Methylation data analysis method, computing device and program product

Abstract

The present application relates to a methylation data analysis method, a computing device and a program product. The method comprises: determining, based on sequencing data of a to-be-tested sample, a number of methylation reads supporting that each target methylation site is methylated and a number of unmethylation reads supporting that each target methylation site is not methylated; performing standardization correction on the number of methylation reads and the number of unmethylation reads respectively to obtain an effective number of methylation reads and an effective number of unmethylation reads; and identifying inter-group differential methylation regions of a plurality of sample groups based on the effective number of methylation reads and the effective number of unmethylation reads of the to-be-tested sample at each target methylation site.

Description

Technical Field

[0001] This invention relates generally to bioinformatics processing, and more specifically, to a methylation data analysis method, computing device, computer storage medium, and computer program product. Background Technology

[0002] DNA methylation is a form of DNA chemical modification that can alter genetic expression without changing the DNA sequence. In a broad sense, DNA methylation refers to the chemical modification process in which a specific base in the DNA sequence acquires a methyl group through covalent bonding, catalyzed by DNA methyltransferase (DNMT) using S-adenosylmethionine (SAM) as the methyl donor. DNA methylation can occur at sites such as the C-5 position of cytosine, the N-6 position of adenine, and the G-7 position of guanine. Generally, the DNA methylation discussed in research primarily refers to the methylation of the 5th carbon atom of cytosine in CpG dinucleotides, the product of which is called 5-methylcytosine (5-mC), the main form of DNA methylation in eukaryotes such as plants and animals. DNA methylation is a widely studied epigenetic modification that, along with histone and RNA modifications, plays a crucial role in regulating gene expression and altering chromatin conformation.

[0003] HiFi sequencing (High Fidelity Sequencing) is a sequencing modality launched by PacBio in 2019. Based on Circular Consensus Sequencing (CCS), it balances long read lengths (10-20 kb) with high accuracy (>99%). During HiFi sequencing, the kinetic information of each base is recorded, and bioinformatics analysis methods can be used to extract 5mCG methylation information from the sequencing data. Currently, PacBio has developed the pb-CpG-tools tool for methylation information extraction. However, this tool can only extract information from 5mCG methylation sites and cannot perform other analyses, and it can only analyze a single sample. Summary of the Invention

[0004] This invention provides a methylation data analysis method, computing device, computer storage medium, and computer program product. The method integrates the multi-dimensional analysis needs of 5mCG in HiFi data, achieving one-click methylation analysis of 5mCG in HiFi data.

[0005] According to a first aspect of the present invention, a method for methylation data analysis is provided. The method includes: determining, based on sequencing data of a sample to be tested, the number of methylated reads supporting methylation at each target methylation site and the number of unmethylated reads supporting non-methylation at that site; standardizing and correcting the number of methylated reads and the number of unmethylated reads respectively to obtain the number of effective methylated reads and the number of effective unmethylated reads; and identifying differentially methylated regions between multiple sample groups, wherein the sample groups comprise multiple samples to be tested, based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site for each sample to be tested.

[0006] According to a second aspect of the present invention, a computing device is also provided, the computing device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions, when executed by the at least one processor, causing the computing device to implement the steps of the method described in the first aspect of the present invention.

[0007] According to a third aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect of the present invention.

[0008] According to a fourth aspect of the present invention, a computer program product is also provided. This computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in the first aspect of the present invention.

[0009] In some embodiments, the sequencing data is single-molecule long-read sequencing data.

[0010] In some embodiments, standardizing the number of methylated reads and the number of unmethylated reads includes: selecting methylated reference samples for the number of methylated reads and unmethylated reference samples for the number of unmethylated reads based on the statistical characteristics of the total number of methylated reads and the total number of unmethylated reads among the test samples; determining methylation standardization factors for the number of methylated reads and unmethylation standardization factors for the number of unmethylated reads based on the weighted truncated mean of the logarithmic ratio of the relative abundance of methylated reads of each test sample and the methylated reference samples at each target methylation site, and the weighted truncated mean of the logarithmic ratio of the relative abundance of unmethylated reads of each test sample and the unmethylated reference samples at each target methylation site; and standardizing the number of methylated reads and the number of unmethylated reads of each test sample at each target methylation site based on the methylation standardization factors and the unmethylation standardization factors to obtain the effective number of methylated reads and the effective number of unmethylated reads.

