Gene assisted assembly method using hi-c technology, chromosome level genome and application

Abstract

The application relates to a gene-assisted assembly method using a Hi-C technology, a chromosome-level genome and application. The assembly method comprises the following steps: performing Hi-C library construction and sequencing on a sample to obtain a first data set; performing comparison and selection on the first data set and a reference genome data set to obtain a second data set; performing preliminary assembly on the second data set to obtain a preliminary assembly data set; and performing processing and screening on the preliminary assembly data set. According to the method, the mounting rate of Hi-C assisted assembly can be increased to more than 95%.

Description

Technical Field

[0001] This invention relates to a gene-assisted assembly method using Hi-C technology, the assembled chromosome-level genome, and its applications. Background Technology

[0002] The construction of chromosome-level reference genomes is fundamental to research on chromosome interactions, whole-genome resequencing, chromosome evolution and differentiation, and epigenomics. It can be applied to multiple fields and is therefore essential.

[0003] Hi-C technology (High-throughput / resolution Chromosome Conformation Capture) originates from Chromosome Conformation Capture-3C technology. It uses the entire cell nucleus as the research object, employing high-throughput sequencing technology combined with bioinformatics methods to study the interactions of chromosomal DNA across the entire genome. Hi-C-assisted assembly captures these interactions, and based on the principle that intrachromosomal interactions are significantly more frequent than interchromosomal interactions, and that interaction frequencies on the same chromosome decrease with increasing interaction distance, scaffolds or contigs are clustered into groups. Further, contigs / scaffolds within these groups are sorted and oriented to achieve genome mounting approaching the chromosome level.

[0004] In recent years, the proportion of high-quality plant and animal genomes published at the chromosome level as reference genomes has been increasing year by year, and Hi-C has gradually shown its advantages in chromosome mounting applications.

[0005] The current general steps for Hi-C assisted assembly are as follows: 1. Data statistics and filtering; 2. Hi-C library evaluation; 3. Correction; 4. Hi-C assisted assembly; 5. Post-assembly evaluation. However, the current Hi-C assisted assembly has a lower mounting rate and requires a more mature process to support it. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a gene-assisted assembly method utilizing Hi-C technology. The method of this invention can increase the loading rate of Hi-C-assisted assembly to over 95%, and the method is simpler and the process is more mature.

[0007] Specifically, the present invention relates to the following method.

[0008] 1. A method for gene-assisted assembly using Hi-C technology, comprising:

[0009] The samples were subjected to Hi-C library construction and sequencing to obtain the first dataset;

[0010] The first dataset is compared with and selected from the reference genome dataset to obtain the second dataset;

[0011] The second dataset is initially assembled to obtain a preliminary assembled dataset;

[0012] The preliminary assembled dataset is processed and filtered;

[0013] The method for comparing and selecting the first dataset with the reference genome dataset includes:

[0014] Each data point in the first dataset is first aligned with the reference genome dataset to obtain a first subset that can be aligned to the reference genome dataset and a second subset that cannot be aligned to the reference genome dataset.

[0015] After digesting each data in the second subset, the link sites of the library are searched. The data in the second subset are broken up from the link sites of the digested library. The broken data are then compared with the reference genome dataset again to obtain the third subset that can be compared with the reference genome dataset.

[0016] The first subset and the third subset are merged and selected to obtain the second data.

[0017] 2. According to the method described above, the data includes Reads, and / or the dataset includes multiple Reads.

[0018] 3. According to the above method, the selection criteria include: selecting data whose two ends are both aligned to a unique position in the reference genome dataset.

[0019] 4. According to the above method, the preliminary assembly method includes: performing preliminary assembly of the second dataset based on the fact that the interaction frequency on the same chromosome in the cell nucleus is higher than the interaction frequency on different chromosomes, to obtain a preliminary assembled Contig / Scaffold;

[0020] Preferably, the initial assembly is based on LACHESIS.

[0021] 5. According to the above method, the method for processing and filtering the initially assembled dataset includes:

[0022] The initial assembly data was allocated to chromosome groups;

[0023] The preliminary assembly data assigned to each chromosome group are sorted, oriented, and filtered.

[0024] Preferably, the processing and filtering of the preliminary assembled dataset is based on LACHESIS;

[0025] Preferably, the allocation method includes a clustering method;

[0026] Preferably, the sorting method includes: constructing an acyclic spanning tree for the contigs / scaffolds within a chromosome group based on their interaction relationships, and selecting the root tree with the highest confidence from it;

[0027] Preferably, the orientation method includes: traversing the possible directions of Contig / Scaffold through a weighted directed acyclic graph; for the order judgment of each Contig / Scaffold, constructing a scoring function based on the difference between positive and negative interaction relationships and using it as the basis for confidence determination; and selecting the orientation with the highest confidence.

[0028] Preferably, the screening parameters are selected from one or more of the following: number of clusters, proportion of Contig / Scaffold used during orientation, proportion of Contig / Scaffold in the root tree, and ratio of the shortest chromosome length to the longest chromosome length.

[0029] 6. According to the above method, when the optimal preliminary assembly data cannot be used as the assembly data output, the conditions for preliminary assembly are changed again to obtain multiple preliminary assembly data in the preliminary assembly of the second data, and the preliminary assembly data is reprocessed and filtered to select the optimal preliminary assembly data as the assembly data.

[0030] 7. According to the above method, the conditions are selected from one or more of the following: the minimum number of restriction enzyme sites in the Contig / Scaffold sequence in cluster analysis, the maximum Link depth in the Contig / Scaffold sequence in cluster analysis, the ratio of the number of interactions between the Contig / Scaffold sequence and the target cluster to the number of interactions between other clusters in cluster back-interpolation, and the minimum number of restriction enzyme sites in the Contig / Scaffold sequence within the root tree in ordination analysis.

