Method, device, terminal, medium and product for analyzing extrachromosomal circular DNA based on long read sequencing data

Abstract

The application provides an extrachromosomal circular DNA analysis method and device based on long-read sequencing data, a terminal, a medium and a product. After obtaining long-read sequencing data to be analyzed and establishing a reference genome database, firstly, tandem repeat sequence detection and first alignment analysis are sequentially performed on the long-read sequencing data to obtain a first extrachromosomal circular DNA analysis result, second alignment analysis and third alignment analysis are performed on the data in the long-read sequencing data that has not been identified to obtain a second extrachromosomal circular DNA analysis result and a third extrachromosomal circular DNA analysis result, respectively, and finally, the extrachromosomal circular DNA analysis results are combined to obtain a final extrachromosomal circular DNA analysis result. Through three alignment analyses, the accuracy of circular DNA analysis is improved, and the utilization rate of long-read data is improved.

Description

Technical Field

[0001] This application relates to the field of bioinformatics, and in particular to a method, apparatus, terminal, medium, and product for analyzing extrachromosomal circular DNA based on long-read sequencing data. Background Technology

[0002] Extrachromosomal circular DNA (eccDNA) is a class of circular DNA molecules widely found in eukaryotic cells, playing a crucial role in biological processes such as genome plasticity, gene expression regulation, and cellular stress responses. Studies have shown that eccDNA is closely related to the occurrence and development of various diseases; therefore, eccDNA analysis is of great significance in disease research, particularly in the development and progression of tumors.

[0003] With the rapid development of high-throughput sequencing technology, third-generation sequencing technology has become a popular choice in scientific research and clinical applications, and has been widely adopted and applied. Third-generation sequencing platforms (such as nanopore sequencing and single-molecule real-time sequencing) have brought long-read data to genomics. However, third-generation sequencing often suffers from a high sequencing error rate, which has led existing eccDNA analysis tools to focus on error correction strategies, tolerating larger false matches, and to some extent sacrificing the ability to accurately measure complex chimeric structures or low-abundance circular fragments. In recent years, PacBio HiFi sequencing technology has provided long-read data (ranging from thousands to tens of thousands of base pairs) with an average accuracy of 99% or higher, bringing higher accuracy to the identification of complex genomic structures (including eccDNA). However, existing eccDNA analysis tools (such as CReSIL, SMOOTH-seq, etc.) have the following main problems:

[0004] (1) Most existing analysis tools have adopted the analysis process for dealing with high error rate data, which cannot give full play to the advantages of HiFi data;

[0005] (2) Existing analytical tools often treat all circular DNA as the same type, lacking a systematic classification standard, which cannot meet the needs of accurately studying the biological characteristics and functions of eccDNA.

[0006] (3) Existing analytical tools have low analytical efficiency, low reliability of analytical results and high repeatability;

[0007] (4) Researchers often need to use multiple tools in combination or make a lot of parameter adjustments, which not only increases the complexity of the analysis but may also introduce additional errors. At the same time, using different analytical procedures makes it difficult to standardize and compare research results. Summary of the Invention

[0008] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a method, device, terminal, medium and product for analyzing extrachromosomal circular DNA based on long read sequencing data, so as to solve at least one of the problems in the prior art.

[0009] To achieve the above and other related objectives, a first aspect of this application provides a method for analyzing extrachromosomal circular DNA based on long-read sequencing data, comprising: acquiring long-read sequencing data to be analyzed and establishing a reference genome database; detecting tandem repeat sequences in the long-read sequencing data to obtain candidate extrachromosomal circular DNA sequence data; filtering and processing repeat regions in the candidate extrachromosomal circular DNA sequence data to obtain potential extrachromosomal circular DNA sequence data; and performing a first alignment analysis on the potential extrachromosomal circular DNA sequence data based on the reference genome database to obtain a first extrachromosomal circular DNA analysis result. First unidentified data is extracted from the long-read sequencing data; a second alignment analysis is performed on the first unidentified data based on the reference genome database to obtain the second extrachromosomal circular DNA analysis result; second unidentified data is extracted from the long-read sequencing data; a third alignment analysis is performed on the second unidentified data based on the reference genome database to obtain the third extrachromosomal circular DNA analysis result; the first extrachromosomal circular DNA analysis result, the second extrachromosomal circular DNA analysis result, and the third extrachromosomal circular DNA analysis result are merged and classified to obtain the final extrachromosomal circular DNA analysis result.

[0010] In some embodiments of the first aspect of this application, the specific process of filtering and repeating region processing includes: sequentially performing integer processing and average matching rate filtering on candidate extrachromosomal circular DNA sequence data to obtain filtered extrachromosomal circular DNA sequence data; sequentially performing repeating region merging and circularization processing on the filtered extrachromosomal circular DNA sequence data to obtain potential extrachromosomal circular DNA sequence data.

[0011] In some embodiments of the first aspect of this application, based on the reference genome database, a first alignment analysis is performed on potential extrachromosomal circular DNA sequence data to obtain a first extrachromosomal circular DNA analysis result, including: aligning the potential extrachromosomal circular DNA sequence data with the reference genome database to obtain a first alignment result; performing circular DNA analysis on the first alignment result according to a first circular DNA analysis rule to obtain a first extrachromosomal circular DNA analysis result; wherein, the specific process includes: determining a first alignment parameter and alignment strand parameter for each read in the first alignment result; and wherein, the first alignment parameter includes: alignment region length, consistency length, difference from the target sequence, difference from the target sequence, and number of alignment sites; the alignment strand parameter includes: number of alignment strands, alignment strand length, and sum of alignment strand lengths; and, according to the first circular DNA analysis rule, classifying each read into circular DNA based on the first alignment parameter and alignment strand parameter for each read to obtain a circular DNA classification result for each read, thereby obtaining the first extrachromosomal circular DNA analysis result.

[0012] In some embodiments of the first aspect of this application, the first circular DNA analysis rule includes: classifying reads that meet any one of the following UeccDNA classification conditions as UeccDNA: First UeccDNA classification condition: the number of alignment sites read is equal to 1; the difference between the read and the target sequence is less than a set difference threshold, or the percentage difference between the read and the target sequence is less than a set difference percentage threshold; Second UeccDNA classification condition: the number of alignment sites read is greater than 1; the distance between the start position or the distance between the end position of any two adjacent alignment sites read is less than a set distance threshold; classifying reads that meet the UeccDNA classification conditions as UeccDNA. The classification criteria for CeccDNA include: multiple alignment sites read; distance between alignment sites read greater than a set distance threshold; difference between the read and the target sequence less than a set difference threshold, or percentage difference between the read and the target sequence less than a set percentage difference threshold; reads that meet the CeccDNA classification criteria are classified as CeccDNA; wherein, the CeccDNA classification criteria include: the number of alignment strands read is not zero; length of alignment strands read is greater than the length of concordance read; number of alignment sites read is at least two; and the ratio between the total length of alignment strands read and the length of concordance read is within a set ratio range.

