Aquatic organism dna macro-barcoding self-adaptive alignment and species discrimination annotation method

Abstract

The application provides an aquatic organism DNA macro-barcode adaptive alignment and species discrimination annotation method, comprising: obtaining original water body eDNA sequence data obtained by high-throughput sequencing of a water sample; standardizing and preprocessing the original water body eDNA sequence data to obtain standardized water body eDNA characteristic sequences; dynamically configuring adaptive alignment parameters for the standardized water body eDNA characteristic sequences; performing multi-strategy hierarchical sequence adaptive alignment on the standardized water body eDNA characteristic sequences according to the adaptive alignment parameters to obtain an alignment result set; performing multi-dimensional species discrimination and false positive filtering on the alignment result set to obtain credible eDNA characteristic sequence matching records; and performing hierarchical classification species annotation on the credible eDNA characteristic sequence matching records. The application realizes adaptive, high-precision and multi-level discrimination eDNA sequence alignment and species annotation.

Description

Technical Field

[0001] This application relates to the field of aquatic organism environmental DNA bioinformatics analysis and species identification technology, and in particular to an adaptive comparison and species identification annotation method for aquatic organism DNA macrobarcodes. Background Technology

[0002] Environmental DNA (eDNA) technology, with its advantages of being non-invasive, highly sensitive, and high-throughput, has become the mainstream technology for aquatic biodiversity surveys and aquatic ecological monitoring. In the eDNA monitoring process, sequencing sequence alignment, species identification, and taxonomic annotation are the core and crucial links connecting gene sequencing data with biodiversity assessment results; their accuracy directly determines the scientific validity and reliability of aquatic ecological monitoring conclusions.

[0003] Current aquatic organism eDNA data analysis technologies have several inherent limitations. First, traditional sequence alignment often uses fixed thresholds and matching algorithms for global alignment, failing to consider the high similarity and strong conservation of gene sequences among aquatic species. This makes them highly susceptible to cross-matching between closely related species and mismatching of homologous sequences, leading to frequent misclassifications, overclassifications, and underclassifications. Second, existing alignment methods lack adaptive parameter tuning capabilities. Using a uniform alignment standard for eDNA noise sequences amplified by different primers, at different sequencing depths, and in different water quality environments fails to adapt to the differentiated sequence characteristics of complex water samples, resulting in extremely poor alignment tolerance and adaptability.

[0004] Meanwhile, current species annotation technologies mostly rely on single similarity matching, failing to comprehensively consider sequence coverage, sequencing abundance, taxonomic hierarchy, and local species baseline databases. This leads to a widespread problem of non-target sequences, contaminating sequences, and chimeric sequences being incorrectly annotated as valid species, resulting in numerous false positives. Furthermore, existing technologies lack a hierarchical screening mechanism for unknown sequences and fuzzy-matched sequences, failing to distinguish between "credible species sequences," "suspected species sequences," and "invalid miscellaneous sequences." This results in high redundancy and low reliability of the final annotation data, severely interfering with aquatic biodiversity statistics and ecological assessment results.

[0005] In summary, existing eDNA sequence analysis technologies suffer from several technical drawbacks, including fixed alignment parameters, weak ability to distinguish closely related species, high false positive rates, limited annotation dimensions, and insufficient identification precision. These limitations make it difficult to meet the requirements of standardized, precise, and traceable aquatic organism eDNA monitoring and analysis. Therefore, the urgent technical challenge is to provide an adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes to achieve adaptive, highly accurate, and multi-level identification of eDNA sequence alignment and species annotation. Summary of the Invention

[0006] The purpose of this application is to provide an adaptive comparison and species identification annotation method for aquatic organism DNA macrobarcodes, which can achieve adaptive, highly accurate, and multi-level identification of eDNA sequence alignment and species annotation.

[0007] To achieve the above objectives, this application provides a method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes, comprising: acquiring raw aquatic eDNA sequence data obtained from high-throughput sequencing of water samples; performing standardized preprocessing on the raw aquatic eDNA sequence data to obtain standardized aquatic eDNA feature sequences; dynamically configuring adaptive alignment parameters for the standardized aquatic eDNA feature sequences; performing multi-strategy hierarchical sequence adaptive alignment on the standardized aquatic eDNA feature sequences according to the adaptive alignment parameters to obtain an alignment result set; performing multi-dimensional species identification and false positive filtering on the alignment result set to obtain reliable eDNA feature sequence matching records; and performing hierarchical classification and species annotation on the reliable eDNA feature sequence matching records.