[0031] 8. According to the method described above, the reference genome dataset is selected from a reference genome draft based on second-generation sequencing or a reference genome draft based on third-generation sequencing.

[0032] 9. The chromosome-level genome assembled using the gene-assisted assembly method described above with Hi-C technology.

[0033] 10. The above-described gene-assisted assembly method using Hi-C technology and the application of the assembled chromosome-level genome in chromosome interactions, whole-genome resequencing, chromosome evolution and differentiation, and epigenomics. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions involved in the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the specific embodiments described 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 skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] To facilitate understanding of this plan, some technical terms appearing in this document are explained.

[0037] Terminology Explanation:

[0038] Gene interaction: Gene interaction refers to the phenomenon where non-allelic genes influence the expression of the same trait through mutual interaction. Broadly speaking, gene interaction can be divided into intragene interaction and intergene interaction. Intragene interaction refers to the dominant-recessive effect between alleles. Intergene interaction refers to the interaction between non-allelic genes at different loci, manifested as complementarity, repression, epistasis, etc.

[0039] Positive interaction: When the directions of the interacting regions are consistent, it is a positive interaction.

[0040] Reverse interaction: When the directions of the interacting regions are opposite, it is a reverse interaction.

[0041] Hi-C technology: a chromatin conformation capture technology based on 3C technology and utilizing high-throughput sequencing. Hi-C technology is a novel approach that combines chromatin conformation capture with high-throughput sequencing. This technology cross-links and enriches DNA fragments with large linear distances and similar spatial structures, followed by pair-end sequencing. Analysis of the sequencing data reveals the interactions between different DNA segments in chromatin, thereby deriving the three-dimensional spatial structure of the genome and possible regulatory relationships between genes.

[0042] With the continuous development of NGS, obtaining a draft genome of a species through sequencing is now relatively easy using Hi-C gene-assisted assembly technology. However, obtaining a complete genome from this draft is not simply a matter of increasing sequencing throughput. To determine the chromosomes corresponding to each contig / scaffold in the draft and their respective sequences on the genome, the classic solution is to construct long-fragment mate pair libraries to determine the connection order of contigs / scaffolds, thus linking them together and extending the assembly to the chromosome level. Due to numerous limitations of NGS, such as unavoidable issues like GC content, sequencing read length, and mapping accuracy, assembling genomes with high repetitive sequences and high heterozygosity using NGS sequencing data to the chromosome level is extremely difficult, especially for the genome assembly of large plants and animals. Third-generation sequencing, with its long read advantage, occupies an important position in the field of genome assembly and has overcome many genome assembly challenges for various species; however, its high cost also restricts its widespread application.

[0043] Chromosome mounting rate: The proportion of contigs / scaffolds assembled into chromosomes to the total number of contigs / scaffolds in the sketch.

[0044] Reference genome draft: A genome sequence map with over 90% sequenced and sequencing accuracy at 1%. The reference genome draft contains gaps. The completed genome map performs gap closure on the entire genome to obtain the completed genome map. The completed map is assembled from the draft map using techniques such as gap filling to create sequences that meet evaluation parameters such as N50.

[0045] Raw Reads: The raw image data obtained from sequencing is converted into sequence data through base calling, which is called Raw Data or Raw Reads. The results are stored in fastq file format. The fastq file is the most raw file obtained by the user, which stores the sequence of the reads and the sequencing quality of the reads.

[0046] Clean Reads: In this invention, the high-quality data obtained after filtering the original offline data is called Clean Reads.

[0047] Heatmap: An image showing the interaction between different regions, with the horizontal and vertical axes representing the region names.

[0048] Contig / Scaffold: The genome at the level of Contig and / or Scaffold.

[0049] The software can determine the first subset that can be aligned to the reference genome dataset and the second subset that cannot: Using the software's default parameters and standards, and employing a penalty scoring method, the software can provide both the mapped first subset and the unmapped second subset. For example, using Bowtie2 software, Read1 and Read2 are aligned one-end to the reference genome, respectively, yielding BAM format alignment result files. Those skilled in the art can then obtain the first subset that can be aligned to the reference genome dataset and the second subset that cannot.

[0050] Linkage sites of the library after enzyme digestion: In the construction of Hi-C libraries, the typical procedure includes formaldehyde cross-linking, restriction endonuclease digestion, biotin labeling, DNA ligation, streptavidin enrichment, biotin fishing, final repair, 3' A addition, adapter addition, PCR amplification, and sequencing. In this invention, the linking sites of the library after enzyme digestion are the DNA ligation sites after biotin labeling and before streptavidin enrichment.

[0051] This invention is based on the fact that chromosomes have independent regions in the cell nucleus, that intrachromosomal interactions are stronger than interchromosomal interactions, and that interactions between closely spaced chromosomes are stronger than interactions between distant chromosomes. It utilizes optimized Hi-C technology to link genome sequences into a chromosome-level genome.

[0052] The first aspect of this invention provides a method for gene-assisted assembly using Hi-C technology, comprising:

[0053] The DNA samples were used to construct Hi-C libraries and sequence them to obtain the first dataset;

[0054] The first dataset is compared with and selected from the reference genome dataset to obtain the second dataset;

[0055] The second dataset is initially assembled to obtain a preliminary assembled dataset;

[0056] The preliminary assembled dataset is processed and filtered;

[0057] The method for comparing and selecting the first dataset with the reference genome dataset includes:

[0058] Each data point in the first dataset is first aligned with the reference genome dataset to obtain a first subset that can be aligned to the reference genome dataset and a second subset that cannot be aligned to the reference genome dataset.

