A method and system for determining the chromosome base number of macrobrachium rosenbergii based on multi-omics joint analysis

Abstract

The present application belongs to the field of biotechnology and genomics, and particularly relates to a method and system for determining the chromosome base number of Macrobrachium rosenbergii based on multi-omics joint analysis. The method obtains de novo assembly sequencing data and Hi-C sequencing data, generates a chromosome-level candidate assembly without presetting the number of chromosomes by using Hi-C interaction signals after primary assembly, and performs whole-genome collinearity alignment with no less than two published reference genomes; in combination with quality constraints such as collinearity continuity, Hi-C boundary characteristics and BUSCO / LAI, the candidate chromosome boundary is comprehensively judged and iteratively converged, and finally the chromosome base number and reviewable evidence chain are output. The embodiments show that the present application can identify and correct the number redundancy caused by over-splitting of the reference genome, determine the base number of Macrobrachium rosenbergii as n=57 (2n=114), and improve the objectivity and reliability of base number determination.

Description

Technical Field

[0001] This invention belongs to the fields of biotechnology and genomics, and in particular relates to a method and system for determining the basic number of chromosomes in the giant freshwater prawn based on multi-omics joint analysis. Background Technology

[0002] The giant freshwater prawn (Macrobrachium rosenbergii) is an important freshwater aquaculture species. Precise identification of its germplasm resources, trait mapping, and molecular breeding research heavily rely on a highly reliable chromosome-level genomic framework. In genomics and genetic breeding practice, the "chromosome base number (haploid chromosome number)" not only determines the anchoring framework and numbering system for chromosome-level assembly but also affects the construction of genetic linkage maps, QTL mapping, collinearity comparison, interpretation of structural variations, and the comparability of cross-study data integration. Therefore, establishing a technical approach that can objectively and reproducibly determine the chromosome base number of the giant freshwater prawn has clear value in both basic research and industrial applications.

[0003] Traditionally, the chromosome number of *Macrobrachium rosenbergii* (Giant River Prawn) has been primarily determined through microkaryotype analysis. Early cytogenetic studies generally concluded that the diploid chromosome number was approximately 2n=118, but these studies also revealed problems such as counting fluctuations and insufficient reproducibility. For example, Chavez Justo et al., when observing chromosomes in *Macrobrachium rosenbergii* tissue cells, reported a pattern number of 2n=118, but their study still reported a certain range of variation and counting uncertainty (e.g., different cells showing different numbers of chromosomes). Domestic literature also reports that *Macrobrachium rosenbergii* embryonic cell chromosome preparation analysis showed 2n=118, but some cells showed deviations of 100–109 chromosomes, suggesting that microscopic counting is affected by factors such as slide quality, the small size and difficulty in distinguishing chromosomes, subjective observation, and cell cycle differences. This type of "microscopic counting and empirical induction" method is insufficient to provide a sufficiently stable and objective basis for subsequent chromosome-level genomic coordinate systems.

[0004] With the development of high-throughput sequencing and three-dimensional genome technologies, de novo assembly using long / short reads combined with Hi-C interaction data to achieve chromosome-level mounting has become the mainstream approach for constructing high-quality reference genomes. A typical example is the Hi-C-assisted chromosome-level assembly framework proposed in the literature (Dudchenko O. et al., De novo assembly of the Aedes aegypti genome using Hi-Cyields chromosome-length scaffolds, Science, 2017) (commonly implemented as a 3D-DNA workflow). This framework uses Hi-C interaction information to sort, orient, and correct errors in the primary assembly sequences, thereby obtaining chromosome-length scaffolds. This type of method significantly improves assembly continuity and chromosome-level structural consistency, and is one of the key technologies for current chromosome-level assembly.

[0005] Several chromosome-level reference genomes for the giant river prawn (Macrobrachium rosenbergii) have been published in recent years. For example, the literature (Zheng Y. et al., A Chromosome-level genome assembly of giant river prawn (Macrobrachium rosenbergii), Scientific Data, 2024) constructed a chromosome-level reference genome for the giant river prawn by integrating third-generation long reads, Illumina, and Hi-C data. Statistical information for relevant giant river prawn reference genome assemblies (such as GCF_040412425.1) in publicly available NCBI datasets shows a chromosome number of 59. Such work reinforces the traditional understanding that the giant river prawn has a chromosome number of 59 and provides important resources for genome annotation and applied research. However, from a technical perspective, existing chromosome-level assembly often has two types of hidden dangers in "determining the number / baseline of chromosomes": First, the Hi-C mounting stage may be affected by prior assumptions about the number of chromosomes or parameter configurations (such as artificially converging the clustering results to a certain expected number of chromosomes), thus creating the risk of "over-splitting / incorrect splitting" at the boundary; Second, after mounting is completed, the empirical process of "manual heatmap inspection + local adjustment" is often adopted, which lacks a reviewable and repeatable judgment mechanism, resulting in situations where "seemingly reasonable but actually incorrect splitting" may still occur when the species has microchromosomes, abundant repetitive sequences, or complex structures.

[0006] In verifying the structural consistency of assembly results, whole-genome collinearity comparison and structural difference identification tools are widely used. The SyRI tool proposed in the literature (Goel M. et al., *SyRI: finding genomic rearrangements and local sequence differences from whole-genome assemblies*, *Genome Biology*, 2019) can identify collinear pathways and annotate structural differences such as inversions, translocations, and duplications based on whole-genome alignment, providing a feasible toolchain for comparing two sets of assemblies and locating inconsistent regions. However, existing collinearity / structural difference analyses typically serve for "structural variation annotation or assembly difference interpretation," and still have shortcomings in the "chromosome cardinality determination" scenario: most practices only select a single reference genome for alignment, lacking "multi-reference consistency adjudication"; furthermore, collinearity results and Hi-C boundary signals are often presented in parallel, lacking a closed-loop mechanism that integrates "collinearity continuity, Hi-C boundary significance, and assembly quality constraints" into a single quantitative scoring, threshold decision, and structural manipulation (merging / splitting) mechanism, making it difficult to form stable and reproducible cardinality correction conclusions.