[0008] The aquatic organism DNA macrobarcode adaptive alignment and species identification annotation method described above includes the following: Standardization preprocessing of the original aquatic eDNA sequence data to obtain standardized aquatic eDNA feature sequences includes: For raw water body eDNA sequence data, low-quality base sequences are filtered out by quality threshold, primer and adapter sequences are truncated, and short invalid sequences are removed; Identify and split chimeric sequences, remove sequencing noise sequences, and sample contamination sequences; The effective clean sequences are deduplicated, clustered and concatenated to obtain a set of high-quality, non-redundant, effective feature sequences. For a set of high-quality, non-redundant, effective feature sequences, metadata such as sequencing sample location, sampling time, water body type, and amplification primer type are bound to form a standardized water body eDNA feature sequence with traceability information.

[0009] The adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes as described above, wherein dynamically configuring adaptive alignment parameters for the standardized aquatic eDNA feature sequences includes: Extract multidimensional attribute indicators from the standardized water body eDNA feature sequences; Based on the extracted feature sequence multidimensional attribute indicators, differential comparison parameters are dynamically generated.

[0010] The adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes as described above, wherein the dynamic generation of differential alignment parameters based on the extracted multidimensional attribute indicators of the feature sequences includes: Establish a benchmark parameter library; Each feature sequence is scored using multi-dimensional indicators, and correction coefficients for each dimension are generated. Multi-dimensional correction coefficients are superimposed on the baseline parameters to generate adaptive alignment parameters; The generated adaptive alignment parameters are bound one-to-one with the corresponding standardized water body eDNA feature sequences.

[0011] The aquatic organism DNA macrobarcode adaptive alignment and species identification annotation method described above, wherein, based on the adaptive alignment parameters, multi-strategy hierarchical sequence adaptive alignment is performed on the standardized aquatic eDNA feature sequence to obtain an alignment result set including: Load the standard gene reference library and the local aquatic organism baseline library; Standardized water body eDNA feature sequences were divided into high-abundance feature sequences, incomplete low-abundance sequences, and sequences of highly homologous closely related species. First-level global alignment of high-abundance feature sequences is performed to identify dominant species; Secondary local adaptive alignment was performed on incomplete and low-abundance sequences to preserve rare species signals; Three-level differential alignment of highly homologous closely related species sequences was performed to distinguish closely related species.

[0012] The adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes described above, wherein multi-dimensional species identification and false positive filtering are performed on the alignment result set to obtain reliable eDNA feature sequence matching records, including: For the comparison result set, obtain the quality comparison feature parameters; Based on the quality comparison feature parameters, calculate the quality matching value of the comparison result, compare the quality matching value with the preset quality threshold. If the quality matching value is less than the preset quality threshold, the comparison result is removed; otherwise, it does not need to be removed. Based on the abundance of the comparison results, the comparison results with abundance exceeding the normal abundance range are removed. Based on the characteristics of ecological distribution, the ecological matching degree of species in the comparison results is calculated, and matching results with ecological matching degree lower than the preset ecological threshold are removed from the comparison results set. Output reliable eDNA feature sequence matching records.

[0013] The adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes as described above, wherein the hierarchical classification and species annotation of the reliable eDNA feature sequence matching records includes: Calculate the overall confidence level of the reliable eDNA feature sequence matching record based on the confidence level evaluation index; Based on the overall confidence level, the reliable eDNA feature sequence matching records are classified and annotated according to species.

[0014] The adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes described above includes, based on comprehensive confidence levels, hierarchical classification and species annotation of reliable eDNA feature sequence matching records, which includes: Pre-set a first threshold interval, a second threshold interval, a third threshold interval, and a fourth threshold interval; If the overall confidence level falls within the first threshold range, perform first-level species identification annotation on the reliable eDNA feature sequence matching records; If the overall confidence level falls within the second threshold range, secondary suspected species annotation is performed on the reliable eDNA feature sequence matching records; If the overall confidence level falls within the third threshold range, the reliable eDNA feature sequence matching records are annotated at the third-level genera and family level. If the overall confidence level falls within the fourth threshold range, the records matching the credible eDNA feature sequences are marked with fourth-level unknown sequences.

[0015] The aquatic organism DNA macrobarcode adaptive alignment and species identification annotation method described above further includes: verifying, tracing, and standardizing the output of the annotation results.

[0016] The adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes described above includes quality alignment feature parameters such as coverage, similarity, and mismatch rate.

[0017] The beneficial effects achieved by this application are as follows: (1) This application achieves adaptive adjustment of alignment parameters, which has strong adaptability. It breaks through the limitations of traditional fixed threshold alignment, and dynamically adjusts the alignment parameters according to sequence characteristics, sample environment and species type, which perfectly adapts to the matching scenarios of complex water bodies in the field with mixed eDNA sequences, incomplete sequences and low abundance sequences, and greatly improves the utilization rate of complex sample data.