[0059] After digesting each data in the second subset, the link sites of the library are searched. The data in the second subset are broken up from the link sites of the digested library. The broken data are then compared with the reference genome dataset again to obtain the third subset that can be compared with the reference genome dataset.

[0060] The first subset and the third subset are merged and selected to obtain the second data.

[0061] In this invention, the sample can be a DNA sample.

[0062] According to the method of the present invention, preferably, the data includes Read.

[0063] According to the method of the present invention, preferably, the dataset includes multiple reads. It also includes multiple pairs of reads. A pair of reads includes Read1 and Read2. Read1 and Read2 obtained from paired-end sequencing data are two sequences with opposite directions in the insert sizes of two complementary strands. Before alignment to the single-stranded reference genome, one of the reads is escaped and then the alignment is performed.

[0064] According to the method of the present invention, preferably, the selection criteria include: selecting data whose ends are both aligned to a unique position in the reference genome dataset.

[0065] According to the method of the present invention, preferably, the first alignment is a single-end alignment or a two-end alignment; more preferably, it is a single-end alignment. In the present invention, the result file of the single-end alignment can be stored separately, making file processing more convenient.

[0066] According to the method of the present invention, preferably, the second alignment is a single-end alignment or a two-end alignment; more preferably, it is a single-end alignment. In the present invention, the result file of the single-end alignment can be stored separately, making file processing more convenient.

[0067] According to the method of the present invention, preferably, the preliminary assembly method includes: performing preliminary assembly of the second dataset based on the fact that the interaction frequency on the same chromosome in the cell nucleus is higher than the interaction frequency on different chromosomes, to obtain a preliminary assembled Contig and / or Scaffold.

[0068] According to the method of the present invention, preferably, the preliminary assembly is based on LACHESIS.

[0069] According to the method of the present invention, preferably, the method for processing and filtering the preliminary assembled dataset includes:

[0070] The initial assembly data was allocated to chromosome groups;

[0071] The preliminary assembly data assigned to each chromosome group are sorted, oriented, and filtered.

[0072] According to the method of the present invention, preferably, the processing and filtering of the preliminary assembled dataset is based on LACHESIS.

[0073] According to the method of the present invention, preferably, the allocation method includes a clustering method.

[0074] According to the method of the present invention, preferably, the number of chromosome groups is 1-100, more preferably 2-70. The number of chromosome groups can be set according to prior values; for example, the number of chromosome groups in fruit flies can be 8, and the number of chromosome groups in some aquatic plants can be 60, etc. The number of chromosome groups can be consistent with the number measured in the species laboratory.

[0075] According to the method of the present invention, preferably, the sorting method includes: sorting the preliminary assembly data assigned to each chromosome group based on the strength of the interaction relationships of the preliminary assembly data; more preferably, constructing an acyclic spanning tree for the contigs / scaffolds within a chromosome group based on their interaction relationships, and selecting the root tree with the highest confidence from it. Even more preferably, constructing an acyclic spanning tree for the contigs / scaffolds within a chromosome group based on their interaction relationships, selecting the root tree with the highest confidence from it, and inserting the remaining fragmented contigs / scaffolds into appropriate positions at the root according to their association relationships, ultimately constructing the accurate contig / scaffold order of the internal groups of a chromosome. Some extremely short contigs / scaffolds are discarded. This operation can be performed based on LACHESIS.

[0076] According to the method of the present invention, preferably, the orientation method includes: traversing the possible directions of Contig / Scaffold through a weighted directed acyclic graph; for the order determination of each Contig / Scaffold, constructing a scoring function based on the difference between positive and negative interaction relationships and using it as the basis for confidence determination; and selecting the orientation with the highest confidence.

[0077] According to the method of the present invention, preferably, the screening conditions are selected from one or more of the following: the number of clusters, the proportion of Contig / Scaffold used during orientation, the proportion of Contig / Scaffold in the root tree, and the ratio of the shortest chromosome length to the longest chromosome length.

[0078] According to the method of the present invention, preferably, when the optimal preliminary assembly data selected cannot be output as assembly data, the conditions for preliminary assembly are changed again to obtain multiple preliminary assembly data in the preliminary assembly of the second data, and the preliminary assembly data is reprocessed and selected to select the optimal preliminary assembly data as assembly data.

[0079] According to the method of the present invention, preferably, the conditions are selected from the minimum number of restriction enzyme sites in the Contig / Scaffold sequence in cluster analysis, the maximum link depth in the Contig / Scaffold sequence in cluster analysis, the ratio of the number of interactions between the Contig / Scaffold sequence and the target cluster to the number of interactions between other clusters in cluster back-interpolation, and the minimum number of restriction enzyme sites in the Contig / Scaffold sequence within the root tree in ordination analysis.

[0080] According to the method of the present invention, preferably, the method for constructing a Hi-C library and sequencing a DNA sample to obtain first data includes:

[0081] DNA samples were cross-linked and cells were lysed to obtain lysed cells;

[0082] The lysed cells were subjected to chromatin digestion using restriction endonucleases;

[0083] The digested samples were biotin-labeled, blunt-end ligated, and DNA purified and extracted to prepare Hi-C samples;

[0084] Library construction was performed on the Hi-C samples to obtain the library products;

[0085] The library products were sequenced to obtain the raw data set.

