Method and apparatus for identifying conserved non-coding elements (CNEs) in a genome

Abstract

The application discloses a method and device for identifying CNEs (Conserved Non-coding Elements) in a genome. The method comprises: aligning each of a plurality of high-quality genomes with a reference genome to obtain genome alignment results; identifying a species evolution relationship topological structure and the genome alignment results to obtain a conserved sequence position information gff file; filtering the gff file to obtain a CNEs.gff file; extracting CNEs sequences from the CNEs.gff file; determining CNEs differentiation sequences according to the CNEs sequences; and performing functional enrichment analysis on target genes of the CNEs differentiation sequences to obtain CNEs identification results in the plurality of high-quality genomes. The application solves the technical problem in the related art that the identification of CNEs is often affected by genome structural variations, resulting in incomplete identification of CNEs or misjudgment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological analysis, and in particular, to a method and device for identifying conserved non-coding elements (CNEs) in a genome. BACKGROUND

[0002] With the rapid development of bioinformatics and high-throughput sequencing technology, scientists can obtain and analyze the complete genome information of various organisms. Among them, the emergence of existing single-molecule real-time fluorescent sequencing technology and nanopore sequencing technology not only improves the accuracy of sequencing, but also greatly improves the length of the sequence, enabling researchers to obtain chromosome-level reference genomes from telomere to telomere. This level of genomic information provides a foundation for in-depth study of biological problems such as the function, evolution, and morphological innovation of genomes.

[0003] In genome research, a special class of sequences has attracted widespread attention, namely conserved non-coding elements (CNEs). CNEs are DNA sequences in the genome that are conserved across different species but do not encode proteins. They play an important role in regulating gene expression, influencing species characteristics, and adaptive evolution. Studies have shown that CNEs, as cis-regulatory elements, can act as enhancers, suppressors, and insulators, regulating gene expression. In addition, the loss or variation of CNEs is closely related to differences in species phenotypes and morphological innovation, and their dynamic changes have a significant impact on species adaptation to the environment and survival ability.

[0004] However, existing technologies have limitations in the comprehensive identification, differentiation analysis, and functional prediction of CNEs. Traditional CNE identification methods are often based on sequence alignment, but ignore the impact of structural variations, which may lead to inaccurate identification. For example, when using BLAST for conservation analysis, due to the limitations of alignment fragments, it may not be able to detect conservation across structural variation breakpoints, thus missing some CNEs. In addition, most methods only focus on the absolute difference in sequence when analyzing the differences in CNEs between species, ignoring the sequence convergent differentiation of species in the evolutionary process. This single analysis perspective limits the in-depth understanding of the function and evolutionary significance of CNEs.

[0005] Currently, the multi-species CNEs analysis method mainly adopts whole genome alignment, and the highly conserved sequences are screened through the alignment results, and then the CNEs are obtained after removing the coding regions. This method can provide relatively accurate identification results under certain conditions, but its limitations become obvious when facing complex structural variations and large-scale species analysis. For example, when there are large-scale inversion and translocation variations between species, the existing conservation analysis tool may not accurately identify the true position of CNEs, resulting in misjudgment or missed detection. At the same time, for the identified CNEs, there is a lack of systematic difference analysis and function prediction process, so that the research of CNEs stays at the preliminary sequence alignment level, and it is difficult to further explore the biological function and evolutionary significance of CNEs.

[0006] At present, an effective solution has not been proposed for the above problems. SUMMARY

[0007] The embodiment of the present application provides a method and device for identifying conserved non-coding elements CNEs in a genome, to at least solve the technical problems that the identification of CNEs is often affected by genomic structural variations in the related art, resulting in incomplete identification of CNEs or misjudgment.

[0008] According to an aspect of an embodiment of the present application, a method for identifying conserved non-coding elements CNEs in a genome is provided, comprising: selecting a reference genome from a plurality of high-quality genomes, wherein the high-quality genome is a genome that meets the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a Contig N50 not less than a second predetermined threshold; performing whole genome alignment on each of the other genomes in the plurality of high-quality genomes except the reference genome and the reference genome to obtain a genome alignment result; generating a multi-species genome alignment matrix according to a species evolution relationship topological structure and the genome alignment result; performing conserved sequence identification on the multi-species genome alignment matrix to obtain a conserved sequence position information gff file with the reference genome as a reference; filtering out sequences containing exon regions in the conserved sequences in the conserved sequence position information gff file according to the coding region position information of the reference genome to obtain a CNEs.gff file; extracting CNEs sequences in the plurality of high-quality genomes from the CNEs.gff file; determining CNEs differentiation sequences appearing in a species differentiation period according to the CNEs sequences; performing functional enrichment analysis on target genes in a predetermined CNEs range in the CNEs differentiation sequences to obtain CNEs identification results in the plurality of high-quality genomes.

[0009] Optionally, the reference genome is selected from the plurality of high-quality genomes, comprising: determining a model species or an evolutionary species at a root position of an evolutionary tree in the plurality of high-quality genomes; and selecting the model species or the evolutionary species from the plurality of high-quality genomes as the reference genome.

[0010] Optionally, the other genomes in the plurality of high-quality genomes except the reference genome are respectively aligned with the reference genome for whole genome alignment to obtain genome alignment results, comprising: using a global alignment software lastz to perform whole genome alignment of the other genomes with the reference genome respectively to obtain original alignment maf files of the other genomes with the reference genome respectively; and using a chainNet software tool to preprocess the original alignment maf files respectively to obtain synnet.axt.maf files corresponding to the genome alignment results, wherein the preprocessing comprises the following operations: sorting, filtering, and collinear region searching.

[0011] Optionally, a multi-species genome alignment matrix is generated according to a species evolutionary relationship topology and the genome alignment results, comprising: merging the synnet.axt.maf files to obtain a multi-species alignment maf file; and processing the multi-species alignment maf file according to the species evolutionary relationship topology to obtain a multi-species genome alignment matrix.

[0012] Optionally, a conserved sequence position information gff file is obtained by performing conserved sequence identification on the multi-species genome alignment matrix, comprising: using phastCons software to perform conserved sequence identification on the multi-species genome alignment matrix to obtain the conserved sequence position information gff file with the reference genome as a reference.

[0013] Optionally, a CNEs differentiation sequence appearing during species differentiation is determined according to the CNEs sequence, comprising: generating a sequence adjacency tree according to the CNEs sequence using a FastTree adjacency method; performing ancestral CNEs prediction using an ancestral module in Paml software according to the sequence adjacency tree to obtain an ancestral CNEs prediction result; calculating a differentiation time of each of the species and an ancestral sequence of a root node in the sequence adjacency tree using a python script; and determining the CNEs differentiation sequence appearing during species differentiation according to the differentiation time.

[0014] Optionally, a functional enrichment analysis is performed on target genes in a predetermined CNE range in the CNE differentiation sequence to obtain a CNE identification result in the plurality of high-quality genomes, including: using annovar software to perform gene annotation on CNEs with convergent differences and CNEs with synchronous differentiation with species in the CNE differentiation sequence to obtain an annotated CNE differentiation sequence; screening the target genes in the predetermined CNE range from the annotated CNE differentiation sequence; and performing the functional enrichment analysis on the target genes to obtain the CNE identification result.