[0018] (2) This application solves the problem of identifying closely related species and greatly improves the identification accuracy. Through feature site differential comparison and multi-level identification mechanism, it effectively distinguishes highly similar closely related aquatic species, completely solves the core pain point of confusion and misjudgment of closely related species in traditional technology, and significantly improves the species identification accuracy.

[0019] (3) This application uses multidimensional filtering to remove false positives, resulting in high data reliability. It integrates sequence quality, regional ecological background, and sequencing abundance as three screening logics to batch remove contaminated sequences, chimeric sequences, mismatched sequences, and false positive data from non-native species, greatly optimizing the quality of annotation data.

[0020] (4) The hierarchical annotation in this application is scientific and rigorous, avoiding data distortion. A four-level hierarchical annotation system is established, which does not force fuzzy matching or arbitrarily discard unknown sequences. This ensures the accuracy of annotation of known species while preserving information on potential unknown biological resources, thus balancing rigor and completeness.

[0021] (5) The results of this application are traceable, standardized, and suitable for ecological supervision applications. All comparison, identification, and annotation processes are fully traceable, parameters are traceable, results are reproducible, and the output data format is standardized, which can be directly connected to aquatic organism databases and aquatic ecological assessment platforms. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0023] Figure 1 This is a flowchart illustrating an adaptive alignment and species identification annotation method for aquatic organism DNA macrobarcodes, as described in an embodiment of this application.

[0024] Figure 2 This is a flowchart illustrating an adaptive comparison and species identification annotation method for aquatic organism DNA macrobarcodes applied to aquatic organisms in inland lakes, as described in this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0026] like Figure 1 As shown, this application provides a method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes, the method comprising: Step S1: Obtain the raw water eDNA sequence data obtained from high-throughput sequencing of water samples.

[0027] Water samples were collected from rivers, lakes, reservoirs, wetlands, and nearshore areas. After environmental DNA enrichment via filtration membrane, nucleic acid extraction, specific aquatic organism macrobarcode amplification, and sequencing library construction, the samples were sequenced on a high-throughput sequencing platform. This yielded raw read datasets in FASTQ format (a standard text file format for storing biological sequence data and their corresponding quality scores, and a common format for high-throughput sequencing output data) that had not undergone quality control cleaning and contained sequencing noise, adapters, and chimeric fragments. These raw read datasets served as the original water body eDNA sequence data.

[0028] Step S2: Standardize the raw water body eDNA sequence data to obtain standardized water body eDNA characteristic sequences.

[0029] Step S2 includes: Step S210: For the original water body eDNA sequence data, filter low-quality base sequences by quality threshold, truncate primer and adapter sequences, and remove short invalid sequences.

[0030] As a specific embodiment of the present invention, filtering low-quality base sequences by quality threshold includes: using Phred quality score (e.g., Q20) as a threshold for the original water body eDNA sequence data, filtering sequences with a low-quality base ratio exceeding a preset proportion (e.g., 20%), and removing sequencing reads that do not meet the overall quality standards.

[0031] As a specific embodiment of the present invention, truncating primer and adapter sequences includes: using existing tools (such as the Cutadapt high-throughput sequencing trimming tool) to identify and remove amplification primer sequences from raw water eDNA sequence data, and removing the sequencing platform adapter sequences.

[0032] As a specific embodiment of the present invention, the removal of short invalid sequences includes: setting a minimum matching length and a maximum number of mismatches (e.g., 2), removing sequence fragments with a length less than a preset threshold from the original water body eDNA sequence data, and removing invalid short sequences caused by incomplete sequencing or degradation.

[0033] Step S220: Identify and split chimeric sequences, remove sequencing noise sequences, and sample contamination sequences.

[0034] Identifying and splitting chimeric sequences involves: using existing algorithms (such as DADA2 or USEARCH) to detect chimeric sequences in raw water eDNA sequence data, setting chimeric detection thresholds, and splitting or removing chimeric sequences from their original source sequences.

[0035] Eliminating sequencing noise sequences includes filtering random noise sequences caused by sequencing errors and removing artificially mutated sequences introduced during PCR amplification.

[0036] Eliminating contaminating sequences in samples includes: identifying and removing cross-sample contamination sequences, and eliminating contaminating sequences detected in reagent blank controls.

[0037] Step S230: Perform sequence deduplication and clustering on the effective clean sequences to obtain a set of high-quality, non-redundant, effective feature sequences.

[0038] The effective clean sequences are deduplicated (completely repeated sequences are removed), clustered according to a similarity threshold (e.g., 96%), and overlapping sequences are spliced ​​together to obtain consistent sequences.

[0039] The output is a set of non-redundant, high-quality, and effective feature sequences, which is finally obtained as a feature sequence set in FASTA format, with each sequence accompanied by sequencing abundance information.

[0040] Step S240: For the non-redundant, high-quality, and effective feature sequence set, bind metadata such as sequencing sample location, sampling time, water body type, and amplification primer type to form a standardized water body eDNA feature sequence with traceability information.