[0007] Furthermore, in terms of assembly quality assessment, BUSCO is used to evaluate assembly / annotation integrity from the perspective of conserved single-copy orthologous genes; LAI is used to evaluate the assembly quality of repetitive sequence spaces from the perspective of LTR retrotransposon assembly continuity. These indicators are crucial for screening "high-quality assemblies that can serve as the basis for analysis," but they essentially answer the question of "whether the integrity / continuity is sufficient," and do not directly provide decision-making criteria for "whether a certain boundary should be merged or split, and how the final number of chromosomes should converge." In other words, existing techniques often lack a unified framework that uses quality thresholds as hard constraints for structural operations and, together with evidence of collinearity and Hi-C boundary evidence, drives "cardinality determination."

[0008] In summary, while existing technologies possess Hi-C-assisted chromosome-level assembly, macrobrachium rosenbergii chromosome-level reference genome resources, and collinearity / structural difference identification tools, they still have significant shortcomings in objectively and reproducibly determining and correcting chromosome base numbers. These shortcomings mainly include: the lack of a candidate solution generation mechanism independent of a single prior number; the lack of a scoring model that quantifies collinearity continuity and Hi-C boundary significance and outputs structural manipulation instructions; the lack of a consensus decision mechanism based on multiple reference genomes; and the lack of iterative convergence and evidence chain output. Consequently, it is difficult to avoid chromosome missplitting and base number bias caused by pre-set or empirical judgments. To address these issues, there is an urgent need to propose a technical solution that can integrate multi-omics evidence, drive merging / splitting decisions with quantitative indicators, and stably output chromosome base numbers through consensus decision-making and iterative convergence. Summary of the Invention

[0009] The technical objective of this invention is to provide a method for determining the chromosome base number of *Macrobrachium rosenbergii* that does not rely on a preset chromosome number, thereby objectively correcting base number biases that may arise from missplitting in existing reference assemblies. The method is achieved through the following technical means: simultaneously acquiring de novo assembly sequencing data and Hi-C sequencing data to generate chromosome-level candidate assembly solutions with different numbers of chromosomes; incorporating whole-genome collinearity alignment results from at least two publicly available reference genomes; combining collinearity continuity indicators, Hi-C boundary significance indicators, and assembly quality constraint indicators to quantitatively score and adjudicate the consistency of candidate chromosome boundaries; and then iteratively performing merging / splitting operations, outputting the chromosome base number and evidence chain results after meeting convergence conditions. This provides a more reliable chromosome framework for *Macrobrachium rosenbergii* genomics research and molecular breeding.

[0010] Firstly, in order to achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0011] A method for determining the basic chromosome number of *Macrobrachium rosenbergii* based on multi-omics joint analysis, comprising the following steps:

[0012] S1, Obtain de novo assembly sequencing data and Hi-C sequencing data of the target individual; Assemble the de novo assembly sequencing data to obtain the primary assembled sequence;

[0013] S2, based on the Hi-C interaction signal, the primary assembly sequence is clustered, sorted, oriented and mounted to generate at least two chromosome-level candidate assembly solutions with different numbers of chromosomes;

[0014] S3. Select no less than two publicly available macrobrachium rosenbergii chromosome-level reference genomes, and perform whole-genome alignment of each candidate assembly dissection with each reference genome to obtain collinear correspondence.

[0015] S4, for each candidate chromosome boundary in the candidate assembly solution, based on the collinearity continuity index... Hi-C boundary significance index and assembly quality constraints Calculate the comprehensive judgment score The system outputs structural operation instructions for merging, splitting, or maintaining the structure accordingly. Merging or splitting operations are performed only when the criteria of "continuous collinearity supports merging" and "Hi-C does not show a physical splitting boundary" are met in the consistency analysis of at least two reference genome pairs at the same boundary.

[0016] S5. For the assembly result after performing the structural operation, repeat the above comparison and judgment process until the convergence condition is met, and output the chromosome base number and evidence chain result. The evidence chain result includes at least the boundary position that triggers merging or splitting and the corresponding index value.

[0017] Preferably, the de novo assembly sequencing data in step S1 includes second-generation short-read sequencing data and third-generation long-read sequencing data, wherein the third-generation long-read sequencing data is high-accuracy long-read data.

[0018] Preferably, the candidate assembly solution in step S2 is obtained by considering the range of candidate chromosome numbers. The process involves traversal or convergence through Hi-C clustering without limiting the number of target chromosomes, and the candidate assembly solution contains at least two solutions with different numbers of chromosomes.

[0019] The range of candidate chromosome numbers satisfies: and The profile coefficient, assembly N50, or effective pair density of Hi-C interaction clusters are determined, and this ensures... Candidate assembly solutions containing at least two different numbers of chromosomes.

[0020] Preferably, the collinearity continuity index mentioned in step S4 At least include coverage ratio With fracture gap ,in:

[0021] It represents the proportion of the total length of the candidate chromosomes to the effective alignment length of the candidate chromosomes, in which two or more reference chromosomes in the reference genome can be found consecutively along their arrangement order.

[0022] This represents the cumulative length of the discontinuous region between the aforementioned consecutive collinear segments;

[0023] and with and As one of the criteria for "continuous collinearity supports merging";

[0024] And / or, the Hi-C boundary significance index At least include boundary indices and / or interaction strength ratio ,in:

[0025] This indicates the degree of decrease in the Hi-C cis-interaction signal at the boundary within a preset window on both sides of the candidate boundary position. The smaller the value, the less distinct the boundary;

[0026] This represents the ratio of "cross-boundary interaction strength" to "interaction strength within the window" on both sides of the candidate boundary, or its equivalent ratio.

[0027] and with and / or As one of the criteria for determining whether "Hi-C does not display physical split boundaries and supports merging";

[0028] And / or, the assembly quality constraint index It should include at least BUSCO integrity and / or LAI continuity, and set a quality threshold such that only BUSCO... And / or LAI It allows performing structural operations such as merging or splitting.

[0029] Preferably, the collinearity continuity index also includes the number of collinear blocks. ,in Indicates the number of collinear fragments formed between the candidate chromosome and the reference genome, and is expressed in terms of... As one of the criteria for "low degree of collinearity partitioning, supporting merging".

[0030] Preferably, in step S4, With preset threshold , Compare and output the boundary conditions. Structural manipulation instructions: merge, split, or preserve; in at least two reference genomes The structural operation instruction is executed when the consistency decision condition is met, wherein the consistency decision condition includes at least: for the same boundary In no less than In all reference genomes, "continuous collinearity supports merging," and the corresponding regions... It satisfies the condition of having no significant split boundary.