[0015] According to another aspect of the embodiments of the present application, a device for identifying CNEs in a genome is also provided, including: a selection unit configured to select a reference genome from a plurality of high-quality genomes, wherein the high-quality genome is a genome satisfying the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a Contig N50 not less than a second predetermined threshold; an alignment unit configured to perform whole-genome alignment between each of the genomes other than the reference genome in the plurality of high-quality genomes and the reference genome to obtain a genome alignment result; a generation unit configured to generate a multi-species genome alignment matrix according to a species evolution relationship topology and the genome alignment result; a conserved sequence identification unit configured to identify conserved sequences in the multi-species genome alignment matrix to obtain a conserved sequence position information gff file with the reference genome as a reference; a filtering unit configured to filter out sequences containing exon regions in the conserved sequences in the conserved sequence position information gff file according to coding region position information of the reference genome to obtain a CNE.gff file; an extraction unit configured to extract CNE sequences in the plurality of high-quality genomes from the CNE.gff file; a determination unit configured to determine CNE differentiation sequences appearing in a species differentiation period according to the CNE sequences; and a functional enrichment analysis unit configured to perform a functional enrichment analysis on target genes in a predetermined CNE range in the CNE differentiation sequence to obtain a CNE identification result in the plurality of high-quality genomes.

[0016] Optionally, the selection unit includes: a first determination module configured to determine a model species or an evolutionary species at a root position of an evolutionary tree in the plurality of high-quality genomes; and a second determination module configured to select the model species or the evolutionary species as the reference genome from the plurality of high-quality genomes.

[0017] Optionally, the alignment unit includes: a whole-genome alignment module, used to perform whole-genome alignment of the other genomes with the reference genome using the global alignment software lastz, to obtain raw alignment MAF files of the other genomes and the reference genome respectively; and a preprocessing module, used to preprocess the raw alignment MAF files using the chainNet software tool to obtain the synnet.axt.maf file corresponding to the genome alignment result, wherein the preprocessing includes the following operations: sorting, filtering, and collinear region search.

[0018] Optionally, the generation unit includes: a merging module for merging the synnet.axt.maf files to obtain a multi-species alignment maf file; and a processing module for processing the multi-species alignment maf file according to the species evolutionary relationship topology to obtain a multi-species genome alignment matrix.

[0019] Optionally, the conserved sequence identification unit includes: a conserved sequence identification module, used to use phastCons software to identify conserved sequences in the multi-species genome alignment matrix, and obtain a gff file of conserved sequence position information with reference to the reference genome.

[0020] Optionally, the determining unit includes: a generation module, used to generate a sequence adjacency tree based on the CNEs sequences using the FastTree adjacency method; a prediction module, used to predict ancestral CNEs using the ancestral module in Paml software based on the sequence adjacency tree, and obtain ancestral CNEs prediction results; a calculation module, used to calculate the divergence time of each species and the root node ancestral sequence in the sequence adjacency tree using a Python script; and a third determining module, used to determine the divergence sequences of the CNEs that appear during species divergence based on the divergence time.

[0021] Optionally, the functional enrichment analysis unit includes: a gene annotation module, used to annotate the CNEs with convergent differences and the CNEs that diverged synchronously with the species in the CNEs differentiation sequences using Annovar software, to obtain annotated CNEs differentiation sequences; a screening module, used to screen the target genes within the predetermined CNEs range from the annotated CNEs differentiation sequences; and a functional enrichment analysis module, used to perform the functional enrichment analysis on the target genes, to obtain the CNEs identification results.

[0022] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium comprising a stored program, wherein the program performs the method for identifying conserved non-coding elements (CNEs) in the genome as described in any of the preceding embodiments.

[0023] According to another aspect of the present invention, a processor is also provided, the processor being configured to run a program, wherein the program, when running, executes the method for identifying conserved non-coding elements (CNEs) in the genome as described in any of the preceding embodiments.

[0024] According to another aspect of the present invention, a computer program product is also provided, including computer instructions that, when executed by a processor, perform the method for identifying conserved non-coding elements (CNEs) in the genome as described in any of the preceding embodiments.

[0025] In this embodiment of the invention, a reference genome is selected from multiple high-quality genomes. A high-quality genome is defined as a genome that meets the following conditions: chromosome-level genome, BUSCO value not less than a first predetermined threshold, and ContigN50 not less than a second predetermined threshold. All genomes in the multiple high-quality genomes except the reference genome are aligned to the reference genome to obtain genome alignment results. A multi-species genome alignment matrix is ​​generated based on the species evolutionary topology and the genome alignment results. Conserved sequences are identified in the multi-species genome alignment matrix to obtain a conserved sequence position information (gff) file with the reference genome as a reference. Sequences containing exon regions in the conserved sequences of the gff file are filtered out based on the coding region position information of the reference genome to obtain a CNEs.gff file. CNEs sequences from multiple high-quality genomes are extracted from the CNEs.gff file. CNEs differentiation sequences appearing during speciation are determined based on the CNEs sequences. Functional enrichment analysis is performed on target genes within a predetermined CNEs range in the CNEs differentiation sequences to obtain CNEs identification results from multiple high-quality genomes. The technical solution provided by this invention achieves the following: a high-quality reference genome is selected, and a multi-species sequence alignment matrix is ​​constructed using global alignment software. Structural variation is considered to identify CNE fragments missing in a single species. PhashCons software is used to identify highly conserved sequences from the alignment matrix, and coding region sequences are removed for preliminary identification. Sequence convergence analysis is performed to screen candidate CNEs with functional or environmental convergence. Paml software is used to predict ancestral CNE sequences and calculate CNE divergence times. Combining divergence times, convergence analysis results, and the biological characteristics of the target species, functional prediction analysis of CNEs is conducted. This ensures the comprehensiveness and accuracy of identification and prediction, thereby solving the technical problem in related technologies where the identification of CNEs is often affected by genomic structural variations, leading to incomplete or misjudged CNEs. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0027] Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention.

[0028] Figure 2 This is a flowchart of a method for identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of the CNEs tree construction results based on convergence differences according to an embodiment of the present invention;

[0030] Figure 4 This is a flowchart of an optional method for identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention;

[0031] Figure 5 This is a schematic diagram of a device for identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention.

[0032] The above figures include the following reference numerals:

[0033] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all 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] As described in the background section, related technologies often suffer from incomplete or misidentified identification of conserved non-coding elements (CNEs) due to genomic structural variations. This invention provides a method and apparatus for identifying conserved non-coding elements (CNEs) in the genome, a computer-readable storage medium, a processor, and a computer program product.