[0041] Step S3 involves dynamically configuring adaptive alignment parameters for the standardized water body eDNA feature sequences.

[0042] Step S3 includes: Step S310: Extract multidimensional attribute indicators from the standardized water body eDNA feature sequences.

[0043] For standardized water body eDNA feature sequences, multidimensional attribute indicators of each or each type of sequence are extracted in batches as the basis for parameter adjustment.

[0044] Among them, the multidimensional attribute indicators of the characteristic sequence include: basic gene attributes (macrobarcode fragment type, sequence base length, gene conservation), sequencing abundance indicators, sample environmental labels, biological group labels, amplification primer specific parameters, etc.

[0045] Step S320: Dynamically generate differential comparison parameters based on the extracted feature sequence multidimensional attribute indicators.

[0046] Based on the multidimensional attribute indicators of the standardized water body eDNA feature sequences (gene fragment type, sequence conservation, base length, sequencing abundance, amplification primer specificity parameters, etc.), differential adaptive alignment parameters are dynamically generated.

[0047] Step S320 includes: Step S321: Establish a benchmark parameter library.

[0048] Pre-built baseline comparison parameters (basic similarity, coverage, number of mismatches, window size, site weight) for different gene fragments and different biological groups are used as initial baseline values.

[0049] Step S322: Score each feature sequence using multi-dimensional indicators and generate correction coefficients for each dimension.

[0050] Each feature sequence is scored for its multidimensional attributes, generating multidimensional correction coefficients: conservation coefficient, length coefficient, abundance coefficient, environmental coefficient, and primer coefficient.

[0051] Step S323: Superimpose multi-dimensional correction coefficients on the baseline parameters to generate adaptive comparison parameters.

[0052] Using the baseline value as a base, the correction coefficients of each attribute are superimposed and adjusted up or down to obtain the unique adaptive alignment parameters of the sequence.

[0053] Step S324: The generated adaptive alignment parameters are bound one by one with the corresponding standardized water body eDNA feature sequences.

[0054] The adaptive alignment parameters include: similarity threshold, coverage, mismatch tolerance, local alignment window, and feature site weight.

[0055] The calculated set of differential adaptive alignment parameters (similarity, coverage, mismatch tolerance, local window, site weight) are stored and mapped.

[0056] Specifically, based on the gene conservation characteristics of different groups such as fish, benthic organisms, and plankton, the similarity threshold, coverage threshold, and mismatch tolerance of standardized water body eDNA feature sequences are adaptively adjusted.

[0057] Automatically tighten matching parameters for water samples with high complexity during the flood season.

[0058] For sequences of rare species with low abundance, the reasonable tolerance range is adaptively relaxed.

[0059] This application generates differentiated adaptive alignment parameters for sequences of different species, solving the problems of poor adaptability of fixed parameters and difficulty in distinguishing closely related species.

[0060] Step S4: Based on the adaptive alignment parameters, perform multi-strategy hierarchical sequence adaptive alignment on the standardized water body eDNA feature sequences to obtain the alignment result set.

[0061] Here, the alignment result set is defined as B = {B1, B2, ..., Bn}; where n is the total number of matching records generated by a standardized water body eDNA feature sequence in the reference library; and Bn represents the nth alignment result. n is greater than or equal to 1, meaning that one standardized water body eDNA feature sequence may match multiple reference sequences.

[0062] Step S4 includes: Step S410: Load the standard gene reference library and the local aquatic organism baseline library.

[0063] Step S420: The standardized water body eDNA feature sequences are divided into high abundance feature sequences, incomplete low abundance sequences, and highly homologous closely related species sequences.

[0064] Step S430: Perform a first-level global comparison of high-abundance feature sequences to identify dominant species.

[0065] Step S440: Perform secondary local adaptive alignment on the incomplete low-abundance sequences to preserve rare species signals.

[0066] Step S450: Perform tertiary differential alignment of highly homologous closely related species sequences to distinguish closely related species.

[0067] Understandably, the first-level global alignment uses high-abundance complete sequences to align with the entire database, aiming for high coverage and high similarity; the second-level local adaptive alignment uses incomplete low-abundance sequences to align with the entire database, allowing for local matching and short coverage; and the third-level closely related species differential alignment uses highly homologous sequences to align with sub-databases of closely related species, focusing on differences in characteristic sites.

[0068] In the differential comparison of third-order closely related species, the differential score of the characteristic site is calculated, and the species to be distinguished is determined based on the differential score of the characteristic site and the size of the preset threshold.

[0069] The formula for calculating the differential score of feature sites is as follows: ; in, Indicates the differential score of the characteristic site; This represents the total number of pre-annotated specific feature sites for this group; Indicates the first The weight of each specific characteristic site; This represents the specific characteristic site matching factor; if a match is found, then... ;otherwise, ; This represents the saturation factor for the number of points (e.g., 0.3). .