[0011] In some embodiments, selecting methylated reference samples and unmethylated reference samples respectively includes: selecting the test sample with the median total number of methylated reads as the methylated reference sample based on the ranking results of the total number of methylated reads of each test sample; and selecting the test sample with the median total number of unmethylated reads as the unmethylated reference sample based on the ranking results of the total number of unmethylated reads of each test sample.

[0012] In some embodiments, the relative abundance of methylated reads indicates the proportion of methylated reads at the target methylation site in the test sample to the total number of methylated reads in the test sample, and the relative abundance of unmethylated reads indicates the proportion of unmethylated reads at the target methylation site in the test sample to the total number of unmethylated reads in the test sample.

[0013] In some embodiments, the calculation of the weighted truncated mean includes: removing the highest and lowest values ​​of a predetermined proportion from the log ratios; and calculating the weighted mean of the remaining log ratios, wherein the weights are negatively correlated with the approximate variance of the corresponding log ratios, the approximate variance being determined based on the number of methylated or unmethylated reads at the corresponding target methylation site of the test sample.

[0014] In some embodiments, the standardization factor is the exponential function value of the weighted truncated mean.

[0015] In some embodiments, identifying differentially methylated regions between groups includes: determining the effective methylation level of each test sample at each target methylation site based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site for each test sample; dividing the genome into multiple genomic intervals according to a predetermined window size; for each genomic interval, performing a statistical test on the effective methylation levels of different sample groups within that genomic interval based on the effective methylation levels of each test sample included in each sample group within that genomic interval; and determining that the genomic interval is a differentially methylated region between groups in response to the statistical test results meeting a predetermined significance condition.

[0016] In some embodiments, statistical testing of the effective methylation levels of different sample groups within genomic intervals includes: for each genomic interval, performing parametric testing on the effective methylation levels of each sample group within the genomic interval, determining the statistical significance between sample groups, performing multiple test correction, and screening genomic intervals where the statistical significance between sample groups meets a predetermined significance condition after correction as differentially methylated regions between groups.

[0017] In some embodiments, the methylation data analysis method further includes: identifying haplotype-differential methylation regions of the sample based on the sequencing data of the sample to be tested and the number of methylated reads and the number of unmethylated reads at each target methylation site of the sample to be tested.

[0018] In some embodiments, identifying haplotype-differential methylation regions includes: dividing the genome into multiple genomic intervals according to a predetermined window size; for each genomic interval, based on the phasing results of the sequencing data of the sample to be tested, determining the total number of methylated reads and the total number of unmethylated reads of each haplotype in the sample to be tested within that genomic interval, so as to perform a statistical test on the methylation level of different haplotypes within that genomic interval; and in response to the statistical test results meeting a predetermined significance condition, determining that genomic interval as a haplotype-differential methylation region.

[0019] In some embodiments, statistical testing of the methylation levels of different haplotypes within a genomic region includes: for each genomic region, performing a nonparametric test on the total number of methylated reads and the total number of unmethylated reads of each haplotype in the sample to be tested within that genomic region, determining the statistical significance between haplotypes, performing multiple test correction, and screening genomic regions whose statistical significance between haplotypes meets the predetermined significance condition after correction as differentially methylated regions between haplotypes.

[0020] In some embodiments, dividing the genome into regions further includes: counting the number of target methylation sites contained in the genome region; and removing genome regions whose number of target methylation sites does not meet a predetermined threshold.

[0021] In some embodiments, the methylation data analysis method further includes: imprinting gene identification based on the identified haplotype-differential methylation regions.

[0022] In some embodiments, imprinting gene identification includes: identifying haplotype differentially methylated genes associated with the identified haplotype differentially methylated regions based on the identified haplotype differentially methylated regions; and identifying haplotype differentially methylated genes existing in a known imprinting gene database as candidate imprinting genes.

[0023] In some embodiments, the methylation data analysis method further includes: positional annotation of the identified differentially methylated regions; and, optionally, functional enrichment analysis or pathway enrichment analysis of the genes within the identified differentially methylated regions.