[0013] In some embodiments of the first aspect of this application, the second alignment and classification analysis of the first unidentified data based on the reference genome database to obtain the second extrachromosomal circular DNA analysis result includes: aligning the first unidentified data with the reference genome database to obtain a second alignment result; determining a second alignment parameter for each read in the second alignment result; performing linear read identification analysis and circular DNA identification analysis on the first unidentified data based on the second alignment parameter for each read to obtain linear read identification results and circular DNA identification results; calculating the identification result parameter for each read in the circular DNA identification result, and classifying the circular DNA identification result according to the second circular DNA analysis rules based on the identification result parameter for each read to obtain the second extrachromosomal circular DNA analysis result.

[0014] In some embodiments of the first aspect of this application, second unidentified data is extracted from the long-read sequencing data; based on the reference genome database, a third alignment and classification analysis is performed on the second unidentified data to obtain a third chromosome extrachromosomal circular DNA analysis result, including: using Minimap2 to align the second unidentified data with the reference genome database to obtain a third alignment result, and sorting the third alignment result; identifying abnormal coverage regions based on the coverage map of the generated sorted results, and merging the identified abnormal coverage regions; and processing the merged third alignment result using a segmentation-based inference method to obtain the third chromosome extrachromosomal circular DNA analysis result.

[0015] In some embodiments of the first aspect of this application, the merged third alignment result is processed by employing a segmentation-based inference method to obtain the third chromosome outer circular DNA analysis result, including: outputting the segmentation-based identification conditions satisfied in the merged third alignment result as the third chromosome outer circular DNA analysis result; wherein, the segmentation-based identification conditions include: the read contains a soft trimming operation and the read crosses both ends of the target region.

[0016] To achieve the above and other related objectives, a second aspect of this application provides an extrachromosomal circular DNA analysis device based on long-read sequencing data, comprising: an acquisition module for acquiring long-read sequencing data to be analyzed and establishing a reference genome database; a first alignment analysis module for identifying candidate extrachromosomal circular DNA sequence data from the long-read sequencing data through tandem repeat sequence detection and filtering and repeating region processing to obtain potential extrachromosomal circular DNA sequence data; performing a first alignment analysis on the potential extrachromosomal circular DNA sequence data based on the reference genome database to obtain a first extrachromosomal circular DNA analysis result; and a second alignment analysis module for... The long-read sequencing data is used to extract the first unidentified data; based on the reference genome database, the first unidentified data is subjected to a second alignment analysis to obtain the second extrachromosomal circular DNA analysis result; a third alignment analysis module is used to extract the second unidentified data from the long-read sequencing data; based on the reference genome database, the second unidentified data is subjected to a third alignment analysis to obtain the third extrachromosomal circular DNA analysis result; a merging module is used to merge and classify the first extrachromosomal circular DNA analysis result, the second extrachromosomal circular DNA analysis result, and the third extrachromosomal circular DNA analysis result to obtain the final extrachromosomal circular DNA analysis result.

[0017] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for analyzing extrachromosomal circular DNA based on long-read sequencing data.

[0018] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to implement the method for analyzing extrachromosomal circular DNA based on long-read sequencing data.

[0019] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the method for analyzing extrachromosomal circular DNA based on long-read sequencing data.

[0020] As described above, the method, apparatus, terminal, medium, and product for analyzing extrachromosomal circular DNA based on long-read sequencing data of this application have the following beneficial effects:

[0021] This application leverages the high accuracy and long read length characteristics of HiFi sequencing data to improve the sensitivity and accuracy of eccDNA detection; the method in this application can simultaneously detect and classify multiple types of eccDNA, providing comprehensive eccDNA analysis. Attached Figure Description

[0022] Figure 1 The diagram shown is a flowchart of a method for analyzing extrachromosomal circular DNA based on long-read sequencing data in one embodiment of this application.

[0023] Figure 2 The diagram shows a specific process for analyzing extrachromosomal circular DNA based on long-read sequencing data in one embodiment of this application.

[0024] Figure 3 The diagram shown is a schematic block diagram of a chromosome extrachromosomal circular DNA analysis device based on long-read sequencing data in one embodiment of this application.

[0025] Figure 4 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0026] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0027] In the embodiments of this application, terms such as "first" and "second" are used to distinguish identical or similar items with essentially the same function and purpose. For example, the first comparison parameter and the second comparison parameter are merely used to distinguish different comparison parameters and do not limit their order. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that terms such as "first" and "second" do not necessarily imply that they are different.

[0028] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0029] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0030] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0031] eccDNA can be classified into the following categories:

[0032] (1) U-eccDNA (Unique-locus eccDNA): Only one alignment site is found in the genome;

[0033] (2) M-eccDNA (Multi-locus eccDNA): The same eccDNA sequence can be found at multiple sites in the genome, but it is not "chimerism";

[0034] (3) C-eccDNA (Chimeric eccDNA): composed of multiple sequences from different genome fragments spliced ​​together;

[0035] (4) MC-eccDNA (Multiple-locus Chimeric eccDNA): It has both the characteristics of "multiple-locus matching" and "chimerism";

[0036] (5) X-eccDNA (Unknown eccDNA): The type of eccDNA that cannot be determined at present.

[0037] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 This document illustrates a flowchart of a method for analyzing extrachromosomal circular DNA based on long-read sequencing data, as described in an embodiment of the present invention. The method for analyzing extrachromosomal circular DNA based on long-read sequencing data in this embodiment mainly includes the following steps:

[0038] Step S11: Obtain the long-read sequencing data to be analyzed and establish a reference genome database.

[0039] In one embodiment, the long-read sequencing data is long-read (HiFi reads) data from the PacBio HiFi sequencing platform.

[0040] In one embodiment, the reference genome database is a BLAST nucleic acid database constructed using a reference genome (e.g., the human reference genome T2T-CHM13v2.0).

[0041] Step S12: Detect tandem repeat sequences in the long read sequencing data to obtain candidate extrachromosomal circular DNA sequence data; filter and process repeat regions in the candidate extrachromosomal circular DNA sequence data to obtain potential extrachromosomal circular DNA sequence data; perform a first alignment analysis on the potential extrachromosomal circular DNA sequence data based on the reference genome database to obtain the first extrachromosomal circular DNA analysis results.

[0042] In one embodiment, the TideHunter tool is used to detect tandem repeat sequences in the long-read sequencing data to obtain candidate extrachromosomal circular DNA sequence data.

[0043] It should be understood that tandem repeats refer to identical or nearly identical DNA sequence segments that appear repeatedly in a genome.

[0044] It should be noted that TideHunter is a tool specifically designed to efficiently and sensitively detect tandem repeats from noisy long-read sequence data and invoke consensus sequences.

