A t2t gapless complete genome assembly method based on genomic automatic gap filling

Abstract

The present application relates to a kind of T2T gapless complete genome assembly method based on genome automatic Gap filling.The method comprises: obtaining the first genome of at least one Gap containing chromosome level and the second genome in the form of Contig set;Telomere extension is carried out to the first genome, and the target Contig containing telomere sequence is selected from the second genome and extended to the chromosome end missing telomere;Adaptive Gap filling is carried out to the genome after telomere extension, and candidate Contig is selected to fill Gap in dynamic search range;Telomere and centromere analysis is carried out to the genome after filling, and complete T2T genome is output.The present application realizes the full-process automation of Gap filling and telomere, centromere analysis, effectively improves genome accuracy and continuity, and accurately restores complete structure of chromosome.

Description

Technical Field

[0001] This invention relates generally to the processing of biological information, and more specifically, to a method, computing device, computer storage medium, and computer program product for achieving T2T gap-free complete genome assembly based on automatic genome gap filling. Background Technology

[0002] High-quality, complete genome assembly is the core foundation for elucidating species genetic evolution, trait regulation mechanisms, and conducting molecular breeding. While second-generation short-read sequencing has promoted the construction of reference genomes for plants and animals, its limited read length prevents it from traversing highly repetitive regions of the genome. This results in traditional assemblies generally suffering from sequence fragmentation, insufficient integrity, and numerous gaps, with most genomes remaining at a draft level, failing to cover all the genetic information of chromosomes and thus hindering in-depth genomics research.

[0003] With the iteration of PacBio HiFi, ONT ultra-long read sequencing, and Hi-C assisted assembly technologies, gap-free telomere-to-telomere (T2T) genome assembly technology has made breakthrough progress. This technology can accurately resolve genomic "dark matter" such as repetitive sequences and structural variations, achieving complete and continuous chromosome assembly. It has the advantages of being gap-free, highly accurate, and highly intact, and can comprehensively resolve chromosome topology, telomere centromere characteristics, and gene regulatory elements, providing a precise and complete genetic background for species evolution, functional gene mining, and trait mechanism analysis.

[0004] The vast majority of published genomes of various species have not yet met the T2T assembly standard, containing numerous unfilled gaps and exhibiting critical defects such as complete or partial telomere deletion. Telomeres are core repetitive structures at the ends of chromosomes, playing crucial roles in maintaining chromosome stability, protecting genome integrity, and regulating cell division and homologous recombination. Traditional assembly methods struggle to splice highly repetitive telomere sequences, leading to the complete loss of genetic information at chromosome ends. This not only hinders specialized research on telomere evolution and chromosome stability but also causes gene truncation, blurred boundaries, and the loss of distant regulatory elements, severely limiting the systematic understanding of chromosome structure and function.

[0005] Genome gaps are often enriched in complex repetitive regions such as telomeres, centromeres, and transposons, making them difficult to fill effectively using conventional methods like alignment extension and second-generation gap-filling, thus becoming a core bottleneck in genome assembly. Unclosed gaps can conceal functional genes, structural variations, and epigenetic regulatory sites, resulting in incomplete gene annotation and pathway analysis. They also disrupt genome sequence continuity, interfering with three-dimensional genome analysis, including TAD structure, chromatin loops, and CTCF regulatory architecture, making it impossible to accurately reconstruct the spatial regulatory mechanisms of the genome. Ultimately, this leads to significant biases in evolutionary analysis, subgenome alignment, and trait localization.

[0006] In summary, the problems of telomere deletion and gap filling in traditional genomes severely restrict in-depth research in functional genomics, three-dimensional genomics, and evolutionary genomics. Conducting gap-free, complete genome assembly of species from T2T, accurately filling genomic gaps, fully restoring telomere structures, and overcoming the challenges of assembling complex repetitive regions, is of significant scientific importance for improving species genetic information, elucidating the complete function of chromosomes, and exploring core gene resources. Summary of the Invention

[0007] This invention provides a method, computing device, computer storage medium, and computer program product for achieving T2T gap-free complete genome assembly based on automated genome gap filling. It includes modules for error detection, telomere extension, adaptive gap filling, local assembly gap filling, reads-based extended gap filling, telomere analysis, and centromere analysis.

[0008] This invention utilizes a hybrid architecture combining Perl, R, and Shell to automate the entire process of genome gap filling, offering advantages such as high efficiency, scalability, and result visualization.

[0009] The method described in this invention involves two sets of input data: a first genome and a second genome. The first genome is a chromosome-level genome of the target species, obtained by assembling sequencing data and attaching it to chromosomes. It contains at least one gap, which serves as the repair target for this method. This method does not limit the source of the first genome; any genome already attached to the chromosome level but not yet achieving T2T integrity can be used as input. The first genome is obtained by assembling PacBio HiFi sequencing data and ONT ultra-long sequencing data, followed by attachment to HiC sequencing data.

[0010] The second genome is a collection of contigs from the same species as the first genome, obtained through independent assembly of sequencing data from that species. The second genome serves as a candidate sequence for gap filling and telomere extension of the first genome. This method does not limit the assembly software or specific data type of the second genome. Preferably, the second genome is assembled using long-read sequencing data, including but not limited to ONT ultra-long sequencing data, PacBio HiFi sequencing data, or a combination of both.