[0024] It should be understood that the summary section is provided to present the chosen concepts in a simplified form, which will be further described in the detailed description below. The summary section is not intended to identify key or principal features of the invention, nor is it intended to limit the scope of the invention. Attached Figure Description

[0025] Figure 1 A schematic diagram of a system for implementing a methylation analysis method according to an embodiment of the present invention is shown.

[0026] Figure 2 A flowchart of a methylation analysis method according to an embodiment of the present invention is shown.

[0027] Figure 3 A block diagram schematically illustrates a device suitable for implementing embodiments of the present invention.

[0028] In the various figures, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

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

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

[0031] As described above, DNA methylation is a widely studied epigenetic modification that plays a crucial role in regulating gene expression and altering chromatin conformation. HiFi sequencing (High Fidelity Sequencing), launched by PacBio in 2019, is a sequencing mode that balances long read lengths (10-20 kb) and high accuracy (>99%) based on Circular Consensus Sequencing (CCS). During HiFi sequencing, the kinetic information of each base is recorded, and bioinformatics analysis methods can be used to extract 5mCG methylation information from the sequencing data. Currently, PacBio has developed the pb-CpG-tools tool for extracting methylation information. However, this tool can only extract information from 5mCG methylation sites and cannot perform other analyses, and it can only analyze a single sample. To at least partially address one or more of the above-mentioned problems and other potential issues, the example embodiments of this application propose a methylation data analysis method, computing device, and program product.

[0032] Figure 1 A schematic diagram of an exemplary system 100 for implementing a methylation data analysis method according to an embodiment of the present invention is shown. Figure 1 As shown, system 100 includes at least a computing device 110. In some embodiments, system 100 may further include a sequencing device 130, a server 140, and a network 150. In some embodiments, the computing device 110, sequencing device 130, and server 140 may communicate and interact with each other via wired or wireless means (e.g., via network 150).

[0033] Regarding sequencing device 130, it is used, for example, to sequence a sample to be tested to generate sequencing data about the nucleic acid sequence information of the sample. In some embodiments, sequencing device 130 can also send the generated sequencing data about the sample to computing device 110. In some embodiments, server 140 can also send the sequencing data about the sample to computing device 110. In some embodiments, sequencing device 130 is, for example, a sequencing device based on circular consensus sequence (CCS). In some embodiments, the sequencing data can be HiFi sequencing (High Fidelity reads) data, particularly PacBio's sequencing data based on circular consensus sequence mode, which balances long read length (10-20kb) and high accuracy (>99% accuracy).

[0034] Regarding server 140, it is used, for example, to store or provide reference genome sequence information. For instance, server 140 may store or provide human reference genome sequence information obtained from public databases, including but not limited to human reference genome GRCh37, human reference genome GRCh38, or the T2T-CHM13 reference sequence released by the Telomere-to-Telomere Consortium. In some embodiments, server 140 may also store and maintain information on 5mCG methylation sites, particularly methylation sites extracted by the pb-CpG-tools tool developed by PacBio, for reference in subsequent analyses.

[0035] Regarding the computing device 110, it is used, for example, to perform a methylation data analysis method. In some embodiments, the computing device 110 is used to determine, based on sequencing data of a test sample, the number of methylated reads supporting methylation at each target methylation site and the number of unmethylated reads supporting non-methylation at that site; to standardize and correct the number of methylated reads and the number of unmethylated reads respectively to obtain the number of effective methylated reads and the number of effective unmethylated reads; and, based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site of each test sample, to identify differentially methylated regions between multiple sample groups, wherein the sample group comprises multiple test samples.

[0036] In some embodiments, the computing device 110 may have one or more processing units, including dedicated processing units such as GPUs, FPGAs, and ASICs, and general-purpose processing units such as CPUs. The computing device 110 may be a single physical device, or an integration of multiple physical servers, an integration of multiple processing units, etc. Additionally, one or more virtual machines may run on each computing device 110. The specific structure of the computing device 110 may be, for example, combined as follows: Figure 3 In some embodiments, the computing device 110 and the server 140 may be integrated together or set up separately.