[0045] In one embodiment, the candidate extrachromosomal circular DNA sequence data output by the TideHunter tool includes multiple extrachromosomal circular DNA reads; each extrachromosomal circular DNA read includes: read name (readName), tandem repeat ID number (repN), copy number of tandem repeat (copyNum), read length (readLen), start coordinate of tandem repeat (start), end coordinate of tandem repeat (end), length of consensus sequence (consLen), average match rate between each unit sequence and consensus sequence (aveMatch), specific position of tandem repeat in the original read (subPos), and consensus sequence (consSeq).

[0046] For example, the output of the TideHunter tool can be seen in Table 1 below.

[0047] Table 1. Candidate extrachromosomal circular DNA sequence data

[0048] read1 rep1 3.2 1000 50 400 100 99.5 50,150,250 ATCG… read2 rep1 2.8 800 30 350 110 99.8 30,140 GCTA…

[0049] In one embodiment, the specific process of filtering and repeating region processing includes: sequentially performing integerization and average matching rate filtering on candidate extrachromosomal circular DNA sequence data to obtain filtered extrachromosomal circular DNA sequence data; and sequentially performing repeating region merging and circularization on the filtered extrachromosomal circular DNA sequence data to obtain potential extrachromosomal circular DNA sequence data. It should be noted that filtering and repeating region processing are used to reduce noise and highlight potential circular structures.

[0050] The following section will explain the specific process of filtering and handling duplicate regions:

[0051] The candidate extrachromosomal circular DNA sequence data were sequentially integerized. Integerization involved rounding the copyNum of each extrachromosomal circular DNA sequence read to an integer. It should be noted that integerization facilitates subsequent identification of MeccDNA or UeccDNA and allows for the merging of repetitive regions, avoiding errors caused by decimal copy numbers.

[0052] Furthermore, the integerized extrachromosomal circular DNA sequence data is filtered using the average match rate. The specific process of the average match rate filtering includes: reading and outputting the extrachromosomal circular DNA sequence data with an average match rate greater than 99 (aveMatch>99) from the integerized extrachromosomal circular DNA sequence data as the filtered extrachromosomal circular DNA sequence data.

[0053] Furthermore, the filtered extrachromosomal circular DNA sequence data is subjected to repeating region merging processing, including: calculating the effective length (Effective_Length) of each extrachromosomal circular DNA read in the filtered extrachromosomal circular DNA sequence data according to the following formula 1;

[0054] Effective_Length = (end – start + 1) / readLen × 100%; (Formula 1)

[0055] Where readLen is the length of the original read, start is the starting coordinate of the serial repetition, and end is the ending coordinate of the serial repetition;

[0056] Based on the effective length of each extrachromosomal circular DNA read in the filtered extrachromosomal circular DNA sequence data, and according to the merging conditions, the repetitive regions of each extrachromosomal circular DNA read in the filtered extrachromosomal circular DNA sequence data are merged to obtain the merged extrachromosomal circular DNA sequence data.

[0057] The merging conditions include: (1) the difference in consLen between the two repeating regions is ≤15bp (bp represents a base pair); the effective length of the merged region is ≤102%. It should be noted that the effective length of the merged region can be calculated with reference to Formula 1, which will not be repeated here. It should be noted that 15bp is an exemplary threshold, and those skilled in the art can set it to other values, which is not limited in this invention.

[0058] Each read in the merged extrachromosomal circular DNA sequence data includes: read name (readName), tandem repeat ID number (repN), copy number of tandem repeat (copyNum), start coordinate of tandem repeat (start), end coordinate of tandem repeat (end), and length of consensus sequence (consLen).

[0059] For example, read1 has two repeated regions:

[0060] Region 1: start=50, end=400, consLen=100

[0061] Region 2: start=450, end=750, consLen=98

[0062] The two duplicate regions were merged according to the above merging criteria. The merging results are shown in Table 2.

[0063] Table 2. Readings after merging

[0064] read1 repM0 6 50 750 100 70％

[0065] Furthermore, according to the first CtcR classification rule, the merged extrachromosomal circular DNA sequence data are divided into four categories to obtain the first CtcR classification results; wherein, the first CtcR classification rule includes:

[0066] Reads with an Effective_Length ≥ 99% are classified as CtcR-perfect. Specifically, an Effective_Length ≥ 99% read indicates that the serial repeating region almost covers the entire read length and is very close to the ring structure. This type of read refers to reads with very complete repeating segments, high average matching rate, and may be quite consistent with the ring structure.

[0067] Reads with 70% ≤ Effective_Length < 99% are classified as CtcR-hybrid. Specifically, these reads indicate that the serial repetition portion is large, but has not yet achieved complete coverage or has some mismatched regions. These reads have both partial looping characteristics and contain a certain linear component, similar to the looping possibility of a "hybrid" state.

[0068] Reads containing multiple repeating regions are classified as CtcR-multiple; specifically, these reads contain multiple relatively independent repeating regions (i.e., multiple tandem repeats that can be merged), indicating multiple copies / multiple segments of the repeating regions stacked; these reads may present a multi-segment splicing form due to the high number of repeats and significant amplification, and are more likely to have high copy (multi-ring) potential.

[0069] Reads with a single repN tag are classified as CtcR-inversion; specifically, such reads have a single repN tag but detect inversion information, or exhibit some kind of inversion after merging, and do not belong to the aforementioned perfect, hybrid, or multiple categories; this may indicate that the read has a special case of local ringing or inversion of repeating units; its ring structure has the feature of "reverse repetition / inversion".

[0070] It should be noted that CtcR (CtcReads, Concatemeric tandem copies reads) refers to reads containing tandem repeat sequences, which may be a characteristic of circular DNA.

[0071] Furthermore, the merged extrachromosomal circular DNA sequence data is circularized. This circularization process includes: circularizing each read from the merged extrachromosomal circular DNA sequence data to obtain circularized sequence data for each read; and outputting all circularized sequence data as potential extrachromosomal circular DNA sequence data. It should be understood that DNA circularization refers to the process of treating DNA fragments using specific biochemical methods to form a circular structure.

[0072] For example, one of the sequences read from the merged extrachromosomal circular DNA sequence data is ATCGATCG (8 bp in length); the sequence obtained after circularization is ATCGATCGATCGATCG (joined end to end, 16 bp in length).

[0073] Furthermore, the output format for each read in the potential extrachromosomal circular DNA sequence data is query ID + specific sequence; where the query ID is formatted as read name / ID number of tandem repeat / length of consensus sequence / copy number of tandem repeat. For example, a read in the potential extrachromosomal circular DNA sequence data is read1|repM0|100|6ATCGATCGATCGATCG.