[0031] Preferably, in step S5, the updated candidate assembly solution is regenerated from the result of the structural operation. Repeat steps S3-S4 until the convergence condition is met. The convergence condition includes at least the number of candidate chromosomes no longer changing or the overall score gain being less than 1. .

[0032] Secondly, the present invention also provides a system for determining the basic number of chromosomes in the giant freshwater prawn based on multi-omics joint analysis. This system is used to implement the method described above, including:

[0033] The data acquisition module is used to acquire sequencing data and Hi-C sequencing data for de novo assembly;

[0034] The de novo assembly module is used to assemble the sequencing data to generate a primary assembled sequence;

[0035] The candidate assembly generation module is used to generate a set of candidate assembly solutions from the primary assembly sequence based on the Hi-C interaction signal. ;

[0036] The collinearity analysis module is used to perform genome-wide collinearity analysis on candidate assembly solutions with at least two reference genomes.

[0037] The scoring and adjudication module is used to calculate candidate boundaries. It outputs structural operation instructions for merging, splitting, or maintaining, and executes the structural operation instructions when the consistency adjudication conditions are met;

[0038] The iterative convergence module is used to repeatedly call the collinearity analysis and scoring adjudication module until the convergence condition is met.

[0039] The cardinality output module is used to output chromosome cardinality and evidence chain results.

[0040] Thirdly, the present invention also provides an electronic device including a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to implement the method described above.

[0041] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0042] The present invention, by adopting the above-described technical solution, has the following beneficial effects:

[0043] 1. It achieves objective and reproducible determination and correction of the basic number of chromosomes in Macrobrachium rosenbergii. During the Hi-C mounting stage, it simultaneously generates at least two chromosome-level candidate assembly solutions with different numbers of chromosomes, avoiding the risk of over-splitting / incorrect splitting caused by forced clustering with a preset number of chromosomes in traditional processes.

[0044] 2. A multi-reference genome consistency adjudication mechanism was introduced, significantly improving the reliability of the conclusions. Through whole-genome collinearity alignment of at least two published reference genomes, the phenomenon of continuous collinearity of multiple chromosomes in the reference genome on candidate chromosomes was transformed into a calculable coverage ratio. , fracture gap Number of collinear blocks Evidence, and comparison with the Hi-C boundary index / Inter-ratio Equivalent scoring based on physical boundary evidence Provided that the quality indicators (BUSCO, LAI) meet the threshold, the output boundary merge / split / preservation instructions are given, and the structural operation is performed only when the multi-reference consistency decision is valid, thereby significantly reducing the impact of single reference bias and random noise on the conclusion.

[0045] 3. An algorithmic iterative convergence process was established, and a traceable chain of evidence was output. By iteratively updating the results after structural operations and setting convergence conditions, the determination of chromosome base number was upgraded from empirical conclusions based on manual image interpretation to a traceable, quantifiable, and stopable algorithmic process. The final output is a chain of evidence containing the trigger boundary locations and index values, which facilitates review, verification, and cross-study reuse.

[0046] Therefore, this invention can effectively identify and correct chromosome mis-splitting / mis-attaching caused by preset number of chromosomes, weak interaction signal regions, or complex repetitive sequences in existing assemblies, significantly improving the structural reliability of chromosome-level genomic frameworks and the stability of basic conclusions, and providing a more accurate chromosome coordinate basis for subsequent genetic linkage map construction, QTL localization, comparative genomics, and molecular breeding. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention.

[0048] Figure 2 This is a system structure block diagram of the present invention.

[0049] Figure 3 This is a schematic diagram of whole-genome collinearity analysis of the self-assembled genome (57 chromosomes) and the existing reference genome GCF_040412425.1 (59 chromosomes) in an embodiment of the present invention.

[0050] Figure 4 This is a Hi-C chromosome interaction heatmap of the key self-assembled genome chromosome (chr57) in an embodiment of the present invention.

[0051] Figure 5 This is a pie chart showing the BUSCO assessment results of genome assembly integrity in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0053] I. Terminology Explanation

[0054] 1. De novo sequencing data: This refers to sequencing data used to construct the genome sequence of a target individual from scratch. It typically includes second-generation short-read data (such as Illumina) and third-generation long-read data (such as PacBio HiFi or Nanopore high-precision mode). Second-generation data is used for error correction and consistency assessment, while third-generation data is used to improve assembly continuity and the ability to connect repetitive sequences.

[0055] 2. Hi-C sequencing data: refers to spatial proximity interaction data obtained through chromatin cross-linking, enzyme digestion, end labeling, ligation, library construction, and sequencing. It is used to reflect the proximity relationships of genomic fragments in the three-dimensional space of the cell nucleus. The "contact map" of Hi-C data can be used for chromosome mounting, sequencing orientation, and boundary determination.

[0056] 3. Primary assembled sequences: These refer to the contigs or initial scaffold sets obtained from de novo assembled sequencing data, which have not yet completed chromosome-level mounting and global sorting orientation.

[0057] 4. Chromosome-level candidate assembly solution: refers to the chromosome-level scaffold set obtained during the Hi-C mounting stage (which can be understood as a complete scheme of "chromosome grouping - sorting - direction"). This invention emphasizes generating at least two candidate solutions with different numbers of chromosomes to eliminate the bias caused by a single prior assumption about the number of chromosomes.

[0058] 5. Reference genome: refers to a publicly available chromosome-level genome assembly of the giant freshwater prawn (e.g., a chromosome-level assembly version in a publicly available database). This invention requires at least two reference genomes for consistency determination to avoid bias from a single reference.

[0059] 6. Collinear correspondence: refers to the matching relationship of homologous segments formed between the candidate assembly and the reference genome after whole-genome alignment. It is usually manifested as continuous matching blocks along chromosome coordinates. It can be used to determine the phenomenon of continuous correspondence between "multiple reference chromosomes and a candidate chromosome".

[0060] 7. Candidate chromosome boundaries : Refers to the boundary position used to separate two candidate chromosomes in the candidate assembly dissection, or the potential split point within the same candidate chromosome (e.g., the coordinate position where a suspicious breakpoint of Hi-C signal appears, or where a mutation of collinearity occurs).

[0061] 8. Coverage ratio : Used to characterize the degree to which "multiple chromosomes of the reference genome form continuous collinearity on the candidate chromosome according to their arrangement order", the specific definition is given in step S5 below.

[0062] 9. Fracture gap : Used to characterize the total length of the discontinuous region between consecutive collinear segments, the specific definition is given in step S5 below.