[0011] According to a first aspect of the present invention, a T2T gap-free complete genome assembly method based on automated gap filling is provided. The method includes: acquiring a first genome and a second genome, wherein the first genome is a genome containing at least one gap obtained after assembling sequencing data of a sample to be tested and mounting chromosomes, and the second genome is a set of contigs obtained after assembling sequencing data of the sample to be tested; performing telomere extension on the first genome, wherein for chromosome ends in the first genome lacking telomeres, a target contig containing telomere sequences is selected from the second genome, and the fragment of the telomere sequence in the target contig is extended to the chromosome end; performing adaptive gap filling on the telomere-extended first genome, wherein the second genome is used as a candidate sequence library, and a candidate contig sequence is searched within a dynamic search range using an adaptive search strategy, and candidate contigs that meet preset conditions are selected to fill the gaps; performing telomere analysis and centromere analysis on the gap-filled genome; and outputting a complete telomere-to-telomere genome.

[0012] In some optional embodiments, gaps that still exist after adaptive gap filling are filled using local assembly gap filling; gaps that still exist after local assembly gap filling are filled using gap filling based on sequencing data alignment.

[0013] In some optional embodiments, before telomere extension, the presence of misaligned contigs in the first genome is detected and corrected if present.

[0014] In some embodiments, for chromosome ends lacking telomeres in the first genome, selecting target contigs containing telomere sequences from the second genome and extending the telomere sequence fragments in the target contigs to the chromosome ends includes: searching for forward and reverse complementary telomere repeat motifs in segmented windows of each sequence in the first and second genomes, respectively, and statistically analyzing their positions, lengths, and end types; aligning contigs containing telomere sequences in the second genome to the chromosome end regions lacking telomeres in the first genome; performing clustering filtering on the collinear alignment results and selecting the best matching chromosome for each contig; calculating the Pearson correlation coefficient based on the reference coordinates and query coordinates of the aligned blocks after clustering filtering; and determining the position type of the aligned blocks on the chromosome based on the coordinates of the aligned blocks on the reference sequence.

[0015] In some embodiments, for chromosome ends lacking telomeres in the first genome, selecting a target contig containing telomere sequences from the second genome and extending a fragment of the telomere sequence in the target contig to the chromosome end further includes: determining the extension direction based on the sign of the Pearson correlation coefficient, the position type, and the end type of the corresponding contig telomere; if the chromosome telomere is anteriorly missing, the alignment region covers the anterior end of the chromosome, the Pearson correlation coefficient is greater than 0, and the anterior end of the contig contains a telomere sequence, then the anterior end sequence of the contig is used as the extension direction; if the chromosome telomere is anteriorly missing, the alignment region... If the contig covers the anterior end of the chromosome, has a Pearson correlation coefficient less than 0, and contains a telomere sequence at the posterior end, then the extension direction is the reverse complementary sequence of the posterior end sequence of the contig. If the chromosome lacks a posterior telomere, and the alignment region covers the posterior end of the chromosome, has a Pearson correlation coefficient greater than 0, and contains a telomere sequence at the posterior end of the contig, then the extension direction is the posterior end sequence of the contig. If the chromosome lacks a posterior telomere, and the alignment region covers the posterior end of the chromosome, has a Pearson correlation coefficient less than 0, and contains a telomere sequence at the anterior end of the contig, then the extension direction is the reverse complementary sequence of the anterior end sequence of the contig.

[0016] In some embodiments, using the second genome as a candidate sequence library, an adaptive search strategy is used to search for candidate contig sequences within a dynamic search range, and selecting candidate contigs that meet preset conditions to fill the gap includes: for each gap on the first genome, starting from a preset minimum flanking length, the flanking length is gradually increased with a preset step size until a preset maximum flanking length is reached; after each increase in flanking length, flanking sequences on both sides of the gap are extracted, and the contigs of the second genome are aligned to the flanking sequences; the collinearity alignment results are verified, and the alignment results that reach a preset similarity threshold are further clustered and filtered to select candidate contigs that meet preset conditions to fill the gap; if a candidate contig that meets the preset conditions is found, the increase in flanking length is stopped.

[0017] In some embodiments, candidate contigs that meet preset conditions are selected to fill the gap. The preset conditions include: the similarity reaches a preset similarity threshold after comparison verification (e.g., the preset similarity threshold is 99% after MUMmer comparison verification), and the clustered regions with a total comparison length reaching a preset length threshold (e.g., Lmin=100,000bp as described herein) are retained after clustering filtering.

[0018] In some embodiments, retaining clustered regions whose total alignment length reaches a preset length threshold after clustering filtering includes: reading all alignment blocks that reach a preset similarity threshold; establishing a new clustered region using each unlabeled output block as a seed; scanning adjacent blocks backward; merging the block into the current clustered region when both the distance on the reference sequence and the distance on the query sequence are less than the maximum distance threshold Dmax; updating the cluster boundary to the boundary of the latest merged block; repeating the scan until all blocks are processed; for each clustered region, if its total alignment length is greater than or equal to a preset length threshold Lmin, retaining all alignment blocks in that clustered region, otherwise discarding them; and selecting candidate contigs to fill the gaps based on the filtered alignment results.

[0019] In some embodiments, telomere and centromere analysis of the gap-filled genome includes: searching for telomere repeat sequences in the terminal regions of each chromosome of the gap-filled first genome using a sliding window method; if telomere repeat sequences are detected at both ends of each chromosome, the genome is confirmed to be a complete telomere-to-telomere genome.

[0020] In some embodiments, telomere and centromere analysis of the gap-filled genome includes: centromere analysis using centromeres, bidirectional mining of centromere feature repeats using the assembled genome sequence and the original long reads, first extracting species-specific centromere satellite monomers from the assembled genome sequence, then reattaching the genome to identify continuous enriched regions, and outputting candidate centromere coordinates.