[0074] It should be noted that after filtering and processing of repetitive regions, you will get a main file (in .csv format) containing all the filtered and merged sequence information, a file containing the first CtcR classification results (in .csv format), and a file containing potential extrachromosomal circular DNA sequence data (in .fasta format).

[0075] In one embodiment, based on the reference genome database, a first alignment analysis is performed on potential extrachromosomal circular DNA sequence data to obtain a first extrachromosomal circular DNA analysis result. This includes: aligning the potential extrachromosomal circular DNA sequence data with the reference genome database to obtain a first alignment result; performing circular DNA analysis on the first alignment result according to first circular DNA analysis rules to obtain a first extrachromosomal circular DNA analysis result. Specifically, this process includes: determining the first alignment parameters and alignment strand parameters for each read in the first alignment result; wherein the first alignment parameters include: alignment region length, consistency length, difference from the target sequence, difference from the target sequence, and number of alignment sites; the alignment strand parameters include: number of alignment strands, alignment strand length, and sum of alignment strand lengths; and classifying each read into circular DNA based on the first alignment parameters and alignment strand parameters according to the circular DNA analysis rules to obtain a circular DNA classification result for each read, thereby obtaining the first extrachromosomal circular DNA analysis result.

[0076] In one embodiment, the first circular DNA analysis rule includes: classifying reads that meet any one of the following UeccDNA classification conditions as UeccDNA: First UeccDNA classification condition: the number of alignment sites read is equal to 1; the difference between the read and the target sequence is less than a set difference threshold, or the percentage difference between the read and the target sequence is less than a set difference percentage threshold; Second UeccDNA classification condition: the number of alignment sites read is greater than 1; the distance between the start position or the distance between the end position of any two adjacent alignment sites read is less than a set distance threshold; classifying reads that meet the UeccDNA classification conditions as UeccDNA... The sequence is classified as CeccDNA. The CeccDNA classification criteria include: multiple alignment sites are read; the distance between read alignment sites is greater than a set distance threshold; the difference between the read sequence and the target sequence is less than a set difference threshold, or the percentage difference between the read sequence and the target sequence is less than a set percentage difference threshold. Reads meeting the CeccDNA classification criteria are classified as CeccDNA. The CeccDNA classification criteria include: the number of read alignment strands is not zero; the length of the read alignment strands is greater than the read consistency length; the number of read alignment sites is at least two; and the ratio between the total length of the read alignment strands and the read consistency length is within a set ratio range.

[0077] The following section will explain the specific process of the first type of comparative analysis:

[0078] Configure the parameters for the BLAST tool; set word_size (minimum matching length) to 100, evalue (expected value threshold) to 1e-50, perc_identity (sequence similarity threshold) to 99, and set the output format to a 6-column table format.

[0079] Furthermore, using the BLAST tool, the potential extrachromosomal circular DNA sequence data are compared with the reference gene database to obtain a first alignment result. Each read in the first alignment result includes: query ID (query_id), target sequence ID (subject_id), sequence alignment consistency percentage (identity), alignment length (alignment_length), start site (q_start) of the alignment region on the query sequence (query_id), end site (q_end) of the alignment region on the query sequence (query_id), start site (s_start) of the alignment region on the target sequence (subject_id), and end site (s_end) of the alignment region on the target sequence (Subject id).

[0080] For example, one of the reads from the first alignment results is shown in Table 3:

[0081] Table 3. Readings from the first comparison results

[0082] read1|rep1|100|3 chr1 99.8 95 1 95 1000 1095

[0083] Furthermore, each query ID read in the first comparison result is parsed to obtain the parsing result of each ID read in the first comparison result.

[0084] For example, a query ID (query_id) is read1|rep1|100|3. The parsing result of this query ID (query_id) is: read name (readName): read1; serial duplicate ID number (repN): rep1; consensus sequence length (consLen): 100; serial duplicate copy number (copyNum): 3.

[0085] Furthermore, based on the ID parsing results of each read in the first comparison result, the alignment region length Rlength, the gap_Length with the target sequence, the gap_Percentage with the target sequence, and the number of alignment sites for each read in the first comparison result are calculated with reference to Formulas 2, 3, and 4 below:

[0086] Rlength = s_end - s_start + 1; (Formula 2)

[0087] gap_Length = consLen – Rlength; (Formula 3)

[0088] Gap Percentage =|gap_Length| / consLen × 100%; (Formula 4)

[0089] Furthermore, reads exceeding 100 alignment sites are removed in the first alignment result. It should be understood that more than 100 alignment sites typically indicate extremely repetitive reads or noise compared to the reference sequence, significantly impacting analysis efficiency and accuracy. This threshold is preferred as a limitation in this invention, but the possibility of modifying the threshold in extreme cases is not excluded.

[0090] Furthermore, the first alignment results obtained after removal are sorted according to q_start to obtain one or more alignment chains; the length of each alignment chain and the sum of the lengths of the alignment chains are calculated.

[0091] Furthermore, reads that meet any of the following UeccDNA classification criteria in the first comparison results are classified as UeccDNA:

[0092] Case 1: (1) Reading with a single alignment site; (2) Gap_Percentage≤10% or |gap_Length|≤50bp.

[0093] Case 2: (1) The reading has multiple alignment sites; (2) The difference between the starting positions of any two adjacent alignment sites is not less than 5 bp, or the difference between the starting positions of any two adjacent alignment sites is not less than 5 bp.

[0094] Reads from the first alignment results that meet the following MeccDNA classification criteria will be classified as MeccDNA:

[0095] Reads have multiple alignment sites; the distance between alignment sites is greater than 5 bp; Gap_Percentage ≤ 10% or |gap_Length| ≤ 50 bp.

[0096] Reads from the first alignment results that meet the following CeccDNA classification criteria will be classified as CeccDNA:

[0097] The read has at least two alignment sites; the length of the alignment chain is greater than the length of the consensus sequence; the ratio of the sum of the lengths of the alignment chains to the length of the consensus sequence (consLen) is between 0.8 and 1.2.

[0098] Furthermore, CeccDNA can be further classified: reads with one alignment strand are classified as Cecc; reads with more than one alignment strand are classified as Ceccm. It should be understood that Cecc indicates that a single chimeric strand can form a loop; Ceccm indicates a complex of multiple chromosomal segments, possibly found in highly complex chimeric structures, which better reflects the amplification characteristics of certain tumor cells in reality.

[0099] It should be noted that the first type of comparison analysis yields CSV files containing UeccDNA classification information, CSV files containing MeccDNA classification information, CSV files containing CeccDNA classification information, FASTA files containing UeccDNA sequences, FASTA files containing MeccDNA sequences, FASTA files containing CeccDNA sequences, and FASTA format record files.