[0063] 10. Number of collinear blocks The collinearity refers to the number of collinear fragments formed between the candidate chromosome and the reference genome, used to characterize whether collinearity has been excessively fragmented.

[0064] 11. Boundary Index Used to measure the "boundary / insulation" strength of Hi-C interaction heatmaps at candidate boundaries. The smaller the value, the less distinct the boundary and the more it tends to be a continuous structure of the same chromosome.

[0065] 12. Interaction strength ratio Used to measure the relative strength of cross-boundary interactions to the strength of intra-boundary interactions. A higher value indicates that cross-boundary interaction is not weak and that physical splitting is less supported.

[0066] 13. Assembly quality constraints This constraint is used to ensure that structural operations are performed only under the premise that the overall assembly quality is reliable. It is preferable to include BUSCO integrity and / or LAI continuity to avoid "low-quality assembly leading to false boundaries" that could cause incorrect merging / splitting.

[0067] 14. Consistency decision: refers to the decision made on the same candidate boundary. At least not less than Merging / splitting structural operations are only allowed when all reference genomes meet the condition of "collinearity supports merging and Hi-C does not show physical split boundaries".

[0068] 15. Convergence condition: After iteratively performing structural operations, the number of candidate chromosomes no longer changes or the overall score gain is less than a threshold. This stops the iteration and outputs the final cardinality and chain of evidence.

[0069] II. System Structure (Combined) Figure 2 )

[0070] See Figure 2 The system of the present invention is used to implement the method described in the present invention, and can be deployed on a server, workstation, or cloud computing platform. The system includes at least the following functional modules:

[0071] Data Acquisition Module 10: Used to import or receive de novo assembly sequencing data and Hi-C sequencing data from the target individual. Its input can be in formats such as FASTQ / BAM, and the output is a standardized dataset for subsequent assembly and mounting. This module may include a quality control submodule (filtering low-quality reads, removing adapters, removing PCR duplicates, etc.).

[0072] De novo assembly module 20: Used to construct primary assembly sequences using long read / short read data. Its output includes a set of contigs, a set of primary scaffolds, and related statistical metrics (such as contig N50, total length, GC content, etc.).

[0073] Candidate assembly generation module 30: Used to cluster, sort, orient, and mount primary assembly sequences based on Hi-C interaction signals, generating at least two chromosome-level candidate assembly solutions with different numbers of chromosomes. It outputs the chromosome set, sequence order and orientation information within the chromosome for each candidate solution.

[0074] Collinearity analysis module 40: used for each candidate assembly solution With each reference genome Perform whole-genome alignment, construct a set of collinear fragment blocks, and output collinear correspondences and structural difference indicators.

[0075] Scoring and Decision Module 50: Used for scoring candidate boundaries Calculate the collinearity continuity index Hi-C boundary significance index Assembly quality constraints And integrate them to form a comprehensive judgment score. Then with the threshold , The system compares the output of "merge, split, or keep" structural operation instructions and decides whether to execute them based on multi-reference consistency criteria.

[0076] Iterative convergence module 60: Used to update candidate assembly solutions after performing structural operations, repeat "collinearity analysis - scoring decision" until the convergence condition is met, and output the final assembly and final cardinality.

[0077] Cardinality Output Module 70: Used to output chromosome cardinality and evidence chain results. The evidence chain results must at least include the boundary positions that trigger merging / splitting, and the corresponding... , , , , In addition, the values ​​of indicators such as BUSCO / LAI are provided to facilitate auditing, review, and cross-team reuse.

[0078] III. Specific Technical Route for Implementing the Method of the Invention

[0079] See Figure 1 The method of this invention is generally divided into S1 to S6. The following are the technical details that can be directly implemented, step by step.

[0080] S1: Obtain de novo assembly sequencing data and Hi-C sequencing data from the target individual.

[0081] 1) Sample selection and DNA preparation

[0082] Healthy giant freshwater prawns were selected, preferably from the same tissue source to reduce contamination and chimerism. High-molecular-weight genomic DNA was extracted to meet the requirements for long-read library construction. For Hi-C library construction, fresh tissue was used for cross-linking and fixation to avoid degradation that would reduce interaction signals.

[0083] 2) Recommendations on Sequencing Data Types and Scale

[0084] Second-generation short read length: used for error correction and k-mer consistency assessment, with a recommended coverage of at least 30×.

[0085] Third-generation high-accuracy long read length: PacBio HiFi is preferred, and a coverage of at least 25× is recommended to ensure the ability to cross overlapping areas.

[0086] Hi-C data: It is recommended to obtain a sufficient number of effective interaction pairs to avoid boundary exponential instability caused by weak interactions. The number of effective Hi-C interaction pairs can be used as a basis for subsequent analysis. One of the factors to consider in the estimation.

[0087] 3) Data quality control

[0088] Perform connector removal and low-quality read filtering on FASTQ data. Identify valid interaction pairs in Hi-C data (e.g., remove self-loops, duplicates, and invalid connections), and output the number of valid interaction pairs. The distribution of this information across the genome provides a basis for subsequent window size selection.

[0089] S2: Obtain the primary assembly sequence from scratch.

[0090] 1) Assembly strategy

[0091] The preferred approach is to perform de novo assembly primarily using long reads with high accuracy to obtain a contig set; then, short reads are used for consistency evaluation and necessary error correction. Alternatively, the common approach of "long read assembly - redundancy removal - polishing" can be adopted.

[0092] 2) Redundancy removal and contamination filtration

[0093] Potential haplotype redundancy is removed (e.g., using purge-like strategies), and obvious exogenous contamination sequences are eliminated to prevent subsequent Hi-C mounting from forming pseudo-clusters.

[0094] 3) Output Indicators

[0095] At least the following outputs should be provided: total assembly length, contig N50, longest contig length, GC content, and read return rate. This output will be used as a basis for subsequent... The composition or prior reference.

[0096] S3: Generate candidate assembly solutions with at least two different numbers of chromosomes based on Hi-C interaction signals.

[0097] This step addresses the common problem in existing technologies where "preset chromosome number leads to forced convergence in clustering." By aggregating candidate solutions, it provides comparable objects for subsequent "quantization adjudication + iterative convergence." Its core lies in not fixing the Hi-C output to a single chromosome number, but instead outputting at least two sets of chromosome-level solutions with different chromosome numbers. .

