide quality assurance for any cross-injection. Even in the absence of contamination, when multiplexing on the same flow cell Sequencing reads or signals from one sample (e.g., up to 5–10%; etc.) In some instances, a simple fingerprinting option may be used to identify a sample that is not identical to the original sample. A quantification and dispensing technique was performed to accurately assess the degree of misassignment for any sample. In a specific example, as shown in Figures 4A to 4D, Accumulation of cross-contamination and index misassignment in wells The effect can be detected with a sensitivity of over 90%. As shown, all 96 combinations of i7 and i5 indexes were used to calculate the Illumina Truseq HT libraries can be prepared, where each well contains an HBB amplicon. Each well contains 400, 200, 100, or 0 mAbs, depending on the precon sequencing experiment. QCT molecules are added; each well contains a QCT molecule that is identified as a cross-contamination. The ratio of reads is shown, where cross contamination and indexing were not considered in this experiment. Smith assignments ranged from less than 1% to 13%; columns D710-D712 are Q Since there are zero CT molecules, the ratios in these wells are the same as in the modified embodiment of method 100. The shape morphology indicates the sensitivity with which cross contamination can be detected; and FIG. 4B shows The number and source of contaminating leads found in D702 / D504 (left); and The number and source of contaminating leads from D702 / D504 are shown in the figure; FIG. 4C illustrates an analysis similar to that of FIG. 4B, but for wells D707 / D504. and where FIG. 4D illustrates the source of contamination for wells D710 / D504 and D711 / D504. Consistent with not adding QCT molecules to these wells, No contaminating reads were found from the ELISA kit.

[0016] Embodiments may additionally or alternatively include, where read depth is sufficient (e.g., separate QCT analysis Accurate molecular counting of biological targets (e.g., greater than 20 read depths per child; etc.) For example, based on the use of a set of variation regions of a set of QCT molecules. This can be useful in obtaining accurate target quantification in techniques that use complex sequence addition. The embodiment defines available biological targets to be assayed if the read depth is sufficiently high. In some instances, non-invasive detection by amplicon sequencing can be used to quantitate the For detection of mutations used in premortem genetic testing, fewer than 400 QCTs per sample Adding molecules allows for such high read depth for complex sequences, e.g., where Mi 96 such samples are multiplexed in a Seq run (e.g., the chromatin shown in FIG. 7C). However, to facilitate molecular counting and / or other suitable functions, one Any suitable number of QCT molecules can be added to the above samples. As shown in Figure 5A to Figure 5B, the number of sequence reads and the number of molecules (e.g., sequence reads and determined based on a process related to the QCT molecule; etc.) Here, the ratio of the number of molecules to the number of reads can vary by 2-3 times, so the number of molecules in a given sample Improvements related to using the QCT molecule to determine (e.g., using the read number itself) Improvements in reliability compared to the conventional method; etc.) are noted.

[0017] In several instances (e.g., to quantify absolute molecular counts of one or more biological targets), For example, the method 100 and / or system 200 may be used to: a) determine the diagnostic outcome of an assay; b) determining parameters to be used in an algorithm for determining the CAM in an experiment or assay; and c) tracking the loss of input DNA at various stages of synthesis and the degradation of target molecules when the number of target molecules is too low. d) return a specific call result (e.g., to determine when an assay is unreliable); Designing assays to detect copy number variations at or between loci, and / or or e) be able to support therapeutic and clinical decision-making based on diagnostic assay results. .

[0018] Embodiments may additionally or alternatively include technician management and / or laboratory management ( Evaluate and / or improve several quality aspects related to, for example, clinical laboratory management; As shown in FIG. 6, in an embodiment, the method 100 and / or system The system 200 can be used to identify problematic sample processing by different technicians or laboratories. Here, the runs of samples A01 to A06 and the corresponding runs of samples B31 to B35 were divided into two different were performed in different laboratories with different pre-PCR / post-PCR separation techniques; The same volume of QCT molecules from the kit (roughly corresponding to approximately 200 molecules) was added to each sample. ; "num_seqs" indicates the number of distinct EMI clusters identified for each sample ;"contam_frac" indicates the total proportion of contaminating reads identified in each sample; "collision_frac" is the degree to which two valid EMI clusters differ from each other. "contam_collision_frac" identifies which samples contain Combining the two previous metrics; "ident_frac" maps to a valid EMI. The number of reads pinged is divided by the total number of reads for that particular sample; "reads_per_qctmol" indicates the average read depth of the EMI; these Use filter thresholds on emitted metrics to determine whether they pass or fail quality control (QC). where only 1 out of 6 samples for laboratory A did not meet the QC metric. for Laboratory B, 5 out of 5 samples passed the QC metric; and These results are now used to explain how sample processing and pre-PCR / post-PCR separation are performed. (For example, in this case, Laboratory A is implementing the following measures involving improved sample processing: In each run, the samples passed the same QC metrics; etc. As described above, in a specific example, the inclusion of multiple QCT species dispensed from the same pool The random error of the pipette can be measured using correlation with the absolute number of QCT molecule counts. (e.g., as shown in FIG. 7C), and / or systematic pipetting and / or quantification. Errors may additionally or alternatively be traceable (e.g., as shown in FIG. 7A). (e.g., based on a comparison of the center panel with the left and right panels). As shown in Figure 7C, absolute quantification of spiked-in QCT molecules could be determined. In a specific example, as shown in FIG. 7A, QCT1, QCT2, and QCT3 laminases can be used. Preparing and pooling libraries (e.g., corresponding to different sets of QCT molecules; etc.); and Spike-in PCR reactions with 100, 200, or 400 molecules per QCT library by aggregating EMI sequence reads with at most two base changes; The EMIs in each QCT library can be clustered by using the may represent the mean value of replicates ± standard deviation; and the graph line represents a linear regression fit. The shading may correspond to a 95% confidence interval for the mean. In a specific example, as shown in FIG. To determine the robustness of QCT counts to read depth, half of all reads were Sequencing reads may be downsampled by randomly selecting ; The number of EMI clusters recovered from the downsampled sequencing reads was It can be plotted against the full data set; the color of the points corresponds to the EMI cluster. The black line may represent the read depth after downsampling from Intercept = 0; QCT analysis is robust when read depth per QCT molecule exceeds 20. This may aid in the reliability of molecular counting; and the number of QCT clusters is 4 When it is 00, the read depth after downsampling is less than 20 per molecule. As shown in Figure 7C, the QCT molecule counts were consistent across the QCT libraries. Here, Figure 7A at 100 QCT molecule input level. The variance of QCT3 cluster number versus QCT1 cluster number for each PCR replicate from A fabric diagram can be shown.

[0019] Embodiments may additionally or alternatively include performing various sequencing library preparation steps (e.g. , sample preparation stage) and / or sequencing stage to expand the QCT library and reduce sample loss In a specific example, a first set of QCT molecules can be traced at the time of sampling. (e.g., a QCT1 molecule; a first QCT molecule that includes a first shared QCT identifier region; etc.) After sample purification, an equal amount of a second set of QCT molecules (e.g., QCT2 molecules; If a second QCT molecule containing a shared QCT identifier region is dispensed, etc., the first QCT molecule By comparing the molecule counts for the child set and the second QCT molecule set (e.g., QCT1 vs. QCT2 molecular counts, etc., and purification yields may be assessed.

[0020] Embodiments may additionally or alternatively include determining the amount of biological material available for assay by: By quantification of biological targets based on the use of QCT molecules, This allows for improved measurement of total available genomic material and calculation of expected biological target concentrations. This can be improved because not all targets are available for assay. In a specific embodiment, this is a non-invasive prenatal genetic test for determining the genetic status of a fetus. Circulating free DNA and circulating tumor DNA assayed in the application of non-invasive prognostic testing (NIPT) As in the case of liquid biopsy applications where A is assayed, DNA is split into short size distributions. In particular embodiments, these applications may involve shearing, depending on the target of interest. The available DNA is less than 25%, where the input DNA is The determination of DNA genome equivalents can be determined using QCT molecules, for example in the present case, Genomic DNA is probed with either Alu or Hpy, which cuts outside and inside, respectively, the gene of interest. Then, add 9 ng to 36 ng of QCT molecules. The digested DNA (corresponding to 2,500 to 10,000 genome equivalents) was spiked in. The nucleotides in each PCR reaction can be PCR amplified and sequenced on a MiSeq. The genome equivalent (GE) of the DNA is determined by the QCT method in the relevant part of the embodiment of method 100. can be measured by molecular-related assays; PCR reactions are performed in duplicate where Figure 8A shows the linear fit line and the shading is the 95% CI of the mean. and where the measurements of input DNA are consistent across replicates and dilution series. 8B, but systematically higher than the Qubit measurements by a factor of 10; As shown in Fig. 1, human genomic DNA is sheared into a size distribution with peaks of 100–150 bp. QCT molecules were then spiked into 2.3 ng to 36 ng of sheared DNA. The amplicon size is approximately 150 bp, which is the genome equivalent of sheared DNA. can be measured, and here FIG. 8B shows that the slope of the line is amplified due to random shear. In a specific example, as shown in FIG. Using QCT molecules, the amount of genome equivalents that can be assayed can be measured, which is may vary from assay to assay and may vary in footprint even for the same assay; amplify the region surrounding the same mutation from sheared DNA to generate 150 bp PCR products vs. bp PCR products (left vs. right) were formed and in both cases amplified using QCT molecules. The number of molecules that were detected was measured; here, 18 nanograms ( ng) of genomic DNA with an average length of approximately 170 bp (e.g., the average length of circulating free DNA) and included in both cases (n=8 for 150bp and n=8 for 72bp). = 4); and consistent with the theoretical model, the number of molecules that can be amplified is much greater than the input DNA. significantly less, with as much as a two-fold difference between different footprints for the same input DNA mass. and here Figure 9 shows why other input DNA measurements (concentration, etc.) require molecular information. This may indicate why accurate molecular diagnosis may not be sufficient, for example, as discussed here. If the number of child counts were to decrease by a factor of about 2, the Poisson noise would increase by about 40%, can vary with accuracy of 95% (2σ) to 99% (3σ).