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0038] The methods and embodiments provided in this invention can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0039] Memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for identifying conserved non-coding elements (CNEs) in the genome in this embodiment of the invention. Processor 102 executes various functional applications and data processing by running the computer programs stored in memory 104, thereby implementing the above-described method. Memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, memory 104 may further include memory remotely located relative to processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one instance, transmission device 106 includes a Network Interface Controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0040] Example 1

[0041] According to an embodiment of the present invention, a method embodiment for identifying conserved non-coding elements (CNEs) in a genome is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0042] Figure 2 This is a flowchart of a method for identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0043] Step S202: Select a reference genome from multiple high-quality genomes, wherein a high-quality genome is a genome that meets the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a Contig N50 not less than a second predetermined threshold.

[0044] In this embodiment, a genome that meets the following criteria can be selected from multiple high-quality genomes: chromosome level, BUSCO integrity greater than 90% (first predetermined threshold), and Contig N50 greater than 500Kb (second predetermined threshold) as a reference. For example, the African killifish (Oreochromis niloticus) genome is an ideal reference for studying fish CNEs due to its high quality.

[0045] That is, based on high-quality genomes of multiple species (the high-quality genomes should meet three conditions: ① chromosome-level genomes; ② BUSCO value ≥ 90%; ③ Contig N50 ≥ 1Mb), the genome of one species is selected as a reference (it is generally recommended to use a model species or a high-quality genome at the root of the evolutionary tree as a reference, and the priority order for selecting the reference genome is: T2T level genomes > model species genomes > genomes with the highest BUSCO and Contig N50 combined. In specific implementation, the appropriate selection can be made according to the actual situation).

[0046] Step S204: Align the genomes other than the reference genome in the multiple high-quality genomes with the reference genome to obtain the genome alignment results.

[0047] Step S206: Generate a multi-species genome alignment matrix based on the topological structure of species evolutionary relationships and genome alignment results.

[0048] Step S208: Conserved sequence identification is performed on the multi-species genome alignment matrix to obtain a gff file containing conserved sequence position information with reference to the reference genome.

[0049] Step S210: Based on the coding region location information of the reference genome, the sequences containing exon regions in the conserved sequence location information gff file are filtered out to obtain the CNEs.gff file.

[0050] Step S212: Extract CNE sequences from multiple high-quality genomes from the CNEs.gff file.

[0051] Step S214: Determine the CNEs differentiation sequences that appear during the speciation period based on the CNEs sequences.

[0052] Step S216: Perform functional enrichment analysis on the target genes within the predetermined CNE range in the CNE differentiation sequence to obtain CNE identification results in multiple high-quality genomes.

[0053] As described above, in this embodiment of the invention, a reference genome is selected from multiple high-quality genomes. A high-quality genome is defined as a genome that meets the following conditions: chromosome-level genome, BUSCO value not less than a first predetermined threshold, and Contig N50 not less than a second predetermined threshold. All genomes in the multiple high-quality genomes except the reference genome are aligned to the reference genome to obtain genome alignment results. A multi-species genome alignment matrix is ​​generated based on the species evolutionary topology and the genome alignment results. Conserved sequences are identified in the multi-species genome alignment matrix to obtain a conserved sequence position information (gff) file with the reference genome as a reference. Sequences containing exon regions in the conserved sequences of the gff file are filtered out based on the coding region position information of the reference genome to obtain a CNEs.gff file. CNEs sequences from multiple high-quality genomes are extracted from the CNEs.gff file. CNEs differentiation sequences appearing during speciation are determined based on the CNEs sequences. The CNEs differentiation sequences are then analyzed. Functional enrichment analysis was performed on target genes within a predetermined CNE range, yielding CNE identification results from multiple high-quality genomes. This process involved selecting high-quality reference genomes, performing whole-genome alignment using global alignment software to construct a multi-species sequence alignment matrix, incorporating structural variation considerations to identify CNE fragments missing in a single species, and using PhastCons software to identify highly conserved sequences from the alignment matrix, removing coding region sequences for preliminary identification. Sequence convergence analysis was conducted to screen candidate CNEs with functional or environmental convergence. Paml software was used to predict ancestral CNE sequences and calculate CNE divergence times. The goal was to combine divergence time, convergence analysis results, and target species biological characteristics to perform CNE functional prediction analysis, ensuring the comprehensiveness and accuracy of identification and prediction. In short, by systematically integrating species evolutionary information, genomic structural variation considerations, and functional annotation, the accuracy and comprehensiveness of CNE identification were significantly improved, enabling a more precise association between CNEs and the traits they may regulate.

[0054] Therefore, the technical solutions provided by the embodiments of the present invention solve the technical problem in the related art that the identification of CNEs is often affected by genomic structural variations, resulting in incomplete identification or misjudgment of CNEs.

[0055] According to the above embodiments of the present invention, selecting a reference genome from multiple high-quality genomes includes: identifying a model species or an evolutionary species located at the root of the evolutionary tree among the multiple high-quality genomes; and selecting the model species or evolutionary species from the multiple high-quality genomes as a reference genome.

[0056] In this embodiment, before performing CNE analysis on multiple high-quality genomes, a model species (such as a model organism) or a species at the root of the evolutionary tree is first identified as a reference. For example, the African killifish is a model species with high-quality genome and a stable position, making it suitable as a reference.

[0057] By selecting root species or model species as reference genomes, a stable comparison benchmark can be provided, which is beneficial to the consistency and reliability of subsequent CNE identification.

[0058] In one application scenario, taking a certain aquatic fish as an example, the detailed implementation steps and application of the invention's technical solution are explained. The target fish A exists in two ecotypes in nature: A1 produces adhesive eggs, denoted as Anian, and A2 produces drifting eggs, denoted as Apiao. Among the spawning types of fish in nature, there are three main egg types: drifting, adhesive, and settling. Adhesive and settling eggs are attached spawning and rearing methods, meaning the mother's spawning location and hatching location are basically in the same area. Adhesive eggs attach to algae or rocky reefs in the environment. Sedimentary fish spawn at the bottom of rivers and lakes, attaching to the riverbed and eventually hatching. Drifting fish, on the other hand, spawn and drift away with the current of rivers or lakes, and their final spawning and hatching sites are generally different. These two types of fish have a significant impact on fish survival and economic morphology. Fish A provides excellent research material for studying egg development. Therefore, we selected three representative fish of both the sedimentary and drifting types, namely A1, B1, C1 and A2, B2, C2, and then identified and analyzed CNEs according to the following steps.

[0059] Using the A1 genome, which has the highest BUSCO ratio of 98% and contig N50 of 20Mb among the six chromosome-level genomes, as a reference, the global alignment software lastz was used to align A2, B1, C1, B2, and C2 with A1 respectively (detailed command execution: "Convert the reference genome to binary file faToTwoBit A1.genome.fa A1.genome.fa.2bit; convert the target genome to binary file faToTwoBit A2.genome.fa A2.genome.fa.2bit; perform whole-genome alignment with lastz to obtain the raw alignment maf file lastz T=2--chain-gfextend--nogapped C=2H=2000 Y=3400 L=6000K=2200--format=maf A1.genome.fa A2.genome.fa | last-split -m1 > A1_A2.raw.maf") This process yields the original alignment MAF files of A2, B1, C1, B2, C2, and A1.