[0086] According to the method of the present invention, preferably, the DNA is purified and extracted to prepare Hi-C samples, and Hi-C sample quality control is performed. Then, the quality-controlled Hi-C samples are used to construct a library to obtain the library product. More preferably, the prepared Hi-C samples are subjected to gel electrophoresis, and the substrate fragment length is selected based on the gel electrophoresis results to select a suitable Hi-C sample as the quality-controlled Hi-C sample.

[0087] According to the method of the present invention, preferably, the method further includes filtering the original dataset.

[0088] According to the method of the present invention, preferably, the filtering method includes one or more of the following methods:

[0089] (1) Remove data with bases greater than 5bp that are contaminated with connectors from the original offline dataset;

[0090] (2) Remove data from the original dataset where bases with a quality value Q ≤ 19 account for more than 50% of the total bases; and

[0091] (3) Remove data from the original data that contains more than 5% N bases.

[0092] According to the method of the present invention, preferably, the reference genome dataset is selected from a reference genome draft based on second-generation sequencing or a reference genome draft based on third-generation sequencing.

[0093] In the gene-assisted assembly process using Hi-C technology in this invention, there is no need to correct errors in the reference genome dataset.

[0094] In the gene-assisted assembly process using Hi-C technology in this invention, it is not necessary to cut the Contig / Scaffold level genome into the bin level genome.

[0095] According to a specific embodiment of the present invention, a method for gene-assisted assembly using Hi-C technology includes, but is not limited to:

[0096] The first step is to construct a Hi-C library and sequence the DNA sample to obtain initial data. Specifically,

[0097] First, the experimental DNA samples are cross-linked. After cross-linking, cell lysis is performed, and samples are taken for extraction and quality testing. Once the quality is deemed satisfactory, the samples proceed to the "Hi-C fragment" preparation process.

[0098] In the preparation process of the "Hi-C fragment", restriction endonucleases, such as HindIII / MboI (the "cleavage site" and "cleavage fragment" mentioned later refer to this enzyme), are used to digest chromatin and samples are taken to detect the cleavage effect.

[0099] The enzyme-digested samples were biotin-labeled, blunt-end ligated, and DNA purified and extracted to prepare Hi-C samples, which were then sampled for DNA quality testing.

[0100] After passing the test, the sample proceeds to the standard library construction process, which includes the following steps: the biotin-labeled ends of the Hi-C sample fragments that have passed the quality test are removed, the fragments are sonicated and repaired, then base A is added, the biotin-containing fragments are extracted, and sequencing adapters are added to form adapter-added products; then PCR conditions are screened and amplified to obtain the library products.

[0101] An ideal library consists of DNA fragments from two different restriction enzymes joined together. The fragment ligation site forms a new restriction enzyme cleavage site (e.g., if HindIII is used in the chromatin digestion step described above, the new cleavage site formed after blunt-end ligation is recognized and cleaved by Nhel). Samples of the library amplification products are subjected to "Hi-C fragment ligation site quality control testing." Once the test is passed, the entire library preparation is complete.

[0102] In obtaining a quality-controlled Hi-C library, before amplification and sequencing, substrate fragments of appropriate length (50–1000 bp) need to be selected using electrophoresis gel mapping. By aligning reads to the draft, the theoretical length distribution of the Hi-C library insert fragments is evaluated to determine if it conforms to the predetermined length range. This is a typical and intuitive quality control method for verifying library quality in the information analysis stage. Generally, an insert fragment length distribution conforming to 50–1000 bp indicates consistency with the substrate selection range in the experimental stage; furthermore, the more paired-end reads greater than 100 bp in the insert fragment length distribution, the better the library.

[0103] After the constructed library passed quality control, it was sequenced using the Illumina platform with the PE150 sequencing strategy.

[0104] Sequencing results include some raw reads, which contain low-quality sequences, adapter-contaminated sequences, sequences with more than 5% N bases, and clean reads. A higher proportion of clean reads indicates better data quality. To ensure the quality of subsequent information analysis, the raw sequences are filtered to obtain high-quality clean reads before further analysis. The data processing steps are as follows:

[0105] (1) Remove adapter-contaminated reads, such as reads with more than 5 bp of adapter-contaminated bases. For paired-end sequencing, if one end is contaminated by the adapter, remove the reads from both ends.

[0106] (2) Remove low-quality reads, such as reads with a quality value of Q≤19 accounting for more than 50% of the total bases. For paired-end sequencing, if one end is a low-quality read, both ends of the reads will be removed.

[0107] (3) Remove reads containing more than 5% N bases. For example, for paired-end sequencing, if one end contains more than 5% N bases, the reads at both ends will be removed.

[0108] The high-quality Clean Reads obtained after the above data processing steps are what this invention refers to as the first data.

[0109] The second step involves comparing and selecting from the first dataset and the reference genome dataset to obtain the second dataset. Specifically:

[0110] In this step, the first data, after filtering out the reads that are prone to causing analytical errors, is used as the first reference data of the filtered genome draft as the reference sequence. Bowtie2 software is called for alignment. Specifically, two reads in the first data, such as Read1 and Read2, are aligned one-to-one with the reference genome dataset to obtain a BAM format alignment result file. This yields a first subset that can be aligned to the reference genome dataset and a second subset that cannot. The unaligned reads in the second subset (also known as Unmap Reads) are then examined for the linking sites of the digested library. The portion after the linking sites of the digested library is truncated (cut off) and aligned one-to-one with the reference genome dataset again to obtain a third subset that can be aligned to the reference genome dataset. The first subset and the third subset are merged, and pairs of reads with both ends aligned to unique positions in the reference genome dataset are selected as the second data in this invention. This second data is used for subsequent analysis.