[0021] In some optional embodiments, if there are still unfilled gaps after adaptive gap filling, the strategy is switched to local assembly filling; the sequencing reads within a preset range flanking the gap are extracted and locally assembled, and the assembled local contigs are used to fill the gap, thereby further improving the accuracy and continuity of the genome; if the local assembly filling strategy fails to fill the gap, the strategy is switched to iterative extension filling based on reads: using a known sequence on one side of the gap as a seed, the sequencing reads are aligned to the seed, and the newly added sequences that extend into the gap are truncated and merged into the end of the seed, and iterative extension is performed until the gap is covered.

[0022] In some optional embodiments, before telomere extension, the presence of oriented contigs in the first genome is detected and corrected if present. This includes: aligning the second genome to the first genome, detecting only contigs in the first genome with a length less than a preset threshold; traversing the collinear alignment results on the contigs less than the preset threshold, classifying each alignment record into an internal region, a left external region, or a right external region based on the positional relationship of its alignment region relative to the contig interval; statistically analyzing the orientation of alignment records belonging to the same best-matching chromosome in the external region (left external and right external) and the internal region, accumulating the external same-direction length for each record if it is a forward alignment, and accumulating the external reverse length if it is a reverse alignment; calculating the internal orientation indicator and the external orientation indicator, where the orientation indicator is defined as the sign function value of the sum of the same-direction alignment lengths minus the difference in the reverse alignment lengths; when the signs of the internal orientation indicator and the external orientation indicator are opposite, and the external same-direction length and the external reverse length are not both zero, the contig is determined to have an orientation error, and its orientation is corrected.

[0023] In some optional embodiments, before telomere extension, the presence of directionally incorrect contigs in the first genome is detected and corrected if present. The method further includes: for contigs in the second genome, calculating the total alignment length of the contigs to each chromosome in the first genome, selecting the chromosome with the largest total alignment length as the best matching chromosome for the sequence, and performing subsequent orientation determination only when the best matching chromosome is equal to the chromosome containing the contig.

[0024] According to a second aspect of the invention, a computing device is also provided, the device comprising: a memory configured to store one or more computer programs; and a processor coupled to the memory and configured to execute one or more programs to cause the device to perform the method of the first aspect of the invention.

[0025] According to a third aspect of the invention, a non-transient computer-readable storage medium is also provided. This non-transient computer-readable storage medium stores machine-executable instructions that, when executed, cause a machine to perform the method of the first aspect of the invention.

[0026] According to a fourth aspect of the invention, a computer program product is also provided. The computer program product includes instructions that, when executed by a machine, implement the method of the first aspect of the invention.

[0027] The summary section is provided to present the chosen concepts in a simplified form, which will be further described in the detailed description below. The summary section is not intended to identify key or principal features of the invention, nor is it intended to limit the scope of the invention. Attached Figure Description

[0028] Figure 1 A schematic diagram of a system for T2T gap-free complete genome assembly based on automated gap filling according to an embodiment of the present invention is shown; Figure 2 A flowchart of a T2T gap-free complete genome assembly method based on automated genome gap filling according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of cross-Gap alignment of candidate contigs during the gap filling process according to an embodiment of the present invention is shown; Figure 4 A block diagram is shown that is suitable for implementing embodiments of the present invention; In the various figures, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0029] Preferred embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0030] The term "comprising" and its variations as used herein signify open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc., may refer to different or the same objects. The terms "not higher than" or "below" mean "less than or equal to", and the terms "not lower than" or "above" mean "higher than or equal to", and are both to be understood as including the stated number.

[0031] A schematic diagram of an exemplary system 100 for a T2T genome construction method based on automated genome gap filling according to an embodiment of the present invention. Figure 1 As shown, system 100 includes at least a computing device 110. In some embodiments, system 100 may further include a sequencing device 130, a server 140, and a network 150. In some embodiments, the computing device 110, sequencing device 130, and server 140 may communicate and interact with each other via wired or wireless means (e.g., via network 150).

[0032] Regarding sequencing device 130, it is used, for example, to sequence a sample to be tested to generate sequencing data about the nucleic acid sequence information of the sample. In some embodiments, sequencing device 130 can also send the generated sequencing data about the sample to computing device 110. In some embodiments, server 140 can also send the sequencing data about the sample to computing device 110. In some embodiments, sequencing device 130 is a single-molecule long-read sequencing platform, such as the Pacific Biosciences (PacBio) platform or the Oxford Nanopore Technologies (ONT) platform. In particular, in some embodiments, the sequencing data can be whole-genome sequencing (WGS) data, or targeted sequencing data based on hybridization capture probes (e.g., including but not limited to whole exome sequencing (WES) or specific gene panel sequencing).

[0033] Regarding server 140, it is used, for example, to store or provide reference genome sequence information and historical genome data. For example, server 140 may store or provide reference genome sequence information obtained from public databases, said reference genomes including, but not limited to, published reference genomes of the same or closely related species. In some embodiments, server 140 may also store historical genome assembly data and sequencing data for reference in subsequent analysis.

[0034] Regarding computing device 110, it is used, for example, to execute a T2T genome construction method based on automated genome gap filling. Computing device 110 may have one or more processing units, including dedicated processing units such as GPUs, FPGAs, and ASICs, and general-purpose processing units such as CPUs. Computing device 110 may be a single physical device, or an integration of multiple physical servers, an integration of multiple processing units, etc. Additionally, one or more virtual machines may run on each computing device 110. In some embodiments, computing device 110 and server 140 may be integrated together or set up separately.

[0035] In some embodiments, the computing device 110 includes, for example, a data acquisition unit 111, an orientation error detection unit 112, a telomere extension unit 113, a gap filling unit 114, a telomere analysis unit 115, and a centromere analysis unit 116.