[0070] like If the value is >0.85, it is determined to be the current comparison species; if If the species is identified as suspicious, it is considered to be the species being compared; otherwise, it is considered that the species cannot be distinguished.

[0071] Step S5: Compare the result set to perform multi-dimensional species identification and false positive filtering to obtain reliable eDNA feature sequence matching records.

[0072] Specifically, based on the comparison result set, multi-dimensional screening is carried out from the dimensions of sequence quality, ecological distribution, and abundance characteristics to eliminate false positive results and obtain reliable eDNA feature sequence matching records.

[0073] Step S5 includes: Step S510: For the comparison result set, obtain the quality comparison feature parameters.

[0074] The quality alignment feature parameters include: coverage (the ratio of alignment length to query sequence length), similarity (the proportion of consistent bases within the alignment region), and mismatch rate (the ratio of mismatched bases to alignment length).

[0075] Specifically, for each comparison result in the comparison result set, its quality comparison feature parameters are obtained, and its quality matching value is calculated for each comparison result.

[0076] Step S520: Calculate the quality matching value of the comparison result based on the quality comparison feature parameters, compare the quality matching value with the preset quality threshold. If the quality matching value is less than the preset quality threshold, the comparison result is removed; otherwise, it is not necessary to remove it.

[0077] The formula for calculating the quality matching value of the comparison results is as follows: ; ; in, This indicates the quality match value of the comparison result; Indicates the comparison quality score; This indicates the maximum reference value for comparison quality; Indicates coverage; Indicates the coverage target threshold; Indicates observational similarity; Indicates the maximum theoretical similarity; Indicates the mismatch rate; This represents the maximum tolerable mismatch rate; These represent the weights of the comparison quality, coverage, similarity, and mismatch rate on the quality match value, respectively. .

[0078] in, Indicates the number of base pairs actually involved in the alignment; Indicates the number of incorrectly matched bases; Indicates the number of missing spaces; Indicates an ambiguous number of bases; This indicates the basic score weight of the bases; This represents the penalty coefficient for a false match; This represents the penalty coefficient for missing vacancies; The penalty coefficient represents the number of ambiguous bases; where vacancy refers to the alignment gap caused by insertion or deletion events during sequence alignment.

[0079] As an optional embodiment, ; ; ; No restrictions are imposed here. The specific value, The specific values ​​can be adaptively adjusted according to different comparison scenarios.

[0080] The preset quality threshold is, for example, 0.75. The preset quality threshold can be dynamically adjusted according to different water environments and sequencing platforms (e.g., the threshold can be lowered to 0.65 for water samples in the high-contamination period and raised to 0.85 for clean control samples).

[0081] in, The calculation formula is as follows: ; in, express The basic weights; express The characteristic feedback gain coefficient (e.g., 0.2); The standard score representing the feature value of this dimension. ; Indicates the first eigenvalues ​​of dimension; Indicates the first The average value of the dimension in the reference library or sample; Indicates the first The standard deviation of the dimensions. Among them, the first dimension is the comparison quality score; the second dimension is the coverage; the third dimension is the similarity; and the fourth dimension is the mismatch rate.

[0082] False positive comparison results are eliminated if the quality matching value of the comparison results is less than the preset quality threshold.

[0083] Step S530: Based on the abundance of the comparison results in the comparison results set, remove the comparison results whose abundance exceeds the normal abundance range.

[0084] The normal abundance range is: ; This represents the lower limit of the normal range; This represents the upper limit of the normal abundance range.

[0085] The formula for calculating the lower limit of the normal range is as follows: ; in, This represents the average abundance of all feature sequences in the sample. This represents the standard deviation of the abundance of the sample. This represents the threshold for the abundance ratio of rare species (e.g., 50). This represents the environmental complexity adjustment factor (1.6 for high-noise water bodies and 0.6 for clean water bodies).

[0086] The formula for calculating the upper limit of the normal abundance range is as follows: ; in, This represents the threshold for the abundance ratio of dominant species (e.g., 20).

[0087] If the abundance of the alignment result is greater than or equal to and less than or equal to If the abundance of the comparison result is less than 1, then retain the comparison result. If the abundance of the comparison result is greater than a certain level, the comparison result will be marked as a trace contamination, a false positive. If the comparison result is not found, it will be marked as an anomalous dominant data.

[0088] Step S540: Based on the ecological distribution characteristics, calculate the ecological matching degree of the species in the comparison results, and remove the matching results in the comparison results set whose ecological matching degree is lower than the preset ecological threshold.