[0111] Based on the experimental principles of Hi-C library construction, different molecular types are generated during the Hi-C library construction process. According to the alignment position and orientation of reads in the genome, they can be specifically classified as self-circles, dangling ends, dumped pairs, and valid pairs. The data used for subsequent analysis are valid paired-end reads (i.e., valid pairs), meaning a pair of reads aligned to two different restriction enzyme fragments, and the actual fragment size matches the theoretical fragment size. In this invention, such valid data is considered the second data set. All other data types are considered invalid paired reads and are excluded in this step.

[0112] The third step is to perform preliminary assembly of the second data to obtain preliminary assembled data.

[0113] In this step, parameter traversal is used to perform assisted assembly. Since the interaction frequency on the same chromosome within the cell nucleus is higher than the interaction frequency between different chromosomes, a clustering algorithm can be used to assign the initially assembled contigs / scaffolds to various chromosome groups. This initial assembly process is performed using LACHESIS.

[0114] The fourth step involves processing and filtering the preliminary assembled dataset. Specifically, the preliminary assembled data is allocated to chromosome groups; and the preliminary assembled data allocated to each chromosome group is sorted, oriented, and filtered. This step is performed based on LACHESIS.

[0115] In this step, the initially assembled data (i.e., Contig / Scaffold) is clustered. Based on the alignment results and the principle that the interaction frequency on the same chromosome within the cell nucleus is higher than the interaction frequency on different chromosomes, the initially assembled Contig / Scaffold is assigned to each chromosome group using agglomerative hierarchical clustering (a bottom-up hierarchical clustering algorithm) with LACHESIS software.

[0116] Chromosome clusters refer to the clustering results obtained by the above algorithm, with each cluster considered as a chromosome cluster.

[0117] The fifth step is to sort and filter the preliminary assembly data assigned to each chromosome group.

[0118] In this step, the order of Contigs / Scaffolds is determined. The initial assembly data (Contigs / Scaffolds) assigned to a chromosome group in the previous step are used to construct the longest acyclic spanning tree based on interactions. The "trunk" (root tree) with the highest confidence is selected from this tree. The remaining fragmented Contigs / Scaffolds are then inserted into appropriate positions in the root tree according to their associations. This process ultimately constructs the accurate Contig / Scaffold order for the internal groups of a chromosome. Some extremely short Contigs / Scaffolds are discarded by the LACHESIS software.

[0119] The sixth step is to determine the orientation of the sorted and filtered preliminary assembled data.

[0120] In this step, within the ordered Contig / Scaffold group of each chromosome, all possible directions of the Contig / Scaffold are traversed using a weighted directed acyclic graph (WDAG). For the order determination of each Contig / Scaffold, a scoring function is constructed based on the difference between forward and reverse interactions to describe the confidence level when determining a direction. This is then determined using LACHESIS software, thereby achieving Contig / Scaffold orientation.

[0121] The seventh step is to evaluate the preliminary assembly data with the determined direction and select the preliminary assembly data with the best results as the final assembly data.

[0122] In this step, the best assembly results are selected. Based on the assembly results obtained by running LACHESIS under different parameters, different weights are assigned to score the results according to the consistency of the number of chromosomes in the cluster with the expected result, the genome mounting rate, the trunk value, and the consistency of chromosome length. The assembly results with higher scores are selected as the best. Preferably, the selection parameters are selected from one or more of the following: the number of clusters, the proportion of contigs / scaffolds used during orientation, the proportion of contigs / scaffolds in the root tree, and the ratio of the shortest chromosome length to the longest chromosome length.

[0123] More preferably, if the optimal preliminary assembly data cannot be used as the output assembly data, the conditions for preliminary assembly are changed again to obtain multiple preliminary assembly data in the preliminary assembly of the second data, and the preliminary assembly data is reprocessed and screened to select the optimal preliminary assembly data as the assembly data. More preferably, the conditions are selected from the minimum number of restriction enzyme sites in the Contig / Scaffold sequence in the cluster analysis, the maximum Link depth in the Contig / Scaffold sequence in the cluster analysis, the ratio of the number of interactions between the Contig / Scaffold sequence and the target cluster to the number of interactions between other clusters in the cluster back-interpolation, and the minimum number of restriction enzyme sites in the Contig / Scaffold sequence in the root tree in the sorting analysis.

[0124] The above methods can be used to assemble a genome at the chromosome level.

[0125] In this invention, verification can be performed by constructing a heatmap based on assembled chromosome data, specifically:

[0126] Heatmap verification of assembled chromosomes is a standard for checking the accuracy of assisted assembly. The method for constructing heatmaps is as follows: For chromosomes used in assisted assembly, they are cut into equal-length bins (common bin sizes include 1 MB and 500 kb). The effective alignment reads between each pair of bins are used as the strength signal of the interaction between them to construct a heatmap.

[0127] Based on the two fundamental principles of Hi-C assisted assembly, two basic rules exist in heatmap verification: (i) the interaction strength between two bins within a chromosome is stronger than the interaction strength between two bins between chromosomes; (ii) on the same chromosome, the interaction strength between two bins that are close to each other is stronger than the interaction strength between two bins that are far apart.

[0128] Ideally, the global heatmap or the internal heatmap of a chromosome shows strong interactions along the diagonal, with the interaction strength gradually weakening outwards from the diagonal. In addition, in some species, due to the presence of AB Compartment, the heatmap may also show a grid-like pattern.

[0129] A second aspect of the present invention provides a chromosome-level genome assembled according to the gene-assisted assembly method using Hi-C technology described above.