[0036] Regarding the data acquisition unit 111, it is used, for example, to acquire a first genome and a second genome, wherein the first genome is a genome containing at least one gap obtained after assembling the sequencing data of the sample to be tested and mounting chromosomes, and the second genome is a set of contigs obtained after assembling the sequencing data of the sample to be tested.

[0037] Regarding the orientation error detection unit 112, it is used, for example, to detect the presence of oriented contigs in the first genome before telomere extension, and to correct them if present. In some embodiments, the orientation error detection unit is optional.

[0038] Regarding the telomere extension unit 113, it is used, for example, to extend the telomere of the first genome. For chromosome ends in the first genome that lack telomeres, a target contig containing a telomere sequence is selected from the second genome, and a fragment of the telomere sequence in the target contig is extended to the chromosome end.

[0039] Regarding the Gap filling unit 114, it is used, for example, to perform adaptive Gap filling on the first genome after telomere extension, using the second genome as a candidate sequence library, searching for candidate contig sequences within a dynamic search range using an adaptive search strategy, selecting candidate contigs that meet preset conditions to fill the gaps; performing local assembly Gap filling on gaps that still exist after adaptive Gap filling; and using gap filling based on sequencing data alignment on gaps that still exist after local assembly Gap filling.

[0040] Regarding the telomere analysis unit 115, it is used, for example, for telomere analysis of the genome after gap filling.

[0041] Regarding the centromere analysis unit 116, it is used, for example, to perform centromere analysis on the gap-filled genome, and to bidirectionally mine centromere feature repeats using the assembled genome sequence and the original long reads, and output centromere coordinates.

[0042] The following will combine Figure 2 A method 200 for T2T gap-free complete genome assembly based on automated gap filling according to an embodiment of the present invention is described. Figure 2 A flowchart of a method 200 for constructing a complete genome assembly according to an embodiment of the present invention is shown. Method 200 can be performed in... Figure 1 The described computing device 110 performs the operation. It should be understood that method 200 may also include additional actions not shown and / or the actions shown may be omitted, and the scope of the invention is not limited in this respect.

[0043] In step 202, computing device 110 acquires a first genome and a second genome. The first genome is a genome containing at least one gap obtained after assembling the sequencing data of the sample to be tested and mounting chromosomes. The second genome is a set of contigs obtained after assembling the sequencing data of the sample to be tested.

[0044] Regarding the two sets of input data involved in the method of this invention, the first genome and the second genome, for example, the first genome is a chromosome-level genome of the target species, which is obtained by assembling sequencing data and then attaching it to chromosomes. This method does not limit the source of the first genome; any genome that has been attached to the chromosome level but has not yet achieved T2T integrity can be used as input. Preferably, the first genome is assembled using HiFi sequencing data and ONT sequencing data, and then obtained after HiC chromosome attachment.

[0045] The second genome is a collection of contigs from the same species as the first genome, obtained through independent assembly of sequencing data from that species. The second genome serves as a candidate sequence for gap filling and telomere extension of the first genome. This method does not limit the assembly software or specific data type of the second genome. Preferably, the second genome is assembled using long-read sequencing data, including but not limited to ONT ultra-long sequencing data, PacBio HiFi sequencing data, or a combination of both.

[0046] In optional step 204, before telomere extension, the computing device 110 detects whether there are misaligned contigs in the first genome, and corrects them if present. Specifically, the computing device 110 performs collinearity alignment between the chromosome-level genome to be filled and the genome to be filled, and performs orientation error detection and correction on the contig sequences in the chromosome-level genome to obtain an orientation-corrected genome.

[0047] In some embodiments, a method for targeted error detection and correction of contig sequences in the chromosome-level genome includes, for example, reading the location information of the contig in the genome to be detected, extracting the location information, including the contig ID, the chromosome in which it is located, the start and end positions, and the length; for contigs whose length exceeds a preset threshold, they are skipped directly without detection; for short contigs, each record in its collinearity alignment result is read, and the query start position, query end position, ref start position, ref end position, alignment length, reference chromosome name, and query sequence name are extracted. The total alignment length of each query sequence to each chromosome is accumulated and used to subsequently determine the best matching chromosome for the query sequence.

[0048] For each contig interval to be detected, the alignment records are divided into three categories according to the positional relationship of their alignment regions relative to the contig interval: if the alignment region falls entirely inside the contig interval, it is classified into the "inner region" list; if the alignment region is located before the start position of the contig, it is classified into the "left outer region" list; if the alignment region is located after the end position of the contig, it is classified into the "right outer region" list.

[0049] For each contig to be detected, orientation determination is performed. For each record in the internal list, it is determined whether it is a forward or backward alignment based on whether the starting coordinate of the ref is less than the ending coordinate of the ref. The alignment lengths of the same direction and the backward alignment are accumulated separately. All query sequences are sorted in descending order of total alignment length, and the sequence with the largest alignment length is selected as the best matching sequence for that contig. Then, it is confirmed that the optimal alignment chromosome of the best matching sequence is indeed equal to the chromosome where the current contig is located, that is, the current chromosome has the largest total length among all chromosomes to which the sequence is aligned, before proceeding to the next step. After confirming the best matching chromosome, the orientation of alignment records belonging to the same best matching sequence in the outer regions (left outer and right outer) is counted. For each record, if it is a forward alignment, the external same-direction length is accumulated; if it is a backward alignment, the external backward alignment length is accumulated.

[0050] The invention calculates the internal and external direction indicators, and defines the direction indicator as follows: in For the sum of internal lengths in the same direction, For the internal reverse length sum, The sum of the external lengths in the same direction. For the external reverse length and, For internal direction indication, The internal direction indicator is the external direction indicator. When the internal direction indicator has the opposite sign to the external direction indicator, and the external same-direction length and external opposite-direction length are not both zero, the Contig is determined to have an orientation error and its orientation is corrected.