[0037] In some embodiments, the computing device 110 includes, for example, a methylation site detection unit 112, a methylation data correction unit 114, an intergroup differential methylation region identification unit 116, an inter-haploid differential methylation region identification unit 118, an imprinted gene identification unit 120, and a differential methylation gene function analysis unit 122. The aforementioned methylation site detection unit 112, methylation data correction unit 114, intergroup differential methylation region identification unit 116, inter-haploid differential methylation region identification unit 118, imprinted gene identification unit 120, and differential methylation gene function analysis unit 122 can be configured on one or more computing devices 110.

[0038] Regarding the methylation site detection unit 112, it is used, for example, to determine, based on the sequencing data of the sample to be tested, the number of methylated reads at each target methylation site that support methylation of that site and the number of unmethylated reads that support non-methylation of that site.

[0039] Regarding the methylation data correction unit 114, it is used, for example, to standardize and correct the number of methylated reads and the number of unmethylated reads respectively to obtain the number of effective methylated reads and the number of effective unmethylated reads.

[0040] Regarding the intergroup differential methylation region identification unit 116, it is used, for example, to identify intergroup differential methylation regions of multiple sample groups based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site for each test sample.

[0041] Regarding the haplotype differential methylation region identification unit 118, it is used, for example, to identify haplotype differential methylation regions of the test sample based on the sequencing data of the test sample and the number of methylated reads and the number of unmethylated reads at each target methylation site of the test sample.

[0042] Regarding the imprinting identification unit 120, it is used, for example, for imprinting identification based on the identified haplotype-differential methylation regions.

[0043] Regarding the differentially methylated gene function analysis unit 122, it is used, for example, to annotate the location of the identified differentially methylated regions; and, optionally, to perform functional enrichment analysis or pathway enrichment analysis on the differentially methylated genes within the identified differentially methylated regions.

[0044] The following will combine Figure 2 A methylation data analysis method according to an embodiment of the present invention is described. Figure 2 A flowchart of a methylation data analysis method 200 according to an embodiment of the present invention is shown. It should be understood that method 200 can, for example, be implemented in... Figure 3 The described device executes at 500 points, and can also be used in Figure 1 The described computing device 110 performs the operation. It should be understood that method 200 may also include additional actions not shown and / or the actions shown may be omitted, and the scope of the invention is not limited in this respect.

[0045] In step 202, the computing device 110 determines, based on the sequencing data of the sample to be tested, the number of methylated reads supporting methylation at each target methylation site and the number of unmethylated reads supporting non-methylation at that site.

[0046] The term "test sample" typically refers to a target biological sample for which methylation analysis is required. In some embodiments, the test sample may be derived from a human. The nucleic acids in the test sample may be derived from any tissue, cell, or body fluid sample of interest.

[0047] Regarding sample sequencing data, it may include, for example, the nucleic acid sequence information of the sample. In some embodiments, the sequencing data is single-molecule long-read sequencing data. More specifically, the sequencing data may be HiFi sequencing (High Fidelity reads) data, particularly PacBio's sequencing data based on circularized concordance sequence patterns, which balances long read lengths (10-20 kb) and high accuracy (>99% accuracy). HiFi sequencing data not only provides base sequence information but also, by recording the kinetic information of each base (such as polymerase kinetics), allows for the extraction of 5 mCG methylation information from the sequencing data using bioinformatics methods.

[0048] Before methylation site detection, raw sequencing data typically undergoes preliminary quality control and filtering processes, including but not limited to removing low-quality sequencing sequences and adapter sequences. Those skilled in the art should understand that such quality control and filtering can be implemented using any suitable quality control and filtering tools, and the specific processes and parameters can be flexibly selected based on the sequencing platform, sample characteristics, and experimental objectives.

[0049] A method for determining the number of methylated and unmethylated reads at each target methylation site in the sample to be tested includes, for example, the following: A computing device 110 uses a methylation detection tool to calculate, based on the 5mCG methylation tag information carried in the HiFi sequencing data, the number of reads supporting methylation Mj and the number of reads supporting unmethylation Uj at each CG site in the genome from the alignment results. In some embodiments, the methylation detection tool may be a pb-CpG-tools tool, or other tools capable of extracting 5mCG methylation information from HiFi sequencing data. The total depth at each CG site Nj = Mj + Uj. The methylation level at each CG site Pj = Mj / Nj.