[0098] 3.1 Construction and Normalization of Hi-C Interaction Matrix

[0099] 1) Comparison and sorting

[0100] Hi-C reads are aligned to the primary assembly sequence, and an interaction counting matrix is ​​constructed according to a preset bin size (e.g., 50 kb, 100 kb, or variable bin). The bin size can be determined based on the number of valid interaction pairs. Determined by assembly size: Larger bins can be used to improve boundary resolution.

[0101] 2) Normalization

[0102] To reduce the bias caused by differences in GC content, comparability, and fragment length, the interaction matrix can be normalized using ICE / VC methods to obtain a normalized interaction matrix. The goal of normalization is to make the interaction strengths of different regions comparable, thereby enabling... The boundary indicators are more stable.

[0103] 3.2 Range of candidate chromosome number The determination

[0104] This invention does not require It must not be equal to any traditional karyotype value, but rather a feasible determination method is given so that a reasonable range can be selected by a person skilled in the art without inventive effort:

[0105] Based on silhouette coefficient: Calculate silhouette coefficient from the clustering results of the interaction matrix. ,choose In the interval near the local peak, as .

[0106] Based on assembly N50: When contig N50 is low, the number of candidate entries can be appropriately increased to avoid over-merging; when N50 is high, the interval can be narrowed.

[0107] Based on efficient pair density: Define pair density ( (for assembly size), when Lower time intervals can be widened and coarser bins can be used.

[0108] In practice, the optimal settings can be configured to make It contains at least two solutions with different numbers of elements, for example and ,or and Etc., but the present invention does not limit the specific values.

[0109] 3.3 Generating a set of candidate assembly solutions

[0110] One of the following two implementation methods or a combination thereof can be used:

[0111] Method A: Generation of traversal count (explicit multiple solutions)

[0112] For each number of candidate entries Running the Hi-C mounting process yields a set of chromosome-level assembly solutions. and all Summarized as .at this time Available This indicates that the solution naturally satisfies the condition of "different numbers of lines, different solutions".

[0113] Method B: Clustering with unlimited number of clusters converges (implicit multiple solutions)

[0114] Clustering and mounting are performed without setting a target number of rows, yielding a convergent solution. Then, variant solutions are generated by perturbing the "low-interaction connectivity boundary" (e.g., changing the clustering cut threshold, changing the bin size, changing the minimum group size). Thus constitute This method still satisfies the requirement of "at least two candidate solutions with different numbers of results".

[0115] 3.4 Basic Quality Screening of Candidate Solutions (Lightweight)

[0116] To prevent obviously inferior solutions from entering the subsequent scoring and decision-making process, each candidate solution can be... First, perform a rapid screening: for example, if the intrachromosomal interaction strength is significantly higher than the interchromosomal interaction strength; or if the intrachromosomal heatmap shows a clear diagonal structure. This screening does not require subjective human judgment and can be achieved using statistical measures (such as "the proportion of interactions on the same chromosome").

[0117] S4: Whole-genome alignment and collinearity identification of multiple reference genomes

[0118] 1) Reference genome selection

[0119] Select at least two publicly available macrophage chromosome-level reference genomes. It is preferable to select references from different sources, with different assembly strategies, or different versions to improve the robustness of consistency decisions.

[0120] 2) Whole genome alignment

[0121] For each candidate solution With each Perform whole-genome sequence alignment (e.g., based on MUMmer-type global alignment or other equivalent implementations) to obtain a set of alignment anchors, and then construct a set of collinear blocks. .

[0122] 3) Collinearity correspondence representation

[0123] Collinear blocks can be represented by triples: ,in Number the candidate chromosomes. For reference chromosome numbering, Candidate coordinate range This is the reference coordinate range. This representation is for subsequent calculations. , , Provides a direct data structure.

[0124] S5: Calculate the score It outputs merge / split / keep instructions and employs multi-reference consistency rulings.

[0125] This step is one of the core innovations of this invention: transforming the experience process of "looking at collinearity graphs, looking at Hi-C heatmaps, and looking at quality assessments" into an index-based, threshold-based, and verifiable decision-making mechanism, and introducing multi-reference consistency adjudication, thereby significantly reducing single reference bias and random noise.

[0126] 5.1 Candidate Boundary The generation

[0127] Candidate Boundary It may come from one or a combination of the following sources:

[0128] Candidate solutions The boundary between two adjacent candidate chromosomes (i.e., the dividing boundary between chromosomes).

[0129] Suspicious breakpoints within candidate chromosomes: for example, a mutation in a collinear block, a weakening of the Hi-C diagonal, or the appearance of a "chessboard-like break" or other characteristic regions.

[0130] The candidate boundaries are defined at the two ends of the splicing positions where "multiple reference chromosomes correspond consecutively on the candidate chromosome".

[0131] To enable direct implementation by those skilled in the art, candidate boundaries can be represented as candidate coordinates. ,in Number the candidate chromosomes. This represents the coordinates on the chromosome.

[0132] 5.2 Collinearity Continuity Index Calculation (including) )

[0133] With a candidate chromosome For example, suppose in the reference genome There are two or more chromosomes in it. In its order of arrangement This forms a sequence of consecutive collinear segments. Let the set of coverage regions of these consecutive collinear segments on the candidate chromosome be... And sort them by coordinates from smallest to largest.

[0134] 1) Coverage ratio

[0135] Defined as the proportion of the total coverage length of consecutive collinear segments to the effective alignment length of the candidate chromosome. The effective alignment length can be taken as the total length of the candidate chromosome covered by any reference alignment. .but:

[0136] ;

[0137] in: For the first The coverage length of collinear segments on candidate chromosomes; The effective length of the candidate chromosome for alignment on the reference genome.

[0138] 2) Fracture gap

[0139] Defined as the cumulative length of discontinuous regions between adjacent collinear segments:

[0140] ;

[0141] in: For the first With the The gap length between collinear segments (if overlap occurs, it is recorded as 0).

[0142] 3) Number of collinear blocks

[0143] The number of collinear fragment blocks can be directly obtained. Alternatively, in actual implementation, short segments can be filtered for minimum length before statistics are performed to reduce the impact of noisy segments.

[0144] In terms of the judgment logic, if the condition "multiple reference chromosomes form continuous collinearity with the candidate chromosome" occurs, and high, Small, Smaller values ​​indicate greater support for the notion that "the reference has been over-split" or "the candidate is closer to physical continuity." Therefore, one of the criteria for supporting merging based on collinearity can be set as follows: and and / or .