[0100] It should also be noted that UeccDNA (Unique eccDNA): confirmed eccDNA obtained based on tandem repeat reads (CtcReads) analysis, and eccDNA inferred based on split reads; MeccDNA (Multiple alignment eccDNA): eccDNA with multiple alignment positions in the genome; CeccDNA (Chimeric eccDNA): chimeric circular DNA composed of DNA fragments from different sources; MCeccDNA (Multiple alignment Chimeric eccDNA): chimeric circular DNA with multiple alignment positions.

[0101] Step S13: Extract the first unidentified data from the long read sequencing data; based on the reference genome database, perform a second type of alignment analysis on the first unidentified data to obtain the second chromosome outer circular DNA analysis results.

[0102] In one embodiment, based on candidate extrachromosomal circular DNA sequence data, a first unidentified data is extracted from the long-read sequencing data using a seqkit tool. Specifically, the candidate extrachromosomal circular DNA sequence data is the identified data, and the data other than the identified data is the first unidentified data.

[0103] In one embodiment, based on the reference genome database, a second alignment analysis is performed on the first unidentified data to obtain a second extrachromosomal circular DNA analysis result, including: aligning the first unidentified data with the reference genome database to obtain a second alignment result; determining a second alignment parameter for each read in the second alignment result; performing linear read identification analysis and circular DNA identification analysis on the first unidentified data based on the second alignment parameter for each read to obtain linear read identification results and circular DNA identification results; calculating the identification result parameter for each read in the circular DNA identification result, and classifying the circular DNA identification result according to the second circular DNA analysis rules based on the identification result parameter for each read to obtain a second extrachromosomal circular DNA analysis result.

[0104] The following section explains the specific process of the second type of comparative analysis:

[0105] Using the BLAST tool, the first unidentified data was compared with the reference genome database to obtain the second alignment result.

[0106] Furthermore, the samtools faidx tool is used to create a FASTA index for the first unidentified data for quick access and processing.

[0107] Further, the second alignment results are merged with the FASTA index to obtain the merged result; the consistency of the tnames in the second alignment results is determined, and repetitive regions are identified (tolerance = 30 bp); the second alignment parameters for each read in the second alignment results are determined, and these parameters are added to the merged result. The second alignment parameters include: strand information, eff_Ratio, and frequency of read occurrences. Specifically, based on the gene's position coordinates on the target sequence, if the start position tstart is less than the end position tend, and the sequence orientation is consistent with the positive strand, then the gene is on the positive strand; if the start position tstart is greater than the end position tend, or the sequence orientation is consistent with the anti-strand, then the gene is likely on the anti-strand. The tname typically indicates the name of the target sequence. Checking its consistency helps ensure that all aligned fragments come from the same or the same batch of reference sequences, thus guaranteeing the validity of circular DNA splicing and analysis. Tolerance is typically used to define the range of variation or difference allowed when identifying repetitive sequences. Efficiency (eff_Ratio) measures the percentage of the alignment length to the read length, roughly reflecting the completeness of the alignment. Read frequency is the number of times the read appears in the second alignment result, used to subsequently distinguish between linear and circular DNA reads.

[0108] Furthermore, reads that appear 1 time and have an efficiency (eff_Ratio) > 98% in the second alignment results are output as linear read recognition results; reads that appear more than 2 times and less than 100 times in the second alignment results are filtered out, and the filtered reads are sorted according to q_start to obtain the alignment strands; the repetitive regions of the alignment strands are merged, and the merged strands are marked (PreCtcRlist_number) to obtain the circular DNA recognition results.

[0109] Furthermore, the identification parameters for circular DNA recognition are determined. These parameters include: circular DNA length (eLength), number of repeat units (eRepeatNum), matching degree (MatDegree), and whether reverse inversion exists. Specifically, the circular DNA length (eLength) is usually estimated based on the difference between the minimum start position and the maximum end position; the number of repeat units (eRepeatNum) is determined based on the occurrence of repeat regions; the matching degree (MatDegree) is generally the average identity of the read alignment fragments; and the presence of reverse inversion is indicated by a value of 1 (indicating inversion exists) and 0 (indicating inversion does not exist).

[0110] Furthermore, based on the second CtcR classification rules, the circular DNA identification results are divided into four categories to obtain the second CtcR classification results; wherein, the second CtcR classification rules include:

[0111] The category with a single PreCtcRlist and no reverse (inversion=0) read is defined as CtcR-perfect;

[0112] Reads with multiple PreCtcRlists and where the difference between the maximum and minimum ring lengths of these PreCtcRlists is >50bp are classified as CtcR-hybrid.

[0113] The category of reading PreCtcRlist (including the 'm' marker) that has been merged is determined to be CtcR-multiple;

[0114] The category that has a single PreCtcRlist but has reverse (inversion=1) reads is identified as CtcR-inversion.

[0115] Furthermore, according to the second circular DNA analysis rules, the circular DNA identification results are classified to obtain preliminary second extrachromosomal circular DNA analysis results. The second circular DNA analysis rules include:

[0116] Reads that meet the second MeccDNA classification criteria are classified as MeccDNA; wherein, the second MeccDNA classification criteria include: the read has multiple PreCtcRlists; the difference between the maximum and minimum values ​​of the circular DNA length eLength of the multiple PreCtcRlists is not greater than 50bp;

[0117] Reads that meet the second UeccDNA classification criteria are classified as UeccDNA; wherein the second UeccDNA classification criteria include: the read contains only a single PreCtcRlist, or the read PreCtcRlist has an 'm' marker.

[0118] Furthermore, the reads with an eRepeatNum difference of less than 2 in the preliminary second chromosome outer circular DNA analysis results are output as the intermediate second chromosome outer circular DNA analysis results; based on the intermediate second chromosome outer circular DNA analysis results, the long read sequencing data to be analyzed are extracted and processed to obtain the final second chromosome outer circular DNA analysis results.

[0119] Specifically, based on the qstart and qend of each category of UeccDNA reads in the preliminary second chromosome extracircular DNA analysis results, the corresponding sequence data is extracted from the long-read sequencing data to be analyzed to obtain the UeccDNA classification results; based on the qstart and qend of each category of MeccDNA reads in the preliminary second chromosome extracircular DNA analysis results, the corresponding sequence data is extracted from the long-read sequencing data to be analyzed to obtain the MeccDNA classification results; the UeccDNA classification results and the MeccDNA classification results constitute the final second chromosome extracircular DNA analysis results.

[0120] Step S14: Extract the second unidentified data from the long read sequencing data; based on the reference genome database, perform a third type of alignment analysis on the second unidentified data to obtain the third chromosome outer circular DNA analysis results.

[0121] It should be noted that the third type of comparison analysis is used to detect circular DNA that was not identified in the first two comparison analyses due to low abundance, high degree of chimerism, or other factors.

[0122] In one embodiment, the seqkit tool is used to extract second unidentified data from the long-read sequencing data.