[0130] The third aspect of this invention provides the above-described method for gene-assisted assembly using Hi-C technology, and the application of the assembled chromosome-level genome in chromosome interactions, whole-genome resequencing, chromosome evolution and differentiation, and epigenomics.

[0131] The beneficial effects of this invention are:

[0132] (1) The method of the present invention can increase the mounting rate of Hi-C assisted assembly to over 95%.

[0133] (2) This invention does not require the use of population data; during assembly, there is no need to cut the genome at the contig / scaffold level into the genome at the bin level, which avoids the loss of some bin information and the resulting incomplete information; this invention also does not require error correction, which avoids the error correction error caused by changing the original draft sequence; moreover, the method of this invention is both time-saving and labor-saving compared to genetic maps.

[0134] (3) The method of comparing and selecting the first dataset with the reference genome dataset of the present invention can obtain more complete and accurate data and improve the mounting rate of Hi-C assisted assembly.

[0135] (4) The present invention processes and filters the preliminary assembled dataset, especially under specific filtering conditions in preferred cases, which can find the optimal result and ensure the reliability of the assembly result.

[0136] (5) The present invention uses a clustering algorithm to assign the initially assembled Contig / Scaffold to each chromosome group. Within each chromosome cluster, the Contig / Scaffold is sorted and the Contig / Scaffold direction is determined. In particular, based on the assembly results under different parameters, screening conditions are set to select the better assembly results and ensure the reliability of the assembly results.

[0137] (6) When the optimal preliminary assembly data selected by the present invention cannot be output as assembly data, the conditions for preliminary assembly will be changed again in the preliminary assembly data obtained by preliminary assembly of the second data to obtain multiple preliminary assembly data again. In addition, through specific screening condition parameters, the mounting rate and reliability of Hi-C assisted assembly can be significantly improved. Attached Figure Description

[0138] Figure 1 This is a schematic diagram of the gene-assisted assembly method using Hi-C technology according to Embodiment 1 of the present invention.

[0139] Figure 2 This is a frequency distribution histogram of the inserted segment length in Embodiment 1 of the present invention.

[0140] Figure 3 This is a heatmap of the global Hi-C interaction of the genome in Example 1 of the present invention.

[0141] Figure 4This is a heatmap of the Hi-C interaction of a single chromosome in Example 1 of the present invention.

[0142] The present invention will now be described with reference to specific embodiments. It should be noted that these embodiments are merely illustrative and should not be construed as limiting the present invention.

[0143] Example 1

[0144] A gene-assisted assembly method using Hi-C technology, illustrated in the diagram below. Figure 1 As shown.

[0145] 1. Construct and sequence a Hi-C library from the DNA sample to obtain initial data.

[0146] First, the DNA sample is cross-linked. After cross-linking, cells are lysed, and samples are taken for extraction and quality testing. Once the quality is satisfactory, the sample proceeds to the "Hi-C fragment" preparation process.

[0147] In the preparation process of the "Hi-C fragment", the restriction endonuclease HindIII / MboI (hereinafter referred to as "restriction site" and "restriction fragment" both refer to this enzyme) is used to digest chromatin, and samples are taken to detect the enzyme digestion effect.

[0148] The enzyme-digested samples were biotin-labeled, blunt-end ligated, and DNA purified and extracted to prepare Hi-C samples, which were then sampled for DNA quality testing.

[0149] After passing the test, the sample proceeds to the standard library construction process, which consists of the following steps: the biotin-labeled ends of the Hi-C sample fragments that have passed the quality test are removed, the fragments are sonicated and repaired, then base A is added, the biotin-containing fragments are extracted, and sequencing adapters are added to form adapter-added products; then PCR conditions are screened and amplified to obtain the library products.

[0150] Some raw sequences obtained from sequencing may contain sequencing adapter sequences and low-quality sequences. In order to ensure the quality of information analysis data, the raw sequences are filtered and duplicate reads are removed to obtain clean reads (high-quality reads).

[0151] 1) Remove adapter-contaminated reads (reads with more than 5 bp of adapter-contaminated bases; for paired-end sequencing, if one end is contaminated by the adapter, remove reads from both ends).

[0152] 2) Remove low-quality reads (more than 50% of the total bases in the reads have a quality value of Q≤19. For paired-end sequencing, if one end is a low-quality read, both ends of the reads will be removed).

[0153] 3) Remove reads containing more than 5% N (for paired-end sequencing, if one end contains more than 5% N, both ends of the reads will be removed). Since the insert fragments in Hi-C library preparation are chimeric fragments, there are cases where single-end reads cross chimeric sites during sequencing. In order to ensure the utilization of sequencing data, we also truncate the reads to a certain length (100bp). The data analyzed later are all based on the truncated reads.

[0154] The data filtering and statistical results are shown in Table 1:

[0155] Table 1

[0156]

[0157] 2. Sequencing data base distribution

[0158] Using the base positions of the filtered Clean Reads sequences as the x-axis and the proportions of ATCG bases and N (unidentified bases) at each position as the y-axis, a base distribution map of the Clean Reads was obtained. In the sequencing, the proportions of A and T bases, and the proportions of G and C bases were equal at each position of the Clean Reads, indicating that there was no sequencing bias.

[0159] 3. Comparison and fragment analysis

[0160] 3.1 Genome alignment analysis

[0161] Bowtie2 is used to align each pair of reads in the first dataset. Each pair of reads contains Read1 and Read2. Read1 and Read2 are then aligned one-end to the reference genome dataset to obtain a BAM format alignment result file. This results in a first subset that can be aligned to the reference genome dataset and a second subset that cannot. For the unaligned reads in the second subset (also known as Unmap Reads), the linking sites of the digested library are located. The portion of the digested library after the linking sites is truncated, and then aligned one-end to the reference genome dataset again to obtain a third subset that can be aligned to the reference genome dataset. The first and third subsets are merged, and the pairs of reads where both ends are aligned to a unique position in the reference genome dataset are selected as the second data in this embodiment. This second data is used for subsequent analysis.