[0145] 5.3 Hi-C Boundary Significance Indicators Calculation (including) )

[0146] To make the indicators feasible, the candidate boundary is preferred. Set left and right windows (For example, in coordinates) Take from both sides (length interval), in the Hi-C normalized interaction matrix The strength of the statistical interaction is calculated.

[0147] 1) Interaction strength within the window With cross-boundary interaction strength

[0148] : Indicates the sum of interaction strengths within the left and right windows (without crossing boundaries), for example, the sum of interactions within the left window and interactions within the right window;

[0149] : Indicates the sum of the cross-boundary interaction strength between the left and right windows.

[0150] In practice, bin-level summation can be used.

[0151] 2) Interaction strength ratio

[0152] Defined as the ratio of cross-boundary interaction to in-window interaction:

[0153] ;

[0154] in: To prevent extremely small constants with a denominator of 0. The larger the value, the stronger the cross-boundary interaction, and it is not supported as a physical split boundary.

[0155] 3) Boundary index

[0156] The boundary index is used to reflect the "degree of interaction descent at the boundary". One feasible definition is:

[0157] ;

[0158] in: Smaller means The smaller the value, the closer the cross-boundary interaction is to the in-window interaction, and the boundary is not obvious; A larger value indicates a significant decrease in cross-boundary interactions, making the boundary more distinct. This definition is consistent with... They are monotonic with each other, which facilitates implementation and explanation.

[0159] Therefore, one of the criteria for "Hi-C does not display physical split boundaries and supports merging" can be set as: and / or .

[0160] 5.4 Assembly Quality Constraints Implementation (BUSCO / LAI threshold)

[0161] This invention emphasizes that structural operations must be based on the premise of reliable overall assembly quality. A quality threshold can be set: BUSCO integrity. (For example, the proportion of complete single-copy genes reaches a certain threshold); LAI (This indicates that the assembly continuity of the repeating sequence space meets the requirements); or an equivalent quality indicator (such as QV, return rate, etc.) can be introduced as an alternative. When the quality threshold is not met, the scoring and adjudication module can directly output "keep" or reduce the priority of merging / splitting to avoid false boundaries caused by low quality.

[0162] 5.5 Comprehensive Judgment Score Output of structural operation instructions

[0163] To unify various types of evidence, this invention preferably constructs a comprehensive judgment score:

[0164] ;

[0165] in: Normalized score for collinearity evidence (which can be obtained from...) , , (obtained by linear or piecewise functions); Normalized scores for Hi-C evidence (which can be derived from...) , (Mapped) The quality constraint score is determined by assigning a higher value if the quality threshold is met, otherwise a lower value or 0 is assigned. These are the weighting coefficients.

[0166] Here is a practical implementation example:

[0167] Will Normalization ;

[0168] Will Normalization and use This means "the smaller the gap, the better";

[0169] Will Normalization and use This means "the fewer the number of blocks, the better";

[0170] make ;

[0171] make And cut off to ;

[0172] make If BUSCO and / or LAI meet the threshold, otherwise .

[0173] Then compare it with the threshold and output the operation instruction:

[0174] like Output "Merge";

[0175] like Output "Split";

[0176] Otherwise, output "Keep".

[0177] The above threshold Recommended ranges may be provided by training sets, empirical settings, or in embodiments, but the present invention does not limit specific values.

[0178] 5.6 Multi-reference Consistency Ruling

[0179] To suppress structural biases from a single reference genome, this invention requires that structural operations be performed only when a consistency determination is satisfied. Specifically, for the same boundary... If not less than All reference genomes satisfy the following:

[0180] Collinearity supports merging (e.g.) and Furthermore, Hi-C does not display physical split boundaries (e.g., ...). or If the condition is met, then the "merge" operation is performed; otherwise, even if a single reference supports the condition, "keep" can be output or the next iteration can be performed for further evaluation.

[0181] This consensus decision can be equivalently implemented as a "voting mechanism": in Of the references, the number that meet the conditions is ,when Execution is triggered at a certain time.

[0182] S6: Iteratively update candidate solutions until convergence, output the cardinality and evidence chain results.

[0183] This step addresses the issue that a single decision may be affected by noise. Through iterative updates and convergence criteria, the final base becomes a stable output, and a traceable chain of evidence is provided.

[0184] 6.1 Candidate Solution Update and Regeneration

[0185] After the scoring and adjudication module "merges" or "splits" certain boundary outputs and the consensus adjudication confirms this, the iterative convergence module performs structural operations:

[0186] Merging: Connect two candidate chromosomes into one according to the Hi-C sorting and orientation results, or remove the over-splitting boundary; if necessary, perform small-scale reorientation at the connection point.

[0187] Splitting: The candidate chromosome is split into two at a significant boundary within the chromosome and reordered locally to maintain the diagonal continuity of the Hi-C chromosome.

[0188] After performing the operation, the updated candidate solution is obtained. And use it as input for the next round.

[0189] 6.2 Iteration Process and Stopping Conditions

[0190] 1) The iterative process can be represented as:

[0191] When inputting the initial candidate solution set For each Execute S4 to S5 repeatedly and perform structural operations to obtain... .

[0192] 2) The convergence condition includes at least one of the following:

[0193] The number of candidate chromosomes remains unchanged in two consecutive iterations;

[0194] The overall score gain is less than the threshold. ,Right now

[0195] ;

[0196] in: This is the set of boundaries for this round of evaluation; Indicates the first Wheel boundary scoring; This is the stopping threshold.

[0197] When the convergence condition is met, stop the iteration and output the final chromosome base number.

[0198] 6.3 Output of the chain of evidence results

[0199] In addition to outputting the final count, the cardinality output module also outputs the evidence chain result for verification. The evidence chain must include at least: the boundary locations that trigger the merge / split. Or chromosome number pairs; corresponding , , ; corresponding , ; corresponding BUSCO / LAI and other quality metrics; reference genome set and number of votes that trigger execution. .

[0200] See Figure 5 The chain of evidence can be output in the form of a table or process log, and the adjudication process of "a certain boundary supports the union on both references and Hi-C has no boundary" can be illustrated in the figure.

[0201] IV. Specific Application Examples

[0202] The following is a specific application example demonstrating that, compared to conventional Hi-C mounting / single reference alignment processes, this invention can objectively correct chromosome mis-splitting, stably output chromosome base numbers, and provide a traceable chain of evidence. The thresholds and data used are feasible, engineered values, facilitating reproduction by those skilled in the art; the values ​​on different experimental platforms can be equivalently adjusted without departing from the spirit of this invention.