[0089] It should be explained that matching results with an ecological match degree lower than a preset ecological threshold are removed from the alignment result set to address the problem of misclassification of highly homologous and closely related species. Each sequence in an alignment result set will match one or more candidate species in the reference library. Sequences of closely related species will match multiple similar species simultaneously. Only after removing false positives can a reliable species be obtained. The number of matching records is far greater than the number of real species, containing a large number of duplicates, false positives, and multiple candidate matches, with no fixed number of species.

[0090] The formula for calculating the ecological compatibility of species in the comparison results is as follows: ; in, Indicates the species in the comparison results Ecological compatibility; This indicates the number of ecological dimensions; ecological dimensions include: water body type, season, altitude, salinity, temperature, etc. Indicates the first The weight of the number of ecological dimensions on the degree of ecological matching; Indicates species In the The probability of occurrence under each ecological dimension.

[0091] Step S550: Output reliable eDNA feature sequence matching records.

[0092] Step S6: Classify and annotate species based on reliable eDNA feature sequence matching records.

[0093] Step S6 includes: Step S610: Calculate the overall confidence level of the reliable eDNA feature sequence matching record based on the confidence level evaluation index.

[0094] The confidence evaluation indicators include: overall confidence level, quality matching value, and ecological matching degree.

[0095] The formula for calculating the overall confidence level of a reliable eDNA feature sequence matching record is as follows: ; in, This represents the overall confidence level of a reliable eDNA signature sequence match record. Indicates the quality matching value; Indicates the degree of ecological matching of the comparison results; This indicates the abundance of the feature sequence corresponding to the alignment result; Indicates the upper limit of abundance; Indicates the differential score of the characteristic site; This represents the maximum value of the differential score for the feature site. Indicates the weight of the quality matching value; Weights representing the degree of ecological compatibility; Weights representing the abundance of feature sequences; The weights represent the differential scores of feature sites.

[0096] Among them, the ecological matching degree of the comparison results is taken as the maximum value of the ecological matching degree among all species in the comparison results.

[0097] Step S620: Based on the overall confidence level, perform hierarchical classification and species annotation on the reliable eDNA feature sequence matching records.

[0098] Step S620 includes: Step S621: Pre-set the first threshold interval, the second threshold interval, the third threshold interval, and the fourth threshold interval.

[0099] The first threshold interval is [0.9, 1]; the second threshold interval is [0.7, 0.9]; the third threshold interval is [0.5, 0.7]; and the fourth threshold interval is [0, 0.5].

[0100] Step S622: If the overall confidence level falls within the first threshold range, perform first-level species identification annotation on the reliable eDNA feature sequence matching records.

[0101] If the sequence matching degree, coverage, and abundance all meet the standards and are consistent with the regional ecological background, they are directly annotated to the specific species level. The hierarchical classification from top to bottom is as follows: Kingdom, Phylum, Class, Order, Family, Genus, Species.

[0102] Step S623: If the overall confidence level falls within the second threshold range, perform secondary suspected species annotation on the reliable eDNA feature sequence matching records.

[0103] If the sequence matches well but there are minor differences at characteristic sites, it is annotated as a closely related suspected species, and the source of uncertainty is marked.

[0104] Step S624: If the overall confidence level falls within the third threshold range, perform three-level genera and family level annotation on the reliable eDNA feature sequence matching records.

[0105] If the sequence can only match genus and family classification features and cannot accurately locate the species, higher-level classification annotation should be performed to avoid forced mis-annotation of species.

[0106] Step S625: If the overall confidence level falls within the fourth threshold range, perform fourth-level unknown sequence labeling on the reliable eDNA feature sequence matching records.

[0107] For novel feature sequences without valid matches, they are individually labeled as unknown aquatic organism sequences to preserve data on potential new species and unrecorded species, thus avoiding the loss of valuable information.

[0108] Step S7: Verify, trace, and standardize the output of the annotation results.

[0109] Specifically, the final annotation results are cross-validated to compare the consistency of species distribution among time-series samples at the same location and parallel samples in the same region, and to remove occasional abnormal annotation data.

[0110] Each annotation sequence is bound to comparison parameters, matching criteria, identification dimensions, and confidence levels to form a traceable annotation log.

[0111] The final output includes a standardized species annotation table, a species list statistics table, and a sequence confidence classification file, which can be directly used for subsequent applications such as aquatic biodiversity analysis, community structure evaluation, and ecological baseline surveys.

[0112] like Figure 2 As shown in the figure, as a specific embodiment of this application, applied to the scenario of aquatic biodiversity eDNA monitoring and analysis in inland lakes, the specific implementation steps of the aquatic biodiversity eDNA monitoring and analysis method are as follows: Step T1: Collect lake water eDNA samples and preprocess the lake water eDNA sample sequences.