[0050] In some embodiments, after determining the number of methylated reads and unmethylated reads at each target methylation site, the computing device 110 can also count the number of CG methylation sites occurring in each sample and statistically analyze and visualize the frequency distribution of methylation levels. One method is to statistically analyze the frequency distribution of methylation levels; another method is to statistically analyze and visualize the average methylation level of CG sites in various genomic functional regions (such as promoter regions, exon regions, intron regions, UTR regions, etc.) for each sample.

[0051] In step 204, the computing device 110 performs standardization correction on the number of methylated reads and the number of unmethylated reads to obtain the number of effective methylated reads and the number of effective unmethylated reads.

[0052] In some embodiments, standardizing the number of methylated reads and the number of unmethylated reads includes: selecting methylated reference samples for the number of methylated reads and unmethylated reference samples for the number of unmethylated reads based on the statistical characteristics of the total number of methylated reads and the total number of unmethylated reads among the test samples; determining methylation standardization factors for the number of methylated reads and unmethylation standardization factors for the number of unmethylated reads based on the weighted truncated mean of the logarithmic ratio of the relative abundance of methylated reads of each test sample and the methylated reference samples at each target methylation site, and the weighted truncated mean of the logarithmic ratio of the relative abundance of unmethylated reads of each test sample and the unmethylated reference samples at each target methylation site; and standardizing the number of methylated reads and the number of unmethylated reads of each test sample at each target methylation site based on the methylation standardization factors and the unmethylation standardization factors to obtain the effective number of methylated reads and the effective number of unmethylated reads.

[0053] Specifically, the selection of methylated reference samples and unmethylated reference samples includes: selecting the test sample with the median total number of methylated reads based on the ranking results of the total number of methylated reads of each test sample as the methylated reference sample; and selecting the test sample with the median total number of unmethylated reads based on the ranking results of the total number of unmethylated reads of each test sample as the unmethylated reference sample.

[0054] The relative abundance of methylated reads indicates the proportion of methylated reads at the target methylation site in the test sample to the total number of methylated reads in the test sample, and the relative abundance of unmethylated reads indicates the proportion of unmethylated reads at the target methylation site in the test sample to the total number of unmethylated reads in the test sample.

[0055] Since multiple samples may have differences in sequencing depth, in order to eliminate the differences in sequencing depth, for each pair of features (Mj / Uj) between the test sample and the reference sample, the logarithmic ratio is calculated, as follows: in It is the depth of the j-methylation site in the sample to be corrected. It is the total depth of all CG methylation sites in the sample to be corrected; It is the depth of the methylation site at site j in the reference sample; This represents the total depth of all CG methylation sites in the reference sample. Mvalue_Uj is calculated using the same method.

[0056] In some embodiments, the calculation of the weighted truncated mean includes: removing a predetermined proportion of the highest and lowest values ​​from the log ratios (e.g., removing 30% of the highest and 30% of the lowest values); and calculating the weighted mean of the remaining log ratios, wherein the weights are negatively correlated with the approximate variance of the corresponding log ratios, the approximate variance being determined based on the number of methylated or unmethylated reads at the corresponding target methylation site in the test sample. The formula for the weights is as follows: in It is the weight value of Mj or Uj at position j. It is the total depth of the sample to be calibrated, either Mj or Uj. It is the depth of site j in the sample to be corrected, either Mj or Uj. It is the total depth of the reference sample of Mj or Uj. It is the depth of site j in the reference sample.

[0057] Calculate the weighted trimmed average The exponential function is used as the standardization factor, and the formula is as follows: In some embodiments, the normalization factor is an exponential function value of the weighted truncated mean. The effective sequencing depth is obtained by multiplying the original sequencing depths of Mj and Uj by the calculated normalization factor, as shown in the following formula: The depths of the corrected Mj and Uj are denoted as and Calculate the methylation level at each CG site: In step 206, the computing device 110 identifies differentially methylated regions between multiple sample groups based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site for each sample to be tested. The sample groups include multiple samples to be tested.