[0021] Embodiments of the method 100 and / or system 200 may be used in connection with one or more disease conditions (e.g., For example, in connection with the characterization, diagnosis, treatment, and / or implementation of processes related to one or more disease conditions; etc.), where the condition can be diagnosed using non-invasive prenatal genetic testing (NIPT) (e.g. For example, trisomy 21 or Down syndrome, trisomy 18 or Edwards syndrome, trisomy 13 or Sex chromosome aneuploidies such as Somy or Patau syndrome, Turner syndrome, or other suitable aneuploidies chromosomal abnormalities, including aneuploidies; genetic testing for the presence of chromosomal abnormalities, including DiGeorge syndrome Regarding genetic screening; Regarding genetic screening for monogenic diseases; other prenatal testing; aneuploidy analysis outside the prenatal context and / or other suitable analysis genetic disorders (e.g., single-gene disorders, including sickle cell disease; chromosomal abnormalities; gene amplification Disorders related to; gene deletion; partial chromosomal abnormality; 22q11.2 deletion syndrome or Dijo Charcot-Marie-Tooth syndrome, cystic fibrosis, Huntington's disease; Hechenne muscular dystrophy; hemophilia, thalassemia; etc.), other diseases related to chromosomal abnormalities application (e.g., additional, missing, irregular chromosomal DNA, etc.), cancer (e.g., any through analysis related to suitable cancer genes, cancer biomarkers, and / or other cancer-related targets. via liquid biopsy-related analysis), and / or any other suitable pathology. In one example, the present invention may include and / or relate in some way to one or more of the following: In the method 100, at least one of non-invasive prenatal genetic testing and liquid biopsy is performed. To facilitate diagnosis related to either one of the two, the target molecule count (e.g., the number of target molecules in a sample) The method may include determining a disease progression corresponding to a number of children; based on the use of a QCT molecule; etc. The condition may additionally or alternatively be psychiatric and behavioral conditions (e.g., psychological disorders; depression; Diagnosis; etc.); communication-related conditions (e.g., expressive speech disorder; stuttering; phonological disorders; autistic disorders; vocal conditions; hearing conditions; eye conditions; etc.); sleep-related conditions (e.g., insomnia; sleep apnea; etc.); cardiovascular-related conditions (e.g., coronary artery disease; hypertension; etc.); metabolic-related conditions ( diabetes, etc.); rheumatoid-related conditions (e.g., arthritis, etc.); weight-related conditions (e.g., , obesity, etc.); pain-related conditions; endocrine-related conditions; chronic diseases; and / or any other suitable It may include a variety of pathologies.

[0022] Sequencing Related to One or More Embodiments of Method 100 and / or System 200 (e.g., for S112) preferably includes high-throughput sequencing; This includes NGS, NGS-related technologies, massively parallel signature sequencing, and Polony sequencing. Sequencing, 454 Pyrosequencing, Illumina Sequencing, SOLi D sequencing, Ion Torrent semiconductor sequencing, DNA nanoballoon sequencing -Sequencing, Heliscope single molecule sequencing, single molecule real-time (SMR T) sequencing, nanopore DNA sequencing, sequencing of any generation sequencing technologies (e.g., second-generation sequencing technologies, third-generation sequencing technologies, fourth-generation sequencing technologies, etc.) sequencing technologies), amplicon-associated sequencing (e.g., target amplicon sequencing) recon sequencing), metagenomics-related sequencing, sequencing by synthesis, Tunneling current sequencing, hybridization sequencing, mass spectrometry Sequencing, microscopy-based techniques, and / or high-throughput sequencing and / or in connection with any one or more of any suitable techniques relating to Additionally or alternatively, sequencing may be performed using any suitable sequencing technique (e.g., Sanger sequencing, capillary sequencing, etc.).

[0023] One or more steps of the method 100 and / or process embodiments described herein; The portions may be asynchronous (e.g., sequential), simultaneous (e.g., in parallel; biological samples simultaneously in a multiplexed, automated manner; increased system throughput (e.g., simultaneous computation of sequence reads for the purpose of determining the time of the trigger event) and / or in any other suitable order, at any suitable time and frequency. The system 200 may include one or more of the components and / or embodiments of the subject matter described herein. The present invention may be implemented by and / or using a process.

[0024] Additionally or alternatively, the data described herein (e.g., clusters, sequence Related parameters for sequencing, identifiers, read depth, sequence reads, sequence region determination, QCT analysis The data (e.g., gene design, primer design, etc.) are collected, determined, transmitted, and received. , and / or a time indicator indicating whether the sequence has been otherwise processed; and time indicators indicating the order of reconstitution and / or sequencing steps. a temporal indicator that gives context to what is being described by the data; Changes in data (e.g., data over time; changes in data; data patterns; data trends; data irregularities) and / or other predictions; etc.); and / or any other suitable indicator of time. Any suitable time indicator, including one or more of (e.g., seconds, minutes, hours, days, weeks , period, point in time, timestamp, etc.

[0025] Additionally or alternatively, the parameters, metrics, inputs, outputs, and and / or other suitable data may be a score, a binary value, a classification, a confidence level, an identifier (e.g., sample identifier, QCT molecular identifier, etc.), values ​​along the spectrum, and / or any other suitable The value types may include any one or more of the value types described herein. The preferred types of data include any of the following that are relevant to embodiments of the method 100 and / or system 200: may be used as input for, produced as output from, and / or optionally The method can be operated in any suitable manner.

[0026] Embodiments of the system 200 may additionally or alternatively include a molecule (e.g., a QCT molecule; QC , generate a T library; etc., process the biological sample, and / or perform other suitable processes. A sample handling network configured to: and sequencing the processed genetic material from the mixture produced in the Sequencing system; Analyze sequence reads and QCT sequence read clusters Determine sequencing-related parameters, facilitate diagnosis, facilitate treatment, and and / or other suitable processes (e.g., computational processes). system (e.g., a remote computing system; a local computing system; etc.); and / or The components of system 200 may be integrated in any manner (e.g., Any suitable distribution of functionality between components, such as with respect to some embodiments of method 100, may be used. However, the method 100 and And system 200 may be configured in any suitable manner.

[0027] 2.1 Generate the QCT molecule. An embodiment of the method 100 may include generating a set of QCT molecules S110; This may be done to facilitate downstream computational processing (e.g., to facilitate determination of sequence-related parameters). QCT sequence read cluster determination for sequencing; etc.), sequencing library preparation and sequencing (e.g., high-throughput sequencing, etc.) Used in one or more stages (e.g., steps, phases, periods, periods, etc.) of the other (e.g., The act of generating a molecule to be (e.g., added, processed, sequenced, etc.) You can do it.

[0028] QCT molecules preferably contain target-associated regions (e.g., one or more targets per QCT molecule). As shown in FIG. 2, the target-associated region preferably includes one or more one of the target molecules (e.g., an endogenous target molecule; corresponding to one or more biological targets; etc.) of or more sequence similarity to the target sequence region (e.g., perfect sequence similarity; sequences that meet a threshold condition similarity; sequence similarity of a specified number of bases; etc.), but may additionally or alternatively include one or more The target molecule may include any suitable association with any suitable component of the target molecule. The region preferably corresponds to a corresponding QCT molecule (e.g., includes a target associated region) and a target sequence region. This allows for co-amplification with a nucleic acid molecule (e.g., a nucleic acid, a nucleic acid fragment, etc.) containing a region, which is a molecular co-amplification in the determination of molecular count parameters; taking amplification bias into account etc.) may facilitate improved accuracy, but in addition or instead, library preparation, sequencing, and / or other aspects of the method 100. Any suitable process may be used. In one example, sequencing library preparation (e.g., S112, performing sequencing library preparation) and a nucleic acid molecule comprising a biological target, the alignment of a target associated region with a target sequence region of the biological target. Co-amplification based on sequence similarity may also be included, and wherein sequencing-related parameters are determined. Determining the number of molecules of a biological target that are relevant for sequencing is called a target molecule count. The method may include determining a number of counts based on the QCT sequence read cluster set.