[0051] In step 206, the computing device 110 performs telomere extension on the first genome. For chromosome ends in the first genome that lack telomeres, a target contig containing telomere sequences is selected from the second genome, and the telomere sequence fragment in the target contig is extended to the chromosome end. For example, the computing device 110 performs telomere extension on the directionally corrected genome to fill in the missing telomere sequences at both ends of the chromosome, resulting in a telomere-extended genome.

[0052] Regarding telomere extension, examples include: telomere extension based on contig sequences and telomere extension based on third-generation sequencing data.

[0053] In some embodiments, a method for telomere extension based on contig sequences includes, for example, searching for sequences containing telomere repeat motifs within genomic contig sequences. Then, it is determined whether the contig matches a chromosome terminal sequence. If the contig contains both telomere features and matches a chromosome terminal, the contig sequence is used to complete the missing telomeres on the chromosome.

[0054] In some embodiments, the method for telomere extension based on third-generation sequencing data includes, for example,: calculating the inverse complementary sequence of telomere repeat motifs; reading each sequence in the genome FASTA file, segmenting each sequence into segments of a fixed window size W, with an overlap length O between adjacent windows, starting from the beginning of the sequence, each time a subsequence of length W+O is truncated as a segment, and then stepping one window size W, continuing to truncate the next segment until the entire sequence is covered; in each segment, searching for forward telomere repeats and inverse complementary telomere repeats respectively, and recording the type, length, and position information of the matching sequence mapped back to the original sequence.

[0055] Read the chromosome end information for chromosomes lacking telomeres, and parse the chromosome name and start / end positions in each record: if the start position is 0, mark the chromosome as lacking telomeres at the anterior end; otherwise, mark the chromosome as lacking telomeres at the posterior end. Read the contig information with telomere features, skipping contigs with a length less than 1000 bp. If the telomere feature start position is 0, mark the contig as having telomeres at the anterior end; otherwise, mark the contig as having telomeres at the posterior end.

[0056] Collinearity alignment results are filtered, retaining only alignment records that align to chromosomes lacking telomeres and originate from contigs with telomere features. For each chromosome and contig pair, alignment blocks are clustered: starting from the first block and scanning forward, if the distance between adjacent blocks on both the reference and query sequences is less than the maximum distance threshold of 100kb, they are merged into the same cluster. If the total alignment length of the cluster is greater than or equal to the minimum region length threshold of 100kb, all blocks in that cluster are retained; otherwise, they are discarded.

[0057] In some embodiments, the distance between two adjacent blocks is calculated as follows: the intervals of each block in the query sequence are unified into ordered intervals, and then the difference between the far end of the preceding interval and the near end of the following interval is calculated. The time interval is positive when the two intervals do not overlap, and negative when they overlap.

[0058] For each Contig after filtering and clustering, find its best matching chromosome, which is the chromosome with the largest total alignment length among all chromosomes to which the Contig aligns. Then, determine the extension of the Contig and its best matching chromosome: collect the reference coordinates and query coordinates of all alignment blocks, calculate the Pearson correlation coefficient r, where r > 0 indicates forward alignment and r < 0 indicates reverse alignment. Determine the alignment position type based on the coordinates of the alignment blocks. Based on the sign of the Pearson correlation coefficient, the alignment position type, and the end type of the corresponding Contig telomere, determine the extension direction.

[0059] In some embodiments, the method for determining the alignment position type based on the coordinates of the alignment block and the length of the reference sequence includes, for example: setting an extension threshold of E, and representing the length of the reference sequence as L_ref; if the minimum coordinate of the alignment block on the reference sequence is less than or equal to E and the maximum coordinate is greater than or equal to L_ref minus E, it is determined that both ends are aligned; if the minimum coordinate is less than or equal to E but the maximum coordinate is less than L_ref minus E, it is determined that only the front end is aligned; if the minimum coordinate is greater than E but the maximum coordinate is greater than or equal to L_ref minus E, it is determined that only the back end is aligned; otherwise, it is determined that it is an intermediate alignment.

[0060] In some embodiments, the extension threshold is E = 1,000,000 bp.

[0061] In some embodiments, the Pearson correlation coefficient r for determining the alignment direction is calculated as shown in formula (1): (1) Where xi = the starting coordinates of the i-th block on the reference sequence (chromosome), yi = the starting coordinates of the i-th block on the query sequence (contig), and n = the number of blocks.

[0062] Judgment rules: r>0: Positive alignment (chromosome and contig are aligned in the same direction); r<0: reverse alignment (chromosomes are reversed from contig); In some embodiments, the extended determination logic is shown in Table 1.

[0063] Table 1

[0064] In step 208, the computing device 110 performs adaptive gap filling on the first genome after telomere extension, uses the second genome as a candidate sequence library, searches for candidate contig sequences within a dynamic search range using an adaptive search strategy, and selects candidate contigs that meet preset conditions to fill the gap.

[0065] More specifically, an adaptive search strategy is used to search for candidate contig sequences within a dynamic search range. For each gap in the first genome, starting from a preset minimum flanking length, the flanking length is gradually increased with a preset step size until a preset maximum flanking length is reached. After each increase in flanking length, the flanking sequences on both sides of the gap are extracted, and the contigs of the second genome are aligned to the flanking sequences. Collinearity blocks obtained from the alignment are filtered and screened. If a candidate contig that meets preset conditions is found, the increase in flanking length is stopped. Based on the candidate contigs, the gaps are filled, and the continuity of the genome is improved.