[0058] In some embodiments, identifying differentially methylated regions between groups includes: determining the effective methylation level of each test sample at each target methylation site based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site for each test sample; dividing the genome into multiple genomic intervals according to a predetermined window size; for each genomic interval, performing a statistical test on the effective methylation levels of different sample groups within that genomic interval based on the effective methylation levels of each test sample included in each sample group within that genomic interval; and determining that the genomic interval is a differentially methylated region between groups in response to the statistical test results meeting a predetermined significance condition.

[0059] Specifically, for experimental designs with multiple samples and grouping, the genome is first divided into multiple windows of a specified size, such as 1000 bp. For each sample, the unmethylation depth Uj and methylation depth Mj of each CG site are calculated. The results from all samples are then combined. Assuming there are two groups, denoted as A and B, where n1 represents the number of samples in group A and n2 represents the number of samples in group B, the methylation level of each CG site in all samples is combined. Windows with fewer than 3 CG sites are filtered out. A filtered window is denoted as i, and the methylation level of each CG site in each group is calculated and denoted as Ai,j and Bi,j.

[0060] In some embodiments, statistical testing of the effective methylation levels of different sample groups within genomic intervals includes: for each genomic interval, performing a parametric test on the effective methylation levels of each sample group within the genomic interval to determine the statistical significance between sample groups, performing multiple test correction, and selecting genomic intervals where the statistical significance between sample groups meets a predetermined significance condition after correction as differentially methylated regions between groups. For example, for each window, a t-test analysis is performed, and a Benjamini-Hochberg multiple test is used to correct the p-value to obtain the q-value, selecting intervals with q-values ​​< 0.05 as the inter-group GDMR.

[0061] In some embodiments, the methylation data analysis method further includes positional annotation of the identified differentially methylated regions. For example, statistical annotation of selected GDMRs in different regions of the genome (such as promoter regions, UTRs, exon regions, etc.). Optionally, functional enrichment analysis or pathway enrichment analysis is performed on differentially methylated genes within the identified differentially methylated regions. For example, GO enrichment analysis and / or KEGG enrichment analysis are performed on genes in the GDMR regions.

[0062] In some embodiments, the methylation data analysis method further includes identifying haplotype-differential methylation regions of the test sample based on the sequencing data of the test sample and the number of methylated reads and the number of unmethylated reads at each target methylation site of the test sample.

[0063] Specifically, identifying haplotype-differential methylation regions includes: dividing the genome into multiple genomic intervals according to a predetermined window size; for each genomic interval, based on the phasing results of the sequencing data of the sample to be tested, determining the total number of methylated reads and the total number of unmethylated reads of each haplotype in the sample to be tested within that genomic interval, so as to perform statistical testing on the methylation level of different haplotypes within that genomic interval; and in response to the statistical testing results meeting a predetermined significance condition, determining that genomic interval as a haplotype-differential methylation region.

[0064] In some embodiments, statistical testing of the methylation levels of different haplotypes within a genomic region includes: for each genomic region, performing a nonparametric test on the total number of methylated reads and the total number of unmethylated reads of each haplotype in the sample to be tested within that genomic region, determining the statistical significance between haplotypes, performing multiple test correction, and screening genomic regions whose statistical significance between haplotypes meets the predetermined significance condition after correction as differentially methylated regions between haplotypes.

[0065] Specifically, for the experimental design of a single sample, the genome is divided into multiple windows of a specified size, such as 1000 bp. Windows with fewer than 3 CG sites are filtered out. A filtered window is designated as i, and the sums of unmethylation depths of CG sites in the two haplotypes within window i (HU1i and HU2i) and the sums of methylation depths (HM1i and HM2i) are calculated. For each window, a two-sided Fisher exact test is performed, and the p-value is corrected using a Benjamini-Hochberg multiple test to obtain the q-value. Intervals with q-values ​​< 0.05 are selected as inter-haplotype HDMRs. Annotations are then performed on the selected HDMRs in different regions of the genome (e.g., promoter regions, UTRs, exon regions, etc.). GO enrichment and / or KEGG enrichment analyses are performed on genes within the HDMR regions.

[0066] In some embodiments, dividing the genome into regions further includes: counting the number of target methylation sites contained in the genome region; and removing genome regions whose number of target methylation sites does not meet a predetermined threshold.

[0067] In some embodiments, the methylation data analysis method further includes imprinting gene identification based on the identified haplotype-differential methylation regions.