[0029] In a variation, the QCT molecule can omit the target associated region. For example, The T molecule may be expressed without target relatedness (e.g., with a given similarity to a target sequence region of a biological target). with or without the presence of a target sequence region, and / or a component of the sample (e.g., a nucleic acid molecule containing the target sequence region ; etc.) without the corresponding coamplification of the sample with the biological target. In some instances, the QCT molecules can be pre-treated to make them suitable for sequencing. For example, the pretreated QCT molecules can be treated as treated QCT molecules suitable for sequencing. Adding a co-amplification QCT analysis can be performed simultaneously without the need for pre-sequencing. The product is preferably useful for facilitating the determination of contamination parameters, but may also be useful in addition or Alternatively, any suitable sequencing-related parameter may be used to facilitate the determination of In a specific example, the QCT molecule set is used for subsequent sequencing (e.g., NGS, etc. high throughput sequencing; etc.), where QCT molecular set The generating step includes generating a first subset of QCT molecules of the set of QCT molecules (e.g., a first shared QCT identifier region; etc.; and a second shared QCT identifier region of the set of QCT molecules. a subset of QCT molecules (e.g., each including a second shared QCT identifier region; etc.) wherein the QCT molecular sequencing reads are amplified by a first QCT molecular sequencing read. A first target molecule corresponding to a biological target is included in the child subset and the biological target (e.g., a QCT mixture generated based on the sample, and a second QCT molecular subset; and a biological target (e.g., a second target molecule that corresponds to the biological target; etc.). From the corresponding sequencing of additional QCT mixtures generated based on the additional samples wherein the sample and the additional sample are respectively introduced into a first sample compartment. The sample compartment and the second sample compartment correspond to the target QCT molecules that omit the association region and / or the target associated region may be constructed in any suitable manner. It is possible.

[0030] The QCT molecule preferably contains one or more variable regions (e.g., one or more per QCT molecule). (upper fluctuation region; adjacent fluctuation region; distant fluctuation region; etc.) As shown in Figure 2 Preferably, the variable region is a region that is distinct from one or more sequence regions of the target molecule (e.g., a region that is distinct from the target sequence region). sequence dissimilarity (e.g., complete sequence dissimilarity; dissimilarity of a specified number of bases) with Variation regions may additionally or alternatively contain one or more E In a variation, the EMI region can include a variable "N" base set ( For example, one or more variable "N" bases, where each "N" base can be an "A " base, "G" base, "T" base, and "C" base (e.g., For example, selected randomly; selected according to a predetermined statistical distribution and / or probability; etc. In a variation, the EMI region is determined based on QCT sequence read cluster determination (e.g., By maximizing the pairwise Hamming distance between EMI regions; one or more designated bases (e.g., designed and synthesized bases; etc.), including synthesis regions (e.g., on microarrays; using silicon-based synthesis; etc. In a variation, the QCT molecule may additionally or alternatively include a plurality of EMI areas (e.g., multiple EMI areas; adjacent EMI areas; separate EMI areas; variable Variable regions, including EMI regions that contain "N" bases; EMI regions that contain synthetic regions; etc. For example, each variable region of a set of QCT molecules can be a filler that contains a set of variable "N" bases. The molecular identifier region may include an embedded molecular identifier region, where each "N" base is an "A" base, a "G" base, a base, a "T" base, and a "C" base, Each QCT molecule further comprises an additional EMI region that includes an additional set of variable "N" bases. , where the additional EMI region is separated from the EMI region by a sequence region of the QCT molecule. For example, where the variable "N" base set and the additional variable "N" base set are each A fixed (e.g., predetermined) number of "N" bases (e.g., "N" bases greater than 3, any more than a suitable number of "N" bases, exactly a certain number of "N" bases; etc.; and Here, sequencing-related parameters (e.g., contamination parameters) are determined. The method is based on the EMI region of the QCT molecule set and additional EMI regions (e.g., Based on separate EMI sequence leads corresponding to pairs of EMI areas and additional EMI areas qCT sequence read clusters derived from the sequence of the QCT sequence reads. In some embodiments, the variable region may additionally or alternatively include a synthetic

[0031] In a variant, the QCT molecule belongs to a set of QCT molecules, as shown in FIG. A shared QCT identifier region that identifies the QCT molecule (e.g., a shared sequence region, one or more of the target molecules) (e.g., different QCT identifier regions here have different sequence similarities to the above sequence regions) QCT molecules (and / or other suitable QCT molecules, etc.) In one example, the first QCT may include a QCT identifier region that identifies the QCT molecule. The variability region of each QCT molecule of the molecule set is a region of the second EMI and at least a first QCT identifier. The first EMI region may be separated by a separator region, where the second QC Each additional QCT molecule of the set of T molecules has a second additional EMI region and at least a second The first additional EMI region may be separated by a QCT identifier region. In one example, the first, second, first additional, and second additional EMI regions are A variable "N" base set can be included, where each "N" base can be an "A" base, a "G " base, "T" base, and "C" base, and wherein the QCT system Computationally determining the sequence read cluster set includes first and second QCT discrimination. Based on the child region, and the first, second, first additional, and second additional EMI determining a set of QCT sequence read clusters based on the region. In one example, for each QCT molecule in the first set of QCT molecules, The child sequence is a sequence that is derived from the first QCT identifier region, the first EMI region, and the second EMI region. characterized by perfect sequence similarity with a first sequence template of a biological target; and For each additional QCT molecule of the second set of QCT molecules, The sequence includes a second QCT identifier region, a first additional EMI region, and a second additional EM It is characterized by complete sequence similarity with the second sequence template, except for the I region. In this case, the QCT molecular sequence is determined by a previously determined distinct QCT identifier region (e.g., a unique Except for two separate sections of the 5N sequence separated by an identifier sequence, It may be identical to a molecular sequence (e.g., one or more regions of a target molecular sequence; etc.). In the QCT identifier region (e.g., a unique QCT ID sequence, as shown in FIG. 2), The use of a 100% ... may be added at various stages to track loss of target or other user error. The use of multiple QCT libraries may be possible. Additionally or alternatively, the QCT identifier domain may be The regions can be configured in any suitable manner. However, the QCT molecule can be configured in any suitable manner. Any suitable combination of suitable types of regions may be included (e.g., where different QCT molecules may contain the same or different types and / or numbers of regions; There may be any suitable sequence similarity and / or dissimilarity; etc.

[0032] In a variation, the method 100 may additionally or alternatively include one or more QCT libraries. (e.g., each QCT library comprises a QCT molecule), For example, the QCT library herein can include a set of QCT molecules, e.g., Each set of QCT molecules can be identified by a different QCT identifier domain. Thus, generating a QCT library involves amplifying a set of distinct QCT molecules. Obtain (e.g., for preparation of sequencing, e.g., where QCT molecules are QCT mixtures (e.g., amplified prior to addition to one or more components of a sample to produce Therefore, generating a QCT library determines the number of QCT molecules to be included in the QCT library. In a specific example, given the inherent diversity of QCT molecules, Determining the maximum number of unique QCT molecules that should be included in a sample using the solution of the birthday problem For example, here, 4 can be generated by 10 variable N bases in the QCT molecule. ^For 10 sequences, there are up to 1200 possible single valid EMI collisions with a probability of about 0.5. QCT molecules can be used (exp(-1200×1199 / 2 / 4^10) ≈0.5), and here with 200 QCT molecules, the probability of a single effective collision is about 2%. In an embodiment, generating a QCT library includes: The amplifiable QCT molecules may be expanded in amounts of less than 0.00001 nanograms (and / or other suitable amounts). Development (e.g., sequencing library preparation and high-throughput sequencing) (e.g., in at least one step of However, it is important to determine the number of QCT molecules to include in the QCT library. And generating the QCT library can be performed in any suitable manner.

[0033] In one example, a QCT library is a single-stranded oligonucleotide library that contains a variable "N" sequence. The QCT sequence may be generated by synthesizing a complementary strand to the QCT sequence. The ultramer is resuspended and annealed with the complementary primer sequence; The sequence was extended using Klenow fragment (exo-) and treated with exonuclease I. The final product may be purified and used to generate a double stranded QCT library. The single stranded DNA molecules for amplification may be removed, and the QCT library may be analyzed using the Qubit HS adapter. The antibody can then be added to each sample and quantified using a fluorometric assay such as the ELISA assay. The number of QCT molecules was calculated using the predicted molecular weight of this double-stranded QCT molecule. good.

[0034] However, producing the QCT molecule S110 can be carried out in any suitable manner. This can be done.

[0035] 2.2 Determine QCT sequencing read cluster sets. An embodiment of the method 100 includes determining one or more QCT sequence read clusters. and S120, which may include a step of determining a sequencing-related parameter to facilitate the determination of the sequencing-related parameter. , QCT molecular sequence reads (e.g., sequencing library preparation and sequencing This may serve to form clusters (e.g. after stimulating).

[0036] A QCT sequencing read cluster preferably comprises a QCT molecular sequencing read (e.g. For example, a method for generating a QCT molecule sequence based on one or more sets of QCT molecules and one or more samples containing a biological target. (e.g., derived from sequencing corresponding to one or more QCT mixtures to be Additionally or alternatively, any suitable read and / or sequencing related components. may include.