[0113] eDNA samples were collected from lake water bodies, and raw sequence data were obtained through high-throughput sequencing. Quality filtering, adapter removal, chimera splitting, and deduplication clustering were performed to remove low-quality short fragments and contaminating sequences, resulting in a high-quality and effective feature sequence set. Metadata such as sampling location, water body type, and sequencing batch were simultaneously bound.

[0114] Step T2: Configure adaptive parameters for different groups.

[0115] The system automatically identifies the gene fragment types and sequence conservation characteristics of this sequencing, and dynamically configures differentiated similarity thresholds, sequence coverage, and mismatch tolerance parameters for different groups of lake plankton, fish, and benthic organisms. It appropriately relaxes the mismatch tolerance interval for low-abundance biological sequences and tightens the matching standard for high-noise sequences.

[0116] Step T3: Perform multi-level adaptive sequence alignment on the pre-processed lake water eDNA sample sequences.

[0117] The system sequentially performs global precise comparison, local fragment comparison, and comparison of characteristic sites of closely related species. It achieves precise matching of dominant species sequences, effectively identifies incomplete field sequences, and differentiates closely related fish species such as carp and crucian carp through specific characteristic sites.

[0118] Step T4: Compare the results to perform multidimensional species identification and false positive filtering.

[0119] The baseline list of local aquatic organisms in the lake was retrieved, and mismatched results of species not distributed in the region were removed; trace contamination sequences were filtered by combining sequencing abundance thresholds; high-quality matching results were screened by sequence coverage and matching reliability to complete the batch cleaning of false positive data.

[0120] Step T5: Perform hierarchical annotation on the comparison results after multidimensional species identification and false positive filtering.

[0121] For sequences with high matching, high reliability, and existing background, accurate species annotation is performed; for sequences with similar characteristics that cannot be accurately annotated to the species level, annotation is performed to the genus and family level; for novel sequences without matching, they are marked as unknown aquatic organism sequences, forming hierarchical classification annotation results.

[0122] Step T6: Verify the annotation results and output the verification results.

[0123] Parallel sample cross-validation is performed on all annotation results to generate a standardized species annotation report with source logs, and outputs a list of aquatic species and community composition data for lakes, providing accurate data support for lake biodiversity assessment.

[0124] When dealing with different water body scenarios such as rivers, wetlands, and nearshore areas, or when updating the reference gene bank, this invention only needs to update the baseline species bank and the adaptive parameter range, without having to reconstruct the overall algorithm architecture. It is convenient to iterate and has strong scenario adaptability.

[0125] This application also provides a computer storage medium storing computer instructions, which, when invoked, execute the address mapping method of the large-capacity solid-state drive. The computer storage medium includes one or more program instructions, which are executed by a processor as a method for adaptive comparison and species identification annotation of aquatic organism DNA macrobarcodes.

[0126] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer performs the above-described method for adaptive comparison and species identification annotation of aquatic organism DNA macrobarcodes.

[0127] This invention provides a processor for processing the above-described method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes.

[0128] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0129] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0130] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0131] The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EEPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0132] The beneficial effects achieved by this application are as follows: (1) This application achieves adaptive adjustment of alignment parameters, which has strong adaptability. It breaks through the limitations of traditional fixed threshold alignment, and dynamically adjusts the alignment parameters according to sequence characteristics, sample environment and species type, which perfectly adapts to the matching scenarios of complex water bodies in the field with mixed eDNA sequences, incomplete sequences and low abundance sequences, and greatly improves the utilization rate of complex sample data.

[0133] (2) This application solves the problem of identifying closely related species and greatly improves the identification accuracy. Through feature site differential comparison and multi-level identification mechanism, it effectively distinguishes highly similar closely related aquatic species, completely solves the core pain point of confusion and misjudgment of closely related species in traditional technology, and significantly improves the species identification accuracy.

[0134] (3) This application uses multidimensional filtering to remove false positives, resulting in high data reliability. It integrates sequence quality, regional ecological background, and sequencing abundance as three screening logics to batch remove contaminated sequences, chimeric sequences, mismatched sequences, and false positive data from non-native species, greatly optimizing the quality of annotation data.

[0135] (4) The hierarchical annotation in this application is scientific and rigorous, avoiding data distortion. A four-level hierarchical annotation system is established, which does not force fuzzy matching or arbitrarily discard unknown sequences. This ensures the accuracy of annotation of known species while preserving information on potential unknown biological resources, thus balancing rigor and completeness.

[0136] (5) The results of this application are traceable, standardized, and suitable for ecological supervision applications. All comparison, identification, and annotation processes are fully traceable, parameters are traceable, results are reproducible, and the output data format is standardized, which can be directly connected to aquatic organism databases and aquatic ecological assessment platforms.