[0123] In one embodiment, based on the reference genome database, a third alignment analysis is performed on the second unidentified data to obtain a third chromosome outer circular DNA analysis result, including: using Minimap2 to align the second unidentified data with the reference genome database to obtain a third alignment result (file format SAM or BAM), and using samtools or equivalent tools to sort the third alignment result.

[0124] Furthermore, based on the coverage map of the generated sorted results, abnormal coverage regions are identified and merged to obtain a merged third alignment result. Specifically,

[0125] Statistical methods (such as MAD or Z-score) are used to assess the sequencing depth of each genomic location in the sorted results. If consecutive coverage peaks significantly higher than the background are observed on the alignment coordinates, and the distance between peak segments is small (e.g., ≤10 bp) or there is partial overlap, these "similar regions" are merged into a single candidate gain region. This merging can be determined based on genomic coordinate thresholds (such as the "gap threshold") or overlap ratios to determine whether two coverage peaks should be considered consecutive. Similarly, if the overall coverage level is abnormally high in a large chromosomal segment, it can be considered as an amplified or potentially circular fragment for subsequent analysis. Through this statistical analysis of coverage maps and the merging of similar regions, low-frequency amplification or multicopy chimeric structures that are difficult to detect in the first two alignment analyses can be identified.

[0126] Furthermore, by employing a segmentation-based inference method, the merged third alignment results were processed to obtain the analysis results of the outer circular DNA of the third chromosome.

[0127] In one embodiment, the Minimap2 tool is used to compare the second unidentified data with the reference genome database to obtain a third alignment result. The samtools tool is then used to sort the third alignment result.

[0128] In one embodiment, the merged third alignment result is processed by using a segmentation-based inference method to obtain the third chromosome outer circular DNA analysis result, including: outputting the segmentation-based reading recognition conditions that are met in the merged third alignment result as the third chromosome outer circular DNA analysis result; wherein, the segmentation-based reading recognition conditions include: reading containing soft trimming operations and reading spanning both ends of the target region.

[0129] It should be understood that the Minimap2 tool outputs the third alignment results as a CIGAR string. A CIGAR string is a compact format used to describe the alignment results of DNA, RNA, or protein sequences in bioinformatics. In genomics, a CIGAR string records detailed alignment information between the reads (query sequence) and the reference sequence, including matches, mismatches, insertions, and deletions. The following are the meanings of the various marker characters in a CIGAR string: M: Match or Mismatch; I: Insertion; D: Deletion; N: Skipped region from the reference; S: Soft clip; H: Hard clip; P: Padding; =: Perfect match (where the bases must match the reference); X: Mismatch (where the bases may not match the reference).

[0130] Step S15: The analysis results of the first, second, and third extrachromosomal circular DNA analyses are merged and classified to obtain the final extrachromosomal circular DNA analysis results.

[0131] Specifically, such as Figure 2 As shown, the classification results of UeccDNA in the analysis results of the first chromosome extrachromosomal circular DNA and the analysis results of the second chromosome extrachromosomal circular DNA are merged and duplicate results are removed to obtain the preliminary classification results of UeccDNA; then the preliminary classification results of UeccDNA are merged with the analysis results of the third chromosome extrachromosomal circular DNA to obtain the final classification results of UeccDNA.

[0132] The classification results of MeccDNA from the analysis of extrachromosomal circular DNA on chromosome 1 and chromosome 2 were merged and duplicate results were removed to obtain the final MeccDNA classification results. The classification results of CeccDNA were extracted from the analysis results of extrachromosomal circular DNA on chromosome 1.

[0133] The final classification results of UeccDNA, MeccDNA, and CeccDNA constitute the final analysis results of extrachromosomal circular DNA.

[0134] It should be noted that the extrachromosomal circular DNA analysis method based on long-read sequencing data of the present invention improves the sensitivity and accuracy of eccDNA detection by taking advantage of the high accuracy and long-read characteristics of HiFi sequencing data; the method of the present invention can simultaneously detect and classify multiple types of eccDNA, providing comprehensive eccDNA analysis.

[0135] In one embodiment, the method further includes: summarizing all the above analysis results to generate an eccDNA analysis report to facilitate result interpretation and analysis.

[0136] Specifically, the eccDNA analysis report includes: read count statistics, CtcR classification statistics, and eccDNA statistics; among which, read count statistics include total read count, linear read count (obtained based on linear recognition results), CtcR classification read count, and other read counts (calculating the remaining read count); CtcR classification statistics include: the number and percentage of the four types of CtcRs; eccDNA statistics include: the number and length distribution of each type of eccDNA.

[0137] In one specific embodiment, the method of the present invention can run in a Python 3 computing environment with TideHunter, BLAST, Minimap2, and samtools tools installed. The present invention supports multi-threaded processing, fully utilizing the computing power of multi-core CPUs to accelerate data analysis. In a 48-thread computing environment, the analysis of 10,000 reads can be completed in a short time, improving processing speed by approximately 2-3 times compared to traditional methods, significantly saving computing resources and time costs.

[0138] Figure 3 This is a schematic block diagram of a chromosome extrachromosomal circular DNA analysis device based on long-read sequencing data provided in an embodiment of this application. Figure 3 As shown, the extrachromosomal circular DNA analysis device 3 based on long-read sequencing data includes:

[0139] The acquisition module 31 is used to acquire long-read sequencing data to be analyzed and establish a reference genome database;

[0140] The first alignment analysis module 32 is used to identify candidate extrachromosomal circular DNA sequence data from the long-read sequencing data through tandem repeat sequence detection, and to filter and process repeat regions to obtain potential extrachromosomal circular DNA sequence data; based on the reference genome database, to perform a first type of alignment analysis on the potential extrachromosomal circular DNA sequence data to obtain the first extrachromosomal circular DNA analysis result;

[0141] The second alignment analysis module 33 is used to extract the first unidentified data from the long read sequencing data; and to perform a second type of alignment analysis on the first unidentified data based on the reference genome database to obtain the second chromosome outer circular DNA analysis result.

[0142] The third alignment analysis module 34 is used to extract the second unidentified data from the long read sequencing data; and to perform a third alignment analysis on the second unidentified data based on the reference genome database to obtain the third chromosome outer circular DNA analysis results.

[0143] The merging module 35 is used to merge and classify the analysis results of the first extrachromosomal circular DNA, the second extrachromosomal circular DNA, and the third extrachromosomal circular DNA to obtain the final extrachromosomal circular DNA analysis results.

[0144] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0145] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0146] In one embodiment, the specific process of filtering and repeating region processing includes: sequentially performing integer processing and average matching rate filtering on candidate extrachromosomal circular DNA sequence data to obtain filtered extrachromosomal circular DNA sequence data; sequentially performing repeating region merging and circularization processing on the filtered extrachromosomal circular DNA sequence data to obtain potential extrachromosomal circular DNA sequence data.