[0037] QCT molecular sequence reads represent the identity of a set of one or more QCT molecules dispensed into each sample. Clustering can be done computationally, for example to determine differential information. Clustering techniques can be used, including but not limited to principal component analysis, K-means, hierarchical These include layer clustering, and / or any sequence identity based clustering method. Additionally or alternatively, clustering, computational analyses related to clustering (e.g. pre-processing, filtering, etc.), and / or any other suitable embodiment of method 100. Some of the techniques are based on supervised learning (e.g., logistic regression, backpropagation, etc.). (using neural networks, random forests, decision trees, etc.), learning (e.g., using the Apriori algorithm, using K-means clustering), Semi-supervised learning, deep learning algorithms (e.g., neural networks, restricted Boolean Man-machine, deep belief network method, convolutional neural network method, re Recurrent neural networks, stacked autoencoders, etc.), reinforcement learning (e.g., Q-learning algorithms) algorithms, time difference learning), regression algorithms (e.g., ordinary least squares method, logistic regression, stepwise regression, multivariate adaptive regression splines, local estimates of scatterplots smoothing, example-based methods (e.g., k-nearest neighbors, learning vector quantization, self-organizing machine learning, etc.), , regularization methods (e.g., ridge regression, least absolute shrinkage and selection operator, elastic nets, decision tree learning methods (e.g., classification and regression trees, iterative binarization3 e dichotomiser 3), C4.5, chi-squared automated interaction detection, determining strain, Random forests, multivariate adaptive regression splines, gradient boosting machines, etc.), size methods (e.g., naive Bayes, averaged one-dependence estimator) ndence estimator, Bayesian belief network, etc.), kernel methods (e.g., support vector machines, radial basis functions, linear discriminant analysis, etc.), clustering Clustering methods (e.g., k-means clustering, expectation maximization, etc.), association rule learning algorithms (e.g., Apriori algorithm, Eclat algorithm, etc.), artificial neural networks network models (e.g., perceptron, backpropagation, Hopfield network methods, self-organizing maps, learning vector quantization, etc.), dimensionality reduction methods (e.g. , principal component analysis, partial least squares regression (partial least squares re regression, Sammon mapping, multidimensional scaling, projection pursuit, etc.), ensemble Methods such as boosting, bootstrap aggregation, AdaBoost t), stacked generalization, gradient boosting machines, random forests, etc.), and / or may include any one or more of any suitable artificial intelligence techniques (e.g., machine learning, Methods, etc.) can be applied.

[0038] Determining the QCT sequence read clusters preferably comprises determining a QCT sequence read cluster for one of the QCT molecules. Based on the above domains (e.g., variable domain; QCT identifier domain; etc.) (e.g., QCT domain) based on sequence reads corresponding to those regions of the offspring; etc.), in addition or instead, any In a specific example, the QCT molecule (e.g., a target-associated A quality control template that matches the target sequence region and the QCT molecule sequence ( For example, a biological sample was prepared using primers complementary to both the target-associated regions of the QCT molecule; After the targets (e.g., nucleic acid molecules containing a target sequence region; etc.) are amplified, the molecules are then subjected to The sequence may be indexed and sequenced for duplexing, and Sequencing reads may be separated based on their multiplex index. In the example, the indexed reads are then sequenced using the QCT identifier region (e.g., QC The QCT groups can be clustered according to the T ID sequence, etc., or Identification is based on exact sequence match (excluding variable regions such as EMI regions) with the expected QCT sequence. In some instances, a set of QCT sequence read clusters can be generated (e.g., , computationally, etc.) determining a first condition (e.g., less than a threshold number of dissimilar bases) ; etc.) satisfying a variable region sequence similarity (e.g., a first variable region of a first QCT molecule, and between the second variable region of the second QCT molecule; etc. The third and second QCT molecular sequence reads were then clustered into QCT sequence read clusters. Clustering of QCT sequence reads into clusters of QCT sequence reads For each QCT sequence read cluster in the sequence read cluster set, A sample identifier that identifies the set. Q) to samples relevant for reconstitution and / or sequencing, to sample compartments, etc. Determining CT sequence read cluster assignments, e.g. If necessary, enter the sequencing-related parameters (e.g., contamination parameters, etc.) here. To determine the QCT sequence read cluster set and the sample identifier set, The identification of QCT sequence read clusters may be based on the assignment of QCT sequence read clusters to different sequences. In an example, clustering the first and second QCT sequence reads may include clustering three QCT sequence reads. Based on sequence similarity of variable regions of less than one point substitution, and a second criterion (e.g., QCT sequence >20 read depth per read cluster; >30 read depth; any Related to QCT sequencing read clusters that meet the following criteria: Identify the first and second QCT sequence reads based on read depth. In an embodiment, the QCT molecular sequence may include clustering the QCT molecular sequence. Sequence reads (e.g., sequence reads containing EMI region sequences) were collected in the same well. Additional QCT molecular sequencing reads with ≤2 point substitutions were observed at higher read depths. In a specific example, each EMI corresponds to a specific sample and a corresponding A well and an index or index pair is assigned.

[0039] In a variant, determining the QCT sequence read clusters comprises determining non-validated QCT sequence read clusters. Determine and / or identify CT sequence read clusters (e.g., non-valid EMI clusters, etc.) Or discarding (eg, filtering out, etc.) In one example, non-valid QCT sequencing read clusters, as shown in FIG. are below and / or at a threshold read depth (e.g., 20 reads or less; 30 reads or less). any suitable read depth threshold; etc.), and / or any suitable criteria (e.g. QCT sequence read classification that meets the required read depth criteria; For example, the QCT sequence list may include a star, which is ineffective for molecule counting. In a specific example, valid QCT sequence read clusters can be discarded. ter (e.g., the remaining QCT after discarding non-valid QCT sequence read clusters) Sequence read clusters (e.g., sequence read clusters) were used to identify the sequencing read counts for each sample. The ratio of the number of quality control templates to the number of counts can be determined (e.g., where (e.g., can be used as a correction factor to quantify the number of target molecules). As shown in Figure 10, for average EMI lead depths above 30, the effective vs. non-effective QCT system Sequence read clusters (e.g., EMI clusters) are characterized by significant sequencing depth. This can be clearly identified by the significant decrease in the mean read depth, which is consistent with the adaptive approach (e.g. For example, adaptive lead depth threshold determination; etc.) can be used to distinguish between effective vs. non-effective EMI. In a specific example, determining the QCT sequence read cluster set comprises filtering. A subset of QCT sequence read clusters after filtering (e.g., valid QCT sequence sequence read clusters, etc.) were then filtered using QCT sequence read clusters. The read depth corresponding to the star subset (e.g., read depth threshold criteria and / or other preferred For example, the sequence may include determining whether the sequence satisfies a certain condition; Target molecule counting related parameters (e.g., number of target molecules present in the original sample) Determining the number of reads (counts; etc.) is done by clustering the QCT sequence reads after filtering. Determining QCT molecule counts based on a subset (e.g., where filter QCT sequence read clusters in the QCT sequence read cluster subset after sequencing The number of stars may correspond to the QCT molecular count; etc.); QCT molecular count and QCT Based on the molecular sequence reads (e.g., QCT molecular counts are calculated using QCT molecular sequence (divide by three; etc.) to determine the correction factor ratio; and targeted molecular sequence reads derived from a target sequence that are associated with a biological target (e.g., Based on the target molecule sequence read (e.g., a target sequence region of the target molecule; etc.) (e.g., multiply the target molecule sequence read number by the correction factor ratio; etc.) to determine the target molecule count number. In a specific example, the method 100 includes: and adaptively determining a read depth threshold based on the read depth distribution characteristics. Here, we determine the subset of QCT sequence read clusters after filtering. The read depth is filtered based on whether it satisfies an adaptively determined read depth threshold. In a specific example, each read of the read depth may include determining a post-reading subset. The read depth is the corresponding number of QCT sequence read cluster subsets after filtering. 20 reads (and / or other suitable reads) for each QCT sequencing read cluster In some instances, sequencing errors and Due to PCR errors, non-valid QCT sequencing read clusters are contaminating In addition or instead, valid or ineffective Q Determining CT sequence read clusters may be performed in any suitable manner. However, determining QCT sequence read clusters S120 may This can be done in any suitable manner.

[0040] 2.3 Determine sequencing-related parameters. An embodiment of the method 100 includes determining one or more sequencing-related parameters S 130.

[0041] Sequencing-related parameters include contamination parameters (e.g., different Sequencing library preparation and / or sequencing across users, samples, experiments, etc. molecular count parameters (e.g., describing contamination associated with ELISA) For example, target molecules and / or QCT molecules, which are initially present in a given sample and / or mixture, ; etc.); sample tracking parameters (e.g., related to sample loss; etc.) Sample processing error parameters (e.g., noise; incorrect sample processing operations such as pipetting errors) Quantitative error parameters (e.g., describing the quantitative error) analysis error parameters (e.g., describing computational analysis errors; etc.); and and / or sequencing library preparation, sequencing, relevance analysis, and / or other suitable methods. Any one or more of any suitable parameters relevant to the desired aspect may be included. In the example, the number of QCT molecules determined across multiple samples was used as shown in FIG. By using the above method, sample processing errors describing noise and / or erroneous sample processing can be determined. where, prior to PCR, each sample was diluted with an equal volume of 1000 ribozymes, approximately corresponding to approximately 200 unique QCT molecules. After PCR and sequencing, the QCT molecules can be added to the product and the sequencing data can be obtained. Determine valid QCT sequence read clusters (e.g., EMI clusters) from the data where the expected coefficient of variation (CV) for approximately 200 QCT molecules is sqrt( 200) / 200 ≒ 7%, which is consistent with the observed data shown in Figure 11 among the 12 samples. where any sample meets a certain threshold (e.g., 3σ, 200-3×sqrt(200 )≒150 or a less stringent threshold of about 200 / 2≒100), this result can be used to identify sample processing errors for a particular sample of interest; and The number of QCT molecules can be increased, corresponding to a CV of less than 7% in the process. Additional sample processing error parameters can be determined. Determining sequencing-related parameters is a key step in determining the QCT sequence read cluster set quality. QCT sequence reads that are not assigned to a CT sequence read cluster were and the number of unassigned QCT sequence reads and QCT sequence At least one of the sequencing error rate and polymerase error rate from the total number of reads On the other hand (e.g., end-to-end sequencing and polymerase error rate; etc.) In an embodiment, determining the likelihood of a target or reference sequence. A region that has variation (e.g., a target variation region, a reference variation region, etc.) but is QC-ed in terms of sequence Any sequence not identical to the T read cluster sequence may be due to sequence or polymerase errors. In a specific example, the read counts of these sequences are calculated as the total QCT read counts. The sum of the total sequencing and polymerase error frequencies is given by Synthetic errors may occur in a linear process, whereas polymerase errors occur in an exponential process. may be generated by a process (e.g., unless linear PCR is used), where the initial PCR The effect of an error in a cycle can be amplified exponentially. Thus, in a specific example, Q Analyzing the distribution of read counts of sequences not assigned to CT read clusters By doing this, the contribution of sequencing errors versus polymerase errors can be calculated. However, determining the sequencing error rate and / or polymerase error rate This can be done in any suitable way.