[0060] It's important to note that a high-quality reference genome is a relative concept. In the early days, indicators such as BUSCO and contigN50 were mainly used to judge the level of genome assembly. Generally, the higher the BUSCO and contigN50 values, the better the integrity and continuity of the assembly. With the technological advancements in recent years, genomes are constantly being updated and iterated, and complete genomes at the T2T level are constantly emerging. At this point, a high-quality reference genome is defined as whether it has reached the T2T level or has no gaps. Therefore, when judging the quality of genome assembly, a relative comparison is made among the analyzed species. In CNE analysis, after selecting the species to be analyzed, the genome with the highest assembly quality will always be found based on relevant indicators such as BUSCO and contigN50, and the highest relative quality can be selected.

[0061] According to the above embodiments of the present invention, the other genomes in multiple high-quality genomes, excluding the reference genome, are aligned with the reference genome to obtain genome alignment results. This includes: using the global alignment software lastz to align the other genomes with the reference genome to obtain raw alignment MAF files of the other genomes and the reference genome respectively; and using the chainNet software tool to preprocess the raw alignment MAF files to obtain the synnet.axt.maf file corresponding to the genome alignment results. The preprocessing includes the following operations: sorting, filtering, and collinearity region search.

[0062] In this embodiment, the UCSC database chainNet software tools (axtChain, chainMergeSort, chainPreNet, chainNet, netSyntenic, netToAxt, axtSort, and axtToMaf) are used to sort, filter, and search for collinear regions on the original alignment MAF files to construct synnet.axt.maf files A1_A2.synnet.axt.maf, A1_B1.synnet.axt.maf, A1_C1.synnet.axt.maf, A1_B2.synnet.axt.maf, and A1_C2.synnet.axt.maf (A1_A2.synnet.axt.maf is the final alignment matrix file obtained after A2 is aligned to A1 with A1 as a reference; A1_B1.synnet.axt.maf...) A1_C1.synnet.axt.maf is the final alignment matrix file obtained after B1 is aligned to A1 with A1 as a reference; A1_B2.synnet.axt.maf is the final alignment matrix file obtained after B2 is aligned to A1 with A1 as a reference; A1_C2.synnet.axt.maf is the final alignment matrix file obtained after B1 is aligned to C2 with A1 as a reference.

[0063] The specific commands executed from the original MAF file to the MAF file in this step are as follows: ① Sort the original MAF file and convert it to an AXT format file: `maf-sort A1_A2.raw.maf | maf-convert axt > A1_A2.axt`; ② Convert the AXT file to a chain file: `axtChain -linearGap = medium A1_A2.axt A1.genome.fa.2bit A2.genome.fa.2bit A1_A2.chain` (in this step, A1.genome.fa.2bit A2.genome.fa.2bit is the genome binary file obtained in step 1); ③ Merge and sort the chains: `chainMergeSort A1_A2.chain > A1_A2.sort.chain`; ④ Convert the chain file to a net file: `chainPreNet A1_A2.sort.chain A1_genome.size A2.genome.size A1_A2.sort.chain.filter` (A1_genome.size and A2.genome.size are the genome sizes of A1 and A2, respectively) ⑤ Generate reference net and target net files: chainNet A1_A2.sort.chain.filter A1_genome.size A2.genome.size A1_A2.chain.filter.tnet A1_A2.chain.filter.qnet ⑥ Establish collinearity between A1 and A2 genomes: netSyntenic A1_A2.chain.filter.tnet A1_A2.chain.filter.tnet.synnet; ⑦ Convert collinearly related nets to axt: netToAxt A1_A2.chain.filter.tnet.synnet A1_A2.chain.filter A1.genome.fa.2bit A2.genome.fa.2bit A1_A2.chain.filter.tnet.synnet.axt; ⑧ Sort the converted axt files to obtain genome alignment axt files: axtSort A1_A2.chain.filter.tnet.synnet.axt genomeAlignment.axt⑨ Obtain the final MAF file showing the collinearity correspondence: axtToMaf A1_A2.chain.filter.tnet.synnet.axt A1_genome.sizeA2.genome.size -tPrefix=A1. -qPrefix=A2. A1_A2.synnet.axt.maf. .

[0064] Lastz is suitable for most genomes, especially performing well when processing high-quality genomes. For genomes with extremely high repetitive sequences, such as some plant and animal genomes, Minimap2 can be considered. The chainNet tool is used to preprocess the MAF files generated by Lastz, including sorting (axtSort), filtering (chainFilter), and collinearity analysis (netSyntenic), to ensure that the final synnet.axt.maf file accurately reflects genomic collinearity between species.

[0065] The preprocessing here greatly improves the quality of the alignment results, ensuring the accuracy and reliability of the subsequent multi-species alignment matrix construction.

[0066] According to the above embodiments of the present invention, generating a multi-species genome alignment matrix based on the species evolutionary relationship topology and genome alignment results includes: merging the synnet.axt.maf files to obtain a multi-species alignment maf file; and processing the multi-species alignment maf file according to the species evolutionary relationship topology to obtain a multi-species genome alignment matrix.

[0067] In this embodiment, the synnet.axt.maf file 6 is merged using Multiz software to generate a multi-species alignment maf file. The merged multi-species maf file contains three types of information: the first type is annotation information starting with #; the second type is sequence alignment score information starting with a, for example, a score=53204.0; the third type is alignment information for each species starting with s, for example, s A1.Chr23 44 41 + 37379327 CTTGTTTTAAGGAAAAACTCACTTAATTTTGACTTATTTTT, which includes species information, chromosome information, aligned start position information, aligned length, positive and negative strands, total chromosome length, and sequence information. This information provides important basis for subsequent conserved sequence extraction. The specific execution commands are as follows: ① Use the roast function of the multiz software to generate the merge script do_merge.sh: roast -T=tmp E=A1 "(B2 ((A1 A2) ((B1C1) C2)));" . / mutil_output . / mutil_output / multiz.maf > do_merge.sh ("(B2 ((A1A2) ((B1 C1) C2)))" represents the phylogenetic tree topology of the 6 species involved in the analysis, multi_output is the name of the output folder, multiz.maf is the name of the merged MAF file, and do_merge.sh is the script that generates and executes the merge command); ② Run do_merge.sh: sh do_merge.sh &.

[0068] Here, the preprocessed synnet.axt.maf files are merged to generate a multi-species alignment maf file, which is then processed by Multiz software to form a multi-species genome alignment matrix based on species evolutionary relationships. By constructing the multi-species alignment matrix, this invention can systematically compare CNEs of multiple species, laying a solid data foundation for subsequent conserved sequence identification and functional analysis.

[0069] According to the above embodiments of the present invention, the conserved sequence identification of a multi-species genome alignment matrix to obtain a conserved sequence position information gff file with reference to a reference genome includes: using phastCons software to identify conserved sequences in a multi-species genome alignment matrix to obtain a conserved sequence position information gff file with reference to a reference genome.