[0066] In some embodiments, a method for searching candidate contig sequences within a dynamic search range using an adaptive search strategy includes, for example,: dynamically searching for candidate contig sequences that can fill the gap within a range of 10kb to 100kb using software such as Winnowmap-2.03, starting the search from a 10kb range upstream and downstream of the gap; if no suitable candidate contig is found, expanding the search range to 20kb, and so on, increasing by 10kb each time, until reaching a maximum range of 100kb; for each search range, verifying the collinearity of the candidate contig with the target chromosome using software such as MUMmer v4.0; and finally selecting contigs that meet preset conditions to fill the gap.

[0067] In some embodiments, the preset conditions include, for example, the similarity verified by MUMmer comparison reaching a preset similarity threshold, and the clustered regions whose total comparison length reaches a preset length threshold after clustering filtering.

[0068] In some embodiments, the method for clustering and filtering the alignment results includes, for example,: reading all alignment blocks that reach a preset similarity threshold, establishing a clustering region with each unmarked block as a seed, and scanning adjacent blocks backward; if the distance between adjacent blocks on the reference sequence and the distance on the query sequence are both less than the maximum distance threshold D_max, they are merged into the current clustering region, and the cluster boundary is updated to the boundary of the latest merged block; if the total alignment length of the clustering region is greater than or equal to a preset length threshold L_min, all alignment blocks in the clustering region are retained, otherwise they are discarded; the retained blocks are marked as output and are no longer used as new seeds; after clustering and filtering, regions with significant collinearity support are retained, and scattered and isolated alignment signals are removed; candidate contigs are selected based on the filtered alignment results to fill the corresponding gaps.

[0069] In some embodiments, the method for calculating the distance between two blocks on the query sequence includes, for example, the following: given the intervals [a1,a2] and [b1,b2] of two blocks on the query, they are first unified into ordered intervals, and then the distance between the closer endpoints of the two intervals is calculated, which is positive when they do not overlap and negative when they overlap.

[0070] In some embodiments, the preset similarity threshold reached by MUMmer comparison verification is 99%, the maximum distance threshold D_max=100,000bp, and the preset length threshold L_min=100,000bp.

[0071] In optional step 210, computing device 110 performs local assembly gap filling on gaps that still exist after adaptive gap filling, but before telomere analysis and centromere analysis.

[0072] In some embodiments, if there are still unfilled gaps after the adaptive gap-filling step, a local assembly method is used to further fill them: the gap and the sequencing reads within a preset range on its flanks are extracted and locally assembled, and the assembled local contigs are used to fill the gap, thereby further improving the accuracy and continuity of the genome.

[0073] In optional step 212, computing device 110 fills the gaps that still exist after local assembly gap filling using gap filling based on sequencing data alignment.

[0074] In some embodiments, for unfilled gaps that still exist after adaptive gap filling and local assembly gap filling steps, a reads-based iterative extension filling strategy is adopted; using a known sequence on one side of the gap as a seed, sequencing reads are aligned to the seed, and newly added sequences that extend into the gap are extracted and merged into the end of the seed, and iterative extension is performed until the gap is covered.

[0075] In step 214, the computing device 110 performs telomere analysis on the gap-filled genome, which includes, for example, searching for the number of telomere repeat units in fixed intervals in the end regions of each chromosome of the first genome after gap filling, assuming that telomeres exist if the requirements are met, and finally merging the intervals to determine the telomere location information.

[0076] In step 216, the computing device 110 performs centromere analysis on the gap-filled genome, including: using centromeres to perform centromere analysis, bidirectionally mining centromere feature repeats using the assembled genome sequence and the original long reads, first extracting species-specific centromere satellite monomers from the assembled genome sequence, then back-pasting the genome to identify continuous enriched intervals, and outputting candidate centromere coordinates.

[0077] Example 1 This embodiment uses the genome of a certain species as an example to describe the method of the present invention in detail. It should be understood that this embodiment is only used to illustrate specific implementations of the present invention and is not intended to limit the scope of protection of the present invention. Adjustments and modifications made by those skilled in the art based on the teachings of this embodiment without departing from the concept of the present invention are all within the scope of protection of the present invention.

[0078] 1. Using PacBio HiFi sequencing data and ONT ultra-long sequencing data, preliminary assembly was performed using genome assembly software, such as hifiasm, to obtain the Contig version of the genome. Then, combined with Hi-C library sequencing data, the genome was mounted at the chromosome level to obtain the first genome (Genome A). The second genome (Genome B) was then independently assembled using ONT ultra-long sequencing data. Basic information of Genome A after assembly is shown in Table 2, revealing two gaps located on chromosomes Chr06 and Chr09.

[0079] Table 2

[0080] 2. Perform collinearity analysis on GenomeA and GenomeB to detect and correct any misorienting contigs.

[0081] 3. Based on the collinearity alignment results of Genome A and Genome B, the contig sequence and location information for telomere extension are determined, and telomere extension is performed on the ends of chromosomes lacking telomeres in the first genome. In this embodiment, the telomere repeat motif is set to the corresponding sequence according to the species type, for example, the conserved telomere repeat sequence for vertebrates and ascomycetes is TTAGGG; the classic telomere motif for most terrestrial plants is TTTAGGGG. The telomere deletion cases of chromosomes in Genome A are shown in Table 3, where a start value of 0 indicates that the chromosome lacks telomeres at the beginning, and a non-zero start value indicates that the corresponding chromosome lacks telomeres at the end. Table 3

[0082] The source and sequence location information used for adding telomeres are shown in Table 4: Table 4

[0083] 4. Using Genome B as the candidate sequence library, gap filling is performed on Genome A after telomere extension. For each gap, starting from a preset minimum flanking length (default 10kb), the flanking length is gradually increased with a preset step size (default 10kb) until a preset maximum flanking length (default 100kb) is reached. The flanking sequences on both sides of the gap are extracted, and the contigs of Genome B are aligned to the flanking sequences to determine the source of the contig sequences used to fill the two gaps and visualize them. For example... Figure 3 As shown, ptg000013l can fill the gap on Chr06, and ptg000010l can fill the gap on Chr09.