[0042] In a variant, determining the sequencing-related parameters comprises performing a plurality of QCT analyses. Child sets (e.g., different QCT molecular sets identified by different shared QCT identifier regions) at different stages related to sequencing library preparation and / or sequencing ) that involve different QCT molecule sets being developed; Based on the different QCT sequence read cluster subsets corresponding to the CT molecule set, In one example, the method 100 includes generating a set of QCT molecules, the method comprising: Each QCT molecule is adapted to identify a shared QCT molecule among a set of QCT molecules. generating an additional set of QCT molecules; Thus, each additional QCT molecule represents a shared and additional QCT molecule set. a second QCT identifier region adapted to recognize a QCT molecule; determining a QCT sequence read cluster set based on the QCT identifier region of the sequence; and sequencing-related parameters based on QCT sequence read cluster sets. In a specific embodiment, the set of QCT molecules may include determining Suitable for development in the first stage of library preparation and / or sequencing. wherein the additional set of QCT molecules is used in sequencing library preparation and sequencing. and / or QCT sequencing, Computationally determining the sequence read cluster set is a A first subset of the raster set (e.g., the first QC of the corresponding first QCT molecule) based on the T identifier region and the first variable region; etc. the QCT identifier region corresponds to a first QCT identifier region and is associated with the first stage; and A second subset of the sequence read cluster set (e.g., a corresponding second QCT based on the child's second QCT identifier region and the second variable region; etc. The second subset corresponds to a second QCT identifier region and is associated with a second stage. and wherein determining the sequencing-related parameters includes determining a QCT sequence link. Based on the first and second subsets of the code cluster set, a sample associated with a sample loss is Determining tracking parameters.

[0043] In one example, determining the sequencing-related parameters includes determining a QCT sequence. Based on the lead cluster set, the QCT molecular set and the additional QCT molecular set were determining a first absolute count number and a second absolute count number corresponding to the set; and Pipette error parameters and quantitative error parameters are calculated based on the first and second absolute counts. The method may include determining at least one of the following:

[0044] In a specific example, the use of QCT molecules at different stages results in different This allows for the comparison of different sample preparation techniques; for example, here we add 200 μl of each plasma sample prior to DNA purification. Addition of QCT1 molecules (and / or any suitable number of QCT molecules) to DNA Purification techniques may be evaluated; where DNA is purified from plasma by either Purification Method #1 or Purification Method #2. The DNA samples were purified by PCR amplification and sequencing; Here, 200 QCT2 molecules (and / or any suitable number of QCT molecules) are used in DNA purification. after PCR amplification; here, the valid QCT sequence reads corresponding to the QCT2 molecule were added. The number of dot clusters was similar between the two samples (within about 25%). It is noted that there was no difference in the post-purification treatment of the two samples; The number of valid QCT sequence read clusters for QCT1 for #1 was approximately one-third smaller. As the purification method #1 was low, it was found that it did not result in significant sample loss (e.g., of cfDNA). It has been pointed out that this can be achieved.

[0045] However, determining the sequencing-related parameters S130 may be performed in any suitable manner. It can be carried out in a variety of ways.

[0046] 2.3.A Determine contamination parameters. Determining the sequencing-related parameters S130 may additionally or alternatively include one The method may include determining the above contamination parameters S132. Contamination parameters are cross-contamination parameters (e.g., sequencing and / or Cross-contamination between sample compartments; cross-contamination between different users Describe contamination; etc.), carryover contamination parameters ( For example, multiple sequencing library preparation and / or sequencing may be performed. (e.g., describe carryover contamination between steps of a hopping contamination parameters (e.g., for index-hopping primers) Describe the relevant index hopping contamination, etc. Contamination parameters may include index misassignment (e.g. The extent to which the For example, the contamination parameter here is the cross-contamination (and / or other (e.g., their cumulative effect) and index misassignment Both the preparation and / or sequencing library preparation and / or sequencing Any other suitable associated properties may be described.

[0047] In some instances, determining the contamination parameters may be performed for a particular sample. Contaminating sequences (e.g., found to be associated with a particular sample; The read depths of all the reads found in the compartments; etc. were added together to obtain the total number of reads (or valid Divide by (total number of QCT molecular sequence reads associated with a QCT sequence read cluster) This may include determining the total contamination rate or percentage based on the results of the analysis. In an example, contamination parameters may be determined as shown in FIG. Here, the sequence of the non-valid EMI cluster for sequencing sample A is found to be different from that of another sample. If a valid EMI cluster is seen in (sample B), it is this lead in sample A. indicates that the contaminating sequence is due to contamination from sample B; Add up the lead depths and the total number of leads (or valid EMI classification) The total number of reads that map to the star is then divided by the total number of reads that map to the star for a particular sample. A contamination rate or ratio can be determined; and where the total contamination Proportions or ratios are used to analyze the maximum level of analytical sensitivity and specificity that a clinical assay can report. and / or result in unsuccessful assays and / or no-calls instead of false positives. can be used as a threshold for reporting results; for example, where a particular assay has a threshold of 0. If detection of a 1% allele ratio is required, the sample should be 0.1%. Total contamination rates at or near the threshold may be used to identify no-call results. and where this threshold is instead adopted using knowledge of the allele ratios from the contaminated samples. (i.e., for a measurement of a particular allele in a given sample, A 1% contamination from another sample that is 10% of the allele is (This has the same effect as 10% contamination of the sample.)

[0048] In a specific example, as shown in Figures 4A to 4D, contamination occurs in each sample container. QCT molecular sequence reads (e.g., E This may be measured by identifying the source and destination of contamination (e.g., MI sequence reads). In a specific example, multiple sample compartments (e.g., multiple wells, etc.) may be loaded with the same QCT. If a molecular sequence read (e.g., the same EMI sequence read) is observed, QCT molecular sequencing reads were generated from the samples with the highest read depth across multiple sample compartments. can be characterized as originating from a single sample compartment, in another sample compartment (e.g., another well or wells; etc.) of the sample chamber. In a specific example, the contamination parameters are determined as follows: The first and second QCT sequence reads corresponding to the shared variable region sequences are identifying a raster of the first and second QCT sequencing read clusters; The assignment is made to a distinct sample identifier in the sample identifier set (e.g., a distinct sample comparator). identification of distinct samples; etc.; First read depth and second QCT sequencing read clusters associated with the read clusters generating a read depth comparison between the first read depth associated with the first read depth and a second read depth associated with the second read depth; and Based on the comparison, the sample is identified by a distinct sample identifier among the distinct sample identifiers. The method may include determining a contamination parameter associated with the sample.

[0049] In one example, determining the contamination parameters includes determining a QCT sequence Based on the read cluster set, in the first step of preparing a sequencing library determining a first molecular fingerprint associated with the first amplification; The second step is the preparation of a sequencing library based on the sequence read cluster set. determining a second molecular fingerprint associated with a second amplification in the first and a second molecular fingerprint. Carry-over contamination parameters describing carry-over contamination This may include determining a meter.

[0050] In one variation, determining the contamination parameter comprises indexing. This may include determining a hopping contamination parameter. As shown in Figures 13A and 13B, the use of QCT molecules reduces contamination. Facilitating the identification and removal of primers that cause index hopping and / or index hopping. where each sample has a corresponding D7xx index, as shown in FIG. 13A. Barcodes were added using primers for adding barcodes, and the same sequence was used for validation experiments. -Sequencing flow cell lanes were run; here, D701 and D 707 Indexing oligos are synthesized on the same oligo synthesis column, synthesis error Or, due to index hopping, D701 and D707 are derived from each other. The contamination rate was found to be low, and the level was considered significant at 5%. and may affect clinical outcome; and As described above, indexing primers were not used in subsequent runs on clinical samples. This reduced the maximum contamination level to less than 1%.

[0051] In a specific example, as shown in FIG. 14, the QCT molecule can be used to obtain a unique dual Facilitates measurement of true contamination levels associated with the use of high-index primers Here, standard dual indexing primers are used to identify true samples. Intercontamination, index hopping, and / or indexing oligos A combination of contaminants can cause a contamination of 0.1%. (as shown in samples 1-9); where the addition of a unique dual index The effect of index hopping and index-adding oligo contamination is 0 It is expected that the value will drop to .001 x 0.001 ≒ 1e-6; however, the measured value is A maximum of 0.03% (3e-5) of contacts in reactions with dual unique indexes The ionization rate (as shown for samples 10-29) is the expected 1e-6 higher than contamination, which is the expected This may suggest detection of true contamination levels.