[0070] In this embodiment, for the multi-species genome alignment matrix multiz.maf, conserved sequences are identified using phastCons software to obtain a gff file containing conserved sequence position information with A1 as the reference.

[0071] The specific execution method is as follows: ① Predict the tree structure and generate the mod file: phastCons --target-coverage0.3 --expected-length 45 --estimate-trees mytrees --msa-format MAF multiz.mafmult.mod --no-post-probs (mytrees is the phylogenetic tree of the known species involved in the analysis, multiz.maf is the MAF file obtained in step 3, and mult.mod is the mod file obtained from the prediction) ② Predict conserved sequences: phastCons --target-coverage 0.3 --expected-length 45 --msa-format MAF multiz.maf --most-conserved most-cons.gff mytrees.noncons.mod > scores.wig (multiz.maf is the MAF file obtained in step 3, most-cons.gff is the gff file recording the location information of the conserved sequences, mytrees.noncons.mod is the non-conserved mod file, and scores.wig is the sequence conservation score file in the analysis).

[0072] Additionally, by combining the A1 coding region location information, sequences containing exon regions can be filtered out from conserved sequences, thus obtaining the CNEs.gff file. Based on the conserved non-coding sequence location information in CNEs.gff, and combined with the information in the first line of the multiz.maf file, CNEs sequences for each of the six species are extracted and saved to a designated folder (it is recommended to create the files using chromosome numbers, and write each CNE sequence into a file named with its corresponding CNEs ID and location information for easy tracking and use later, e.g., CNE1_chr1_1000_10115.fasta).

[0073] Here, the PhastCons module is used to perform conservation analysis on the multi-species genome alignment matrix generated by Multiz, outputting a gff file containing CNE location information based on the reference genome. The use of PhastCons ensures the accuracy of CNE identification, accurately identifying non-coding conserved sequences across species, which is crucial for a deeper understanding of genome regulatory networks.

[0074] According to the above embodiments of the present invention, determining the CNE differentiation sequences appearing during the speciation period based on CNE sequences includes: generating a sequence adjacency tree based on the CNE sequences using the FastTree adjacency method; predicting ancestral CNEs using the ancestral module in Paml software based on the sequence adjacency tree to obtain the ancestral CNE prediction results; calculating the differentiation time of each species and the ancestral sequence of the root node in the sequence adjacency tree using a Python script; and determining the CNE differentiation sequences appearing during the speciation period based on the differentiation time.

[0075] In this embodiment, for the above sequence extraction results, a Python script is used to identify the species with missing CNEs, and the position information of the CNE sequence of the nearest source species is recorded. Sequences within a 2kb range upstream and downstream are extracted and compared with the genome of the missing species to determine whether the CNEs are caused by large structural variations such as structural inversions or translocations. The results of the actual missing CNEs are written into CNEs_loss.txt, and the CNE sequences caused by structural variations are supplemented into the corresponding CNE sequence FASTA file and marked in the sequence ID, for example, A2_CNEs_trans_112_176.

[0076] Furthermore, for the updated CNEs sequence results, sequence alignment was performed using mafft (example: mafft CNE1_chr1_1000_10115.fasta > CNE1_chr1_1000_10115.aln ; Perl get_phy_h.pl CNE1_chr1_1000_10115.aln CNE1_chr1_1000_10115.aln.phy), and a sequence adjacency tree was constructed using the FastTree adjacency method (example: FastTree -nt -nj CNE1_chr1_1000_10115.aln.phy > CNE1_tree.nwk). A Python script was used to select CNEs that clustered together with sticky and drifting types as candidate CNEs for trait association.

[0077] Next, based on the phylogenetic tree topology, the ancestral module in the Paml software was used to predict ancestral CNEs. Then, a Python script was used to calculate the divergence time of each species relative to the ancestral sequence of the root node. Specifically, in the calculation of CNE divergence time, the CNE sequences of different species were compared with the ancestral CNE sequences of the root node. Based on the genetic distance between the CNE sequences of each species and the ancestral sequences, the divergence time of each CNE relative to the ancestral sequence was calculated using T=D / 2μ (where D is the genetic distance between each sequence and the ancestral CNE sequence, and μ is the gene mutation rate). There are two cases for calculating the value of μ: First, assuming that the gene mutation rate of all participating species is consistent, the calculation is performed using the gene mutation rate of a certain species in published literature; Second, according to the calculation of the gene mutation rate of each species, there are currently many methods for calculating the value of μ. Common methods are based on the species divergence time: 1. Direct estimation by combining the genetic distance of the species, i.e., μ=D / 2T; 2. Calculation using μ=ds / 2T; 3. If conditions permit, estimation can also be performed based on genome resequencing data. By combining the time of speciation, we can identify the sequences of CNEs that differentiated during speciation and use them as candidate CNEs for shape formation.

[0078] First, Maft was used to perform multiple sequence alignment of CNEs. Then, the FastTree neighbor-joining method was used to construct a phylogenetic tree of CNEs. Ancestor CNEs were predicted using the ancestral state module of PAML. Finally, a Python script was used to determine the divergence events of CNEs in conjunction with speciation time. The CNE divergence analysis method can identify CNE divergence events that occur synchronously with speciation, thus providing a powerful tool for revealing how genomic regulatory sequences change with species evolution.

[0079] According to the above embodiments of the present invention, functional enrichment analysis is performed on target genes within a predetermined range of CNEs in the CNEs differentiation sequences to obtain CNEs identification results in multiple high-quality genomes. This includes: using Annovar software to annotate CNEs with convergent differences and CNEs that diverged synchronously with the species in the CNEs differentiation sequences to obtain annotated CNEs differentiation sequences; screening target genes within a predetermined range of CNEs from the annotated CNEs differentiation sequences; and performing functional enrichment analysis on the target genes to obtain CNEs identification results.

[0080] In this embodiment, gene annotation using Annovar software can be used to annotate CNEs exhibiting convergent differences and those that diverge synchronously with the species, screening for genes associated within a 5Kb range of CNEs, and performing functional enrichment analysis. The functional enrichment results and gene functions are used to screen out CNEs related to oval development, and combined with other omics results, the functions of these CNEs are further validated.

[0081] Here, Annovar software was used to annotate genes associated with CNE differentiation sequences, screen out genes that differentiated synchronously with CNEs, and then perform functional enrichment analysis on these genes using GO or KEGG.

[0082] Through functional enrichment analysis, this invention can reveal the biological processes that CNEs may be involved in, providing important clues for further experimental design and functional verification.