[0203] (a) Sample and multi-omics sequencing data acquisition (corresponding to step S1)

[0204] A healthy giant freshwater prawn was selected, and high-molecular-weight genomic DNA was extracted. The following libraries were constructed and sequenced:

[0205] Second-generation small fragment library: The inserted fragment is about 350bp. Paired-end sequencing was performed using a high-throughput sequencing platform to obtain about 422Gb of high-quality short read data.

[0206] Third-generation high-accuracy long-read library (PacBio HiFi): Sequencing was performed using the PacBio Sequel II platform, yielding approximately 120 Gb of HiFi data with read length N50 > 15 kb.

[0207] Hi-C library: Chromatin was cross-linked and cleaved using Dpn II restriction endonuclease to construct a Hi-C library and perform paired-end sequencing, obtaining approximately 350 Gb of Hi-C data.

[0208] By simultaneously acquiring short read length, HiFi long read length, and Hi-C data, multi-dimensional support is provided for subsequent operations.

[0209] (II) De novo assembly and chromosome mounting (corresponding steps S2-S3)

[0210] 1) De novo assembly (S2)

[0211] Next Denovo (v2.5) was used to perform de novo assembly primarily using HiFi data, resulting in a primary assembled sequence: approximately 3.66 Gb in size and approximately 956 kb in contig N50. Subsequently, purge_haplotigs was used to remove potential haplotype redundancy, reducing the interference of heterozygous redundant splicing on chromosome determination.

[0212] 2) Hi-C mounting (S3) - No preset number of chromosomes

[0213] The Hi-C data were subjected to quality control, alignment, and effective interaction pair screening using the HiC-Pro workflow. Based on this, 3D-DNA was used with primary assembly contigs as input for automated chromosome mounting, sorting, and orientation. Crucially, the target number of chromosomes was not pre-set during mounting (i.e., the parameter "-r59" was not limited); chromosome-level results were obtained autonomously through Hi-C interaction signals. The final chromosome-level genome assembly contained 57 chromosomes, with a total size of approximately 3.09 Gb and a scaffoldN50 of 62.19 Mb.

[0214] (iii) Assembly quality assessment (corresponding to step S5) constraint)

[0215] To ensure that subsequent chromosome number determination is based on reliable assembly, a multidimensional quality assessment of chromosome-level assembly is performed:

[0216] Sequence continuity and backtracking coverage: Second-generation data achieved an alignment rate of 98.87% and a coverage of 96.81%; third-generation data achieved an alignment rate of 99.73% and a coverage of 99.97%.

[0217] Serial Consistency (QV): Merqury evaluation shows that the consensus quality value (QV) based on short read length is 37.58, and the QV based on long read length is 46.15;

[0218] Completeness (BUSCO): On the arthropoda_odb10 dataset, the proportion of complete genes in BUSCO is 95.56% (Compleasm assessment is 96.15%). Figure 5 A visualization of the BUSCO results is provided, showing approximately 95.56% complete, 1.28% fragmented, and 3.16% missing, indicating that the assembly has high integrity in the gene space and can be used as the "gold standard" input for determining chromosome number.

[0219] (iv) Multireference collinearity analysis and Hi-C physical evidence cross-validation (corresponding steps S4 to S6)

[0220] The 57 chromosomes assembled in this example (hereinafter referred to as "self-assembled genome") were systematically aligned with three representative Macrobrachium rosenbergii chromosome-level reference genomes in public databases: GCF_040412425.1, GCA_040167855.1, and GCA_046866055.1. Collinear blocks were constructed using tools such as MUMmer and SyRI, and structural differences were analyzed.

[0221] 1) Comparison with GCF_040412425.1: Identifies over-splitting of "59 models".

[0222] like Figure 3 As shown, in the terminal region of the genome-wide collinearity scatter plot (highlighted / labeled areas in the figure), the three chromosomes labeled chr57, chr58, and chr59 in the reference genome show a continuous and complete collinear correspondence with chr57 in the self-assembled genome. Structurally, this phenomenon implies that the three chromosomes in the reference genome more closely resemble the characteristic of "continuous segments of the same chromosome being split," rather than three independent chromosomes corresponding to three unrelated sequences.

[0223] To further rule out the possibility of "pseudo-continuity caused by incorrect splicing of self-assembled CHR57", this example verifies the Hi-C interaction heatmap of self-assembled CHR57. For example... Figure 4As shown, the interaction signals within the "aligned chr57" region (marked by the arrow) are continuous and strong along the main diagonal, and no "significant physical boundary / insulation break" supporting its splitting into multiple chromosomes appears. Therefore, under the condition of satisfying both "collinearity continuity evidence + Hi-C physical continuity evidence," this invention determines that chr57, chr58, and chr59 in GCF_040412425.1 actually correspond to the same chromosome, and its 59 chromosome frames have numerical redundancy caused by the splitting.

[0224] 2) Compared with GCA_040167855.1 and GCA_046866055.1: Cross-reference consistency supports that chr57 is a single chromosome.

[0225] Further comparison revealed:

[0226] In GCA_040167855.1, the reference chromosome chr9 and the self-assembled genome chr57 are continuous and collinear;

[0227] In GCA_046866055.1, the reference chromosome chr8 and the self-assembled genome chr57 are continuously and completely collinear.

[0228] Two independent references provide a consistent, continuous correspondence for the same self-assembled CHR57, combined with Figure 3 The Hi-C physical evidence forms a chain of evidence with "multiple reference consistency and physical spectrum consistency", thereby enhancing the objectivity of the conclusion and avoiding single reference bias.

[0229] 3) Comprehensive judgment and iterative convergence output: Determine n=57 and correct 2n

[0230] Based on the combined results of the three reference alignments and verification with the Hi-C physical map, the "extra chromosome number" in the existing 59-chromosome reference frame can be attributed to the incorrect splitting of several chromosomes. After excluding this type of splitting error, the remaining 57 chromosomes of the self-assembled genome can form a clearer one-to-one correspondence with the major chromosomes in the reference genome, and each chromosome is supported by continuous diagonal signals of Hi-C interactions. Therefore, the final output of this example is: the basic number of haploid chromosomes in this Macrobrachium rosenbergii germplasm resource is n=57, and the corresponding number of diploids is 2n=114.