[0052] However, determining the contamination parameters S132 may be performed in any suitable manner. It can be carried out in a variety of ways.

[0053] 2.3.B Determine molecular count parameters. Determining the sequencing-related parameters S130 may additionally or alternatively include one The method may further include determining S134 the above molecule count number parameters. The number parameter is the target molecule count (e.g., the number of target molecules in the original sample, etc.). Absolute molecular counts; absolute counts of endogenous target molecules, such as those in the original sample; etc. ); reference molecule counts (e.g., absolute counts of an endogenous reference molecule; in the original sample; etc.); QCT molecule counts (e.g., valid QCT sequence corresponds to the number of distinct QCT molecules added to the sample components; corresponding; etc.); related ratios (e.g., correction factors; molecular counts and related sequences the ratio between the number of reads and the number of reads; etc.); and / or any other suitable parameter related to the number of molecule counts. The device may include one or more of the following meters:

[0054] The molecular count parameters are preferably used to facilitate one or more diagnoses, but may also include: Alternatively or additionally, any suitable part of the embodiment of method 100 (e.g., as an input thereto) may include ) can be used.

[0055] In a variation, a molecule count parameter (e.g., a target molecule count; etc.) is The determination is made by multiplying the number of target molecule sequence reads by the correction factor ratio, Q QCT molecule counts (e.g., number of valid QCT sequence read clusters) T corresponding to the number of sequencing read clusters; etc.) and QCT molecular sequencing reads ( For example, the number of QCT molecular sequencing reads corresponding to a QCT sequencing read cluster ; etc. In a specific example, the effective non-mixing Number of QCT sequence read clusters (e.g., 2 reads or less, and / or any preferred The remaining QC after discarding QCT sequence read clusters with less than the appropriate number of reads T sequencing read clusters; etc.) are calculated based on the QCT molecular counts (e.g., specific sample clusters). for a compartment; for a specific sample; for a specific sample identifier In a specific example, the QCT molecule counts may be calculated from the corresponding QCT molecule. A correction factor may be calculated by dividing the number of sequencing reads obtained from the sequence by the number of sequences obtained from the sequence, for example: Here, the correction factor is the concentration of the target molecule (e.g., in a particular sample compartment; in a particular sample Multiplying by the sequencing reads belonging to a specific sample identifier (e.g., from a specific sample identifier; associated with a specific sample identifier; etc.), Target molecule counts (e.g., the number of biological targets that were originally available for amplification) In one example, the absolute number of endogenous target molecules is obtained. The average QCT sequence used to determine the number of counts and the absolute counts of the endogenous reference molecule. Sequencing depth is determined separately from its corresponding QCT.

[0056] Alternatively, in a variation of the embodiment, the QCT sequence read clusters (e.g. For example, determining molecular count parameters and / or suitable sequencing-related parameters. The read depth threshold for discarding QCT molecular sequencing reads (e.g., E The threshold may be adaptively determined based on the characteristics of the MI sequence read depth distribution. Values ​​are calculated for each indexed sample and the average EMI read depth within each sample. Calculate the square root of this average read depth, and determine whether the read depth is less than the square root of the average read depth. This may be set by discarding QCT sequence read clusters that are less than Additionally or alternatively, the read depth threshold for discarding QCT sequencing read clusters is , can be calculated in any suitable manner.

[0057] However, determining the molecule count parameters S134 may be performed in any suitable manner. This can be implemented by law.

[0058] 2.4 Facilitate diagnosis. An embodiment of the method 100 may additionally or alternatively include facilitating diagnosis S140. and / or a method for determining, providing, or assisting in one or more diagnoses for one or more conditions. and / or otherwise act to promote

[0059] Facilitating one or more diagnoses means determining one or more diagnoses (e.g., determining one or more based on sequencing-related parameters; etc.); providing one or more diagnoses (e.g. to one or more users; to one or more health care providers; to one or more patients by one or more health care providers (e.g., for use in providing a medical diagnosis to a patient; etc.); to assist in one or more diagnoses ( For example, in combination with other data, for use in determining a diagnosis, and providing one or more sequencing-related parameters and and / or other suitable parameters; etc.); and / or provide any suitable parameters relevant to the diagnosis. For example, aiding in diagnosis may include any one or more of the following: Applied immunizations related to at least one of the following: Contamination profiles adapted for use in determining diagnostic outcomes for This may include providing (e.g., to a user; to a health care provider; etc.) In certain instances, target molecule counts (and / or suitable sequencing-related parameters, etc.) ) is one of the few non-invasive prenatal genetic tests and liquid biopsies available. At least one of the target molecule counts (and / or suitable sequence) may be used to facilitate diagnosis. The method may include determining a plurality of parameters (e.g., sequencing-related parameters).

[0060] In a variant, facilitating diagnosis includes prenatal diagnosis (e.g., non-invasive prenatal genetic testing). for relevant genetic disorders and / or suitable pathologies; etc. In some instances, facilitating diagnosis may include detecting a target molecule count. Based on the parameters and the reference molecule count parameters (e.g., endogenous target sequence Based on a comparison between the absolute counts of the sequence and the absolute counts of an endogenous reference sequence; Facilitate prenatal diagnosis of one or more genetic disorders (e.g., single-gene disorders, chromosomal abnormalities, etc.) The method may include:

[0061] In a variation, facilitating diagnosis comprises identifying one or more monogenic diseases (and / or a preferred For example, the detection of absolute cDNA sequences of endogenous target molecules may facilitate the diagnosis of a genetic disorder (e.g., a genetic disorder). Determining the number of copies of a target gene is essential to identify the absolute number of copies of an endogenous target molecule that contains a mutation associated with a monogenic disease. Divide the paired counts (e.g., the number of QCT molecule sequence reads by the number of unique QCT molecules). The average QCT sequencing depth, derived by etc., based on dividing the total read counts of all Determining the absolute counts of the endogenous reference molecule is the determination of the endogenous The absolute counts of the reference molecule (e.g., the total counts for the endogenous reference molecule) determining the number of read counts divided by the average QCT sequencing depth; and the absolute counts of the endogenous target sequence and the absolute counts of the endogenous reference sequence (e.g. Facilitate prenatal diagnosis of monogenic genetic disorders based on, for example, comparisons between In a specific example, as shown in Figures 15A to 15D, By measuring and comparing the number of disease-associated and non-disease alleles, the researchers were able to extract genes from maternal blood to identify the developing fetus. Here, FIG. 15A shows a QCT study of a sickle cell trait (SCT) sample. HbS (mutated hemoglobin) and HbA (normal hemoglobin) as measured by molecular weight Robin) molecule counts, where HbS and HbA alleles are expected to be at equal frequency. This occurs when both the pregnant mother and the developing fetus are heterozygous for the disorder. where FIG. 15B shows the results of QCT in a SCT+10% sickle cell disease (SCD) sample. This includes the number of HbS and HbA molecules measured during pregnancy, which indicates whether the pregnant mother is a carrier of the disorder. Because the developing fetus inherits the disease alleles from both parents, where Figure 15C shows that the fetus has inherited the disorder The relative mutation dosage is the posterior probability of Molecular count and fetal fraction measurements (e.g., maternal and fetal) for use in calculations by RMD analysis. 15D includes measurements at up to 9 loci that differ in genotype between the parent and the offspring; and Samples with 0% vs. 10% SCD added to SCT (e.g., pregnancies that are carriers for the disorder) The mean and 95% confidence intervals for carrier vs. affected fetuses from mothers in the same age group were included. However, facilitating the diagnosis of a single-gene disease may be accomplished in any suitable manner. It is possible.

[0062] In a variant, facilitating diagnosis comprises identifying one or more chromosomal abnormalities (and / or suitable For example, the detection of an absolute count of an endogenous target molecule may be included to facilitate diagnosis of a genetic disorder. Determining the number of counts represents the absolute number of endogenous target molecules associated with the first chromosome ( For example, the total read counts for endogenous target molecules are multiplied by the average QCT sequencing depth. etc.), where the endogenous reference molecule Determining the absolute counts of the endogenous reference molecule associated with the second chromosome Absolute counts (e.g., average of the total read counts for the endogenous reference molecule) and dividing by the average QCT sequencing depth; and Absolute counts of the endogenous reference sequence and absolute counts of the endogenous reference sequence (e.g., comparison between etc., to facilitate prenatal diagnosis of chromosomal abnormalities. As shown in Figures 16A-16B, the QCT molecule was also used to identify chromosome 21 and other 21, which indicates that the fetus has Down syndrome, by counting the number of chromosomes It can be determined whether there is an excess of a chromosome number (e.g., compared to another chromosome). where the difference between the counts of 3 and 2 chromosomes indicates that the signal is a genetic recessive disorder. (e.g., HbSS to HbAS is a 2 to 1 signal; 100 % increase vs. 50% increase), which is a measure of Down syndrome in the developing fetus from circulating DNA in maternal blood. To improve accuracy in measuring and wherein some embodiments of method 100 include: Additionally or alternatively, other de novo mutations, such as trisomy 18 and / or DiGeorge syndrome, The present invention can be used to facilitate diagnosis of genetic mutations and / or chromosomal abnormalities.