[0083] For example, three chromosome-level genomes of bream, crucian carp, and grass carp that produce adhesive eggs, and three genomes of bream, bighead carp, and silver carp that produce drifting eggs can be selected. Using the adhesive bream as a reference, 226,707 CNE fragments were identified using the above method. Muscle alignment was performed on each of the 226,707 CNE sequences to construct an NJ tree. 655 CNEs with adhesive and drifting eggs clustered in separate branches were then selected (e.g., Figure 3 As shown, Figure 3 This is a schematic diagram of the CNE tree structure results based on the embodiment of the present invention. CNEs with convergent differences are defined as candidate CNE sequences affecting egg development. Then, the differentiation time of 655 CNE sequences with convergent differences is calculated and analyzed (differentiation time can also be calculated for all CNEs obtained from the identification). CNEs with similar differentiation times to the two ecotypes of Culter alburnus are screened out, thus discovering that a 52bp and a 174bp CNE play a key regulatory role in the egg development of Culter alburnus. This was verified by a dual fluorescent labeling experiment. These two CNEs do indeed play a key regulatory role in the expression of related genes and exhibit synergistic expression. This provides important insights and perspectives for revealing the molecular mechanisms of egg development and ecological adaptation in Culter alburnus.

[0084] Figure 4 This is a flowchart of an optional method for identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention, such as... Figure 4 As shown, firstly, based on high-quality genomes from multiple species (the high-quality genomes should meet three conditions: ① chromosome-level genomes; ② BUSCO value ≥ 90%; ③ Contig N50 ≥ 1Mb), a genome of one species is selected as a reference (it is generally recommended to use a model species or a high-quality genome at the root of the evolutionary tree as a reference, with the priority order for reference genome selection being: T2T-level genomes > model species genomes > genomes with the highest BUSCO and Contig N50 composite values. In specific implementation, a reasonable selection can be made according to the actual situation) (i.e., species analysis selection).

[0085] Next, a whole-genome alignment was performed using one of the following alignment software: Last, Lastz, Mummer, or Minimap2 (Lastz is generally recommended, while Minimap2 is recommended for highly repetitive genomes. Minimap2 employs a minimal sparse index and dynamic anchor chain algorithm strategy, which can balance the sensitivity and speed of repetitive sequence detection by adjusting the -w and -k parameters according to the complexity of the sequence repetitions in highly repetitive genome alignment, adapting to the problem of repetitive sequence differentiation at different evolutionary distances, and balancing efficiency and accuracy). (Refer to Table 1 for the core algorithms and limitations of the software for reasonable selection. Table 1 provides the core algorithms and advantages and disadvantages of four commonly used whole-genome alignment software). Based on the alignment software selection, the alignment results were converted into a format suitable for Multiz software input.

[0086] Based on the evolutionary topology of the selected species, a multi-species genome multiple sequence alignment matrix is ​​generated. Then, the PhastCons module in the rphast package is used to predict and identify conserved sequences in multiple species. After removing coding region sequences from the conserved sequences and obtaining preliminary CNE identification results, for CNE fragments missing in a single species, CNE sequences existing in the nearest source species are selected. Using the genome of the missing species as a reference, sequences within a 2Kb range upstream and downstream of the CNE sequence of the nearest source species (species adjacent in the evolutionary tree) are extracted and aligned to the missing genome using Blasten. It is then checked whether the missing sequence is caused by structural variation, and a record file is generated. If a true deletion is found, it is written to the CNEs_loss.txt file for final review.

[0087] After completing the systematic and comprehensive identification of CNE sequences, ancestral CNE sequences at each node were constructed using iqtree or paml software. Based on the ancestral CNEs, the genetic distance D between each species' CNEs and their ancestors was calculated. After correcting the genetic distance using the Jukes-Cantor (JC) method, the CNE differentiation time was calculated and analyzed based on T=D / 2μ (where D is the genetic distance and μ is the gene mutation rate). Simultaneously, sequence convergence analysis was performed on each group of CNEs, specifically using Muscle or Maft software. Multiple sequence alignment was performed, and phylogenetic trees were constructed using iqtree or FastTree software based on the NJ method. Scripts were written to screen CNEs with the same biological traits or the same living environment that were clustered into one clade, obtaining differentially expressed candidate CNEs with functional or environmental convergence. Finally, by combining the biological characteristics or living environment and evolutionary history of the target species, the divergence time of CNEs, and the convergence results of CNE clustering, a comprehensive judgment was made on CNEs associated with the target trait, and these were identified as candidate CNEs influencing the formation of the target species' characteristic traits for subsequent experimental verification and analysis.

[0088] Table 1

[0089]

[0090] Therefore, the technical solutions provided in the embodiments of this invention take into account the impact of structural variations on the systematic identification of CNEs, preserving false positives caused by the absence of CNEs in individual species due to inversions or translocations. Furthermore, for the first time, sequence convergence differences are used to mine and analyze candidate functions of CNEs from a sequence evolution perspective. Also, for the first time, an ancestral CNE construction method is introduced, estimating the divergence time of CNEs relative to ancestral sequences in different species, and combining this with species divergence time to grasp the formation time of specific CNEs, thereby determining the relationship between the formation time of specific biological morphologies and the divergence time of CNEs, and focusing on the co-evolutionary relationship between CNEs and the formation of species characteristics.

[0091] Furthermore, CNE identification is more global, comprehensive, and systematic, taking into account the impact of structural variations on CNEs and facilitating functional exploration. The introduction of convergent differences allows CNE research to move from absolute differences to an evolutionary perspective, enabling a more comprehensive analysis of CNE functions from the angle of convergent differences, which is more practically significant. The introduction of a method for constructing ancestral sequences of CNEs for the first time lays the foundation for predicting and estimating CNE divergence time, breaks through the temporal concept of CNE evolution, and provides a new perspective on the co-evolution of CNEs and species characteristic traits.

[0092] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0093] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0094] Example 2

[0095] According to embodiments of the present invention, an apparatus for identifying conserved non-coding elements (CNEs) in the genome is also provided for implementing the above-described method for identifying conserved non-coding elements (CNEs) in the genome. Figure 5 This is a schematic diagram of a device for identifying conserved non-coding elements (CNEs) in the genome according to an embodiment of the present invention, as shown below. Figure 5 As shown, the device includes: a selection unit 501, an alignment unit 503, a generation unit 505, a conserved sequence identification unit 507, a filtering unit 509, an extraction unit 511, a determination unit 513, and a functional enrichment analysis unit 515. The device will be described below.

[0096] Selection unit 501 is used to select a reference genome from multiple high-quality genomes, wherein a high-quality genome is a genome that meets the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a ContigN50 not less than a second predetermined threshold.

[0097] The alignment unit 503 is used to perform whole-genome alignment of the other genomes in multiple high-quality genomes, excluding the reference genome, with the reference genome to obtain the genome alignment results.

[0098] Generating unit 505 is used to generate a multi-species genome alignment matrix based on the topological structure of species evolutionary relationships and genome alignment results.

[0099] The conserved sequence identification unit 507 is used to identify conserved sequences in multi-species genome alignment matrices and obtain a gff file containing conserved sequence position information with reference to the reference genome.

[0100] The filtering unit 509 is used to filter out sequences containing exon regions in the conserved sequence location information gff file based on the coding region location information of the reference genome, so as to obtain the CNEs.gff file.

[0101] Extraction unit 511 is used to extract CNEs sequences from multiple high-quality genomes from the CNEs.gff file.

[0102] Unit 513 is used to determine the CNEs differentiation sequence that appears during the speciation period based on the CNEs sequence.