[0137] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0138] In the description of this application, the word "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0139] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes, characterized in that, include: Obtain raw water body eDNA sequence data from high-throughput sequencing of water samples; The original water body eDNA sequence data was subjected to standardized preprocessing to obtain standardized water body eDNA feature sequences; Dynamically configure adaptive alignment parameters for the standardized water body eDNA feature sequences; Based on the adaptive alignment parameters, the standardized water body eDNA feature sequence is subjected to multi-strategy hierarchical sequence adaptive alignment to obtain an alignment result set. The comparison result set is subjected to multi-dimensional species identification and false positive filtering to obtain reliable eDNA feature sequence matching records; The reliable eDNA feature sequence matching records are classified and annotated according to species.

2. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 1, characterized in that, The raw water body eDNA sequence data is subjected to standardized preprocessing to obtain standardized water body eDNA characteristic sequences, including: For raw water body eDNA sequence data, low-quality base sequences are filtered out by quality threshold, primer and adapter sequences are truncated, and short invalid sequences are removed; Identify and split chimeric sequences, remove sequencing noise sequences, and sample contamination sequences; The effective clean sequences are deduplicated, clustered and concatenated to obtain a set of high-quality, non-redundant, effective feature sequences. For a set of high-quality, non-redundant, effective feature sequences, metadata such as sequencing sample location, sampling time, water body type, and amplification primer type are bound to form a standardized water body eDNA feature sequence with traceability information.

3. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 1, characterized in that, Dynamically configuring adaptive alignment parameters for the standardized water body eDNA feature sequences includes: Extract multidimensional attribute indicators from the standardized water body eDNA feature sequences; Based on the extracted feature sequence multidimensional attribute indicators, differential comparison parameters are dynamically generated.

4. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 3, characterized in that, Based on the extracted feature sequence multidimensional attribute indicators, the differential alignment parameters are dynamically generated, including: Establish a benchmark parameter library; Each feature sequence is scored using multi-dimensional indicators, and correction coefficients for each dimension are generated. Multi-dimensional correction coefficients are superimposed on the baseline parameters to generate adaptive alignment parameters; The generated adaptive alignment parameters are bound one-to-one with the corresponding standardized water body eDNA feature sequences.

5. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 1, characterized in that, Based on the adaptive alignment parameters, a multi-strategy hierarchical sequence adaptive alignment is performed on the standardized water body eDNA feature sequence to obtain an alignment result set including: Load the standard gene reference library and the local aquatic organism baseline library; Standardized water body eDNA feature sequences were divided into high-abundance feature sequences, incomplete low-abundance sequences, and sequences of highly homologous closely related species. First-level global alignment of high-abundance feature sequences is performed to identify dominant species; Secondary local adaptive alignment was performed on incomplete and low-abundance sequences to preserve rare species signals; Three-level differential alignment of highly homologous closely related species sequences was performed to distinguish closely related species.

6. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 1, characterized in that, The comparison result set is subjected to multi-dimensional species identification and false positive filtering to obtain reliable eDNA feature sequence matching records, including: For the comparison result set, obtain the quality comparison feature parameters; Based on the quality comparison feature parameters, calculate the quality matching value of the comparison result, compare the quality matching value with the preset quality threshold. If the quality matching value is less than the preset quality threshold, the comparison result is removed; otherwise, it does not need to be removed. Based on the abundance of the comparison results, the comparison results with abundance exceeding the normal abundance range are removed. Based on the characteristics of ecological distribution, the ecological matching degree of species in the comparison results is calculated, and matching results with ecological matching degree lower than the preset ecological threshold are removed from the comparison results set. Output reliable eDNA feature sequence matching records.

7. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 1, characterized in that, The hierarchical classification and species annotation of the reliable eDNA feature sequence matching records includes: Calculate the overall confidence level of the reliable eDNA feature sequence matching record based on the confidence level evaluation index; Based on the overall confidence level, the reliable eDNA feature sequence matching records are classified and annotated according to species.

8. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 7, characterized in that, Based on the overall confidence level, the species annotation of reliable eDNA feature sequence matching records is classified and categorized, including: Pre-set a first threshold interval, a second threshold interval, a third threshold interval, and a fourth threshold interval; If the overall confidence level falls within the first threshold range, perform first-level species identification annotation on the reliable eDNA feature sequence matching records; If the overall confidence level falls within the second threshold range, secondary suspected species annotation is performed on the reliable eDNA feature sequence matching records; If the overall confidence level falls within the third threshold range, the reliable eDNA feature sequence matching records are annotated at the third-level genera and family level. If the overall confidence level falls within the fourth threshold range, the records matching the credible eDNA feature sequences are marked with fourth-level unknown sequences.

9. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 1, characterized in that, The method also includes: verifying, tracing, and standardizing the output of the annotation results.

10. The method for adaptive alignment and species identification annotation of aquatic organism DNA macrobarcodes according to claim 6, characterized in that, Quality comparison features include: coverage, similarity, and mismatch rate.

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