[0084] 5. After telomere extension, the length of each chromosome and the number of gaps were statistically analyzed. The results are shown in Table 5. It can be seen that the two gaps in the genome were completely filled: Table 5

[0085] 6. In the terminal regions of each chromosome of Genome A after gap filling, telomere repeat sequences were searched using a sliding window method. The copy number and coverage length of the telomere repeat sequences at the ends of each chromosome were counted to determine the position and length of the telomeres at the ends of each chromosome, as shown in Table 6: Table 6

[0086] 7. Centromics was used for centromere analysis. Species-specific centromere satellite monomers were extracted from the genome sequence, and then the genome was re-attached to identify continuous enriched regions. The coordinates of candidate centromeres for each chromosome were output, as shown in Table 7. Table 7

[0087] Figure 4 The schematic diagram illustrates an example device 300 suitable for implementing embodiments of the present invention. For example, device 300 may be as follows: Figure 1 The computing device 110 shown. For example, device 300 may be a device for implementing execution method 200.

[0088] like Figure 4 As shown, device 300 includes a central processing unit (i.e., CPU 301), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (i.e., ROM 302) or loaded from storage unit 308 into random access memory (i.e., RAM 303). RAM 303 may also store various programs and data required for the operation of electronic device 300. CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. Input / output interface (i.e., I / O interface 305) is also connected to bus 304.

[0089] Multiple components in device 300 are connected to I / O output interface 305, including: input unit 306, such as keyboard, mouse, microphone, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless communication transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0090] The various processes and handling described above, such as method 200, can be executed by CPU 301. For example, in some embodiments, method 200 may be implemented as a computer software program stored on a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by CPU 301, one or more actions of method 200 described above may be performed. Alternatively, in other embodiments, CPU 301 may be configured to perform one or more actions of method 200 by any other suitable means (e.g., by means of firmware).

[0091] It should be further noted that the present invention can be a method, apparatus, system, device, computer-readable storage medium, and / or computer program product. The computer program product may include computer-readable program instructions for performing various aspects of the present invention.

[0092] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0093] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0094] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0095] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0096] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0097] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0099] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0100] The above are merely optional embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0101] References [1] Jain C, Rhie A, Hansen N F, et al. Long-read map to repetitivereference sequences using Winnowmap2[J]. Nature Methods, 2022, 19(6): 705-710. (Winnowmap); [2] Marçais G, Delcher A L, Phillippy A M, et al. MUMmer4: A fast andversatile genome alignment system[J]. PLoS computational biology, 2018, 14(1): e1005944. (MUMmer4); [3] Li H, Handsaker B, Wysoker A, et al. The sequence alignment / mapformat and SAMtools. [J].Bioinformatics, 2009, 25(16): 2078-2079. (SAMtools); [4] Quinlan A R, Hall I M. BEDTools: a flexible suite of utilitiesfor comparing genomic features[J]. Bioinformatics, 2010, 26(6): 841-842.(BEDtools); [5] Rhie A , Walenz B P , Koren S ,et al.Merqury: Reference-freequality, completeness, and phasing assessment for genome assemblies[J].GenomeBiology, 2020, 21(1):245.DOI:10.1186 / s13059-020-02134-9.(merquery); [6] Xu G C , Xu T J , Zhu R ,et al.LR_Gapcloser: a tiling path-basedgap closer that uses long reads to complete genome assembly.[J].Gigascience,2019(1).DOI:10.1093 / gigascience / giy157.(LR_Gapcloser)。

Claims

1. A method for T2T gap-free complete genome assembly based on automated genome gap filling, characterized in that, include: A first genome and a second genome are obtained. The first genome is a genome containing at least one gap obtained after assembling the sequencing data of the sample to be tested and mounting chromosomes. The second genome is a set of contigs obtained after assembling the sequencing data of the sample to be tested. Telomere extension is performed on the first genome. For chromosome ends in the first genome that lack telomeres, a target contig containing telomere sequences is selected from the second genome, and the telomere sequence fragment in the target contig is extended to the chromosome end. Adaptive gap filling is performed on the first genome after telomere extension. The second genome is used as a candidate sequence library. Candidate contig sequences are searched within the dynamic search range using an adaptive search strategy. Candidate contigs that meet preset conditions are selected to fill the gaps. Telomere and centromere analyses were performed on the gap-filled genome. Output the complete telomere-to-telomere genome.

2. The method according to claim 1, characterized in that, It also includes: performing local assembly gap filling on gaps that still exist after adaptive gap filling and before telomere analysis and centromere analysis; and using gap filling based on sequencing data alignment on gaps that still exist after local assembly gap filling.

3. The method according to claim 1, characterized in that, Further includes: Before telomere extension, the presence of misaligned contigs in the first genome is detected and corrected if present.

4. The method according to claim 1, characterized in that, The first genome is a genome mounted to the chromosome level via Hi-C, and the second genome is a set of contigs assembled from long-read sequencing data.

5. The method according to claim 1, characterized in that, The first genome was obtained by combining PacBio HiFi sequencing data and ONT sequencing data and then mounting it via Hi-C. The second genome was obtained by combining ONT sequencing data alone or by combining ONT sequencing data and PacBio HiFi sequencing data.