[0063] In a variation, facilitating diagnosis includes facilitating diagnosis of one or more chromosomal microdeletions. For example, determining the absolute counts of the endogenous target molecules may include Divide the total read counts for the causative target molecules by the average QCT sequencing depth Based on this, the absolute counts of endogenous target molecules associated with the microdeletion region can be determined. wherein determining the absolute count number of the endogenous reference molecule comprises determining the absolute count number of the endogenous reference molecule. Divide the total read counts for the sex reference molecule by the average QCT sequencing depth. Based on the results of the chromosomal analysis, a second chromosomal region was predicted to be free of microdeletions. determining an absolute count number of a genetic reference molecule, Facilitating the diagnosis of genetic disorders (e.g., prenatal testing) is based on the comparison of chromosomal microdeletions. This may include facilitating diagnosis (e.g., prenatal diagnosis; etc.).

[0064] In a variation, facilitating diagnosis facilitates diagnosis of one or more copy number variations. For example, determining the absolute counts of the endogenous target molecules may include determining the absolute counts of the endogenous target molecules. The total read counts for each target molecule were divided by the average QCT sequencing depth. Based on this, the absolute counts of endogenous target molecules associated with regions that may have copy number variations are determined. determining an absolute count number of the endogenous reference molecule. This means that the total read counts for the endogenous reference molecule are averaged over the QCT sequence. The number of copies associated with regions expected to be free of copy number alterations based on dividing by the scanning depth determining an absolute count number of an endogenous reference molecule that is Facilitating the diagnosis of genetic disorders (e.g., prenatal testing) would be based on comparison of copy number variations. This may include facilitating diagnosis (e.g., prenatal diagnosis).

[0065] Additionally or alternatively, facilitating diagnosis may relate to any suitable condition. good.

[0066] As shown in Figures 15C and 16B, facilitating diagnosis is one or more fetal fractions. For example, facilitating prenatal diagnosis may be based on fetal fraction measurements, intrinsic Genetic analysis was performed based on the absolute counts of the target sequence and the absolute counts of the endogenous reference sequence. However, the use of fetal fraction measurements may include facilitating prenatal diagnosis of genetic disorders. The application may be performed in any suitable manner for any suitable process of the embodiment of method 100. and facilitating diagnosis S140 may be performed in any suitable manner. can.

[0067] However, embodiments of the method 100 may be implemented in any suitable manner.

[0068] The embodiment of the method 100 and / or the system 200 may be implemented in any of its variations (e.g., the embodiment Various system components and various method processes, including, but not limited to, variations, embodiments, specific examples, diagrams, etc. Any combination and permutation of the processes may be included, where method 100 and / or Some of the process embodiments described herein involve asynchronous (e.g., sequential) Simultaneously (e.g., in parallel), or in any other suitable order, the system 200 and / or One or more steps, elements, components, and / or other aspects of other entities described herein It may be implemented according to and / or using the embodiments.

[0069] Modifications described herein (e.g., embodiments, variations, examples, specific examples, figures, etc.) and / or any of the variations described herein, in addition or instead of: Combine, aggregate, exclude, use, perform sequentially, perform in parallel, and / or otherwise It can be applied in the form of

[0070] Some embodiments of the method 100 and / or system 200 may be implemented, at least in part, by a machine configured to receive a computer readable medium having computer readable instructions stored thereon; The instructions may be embodied and / or implemented as a computer program that may be integrated with the system. The computer-readable medium may include RAM, ROM, flash memory, EEPROM, optical devices (CD or DVD), hard drives Any suitable computer hard disk drive, such as a USB stick, a floppy drive, or any suitable device. The computer-executable components may be general purpose or specific. It may be a dedicated processor for the application, but alternatively or in addition, any suitable dedicated hardware may be used. The instructions may be executed by a single piece of hardware or a combination hardware / firmware device.