[0162] The number of reads and the alignment rate are shown in Table 2:

[0163] Table 2

[0164]

[0165] 3.2 Hi-C Library Insert Fragment Length Distribution

[0166] By aligning reads to a reference genome, the theoretical length distribution of Hi-C library inserts is evaluated to determine whether they conform to the predetermined length range.

[0167] From the filtered high-quality Reads, 50,000 pairs of Reads were randomly selected. The insertion segment length information for each pair of Reads was statistically analyzed. A frequency distribution histogram of the insertion segment length was plotted with the insertion segment length on the x-axis and frequency on the y-axis, as shown below. Figure 2 As shown.

[0168] Typically, the length distribution of inserted fragments should conform to 50–1000 bp, indicating consistency with the substrate selection range in the experimental stage; simultaneously, the more paired-end reads longer than 100 bp in the inserted fragment length distribution, the better the library quality. Figure 2 It can be seen that the substrate selection range is consistent with that in the experimental stage, and the library quality is high.

[0169] 4. Assisted assembly

[0170] This was accomplished using LACHESIS software.

[0171] 4.1 Contig / Scaffold Cluster Analysis

[0172] For diploid genomes, the LACHESIS software uses a bottom-up hierarchical clustering algorithm to assign the initially assembled Contig / Scaffold to n chromosome groups, where n is 14 in this example.

[0173] The initial assembled genome contained a total of 255 contigs / scaffolds, with a total length of 408,446,700 bp. LACHESIS clustering identified 59 contigs / scaffolds in 14 chromosome groups, representing 23.14% of the total, with a total length of 392,622,225 bp, accounting for 96.13% of the total genome length.

[0174] The results of Contig / Scaffold clustering are shown in Table 3:

[0175] Table 3

[0176]

[0177]

[0178] 4.2 Contig / Scaffold Ranking Analysis

[0179] Within each chromosome cluster, contigs / scaffolds are ordered. Based on the standardized interaction strength between contigs / scaffolds, the longest acyclic spanning tree is constructed for each contig / scaffold within a chromosome cluster according to their interaction relationships. The "trunk" (root tree) with the highest confidence is selected, and the remaining fragmented contigs / scaffolds are inserted into appropriate positions at the root according to their association relationships. This process ultimately constructs the accurate contig / scaffold order for the internal clusters of a chromosome.

[0180] As a result, among the 59 contigs / scaffolds in the cluster, 52 were sorted, accounting for 88.14% of the total contigs / scaffolds; the total length of the sorted contigs / scaffolds was 392,306,666 bp, accounting for 99.92% of the total contigs / scaffolds in the cluster. Of the sorted contigs / scaffolds, 51 were located in the root trunk, accounting for 98.08% of the total sorted contigs / scaffolds; the total length of the root trunk contigs / scaffolds was 391,823,533 bp, accounting for 99.88% of the total length of the sorted contigs / scaffolds.

[0181] The Contig / Scaffold sorting results are shown in Table 4:

[0182] Table 4

[0183]

[0184]

[0185] 4.4 Contig / Scaffold Targeted Analysis and Screening

[0186] Orientation: Within the Contig / Scaffold group of each chromosome with a defined order, all possible directions of the Contig / Scaffold are traversed through a weighted directed acyclic graph (WDAG). For the order determination of each Contig / Scaffold, a scoring function is constructed based on the difference between positive and negative interaction relationships and used as the basis for confidence determination. The orientation with the highest confidence is selected to achieve Contig / Scaffold orientation.

[0187] Screening: (1) Number of clusters (number of chromosomes), that is, the number of chromosomes finally assembled is greater than or equal to the expected number of chromosomes and the smaller the difference, the higher the score. (2) The proportion of Contig / Scaffold used during orientation: the closer to 100%, the higher the score. (3) The proportion of Contig / Scaffold in the root tree: the closer to 100%, the higher the score. (4) The ratio of the shortest chromosome length to the longest chromosome length, the higher the ratio, the higher the score. Finally, the scores of the above four types are added together, and the highest score is taken as the final screening result.

[0188] When the optimal preliminary assembly data cannot be used as the final assembly data, the conditions for preliminary assembly are changed again to obtain multiple preliminary assembly data in the preliminary assembly data obtained from the second data. The preliminary assembly data is then reprocessed and filtered to select the optimal preliminary assembly data as the final assembly data. The conditions for preliminary assembly are changed again in the following ways: minimum number of restriction enzyme sites in the Contig / Scaffold sequence in cluster analysis; maximum link depth in the Contig / Scaffold sequence in cluster analysis; ratio of the number of interactions between the Contig / Scaffold sequence and the target cluster to the number of interactions with other clusters in cluster back-interpolation; and minimum number of restriction enzyme sites in the Contig / Scaffold sequence within the root tree in the ordination analysis.

[0189] As a result, among the 52 sorted contigs / scaffolds, all 52 were oriented, accounting for 100% of the sorted contigs / scaffolds; the total length of the oriented contigs / scaffolds was 392,306,666 bp, accounting for 100% of the total length of the sorted contigs / scaffolds; among the contigs / scaffolds with determined sequences in the preliminary genome assembly that belong to the trunk, 51 were oriented, accounting for 100% of the trunk contigs / scaffolds; the total length of the oriented contigs / scaffolds was 391,823,533 bp, accounting for 100% of the total length of the trunk contigs / scaffolds.