6. The method according to claim 1, characterized in that, Telomere extension of the first genome includes: Search for forward and reverse complementary telomere repeat motifs in the segmented windows of each sequence in the first and second genomes, and count their position, length and end type; The contigs containing telomere sequences in the second genome are aligned to the chromosome terminal regions in the first genome that lack telomeres; Clustering filtering is performed on the collinearity ratio results, and the best matching chromosome is selected for each contig; The Pearson correlation coefficient is calculated based on the reference coordinates and query coordinates of the comparison block after clustering and filtering. The location type of the aligned block on the chromosome is determined based on the coordinates of the aligned block on the reference sequence; Based on the sign of the Pearson correlation coefficient, the location type, and the end type of the corresponding contig telomere, the extension direction is determined, and the corresponding segment is extracted from the corresponding contig and extended to the end of the chromosome.

7. The method according to claim 6, characterized in that, The determination of the elongation direction based on the sign of the Pearson correlation coefficient, the location type, and the end type of the corresponding contig telomere includes: If the chromosome telomere deletion is an anterior deletion, the alignment region covers the anterior end of the chromosome, the Pearson correlation coefficient is greater than 0, and the anterior end of the contig contains a telomere sequence, then the anterior end sequence of the contig is taken as the extension direction. If the chromosome telomere deletion is an anterior deletion, the alignment region covers the anterior end of the chromosome, the Pearson correlation coefficient is less than 0, and the posterior end of the contig contains a telomere sequence, then the inverse complementary sequence of the posterior end of the contig is taken as the extension direction. If the chromosome telomere deletion is a posterior deletion, the alignment region covers the posterior end of the chromosome, the Pearson correlation coefficient is greater than 0, and the posterior end of the contig contains a telomere sequence, then the posterior end sequence of the contig is taken as the extension direction. If the telomere deletion on the chromosome is a posterior deletion, the alignment region covers the posterior end of the chromosome, the Pearson correlation coefficient is less than 0, and the front end of the contig contains a telomere sequence, then the inverse complementary sequence of the front end of the contig is used as the extension direction.

8. The method according to claim 1, characterized in that, An adaptive search strategy is used to search for candidate contig sequences for gap filling, including: For each gap on the first genome, starting from the preset minimum flanking length, the flanking length is gradually increased with a preset step size until the preset maximum flanking length is reached; Each time the flanking length is increased, the flanking sequences on both sides of the gap are extracted, and the contigs of the second genome are aligned to the flanking sequences; The collinearity alignment results are compared and verified. The alignment results that reach the preset similarity threshold are further clustered and filtered, and candidate contigs that meet the preset conditions are selected to fill the gap. If a candidate contig that meets the preset conditions is selected, the flank length will stop increasing.

9. The method according to claim 8, characterized in that, Candidate contigs that meet preset conditions are selected to fill the gap. The preset conditions include: the similarity reaches a preset similarity threshold after comparison verification, and the clustered regions whose total comparison length reaches a preset length threshold are retained after clustering filtering.

10. The method according to claim 9, characterized in that, After clustering filtering, clustered regions whose total alignment length reaches a preset length threshold are retained, including: Read all matching blocks that reach the preset similarity threshold; A new clustering region is established with each unlabeled output block as a seed. The adjacent blocks are scanned backward. When the distance on the reference sequence and the distance on the query sequence are both less than the maximum distance threshold Dmax, the block is merged into the current clustering region and the cluster boundary is updated to the boundary of the latest merged block. Repeat the scan until all blocks have been processed; For each cluster region, if its total alignment length is greater than or equal to the preset length threshold Lmin, all alignment blocks in that cluster region are retained; otherwise, they are discarded.

11. The method according to claim 1, characterized in that, The telomere analysis includes: In the end regions of each chromosome of the first genome after gap filling, telomere repeat sequences are searched using a sliding window method. If telomere repeat sequences are detected at both ends of each chromosome, the genome is confirmed to be a complete telomere-to-telomere genome.

12. The method according to claim 3, characterized in that, Detecting the presence of misaligned contigs in the first genome includes: The second genome is aligned to the first genome, and only contigs in the first genome with a length less than a preset threshold are detected. The collinearity alignment results on the Contigs that are less than the preset threshold are traversed, and each alignment record is classified into an internal region, a left external region, or a right external region according to the positional relationship of its alignment region relative to the Contig interval. The orientation of alignment records belonging to the same best-matching chromosome in the left outer region, right outer region, and inner region is counted. For each record, if it is a forward alignment, the outer same-direction length is accumulated; if it is a reverse alignment, the outer reverse length is accumulated. Calculate the internal and external direction indicators. The direction indicator is defined as the sign function value of the difference between the sum of the lengths of the same-direction alignments and the sum of the lengths of the opposite-direction alignments. When the signs of the internal direction indicator and the external direction indicator are opposite, and the external same-direction length and external opposite-direction length are not both zero, the Contig is determined to have an orientation error and its orientation is corrected.

13. The method according to claim 12, characterized in that, Also includes: For a contig in the second genome, the total alignment length of the contig to each chromosome in the first genome is calculated, and the chromosome with the largest total alignment length is selected as the best matching chromosome for the sequence. Subsequent direction determination is only performed when the best matching chromosome is equal to the chromosome containing the contig.

14. A computing device, characterized in that, include: At least one processing unit; At least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the device to perform the steps of the method according to any one of claims 1 to 13.

15. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a machine, implements the method according to any one of claims 1 to 13.

16. A computer program product, characterized in that, The computer program product includes instructions that, when executed by a machine, implement the method according to any one of claims 1 to 13.

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