[0071] As those skilled in the art will appreciate from the foregoing detailed description, and from the drawings and claims, and the method 100, the system 20, and Modifications and variations may be made to the embodiments and / or variations of 0. The present invention provides, for example, the following items. (Item 1) 1. A method for facilitating prenatal diagnosis of a genetic disorder from a maternal sample associated with a pregnant woman, comprising: adding to the maternal sample a set of quality control template (QCT) molecules associated with the genetic disorder, the set of QCT molecules comprising: a target-associated region that has sequence similarity to a target sequence region of an endogenous target molecule, and A variable region that has sequence dissimilarity to a sequence region of the endogenous target molecule. Contains; generating a co-amplification mixture based on co-amplifying the set of QCT molecules and a nucleic acid molecule that includes the target sequence region; sequencing the co-amplified mixture; computationally determining a unique number of the QCT molecule sets based on a number of distinct variable regions detected in QCT molecule sequence reads from the sequencing, wherein the QCT molecule sequence reads correspond to the QCT molecule sets; calculating an average QCT sequencing depth based on the number of QCT molecular sequence reads divided by the number of unique QCT molecules; determining an absolute count number for the endogenous target molecule based on dividing the total read count number for the endogenous target molecule by the average QCT sequencing depth; determining an absolute count number for the endogenous reference molecule based on dividing the total read count number for the endogenous reference molecule by the average QCT sequencing depth; and facilitating the prenatal diagnosis of the genetic disorder based on a comparison between the absolute counts of an endogenous target sequence and the absolute counts of an endogenous reference sequence. The method according to claim 1, further comprising: (Item 2) In the method according to item 1, the genetic disorder comprises a monogenic disease; determining the absolute counts of the endogenous target molecules comprises determining the absolute counts of the endogenous target molecules that include a mutation associated with the monogenic disease based on dividing the total read counts for the endogenous target molecules by the average QCT sequencing depth; determining the absolute count of the endogenous reference molecule comprises determining the absolute count of the endogenous reference molecule that does not include the mutation based on dividing the total read count for the endogenous reference molecule by the average QCT sequencing depth; and facilitating said prenatal diagnosis of said genetic disorder comprises facilitating said prenatal diagnosis of said monogenic disease based on said comparison. (Item 3) In the method according to item 1, the genetic disorder comprises a chromosomal abnormality, determining the absolute count of the endogenous target molecule comprises determining the absolute count of the endogenous target molecule associated with a first chromosome based on dividing the total read count for the endogenous target molecule by the average QCT sequencing depth; determining the absolute count of the endogenous reference molecule includes determining the absolute count of the endogenous reference molecule associated with a second chromosome based on dividing the total read count for the endogenous reference molecule by the average QCT sequencing depth; and facilitating said prenatal diagnosis of said genetic disorder comprises facilitating said prenatal diagnosis of said chromosomal abnormality based on said comparison. (Item 4) 2. The method of claim 1, wherein the genetic disorder comprises a chromosomal microdeletion, determining the absolute counts of the endogenous target molecules comprises determining the absolute counts of the endogenous target molecules associated with a microdeletion region based on dividing the total read counts for the endogenous target molecules by the average QCT sequencing depth; determining the absolute count of the endogenous reference molecule includes determining the absolute count of the endogenous reference molecule associated with a second chromosomal region that is not expected to have a microdeletion based on dividing the total read count for the endogenous reference molecule by the average QCT sequencing depth; and facilitating said prenatal diagnosis of said genetic disorder comprises facilitating said prenatal diagnosis of said chromosomal microdeletion based on said comparison. (Item 5) 2. The method of claim 1, wherein the genetic disorder comprises a copy number variation, determining the absolute counts of the endogenous target molecules comprises determining the absolute counts of the endogenous target molecules associated with regions that may harbor copy number variations based on dividing the total read counts for the endogenous target molecules by the average QCT sequencing depth; determining the absolute counts of the endogenous reference molecules includes determining the absolute counts of the endogenous reference molecules associated with regions not expected to have copy number variation based on dividing the total read counts for the endogenous reference molecules by the average QCT sequencing depth; and facilitating said prenatal diagnosis of said genetic disorder comprises facilitating said prenatal diagnosis of said copy number variation based on said comparison. (Item 6) 2. The method according to claim 1, wherein the average QCT sequencing depth used to determine the absolute counts of the endogenous target molecules and the absolute counts of the endogenous reference molecules is determined separately from their corresponding QCTs. (Item 7) 2. The method of claim 1, wherein facilitating the prenatal diagnosis comprises facilitating the prenatal diagnosis of the genetic disorder based on a fetal fraction measurement, the absolute count number of an endogenous target sequence, and the absolute count number of an endogenous reference sequence. (Item 8) 1. A method for identifying contamination associated with at least one of sequencing library preparation and high throughput sequencing, the method comprising: generating a set of quality control template (QCT) molecules, each QCT molecule comprising: target-associated regions that have sequence similarity to a target sequence region of a biological target, and A variable region that has sequence dissimilarity to a sequence region of said biological target. Contains; and computationally determining a set of QCT sequence read clusters based on the variation regions of the set of QCT molecules, the QCT sequence read cluster set includes QCT molecular sequence reads derived from the high throughput sequencing corresponding to a QCT mixture set generated based on the QCT molecular set and a sample set including the biological target; and the sequencing library preparation comprises co-amplification of the set of QCT molecules with a nucleic acid molecule comprising the biological target based on the sequence similarity between the target associated region and the target sequence region of the biological target; and determining contamination parameters describing contamination associated with at least one of the sequencing library preparation and the high-throughput sequencing based on the QCT sequence read cluster set; The method according to claim 1, further comprising: (Item 9) 9. The method of claim 8, wherein the computationally determining the QCT sequence read cluster set comprises: clustering the first QCT molecular sequence read and the second QCT molecular sequence read into a QCT sequence read cluster of the QCT sequence read cluster set based on variable region sequence similarity satisfying a first condition; and determining, for each QCT sequencing read cluster of the set of QCT sequencing read clusters, an assignment of the QCT sequencing read cluster to a sample identifier of a set of sample identifiers that identify the set of samples. Including, determining the contamination parameter based on the set of QCT sequencing read clusters and the assignment of the QCT sequencing read clusters to the sample identifiers in the set of sample identifiers; The method according to claim 1, further comprising: (Item 10) 10. The method of claim 9, wherein determining the contamination parameter comprises: identifying first and second QCT sequence read clusters corresponding to shared variable region sequences, wherein the assignment of the first and second QCT sequence read clusters to distinct sample identifiers of the set of sample identifiers; generating a read depth comparison between a first read depth associated with the first QCT sequencing read cluster and a second read depth associated with the second QCT sequencing read cluster; and determining the contamination parameters associated with samples identified by distinct sample identifiers of the distinct sample identifiers based on the read depth comparison. The method according to claim 1, further comprising: (Item 11) 10. The method of claim 9, wherein clustering the first and second QCT sequence reads comprises clustering the first and second QCT sequence reads into the QCT sequence read clusters based on the variable region sequence similarity of less than three point substitutions and based on read depth associated with the QCT sequence read clusters that satisfy a second condition. (Item 12) 9. The method according to claim 8, wherein determining the contamination parameter comprises: determining a first molecular fingerprint associated with a first amplification in a first step of the sequencing library preparation based on the QCT sequence read cluster set; determining a second molecular fingerprint associated with a second amplification in a second step of the sequencing library preparation based on an additional set of QCT sequence read clusters; and determining a carryover contamination parameter describing carryover contamination from the first step to the second step based on a comparison between the first and second molecular fingerprints. The method according to claim 1, further comprising: (Item 13) 9. The method of claim 8, wherein the contamination parameter describes the degree of index misassignment associated with the high-throughput sequencing. (Item 14) 14. The method according to claim 13, wherein the contamination parameter is adapted for use in determining a diagnostic outcome for an assay associated with at least one of non-invasive prenatal genetic testing and liquid biopsy. (Item 15) The method of claim 8, further comprising generating a single QCT library comprising the set of QCT molecules, the single QCT library being adapted to develop less than 0.00001 nanograms of amplifiable QCT molecules for each sample of the sample set in at least one of the steps of the sequencing library preparation and the high-throughput sequencing. (Item 16) In the method according to item 8, each variable region of the set of QCT molecules comprises an embedded molecular identifier (EMI) region that includes a set of variable "N" bases, each "N" base being selected from one of an "A" base, a "G" base, a "T" base, and a "C" base; each QCT molecule of the set of QCT molecules further comprises an additional EMI region comprising an additional set of variable "N" bases, the additional EMI region being separated from the EMI region by a sequence region of the QCT molecule, the variable "N" base set and the additional set of variable "N" bases each comprising more than three "N" bases; and The method, wherein determining the contamination parameter comprises determining the contamination parameter based on the QCT sequence read cluster set derived based on the EMI region of the QCT molecule set and the additional EMI region. (Item 17) 1. A characterization method relating to at least one of sequencing library preparation and sequencing, comprising: generating a set of quality control template (QCT) molecules, each QCT molecule comprising a variable region; computationally determining a set of QCT sequence read clusters based on the variation regions of the set of QCT molecules, the set of QCT sequence read clusters including QCT molecule sequence reads derived from the sequencing that correspond to a QCT mixture generated based on the set of QCT molecules and a sample including a biological target; and determining sequencing-related parameters associated with at least one of the sequencing library preparation and the sequencing based on the QCT sequence read cluster set. The method according to claim 1, further comprising: (Item 18) In the method according to item 17, each said QCT molecule comprises a first QCT identifier region that is shared among the set of QCT molecules and adapted to identify said QCT molecule; the method further comprises generating an additional set of QCT molecules, each additional QCT molecule comprising a second QCT identifier region shared among the additional set of QCT molecules and adapted to identify the additional QCT molecule; and The method, wherein computationally determining the QCT sequence read cluster set comprises determining the QCT sequence read cluster set based on the first and second QCT identifier regions. (Item 19) In the method according to item 18, the set of QCT molecules is adapted for deployment in a first stage of at least one of the sequencing library preparation and the sequencing; the additional set of QCT molecules is adapted for deployment in a second stage of at least one of the sequencing library preparation and the sequencing; said computationally determining a set of QCT sequencing read clusters; determining a first subset of the QCT sequence read cluster set, the first subset corresponding to the first QCT identifier region and associated with the first step; and determining a second subset of the QCT sequence read cluster set, the second subset corresponding to the second QCT identifier region and associated with the second step; including; and determining the sequencing-related parameter comprises determining a sample tracking parameter associated with sample loss based on the first and second subsets of the QCT sequence read cluster set. (Item 20) Item 19. The method according to item 18, wherein determining the sequencing-related parameters comprises: determining a first absolute count number and a second absolute count number corresponding to the set of QCT molecules and the additional set of QCT molecules, respectively, based on the set of QCT sequence read clusters; and determining at least one of a pipetting error parameter and a quantification error parameter based on the first and second absolute count numbers; The method according to claim 1, further comprising: (Item 21) Item 19. The method according to item 18, wherein determining the sequencing-related parameters comprises: identifying QCT sequence reads that are not assigned to a QCT sequence read cluster of the set of QCT sequence read clusters; and determining at least one of a sequencing error rate and a polymerase error rate from the number of unassigned QCT sequence reads and the total number of QCT sequence reads. The method according to claim 1, further comprising: (Item 22) In the method according to item 18, the variable region of each of the QCT molecules comprises a first embedded molecular identifier (EMI) region separated from a second EMI region by at least the first QCT identifier region; each said additional QCT molecule comprises a first additional EMI region separated from a second additional EMI region by at least said second QCT identifier region; the first, the second, the first additional, and the second additional EMI regions comprise a variable set of "N" bases, where each "N" base is selected from any one of an "A" base, a "G" base, a "T" base, and a "C" base; and computationally determining the QCT sequence read cluster set includes determining the QCT sequence read cluster set based on the first and second QCT identifier regions and based on the first, second, first additional, and second additional EMI regions. (Item 23) In the method according to Item 22, for each QCT molecule, a corresponding QCT molecule sequence is characterized by perfect sequence similarity with a first sequence template of said biological target except for said first QCT identifier region, said first EMI region, and said second EMI region; for each additional QCT molecule, the corresponding additional QCT molecule sequence is characterized by perfect sequence similarity with the second sequence template except for the second QCT identifier region, the first additional EMI region, and the second additional EMI region. (Item 24) In the method according to item 17, each QCT molecule of the set of QCT molecules comprises a target associated region that has sequence similarity to a target sequence region of the biological target; the sequencing library preparation comprises co-amplification of the set of QCT molecules with a nucleic acid molecule comprising the biological target based on the sequence similarity between the target associated region and the target sequence region of the biological target; and determining the sequencing-related parameters includes determining a target molecule count number describing a number of molecules of the biological target associated with the sequencing based on the set of QCT sequence read clusters. The method further comprising: (Item 25) In the method according to item 24, determining the set of QCT sequencing read clusters includes determining a filtered QCT sequencing read cluster subset based on a read depth corresponding to the filtered QCT sequencing read cluster subset; determining said target molecule counts, determining a QCT molecule count based on the filtered subset of QCT sequencing read clusters; determining a correction factor ratio based on the QCT molecular counts and the QCT molecular sequence reads; and determining the target molecule count based on the correction factor ratio and a target molecule sequence read derived from the sequencing, the target molecule sequence read associated with the biological target; The method according to claim 1, further comprising: (Item 26) 26. The method of claim 25, further comprising adaptively determining a read depth threshold based on a read depth distribution characteristic for the QCT molecular sequence reads, and determining the filtered QCT sequence read cluster subset comprises determining the filtered subset based on whether the read depth satisfies the adaptively determined read depth threshold. (Item 27) 26. The method of claim 25, wherein each read depth corresponds to more than 20 reads for a corresponding QCT sequence read cluster of the filtered QCT sequence read cluster subset. (Item 28) 25. The method of claim 24, wherein determining the target molecule count comprises determining the target molecule count to facilitate diagnosis associated with at least one of non-invasive prenatal genetic testing and liquid biopsy. (Item 29) 18. The method of claim 17, wherein determining the sequencing-related parameters comprises determining contamination parameters including at least one of a cross-contamination parameter describing cross-contamination between sample compartments associated with at least one of the sequencing library preparation and the sequencing, a carry-over contamination parameter describing carry-over contamination between multiple steps of at least one of the sequencing library preparation and the sequencing, and an index-hopping contamination parameter describing index-hopping contamination associated with an index-hopping primer. (Item 30) In the method according to item 29, said set of QCT molecules is adapted for said sequencing; generating said set of QCT molecules, amplifying a first subset of QCT molecules of said set of QCT molecules; and amplifying a second subset of QCT molecules from said set of QCT molecules. Including, said QCT molecular sequencing reads; the QCT mixture generated based on the first QCT molecule subset and the sample containing the biological target; and an additional QCT mixture generated based on the second subset of QCT molecules and an additional sample containing the biological target. wherein the sample and the additional sample correspond to a first sample compartment and a second sample compartment, respectively, of the sample compartment.

Claims

[Claim 1] The invention described in the specification.

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