[0068] Specifically, imprinted gene identification includes: identifying haplotype differentially methylated genes associated with the identified haplotype differentially methylated regions; and identifying haplotype differentially methylated genes existing in a known imprinted gene database as candidate imprinted genes.

[0069] For example, download the imprinted gene list of different species from the Geneimprint website (https: / / www.geneimprint.com / ), obtain the differentially methylated genes by haplotype HDMRs, and compare them with imprinted genes in the imprinted gene database. Identify imprinted genes that are present in both haplotypes as candidate imprinted genes. Analyze the methylation status of these candidate imprinted genes.

[0070] In some embodiments, the methylation data analysis method further includes positional annotation of the identified differentially methylated regions; and, optionally, functional enrichment analysis or pathway enrichment analysis of the differentially methylated genes within the identified differentially methylated regions.

[0071] In some embodiments, this method automates the entire process of HiFi data processing, from raw offline data to methylation analysis, using a hybrid architecture encompassing Python, HTML, and R. Specifically, the method includes data quality control, sequence alignment, variant detection and annotation, variant phasing, 5mCG methylation site detection, haplotype differential methylation region analysis, imprinted gene identification analysis, and intergroup differential methylation region analysis. This method is characterized by high efficiency, scalability, and result visualization.

[0072] In some embodiments, this method allows for flexible configuration of analysis nodes, memory, and CPU usage through the SJM task management system, facilitating troubleshooting and resume from where interrupted. One-click analysis is achieved by configuring sample information and reference genome project information, eliminating the need for separate configuration of input files for each software. This method is applicable to the analysis of single samples or multiple samples in different groups; it can perform analysis in a single dimension or multiple dimensions. The method statistically analyzes and visualizes the results in each analysis module, ultimately generating visualized reports and standardized result files.

[0073] Through the above steps, this application achieves multidimensional analysis of 5mCG methylation in HiFi data, which can effectively eliminate the systematic bias caused by the difference in sequencing depth between samples, stably and accurately identify differentially methylated regions between groups and between haplotypes, and further perform imprinted gene identification and functional enrichment analysis.

[0074] Figure 3The schematic diagram illustrates an example device 300 suitable for implementing embodiments of the present invention. For example, device 300 may be as follows: Figure 1 The computing device 110 shown. For example, device 300 may be used to implement execution Figure 2 The device shown in method 200.

[0075] like Figure 3 As shown, device 300 includes a central processing unit (i.e., CPU 301), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (i.e., ROM 302) or loaded from storage unit 308 into random access memory (i.e., RAM 303). RAM 303 may also store various programs and data required for the operation of electronic device 300. CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. Input / output interface (i.e., I / O interface 305) is also connected to bus 304.

[0076] Multiple components in device 300 are connected to I / O output interface 305, including: input unit 306, such as keyboard, mouse, microphone, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless communication transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0077] The various processes and handling described above, such as method 200, can be executed by CPU 301. For example, in some embodiments, method 200 may be implemented as a computer software program stored on a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by CPU 301, one or more actions of method 200 described above may be performed. Alternatively, in other embodiments, CPU 301 may be configured to perform one or more actions of method 200 by any other suitable means (e.g., by means of firmware).

[0078] It should be further noted that the present invention can be a method, apparatus, system, device, computer-readable storage medium, and / or computer program product. The computer program product may include computer-readable program instructions for performing various aspects of the present invention.

[0079] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0080] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0081] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0082] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0083] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0084] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0085] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0086] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0087] The above are merely optional embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing methylation data, comprising: Based on the sequencing data of the sample to be tested, determine the number of methylated reads that support methylation at each target methylation site and the number of unmethylated reads that support non-methylation at each target methylation site. The number of methylated reads and the number of unmethylated reads are standardized and corrected respectively to obtain the effective number of methylated reads and the effective number of unmethylated reads; and, Based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site of each test sample, differential methylation regions between multiple sample groups are identified, wherein each sample group contains multiple test samples. The standardization correction for the number of methylated reads and the number of unmethylated reads includes: Based on the statistical characteristics of the total number of methylated reads and the total number of unmethylated reads among the test samples, methylated reference samples for the number of methylated reads and unmethylated reference samples for the number of unmethylated reads are selected respectively. Based on the weighted truncated mean of the logarithmic ratios of the relative abundance of methylated reads at each target methylation site between each test sample and the methylated reference sample, and the weighted truncated mean of the logarithmic ratios of the relative abundance of unmethylated reads at each target methylation site between each test sample and the unmethylated reference sample, a methylation normalization factor for the number of methylated reads and an unmethylation normalization factor for the number of unmethylated reads are determined, respectively; and, Based on the methylation normalization factor and the unmethylation normalization factor, the number of methylated reads and the number of unmethylated reads at each target methylation site of each test sample are standardized and corrected to obtain the effective number of methylated reads and the effective number of unmethylated reads.