[0231] (vi) Technical effects demonstrated by this application example

[0232] Through the above application examples and in combination Figures 3-5 It can be seen that the present invention achieves at least the following verifiable technical effects:

[0233] To avoid prior number bias: the number of target rows is not limited during the Hi-C mounting stage, allowing chromosome-level results to converge naturally from interaction signals, thus reducing the systematic risk of "forced clustering / splitting based on 59 rows";

[0234] Multidimensional evidence cross-validation: By utilizing "multi-reference collinearity continuous correspondence, Hi-C physical continuity, and high-quality assembly threshold" to form a closed-loop evidence chain, it is possible to objectively identify and correct over-splitting of the reference genome;

[0235] The conclusions are more stable and verifiable: the output n=57 (2n=114) not only agrees with the high integrity BUSCO assessment, but also explains the fluctuations and controversies in traditional microscopic counting, providing a more reliable chromosome coordinate basis for subsequent genome annotation, comparative genomics and molecular breeding.

[0236] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novelty disclosed herein.

Claims

1. A method for determining the basic chromosome number of *Macrobrachium rosenbergii* based on multi-omics joint analysis, characterized in that, The method includes the following steps: S1, Obtain de novo assembly sequencing data and Hi-C sequencing data of the target individual; Assemble the de novo assembly sequencing data to obtain the primary assembled sequence; S2, based on the Hi-C interaction signal, the primary assembly sequence is clustered, sorted, oriented, and mounted to generate at least two chromosome-level candidate assembly solution sets with different numbers of chromosomes. ; S3, select at least two publicly available macrobrachium rosenbergii chromosome-level reference genomes, and deassemble each candidate assembly. Whole-genome alignment was performed with each reference genome to obtain collinearity correspondences; S4, for each candidate assembly solution set Candidate chromosome boundaries are determined based on the collinearity continuity index. Hi-C boundary significance index and assembly quality constraints Calculate the comprehensive judgment score The system outputs structural operation instructions for merging, splitting, or maintaining the structure accordingly. Merging or splitting operations are performed only when the criteria of "continuous collinearity supports merging" and "Hi-C does not show physical splitting boundaries" are met in the consistency analysis of at least two reference genome pairs at the same boundary. S5. For the assembly result after performing structural operations, repeat the above comparison and judgment process until the convergence condition is met, and output the chromosome base number and evidence chain result. The evidence chain result includes at least the boundary position that triggers merging or splitting and the corresponding index value. The collinearity continuity index mentioned in step S4 At least include coverage ratio With fracture gap ,in: It represents the proportion of the total length of the candidate chromosomes to the effective alignment length of the candidate chromosomes, in which two or more reference chromosomes in the reference genome can be found consecutively along their arrangement order. This represents the cumulative length of the discontinuous region between the aforementioned consecutive collinear segments; and with and As one of the criteria for "continuous collinearity supports merging"; The Hi-C boundary significance index At least include boundary indices and / or interaction strength ratio ,in: This indicates the degree of decrease in the Hi-C cis-interaction signal at the boundary within a preset window on both sides of the candidate boundary position. The smaller the value, the less distinct the boundary; This represents the ratio of "cross-boundary interaction strength" to "interaction strength within the window" on both sides of the candidate boundary, or its equivalent ratio. and with and / or This is one of the criteria for determining whether "Hi-C does not display physical split boundaries and supports merging".

2. The method according to claim 1, characterized in that, The de novo assembly sequencing data mentioned in step S1 includes second-generation short-read sequencing data and third-generation long-read sequencing data, wherein the third-generation long-read sequencing data is high-accuracy long-read data.

3. The method according to claim 1, characterized in that, The candidate assembly solution set mentioned in step S2 By analyzing the range of candidate chromosome numbers The candidate assembly solution set is generated by traversing the cluster or by convergence through Hi-C clustering without limiting the number of target items, and the solution set is... A solution must contain at least two different numbers of chromosomes. The range of candidate chromosome numbers satisfies: and The candidate assembly solution set is determined based on the silhouette coefficient, assembly N50, or effective interaction pair density of Hi-C interaction clustering. Candidate assembly solutions containing at least two different numbers of chromosomes.

4. The method according to claim 1, characterized in that, In step S4 The assembly quality constraint index It should include at least BUSCO integrity and / or LAI continuity, and set a quality threshold such that only BUSCO... and / or LAI It allows performing structural operations such as merging or splitting.

5. The method according to claim 1, characterized in that, The collinearity continuity index also includes the number of collinear blocks. ,in Indicates the number of collinear fragments formed between the candidate chromosome and the reference genome, and is expressed in terms of... As one of the criteria for "low degree of collinearity partitioning, supporting merging".

6. The method according to claim 1, characterized in that, In step S4, With preset threshold , Compare and output the boundary conditions. Structural manipulation instructions: merge, split, or preserve; in at least two reference genomes The structural operation instruction is executed when the consistency decision condition is met, wherein the consistency decision condition includes at least: for the same boundary In no less than In all reference genomes, "continuous collinearity supports merging" and the corresponding regions... It satisfies the condition of having no significant split boundary.

7. The method according to claim 1, characterized in that, In step S5, the updated candidate assembly solution set is regenerated based on the results of the structural operation. Repeat steps S3-S4 until the convergence condition is met. The convergence condition includes at least the number of candidate chromosomes no longer changing or the overall score gain being less than 1. , The stopping threshold, This is the updated set of candidate assembly solutions.

8. A system for determining the basic chromosome number of *Macrobrachium rosenbergii* based on multi-omics joint analysis, characterized in that, The system is used to implement the method according to any one of claims 1-7, comprising: The data acquisition module is used to acquire sequencing data and Hi-C sequencing data for de novo assembly; The de novo assembly module is used to generate primary assembly sequences; The candidate assembly generation module is used to generate a set of candidate assembly solutions based on Hi-C interaction signals. ; The collinearity analysis module is used to analyze the candidate assembly solution set. Perform genome-wide collinearity analysis with at least two reference genomes; The scoring and adjudication module is used to calculate candidate boundaries. It outputs structural operation instructions for merging, splitting, or maintaining, and executes the structural operation instructions when the consistency adjudication conditions are met; The iterative convergence module is used to repeat collinearity analysis and scoring decisions until the convergence condition is met. The cardinality output module is used to output chromosome cardinality and evidence chain results.

9. An electronic device comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

10. 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 described in any one of claims 1 to 7.

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