[0103] Functional enrichment analysis unit 515 is used to perform functional enrichment analysis on target genes within a predetermined CNE range in the CNE differentiation sequence, and obtain CNE identification results in multiple high-quality genomes.

[0104] It should be noted that the selection unit 501, alignment unit 503, generation unit 505, conserved sequence identification unit 507, filtering unit 509, extraction unit 511, determination unit 513, and functional enrichment analysis unit 515 mentioned above correspond to steps S202 to S216 in the above embodiments. The eight units and the corresponding steps are implemented in the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments.

[0105] As can be seen from the above, in the scheme described in the above embodiments of the present invention, a selection unit can be used to select a reference genome from multiple high-quality genomes, wherein the high-quality genome is a genome that meets the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a Contig N50 not less than a second predetermined threshold; a comparison unit is used to perform whole-genome alignment of the other genomes in the multiple high-quality genomes except the reference genome with the reference genome to obtain genome alignment results; a generation unit is used to generate a multi-species genome alignment matrix based on the species evolutionary relationship topology and genome alignment results; a conserved sequence identification unit is used to identify conserved sequences in the multi-species genome alignment matrix to obtain a conserved sequence position information gff file with the reference genome as a reference; a filtering unit is used to filter out sequences containing exon regions in the conserved sequences of the conserved sequence position information gff file based on the coding region position information of the reference genome to obtain a CNEs.gff file; an extraction unit is used to extract CNEs sequences from the multiple high-quality genomes from the CNEs.gff file; and a determination unit is used to determine the CNEs that appear during speciation based on the CNEs sequences. Differentiation sequences; functional enrichment analysis was performed on target genes within predetermined CNE ranges in the CNE differentiation sequences, yielding CNE identification results from multiple high-quality genomes. This involved selecting high-quality reference genomes, performing whole-genome alignment using global alignment software to construct a multi-species sequence alignment matrix, incorporating structural variation considerations to identify CNE fragments missing in a single species, and using PhastCons software to identify highly conserved sequences from the alignment matrix, removing coding region sequences for preliminary identification. Sequence convergence analysis was conducted to screen candidate CNEs with functional or environmental convergence. Paml software was used to predict ancestral CNE sequences and calculate CNE differentiation times. The goal of combining differentiation time, convergence analysis results, and target species biological characteristics for CNE functional prediction analysis was achieved, ensuring the comprehensiveness and accuracy of identification and prediction. In short, by systematically integrating species evolutionary information, genomic structural variation considerations, and functional annotation, the accuracy and comprehensiveness of CNE identification were significantly improved, enabling a more precise association between CNEs and the traits they may regulate.

[0106] Therefore, the technical solutions provided by the embodiments of the present invention solve the technical problem in the related art that the identification of CNEs is often affected by genomic structural variations, resulting in incomplete identification or misjudgment of CNEs.

[0107] Optionally, the selection unit includes: a first determining module for determining a model species or an evolutionary species located at the root of a phylogenetic tree from multiple high-quality genomes; and a second determining module for selecting a model species or evolutionary species from multiple high-quality genomes as a reference genome.

[0108] Optionally, the alignment unit includes: a whole-genome alignment module, used to perform whole-genome alignment of other genomes with the reference genome using the global alignment software lastz, to obtain raw alignment MAF files of other genomes with the reference genome; and a preprocessing module, used to preprocess the raw alignment MAF files using the chainNet software tool to obtain the synnet.axt.maf file corresponding to the genome alignment results, wherein the preprocessing includes the following operations: sorting, filtering, and collinearity region search.

[0109] Optionally, the generation unit includes: a merging module for merging the synnet.axt.maf files to obtain a multi-species alignment maf file; and a processing module for processing the multi-species alignment maf file according to the topological structure of species evolutionary relationships to obtain a multi-species genome alignment matrix.

[0110] Optionally, the conserved sequence identification unit includes: a conserved sequence identification module, used to identify conserved sequences in a multi-species genome alignment matrix using phastCons software, and obtain a gff file containing conserved sequence position information with reference to a reference genome.

[0111] Optionally, the determining unit includes: a generation module for generating a sequence adjacency tree based on the CNEs sequences using the FastTree adjacency method; a prediction module for predicting ancestral CNEs using the ancestral module in Paml software based on the sequence adjacency tree, and obtaining the ancestral CNEs prediction results; a calculation module for calculating the divergence time of each species and the root node ancestral sequence in the sequence adjacency tree using a Python script; and a third determining module for determining the divergence sequences of CNEs that appear during species divergence based on the divergence time.

[0112] Optionally, the functional enrichment analysis unit includes: a gene annotation module for using Annovar software to annotate CNEs with convergent differences and CNEs that diverged synchronously with the species in the CNEs differentiation sequences, to obtain annotated CNEs differentiation sequences; a screening module for screening target genes within a predetermined range of CNEs from the annotated CNEs differentiation sequences; and a functional enrichment analysis module for performing functional enrichment analysis on the target genes to obtain CNE identification results.

[0113] According to another aspect of the present invention, a processor is also provided, which is used to run a program, wherein the program executes the method for identifying conserved non-coding elements (CNEs) in the genome according to any of the above-described methods.

[0114] According to another aspect of the present invention, a computer program product is also provided, including computer instructions that, when executed by a processor, perform a method for identifying conserved non-coding elements (CNEs) in the genome as described above.

[0115] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes the method for identifying conserved non-coding elements (CNEs) in the genome according to any one of the above-described methods.

[0116] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any communication device in a group of communication devices.

[0117] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: selecting a reference genome from multiple high-quality genomes, wherein a high-quality genome is a genome that meets the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a Contig N50 not less than a second predetermined threshold; performing whole-genome alignment of the other genomes in the multiple high-quality genomes, excluding the reference genome, with the reference genome to obtain genome alignment results; generating a multi-species genome alignment matrix based on the species evolutionary topology and genome alignment results; identifying conserved sequences in the multi-species genome alignment matrix to obtain a conserved sequence position information gff file with the reference genome as a reference; filtering out sequences containing exon regions in the conserved sequences in the conserved sequence position information gff file based on the coding region position information of the reference genome to obtain a CNEs.gff file; extracting CNEs sequences from the multiple high-quality genomes from the CNEs.gff file; determining the CNEs differentiation sequences that appear during the speciation period based on the CNEs sequences; and performing functional enrichment analysis on target genes within a predetermined CNEs range in the CNEs differentiation sequences to obtain CNEs identification results in the multiple high-quality genomes.

[0118] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: identifying a model species or an evolutionary species located at the root of a phylogenetic tree from a plurality of high-quality genomes; selecting a model species or evolutionary species from a plurality of high-quality genomes as a reference genome.

[0119] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: using the global alignment software lastz to perform whole-genome alignment of other genomes with the reference genome, obtaining raw alignment MAF files of other genomes with the reference genome; using the chainNet software tool to preprocess the raw alignment MAF files to obtain the synnet.axt.maf file corresponding to the genome alignment results, wherein the preprocessing includes the following operations: sorting, filtering, and collinear region search.