[0190] 5. Results

[0191] 5.1 Statistics of indicators for assisted genome assembly

[0192] The genome was initially assembled and mounted on Contig / Scaffold to obtain Pseudomolecules (chromosome-level genomes). The Pseudomolecules were statistically analyzed, and the results are shown in Table 5.

[0193] Table 5

[0194]

[0195] 5.2 Heatmap Verification

[0196] Figure 3 A global Hi-C interaction heatmap of the genome is displayed, with the horizontal and vertical axes representing chromosomes. The darker the color in the diagram, the stronger the interaction signal.

[0197] Figure 4 A heatmap of Hi-C interactions on a single chromosome is shown.

[0198] As shown in Table 5, the gene-assisted assembly method and system using Hi-C technology of this invention can obtain a near-chromosome-level genome using third-generation assembled genome drafts and Hi-C sequencing data, achieving an mounting rate of over 95%. Furthermore, through... Figure 3 and Figure 4 The heatmaps demonstrate that the gene-assisted assembly method and system utilizing Hi-C technology of this invention produce chromosomes of high quality.

[0199] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0200] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0201] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0202] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0203] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for gene-assisted assembly using Hi-C technology, comprising: The samples were subjected to Hi-C library construction and sequencing to obtain the first dataset; The first dataset is compared with and selected from the reference genome dataset to obtain the second dataset; The second dataset is initially assembled to obtain a preliminary assembled dataset; The preliminary assembled dataset is processed and filtered; The method for comparing and selecting the first dataset with the reference genome dataset includes: Each data point in the first dataset is first aligned with the reference genome dataset to obtain a first subset that can be aligned to the reference genome dataset and a second subset that cannot be aligned to the reference genome dataset. After digesting each data in the second subset, the link sites of the library are searched. The data in the second subset are broken up from the link sites of the digested library. The broken data are then compared with the reference genome dataset again to obtain the third subset that can be compared with the reference genome dataset. By merging and selecting from the first and third subsets, a second dataset is obtained. The methods for processing and filtering the preliminary assembled dataset include: The initial assembly data was allocated to chromosome groups; The preliminary assembly data assigned to each chromosome group are sorted, oriented, and filtered. The screening criteria include: (1) the difference between the number of clusters and the expected number of chromosomes; (2) the proportion of Contig / Scaffold used during orientation; (3) the proportion of Contig / Scaffold in the root tree; (4) the proportion of the shortest chromosome length to the longest chromosome length; the scores of the above four criteria are added together, and the highest score is taken as the final screening result. The scoring criteria corresponding to the filtering conditions are as follows: (1) The number of clusters is greater than or equal to the expected number of chromosomes and the smaller the difference, the higher the score; (2) The closer the proportion of Contig / Scaffold used during orientation is to 100%, the higher the score; (3) The closer the Contig / Scaffold ratio in the root tree is to 100%, the higher the score; (4) The higher the proportion of the shortest chromosome length to the longest chromosome length, the higher the score; When the optimal preliminary assembly data cannot be used as the output assembly data, the conditions for preliminary assembly are changed again in the preliminary assembly dataset obtained by performing preliminary assembly on the second dataset to obtain multiple preliminary assembly data again. The preliminary assembly dataset is then reprocessed and filtered to select the optimal preliminary assembly data as the assembly data. The conditions for preliminary assembly are selected from one or more of the following: the minimum number of restriction enzyme sites in the Contig / Scaffold sequence in cluster analysis, the maximum Link depth in the Contig / Scaffold sequence in cluster analysis, the ratio of the number of interactions between the Contig / Scaffold sequence and the target cluster to the number of interactions between other clusters in cluster back-interpolation, and the minimum number of restriction enzyme sites in the Contig / Scaffold sequence within the root tree in ordination analysis.

2. The method according to claim 1, wherein, The data includes Reads, and / or the dataset includes multiple Reads.

3. The method according to claim 1, wherein, The selection criteria include: selecting data whose two ends are both aligned to a unique position in the reference genome dataset.

4. The method according to claim 1, wherein, The preliminary assembly method includes: performing preliminary assembly of the second dataset based on the fact that the interaction frequency on the same chromosome within the cell nucleus is higher than the interaction frequency on different chromosomes, to obtain a preliminary assembled Contig / Scaffold.

5. The method according to claim 4, wherein, The initial assembly was based on LACHESIS.

6. The method according to any one of claims 1-5, wherein, The initial assembled dataset was processed and filtered based on LACHESIS.

7. The method according to any one of claims 1-5, wherein, The allocation method includes clustering methods.

8. The method according to any one of claims 1-5, wherein, The sorting method includes: constructing an acyclic spanning tree for the contigs / scaffolds within a chromosome group based on their interaction relationships, and selecting the root tree with the highest confidence from it.

9. The method according to any one of claims 1-5, wherein, The orientation method includes: traversing the possible directions of Contig / Scaffold through a weighted directed acyclic graph; for the order judgment of each Contig / Scaffold, constructing a scoring function based on the difference between positive and negative interaction relationships and using it as the basis for confidence determination; and selecting the orientation with the highest confidence.

10. The method according to claim 1, wherein, The reference genome dataset is selected from reference genome sketches based on second-generation sequencing or reference genome sketches based on third-generation sequencing.

11. The application of the gene-assisted assembly method using Hi-C technology according to any one of claims 1-10 in chromosome interaction, whole-genome resequencing, chromosome evolution and differentiation, and epigenomics.

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