[0147] In one embodiment, based on the reference genome database, a first alignment analysis is performed on potential extrachromosomal circular DNA sequence data to obtain a first extrachromosomal circular DNA analysis result. This includes: aligning the potential extrachromosomal circular DNA sequence data with the reference genome database to obtain a first alignment result; performing circular DNA analysis on the first alignment result according to circular DNA analysis rules to obtain a first extrachromosomal circular DNA analysis result. Specifically, this process includes: determining the first alignment parameters and alignment strand parameters for each read in the first alignment result; wherein the first alignment parameters include: alignment region length, consistency length, difference from the target sequence, difference from the target sequence, and number of alignment sites; the alignment strand parameters include: number of alignment strands, alignment strand length, and sum of alignment strand lengths; and classifying each read into circular DNA based on the first alignment parameters and alignment strand parameters according to the circular DNA analysis rules to obtain a circular DNA classification result for each read, thereby obtaining the first extrachromosomal circular DNA analysis result.

[0148] In one embodiment, the circular DNA analysis rules include: classifying reads that meet any one of the following UeccDNA classification conditions as UeccDNA: First UeccDNA classification condition: the number of alignment sites read is equal to 1; the difference between the read and the target sequence is less than a set difference threshold, or the percentage difference between the read and the target sequence is less than a set difference percentage threshold; Second UeccDNA classification condition: the number of alignment sites read is greater than 1; the distance between the start or end positions of any two adjacent alignment sites read is less than a set distance threshold; Reads that meet the MeccDNA classification conditions are classified as MeccDNA. NA; wherein, the MeccDNA classification conditions include: the number of read alignment sites is multiple; the distance between read alignment sites is greater than a set distance threshold; the difference between the read and the target sequence is less than a set difference threshold, or the percentage difference between the read and the target sequence is less than a set difference percentage threshold; the reads that meet the CeccDNA classification conditions are classified as CeccDNA; wherein, the CeccDNA classification conditions include: the number of read alignment strands is not zero; the length of the read alignment strands is greater than the read consistency length; the number of read alignment sites is at least two; the ratio between the sum of the read alignment strand lengths and the read consistency length is within a set ratio range.

[0149] In one embodiment, based on the reference genome database, performing a second alignment and classification analysis on the first unidentified data to obtain a second extrachromosomal circular DNA analysis result includes: aligning the first unidentified data with the reference genome database to obtain a second alignment result; determining a second alignment parameter for each read in the second alignment result; performing linear read identification analysis and circular DNA identification analysis on the first unidentified data based on the second alignment parameter for each read to obtain linear read identification results and circular DNA identification results; calculating the identification result parameter for each read in the circular DNA identification results, and classifying the circular DNA identification results based on the identification result parameter for each read to obtain a second extrachromosomal circular DNA analysis result.

[0150] In one embodiment, second unidentified data is extracted from the long-read sequencing data; based on the reference genome database, a third alignment and classification analysis is performed on the second unidentified data to obtain a third chromosome outer circular DNA analysis result, including: using Minimap2 to align the second unidentified data with the reference genome database to obtain a third alignment result, and sorting the third alignment result; merging similar regions of the sorted results according to the coverage map of the generated sorted results; and processing the merged third alignment result using a segmentation-based inference method to obtain the third chromosome outer circular DNA analysis result.

[0151] In one embodiment, the sorted third alignment results are processed using a segmentation-based inference method to obtain the third chromosome outer circular DNA analysis results. This includes: outputting the segmentation-based reading recognition conditions that are met in the sorted third alignment results as the third chromosome outer circular DNA analysis results; wherein, the segmentation-based reading recognition conditions include: readings containing soft trimming operations and readings that span both ends of the target region.

[0152] Figure 4 This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 4 As shown, the electronic terminal includes at least one processor 401, a memory 402, at least one network interface 403, and a user interface 405. The various components in the device are coupled together via a bus system 404. It is understood that the bus system 404 is used to implement communication between these components. In addition to a data bus, the bus system 404 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 4 The general will label all buses as bus systems.

[0153] The user interface 405 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0154] It is understood that memory 402 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), 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) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0155] In this embodiment of the invention, the memory 402 is used to store various types of data to support the operation of the electronic terminal 400. Examples of this data include: any executable program for operation on the electronic terminal 400, such as the operating system 4021 and application programs 44022; the operating system 4021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 4022 may contain various applications, such as media players, browsers, etc., for implementing various application services. The method for analyzing extrachromosomal circular DNA based on long-read sequencing data provided in this embodiment of the invention can be included in the application program 4022.

[0156] The methods disclosed in the above embodiments of the present invention can be applied to processor 401, or implemented by processor 401. Processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 401 or by instructions in the form of software. The processor 401 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 401 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 401 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0157] In an exemplary embodiment, the electronic terminal 400 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0158] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute... Figure 1 The illustrated embodiment presents a method for analyzing extrachromosomal circular DNA based on long-read sequencing data.

[0159] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when executed on a computer, causes the computer to perform... Figure 1 The illustrated embodiments represent methods for analyzing extrachromosomal circular DNA based on long-read sequencing data.

[0160] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0161] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0162] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0166] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0167] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0168] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0169] In summary, this application provides a method, apparatus, terminal, medium, and product for analyzing extrachromosomal circular DNA based on long-read sequencing data. After acquiring the long-read sequencing data to be analyzed and establishing a reference genome database, the long-read sequencing data is first subjected to tandem repeat sequence detection and a first alignment analysis to obtain the first extrachromosomal circular DNA analysis result. Then, unidentified data in the long-read sequencing data are subjected to a second and a third alignment analysis to obtain the second and third extrachromosomal circular DNA analysis results, respectively. Finally, the results of each extrachromosomal circular DNA analysis are merged to obtain the final extrachromosomal circular DNA analysis result. This application improves the accuracy of circular DNA analysis and increases the utilization rate of long-read data through three alignment analyses. Therefore, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0170] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for analyzing extrachromosomal circular DNA based on long-read sequencing data, characterized in that, include: Acquire long-read sequencing data to be analyzed and establish a reference genome database; The long-read sequencing data is subjected to tandem repeat sequence detection to obtain candidate extrachromosomal circular DNA sequence data; the candidate extrachromosomal circular DNA sequence data is filtered and repetitive region processing is performed to obtain potential extrachromosomal circular DNA sequence data; based on the reference genome database, the potential extrachromosomal circular DNA sequence data is subjected to a first alignment analysis to obtain a first extrachromosomal circular DNA analysis result; wherein, the first alignment analysis includes: aligning the potential extrachromosomal circular DNA sequence data with the reference genome database to obtain a first alignment result; and performing circular DNA analysis on the first alignment result according to the first circular DNA analysis rules to obtain the first extrachromosomal circular DNA analysis result. First unidentified data is extracted from the long-read sequencing data; based on the reference genome database, a second alignment analysis is performed on the first unidentified data to obtain a second extrachromosomal circular DNA analysis result; wherein, the second alignment analysis includes: aligning the first unidentified data with the reference genome database to obtain a second alignment result; determining a second alignment parameter for each read in the second alignment result; performing linear read identification analysis and circular DNA identification analysis on the first unidentified data based on the second alignment parameter for each read to obtain linear read identification results and circular DNA identification results; calculating the identification result parameter for each read in the circular DNA identification results, and classifying the circular DNA identification results according to the second circular DNA analysis rules based on the identification result parameter for each read to obtain a second extrachromosomal circular DNA analysis result; Second unidentified data is extracted from the long-read sequencing data; based on the reference genome database, a third alignment analysis is performed on the second unidentified data to obtain the third chromosome outer circular DNA analysis result; wherein, the third alignment analysis includes: using Minimap2 to align the second unidentified data with the reference genome database to obtain a third alignment result, and sorting the third alignment result; based on the coverage map of the generated sorted result, identifying abnormal coverage regions, and merging the identified abnormal coverage regions to obtain a merged third alignment result; by using a segmentation-based inference method, the merged third alignment result is processed to obtain the third chromosome outer circular DNA analysis result; The results of the analysis of the first, second, and third extrachromosomal circular DNA were merged and classified to obtain the final extrachromosomal circular DNA analysis results.