[0120] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: merging the synnet.axt.maf files to obtain a multi-species alignment maf file; processing the multi-species alignment maf file according to the topological structure of species evolutionary relationships to obtain a multi-species genome alignment matrix.

[0121] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: using phastCons software to identify conserved sequences in a multi-species genome alignment matrix to obtain a gff file containing conserved sequence position information with reference to a reference genome.

[0122] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: generating a sequence adjacency tree based on the CNE sequences using the FastTree adjacency method; predicting ancestral CNEs using the ancestral module in the Paml software based on the sequence adjacency tree, and obtaining the ancestral CNE prediction results; calculating the divergence time of each species and the ancestral sequence of the root node in the sequence adjacency tree using a Python script; and determining the divergence sequences of CNEs that appeared during species divergence based on the divergence time.

[0123] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: using Annovar software to annotate the CNEs with convergent differences and the CNEs that diverged synchronously with the species in the CNEs differentiation sequences to obtain annotated CNEs differentiation sequences; screening target genes within a predetermined range of CNEs from the annotated CNEs differentiation sequences; and performing functional enrichment analysis on the target genes to obtain CNE identification results.

[0124] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0125] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0127] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0131] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying conserved non-coding elements (CNEs) in the genome, characterized in that, include: A reference genome is selected from multiple high-quality genomes, wherein the high-quality genome is a genome that meets the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a Contig N50 not less than a second predetermined threshold; The other genomes among the multiple high-quality genomes, excluding the reference genome, are respectively compared with the reference genome to obtain genome alignment results; A multi-species genome alignment matrix is ​​generated based on the topological structure of species evolutionary relationships and the genome alignment results. Conserved sequences are identified in the multi-species genome alignment matrix to obtain a gff file containing conserved sequence position information with reference to the reference genome; Based on the coding region location information of the reference genome, sequences containing exon regions in the conserved sequence location information gff file are filtered out to obtain the CNEs.gff file; Extract CNE sequences from the multiple high-quality genomes from the CNEs.gff file; Based on the CNEs sequences, determine the CNEs differentiation sequences that appear during the speciation period; Functional enrichment analysis was performed on the target genes within the predetermined CNE range in the CNE differentiation sequences to obtain the CNE identification results in the multiple high-quality genomes. The determination of CNE differentiation sequences appearing during speciation based on the CNE sequences includes: generating a sequence adjacency tree based on the CNE sequences using the FastTree adjacency method; predicting ancestral CNEs using the ancestral module in Paml software based on the sequence adjacency tree to obtain ancestral CNE prediction results; calculating the differentiation time between each species and the ancestral sequence of the root node in the sequence adjacency tree using a Python script; and determining the CNE differentiation sequences appearing during speciation based on the differentiation time.

2. The method for identifying conserved non-coding elements (CNEs) in the genome according to claim 1, characterized in that, A reference genome was selected from multiple high-quality genomes, including: Identify model species or evolutionary species located at the root of the evolutionary tree in the multiple high-quality genomes; The model species or the evolutionary species are selected from the plurality of high-quality genomes as the reference genome.

3. The method for identifying conserved non-coding elements (CNEs) in the genome according to claim 1, characterized in that, The genomes other than the reference genome among the plurality of high-quality genomes are respectively compared with the reference genome to obtain genome alignment results, including: The other genomes were aligned with the reference genome using the global alignment software lastz, resulting in raw alignment MAF files of the other genomes and the reference genome. The original alignment MAF files were preprocessed using the chainNet software tool to obtain the synnet.axt.maf file corresponding to the genome alignment results. The preprocessing included the following operations: sorting, filtering, and collinearity region search.

4. The method for identifying conserved non-coding elements (CNEs) in the genome according to claim 3, characterized in that, A multi-species genome alignment matrix is ​​generated based on the topological structure of species evolutionary relationships and the genome alignment results, including: The synnet.axt.maf files are merged to obtain a multi-species alignment MAF file; The multi-species alignment MAF file is processed based on the evolutionary topology of the species to obtain a multi-species genome alignment matrix.

5. The method for identifying conserved non-coding elements (CNEs) in the genome according to claim 1, characterized in that, Conserved sequence identification is performed on the multi-species genome alignment matrix to obtain a conserved sequence position information gff file with reference to the reference genome, including: The conserved sequences in the multi-species genome alignment matrix were identified using phastCons software, resulting in a gff file containing the conserved sequence position information with reference to the reference genome.

6. The method for identifying conserved non-coding elements (CNEs) in the genome according to claim 1, characterized in that, Functional enrichment analysis was performed on target genes within the predetermined CNE range in the CNE differentiation sequences to obtain CNE identification results in the multiple high-quality genomes, including: Gene annotation was performed on the CNEs with convergent differences and those that diverged synchronously with the species in the CNEs differentiation sequences using Annovar software, resulting in annotated CNEs differentiation sequences. The target gene within the predetermined CNE range is screened from the annotated CNE differentiation sequences; The target gene was subjected to functional enrichment analysis to obtain the identification results of the CNEs.

7. A device for identifying conserved non-coding elements (CNEs) in the genome, characterized in that, include: The selection unit is used to select a reference genome from a plurality of high-quality genomes, wherein the high-quality genome is a genome that meets the following conditions: a chromosome-level genome, a BUSCO value not less than a first predetermined threshold, and a Contig N50 not less than a second predetermined threshold. The alignment unit is used to perform whole-genome alignment of the other genomes in the plurality of high-quality genomes, excluding the reference genome, with the reference genome to obtain genome alignment results; A generation unit is used to generate a multi-species genome alignment matrix based on the topological structure of species evolutionary relationships and the genome alignment results. A conserved sequence identification unit is used to identify conserved sequences in the multi-species genome alignment matrix to obtain a conserved sequence position information gff file with reference to the reference genome. The filtering unit is used to filter out sequences containing exon regions in the conserved sequence location information gff file based on the coding region location information of the reference genome, so as to obtain the CNEs.gff file; Extraction unit, used to extract CNEs sequences from the plurality of high-quality genomes from the CNEs.gff file; A determining unit is used to determine the CNEs differentiation sequence that appears during the speciation period based on the CNEs sequence; The functional enrichment analysis unit is used to perform functional enrichment analysis on target genes within a predetermined range of CNEs in the CNEs differentiation sequence, and to obtain the CNEs identification results in the multiple high-quality genomes. The determining unit includes: a generation module for generating a sequence adjacency tree based on the CNEs sequences using the FastTree adjacency method; a prediction module for predicting ancestral CNEs using the ancestral module in Paml software based on the sequence adjacency tree, and obtaining ancestral CNEs prediction results; a calculation module for calculating the divergence time of each species and the root node ancestral sequence in the sequence adjacency tree using a Python script; and a third determining module for determining the divergence sequences of the CNEs that appear during species divergence based on the divergence time.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program performs the method for identifying conserved non-coding elements (CNEs) in the genome as described in any one of claims 1 to 6.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they perform the method for identifying conserved non-coding elements (CNEs) in the genome as described in any one of claims 1 to 6.

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