2. The method for analyzing extrachromosomal circular DNA based on long-read sequencing data according to claim 1, characterized in that, The specific processes for filtering and handling duplicate regions include: Candidate extrachromosomal circular DNA sequence data are sequentially integerized and filtered by average matching rate to obtain filtered extrachromosomal circular DNA sequence data. The filtered extrachromosomal circular DNA sequence data were sequentially processed by repeating region merging and circularization to obtain potential extrachromosomal circular DNA sequence data.

3. The method for analyzing extrachromosomal circular DNA based on long-read sequencing data according to claim 2, characterized in that, Based on the aforementioned reference genome database, a first alignment analysis is performed on potential extrachromosomal circular DNA sequence data to obtain the first extrachromosomal circular DNA analysis results, including: According to the first circular DNA analysis rules, circular DNA analysis is performed on the first alignment result to obtain the first chromosome extrachromosomal circular DNA analysis result; the specific process includes: Determine the first alignment parameter and alignment chain parameter for each read in the first alignment result; wherein the first alignment parameter includes: alignment region length, consistency length, difference from the target sequence, percentage difference from the target sequence, and number of alignment sites; the alignment chain parameter includes: number of alignment chains, alignment chain length, and total length of alignment chains; According to the first circular DNA analysis rule, based on the first alignment parameter and alignment strand parameter of each read, the circular DNA of each read is classified to obtain the circular DNA classification result of each read, and then the first extrachromosomal circular DNA analysis result is obtained. The first circular DNA analysis rules include: Reads that meet any of the following UeccDNA classification criteria will be classified as UeccDNA: The first UeccDNA classification condition is: the number of alignment sites read is equal to 1; the difference between the read and the target sequence is less than the set difference threshold, or the percentage difference between the read and the target sequence is less than the set difference percentage threshold. The second UeccDNA classification condition is: the number of alignment sites read is greater than 1; the distance between the start position or the end position of any two adjacent alignment sites read is less than the set distance threshold. The category of reads that meet the MeccDNA classification criteria is determined as MeccDNA; wherein, the MeccDNA classification criteria include: the number of alignment sites read is multiple; the distance between the alignment sites read is greater than a set distance threshold; the difference between the read and the target sequence is less than a set difference threshold, or the percentage difference between the read and the target sequence is less than a set percentage difference threshold. Reads that meet the CeccDNA classification criteria are classified as CeccDNA. The CeccDNA classification criteria include: the number of aligned strands read is not zero; the length of the aligned strands read is greater than the concordance length read; the number of aligned sites read is at least two; and the ratio between the total length of the aligned strands read and the concordance length read is within a set range.

4. The method for analyzing extrachromosomal circular DNA based on long-read sequencing data according to claim 1, characterized in that, By employing a segmentation-based inference method, the merged third alignment results were processed to obtain the analysis results of the outer circular DNA of the third chromosome, including: The reads that meet the segmentation and reading recognition conditions in the merged third alignment results are output as the third chromosome outer circular DNA analysis results; wherein, the segmentation and reading recognition conditions include: the read contains a soft trimming operation and the read spans both ends of the target region.

5. A device for analyzing extrachromosomal circular DNA based on long-read sequencing data, characterized in that, include: The acquisition module is used to acquire long-read sequencing data to be analyzed and to establish a reference genome database; The first alignment analysis module is used to identify candidate extrachromosomal circular DNA sequence data from the long-read sequencing data through tandem repeat sequence detection, and to filter and process repeat regions to obtain potential extrachromosomal circular DNA sequence data; based on the reference genome database, to perform a first alignment analysis on the potential extrachromosomal circular DNA sequence data to obtain a first extrachromosomal circular DNA analysis result; wherein, the first alignment analysis includes: aligning the potential extrachromosomal circular DNA sequence data with the reference genome database to obtain a first alignment result; and performing circular DNA analysis on the first alignment result according to the first circular DNA analysis rules to obtain the first extrachromosomal circular DNA analysis result; The second alignment analysis module is used to extract first unidentified data from the long-read sequencing data; and to perform a second alignment analysis on the first unidentified data based on the reference genome database to obtain a second extrachromosomal circular DNA analysis result. The second alignment analysis includes: aligning the first unidentified data with the reference genome database to obtain a second alignment result; determining a second alignment parameter for each read in the second alignment result; performing linear read identification analysis and circular DNA identification analysis on the first unidentified data based on the second alignment parameters for each read to obtain linear read identification results and circular DNA identification results; calculating the identification result parameters for each read in the circular DNA identification results, and classifying the circular DNA identification results according to the second circular DNA analysis rules based on the identification result parameters for each read to obtain a second extrachromosomal circular DNA analysis result. The third alignment analysis module is used to extract second unidentified data from the long-read sequencing data; based on the reference genome database, a third alignment analysis is performed on the second unidentified data to obtain the third chromosome outer circular DNA analysis result; wherein, the third alignment analysis includes: using Minimap2 to align the second unidentified data with the reference genome database to obtain a third alignment result, and sorting the third alignment result; based on the coverage map of the generated sorted result, identifying abnormal coverage regions, merging the identified abnormal coverage regions to obtain a merged third alignment result; and processing the merged third alignment result using a segmentation-based inference method to obtain the third chromosome outer circular DNA analysis result. The merging module is used to merge and classify the analysis results of the first, second, and third extrachromosomal circular DNA analyses to obtain the final extrachromosomal circular DNA analysis results.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 4.

7. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to implement the method as described in any one of claims 1 to 4.

8. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1 to 4.

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