2. The method according to claim 1, wherein, The sequencing data is single-molecule long-read sequencing data.

3. The method according to claim 1, wherein, Selected methylated and unmethylated reference samples respectively include: Based on the ranking of the total methylated reads of each test sample, the test sample with the median total methylated read count was selected as the methylation reference sample; and, Based on the ranking of the total number of unmethylated reads for each test sample, the test sample with the median total number of unmethylated reads was selected as the unmethylated reference sample.

4. The method according to claim 1, wherein, The relative abundance of methylated reads indicates the proportion of methylated reads at the target methylation site in the test sample to the total number of methylated reads in the test sample, and the relative abundance of unmethylated reads indicates the proportion of unmethylated reads at the target methylation site in the test sample to the total number of unmethylated reads in the test sample.

5. The method according to claim 1, wherein, The calculation of the weighted truncated mean includes: Remove the highest and lowest values ​​of the predetermined proportions from the logarithmic ratios; and, Calculate the weighted mean of the remaining logarithmic ratios, where the weights are negatively correlated with the approximate variance of the corresponding logarithmic ratios, and the approximate variance is determined based on the number of methylated or unmethylated reads at the corresponding target methylation site in the test sample.

6. The method according to claim 1, wherein, The standardization factor is the exponential function value of the weighted truncated mean.

7. The method according to claim 1, wherein, Differential methylation regions between groups include: Based on the number of effective methylated reads and the number of effective unmethylated reads at each target methylation site for each test sample, the effective methylation level at each target methylation site for each test sample is determined. The genome is divided into multiple genomic regions according to a predetermined window size; For each genomic region, based on the effective methylation levels of each sample within that genomic region, statistical tests were performed on the effective methylation levels of different sample groups within that genomic region; and, In response to the statistical test results meeting the predetermined significance criteria, the genomic region is determined to be a differentially methylated region between groups.

8. The method according to claim 7, wherein, Statistical tests were performed on the effective methylation levels within genomic regions for different sample groups, including: For each genomic region, the effective methylation level of each sample group within the genomic region is parametrically tested to determine the statistical significance between sample groups. Multiple tests are then performed to correct the statistical significance between sample groups. Genomic regions that meet the predetermined significance criteria after correction are selected as differentially methylated regions between groups.

9. The method according to claim 1, further comprising: Based on the sequencing data of the sample to be tested and the number of methylated reads and the number of unmethylated reads at each target methylation site, haplotype differential methylation regions of the sample to be tested are identified.

10. The method according to claim 9, wherein, Identifying differentially methylated regions between haplotypes includes: The genome is divided into multiple genomic regions according to a predetermined window size; For each genomic region, based on the phasing results of the sequencing data of the sample to be tested, the total number of methylated reads and the total number of unmethylated reads for each haplotype in the sample to be tested within that genomic region are determined, so as to perform statistical testing on the methylation levels of different haplotypes within that genomic region; and, In response to the statistical test results meeting the predetermined significance condition, the genomic region is determined to be a haplotype differentially methylated region.

11. The method according to claim 10, wherein, Statistical tests on the methylation levels of different haplotypes within genomic regions included: For each genomic region, a nonparametric test is performed on the total number of methylated reads and the total number of unmethylated reads of each haplotype in the sample to be tested within that genomic region to determine the statistical significance between haplotypes. Multiple test correction is then performed, and genomic regions whose statistical significance between haplotypes meets the predetermined significance conditions after correction are selected as differentially methylated regions between haplotypes.

12. The method according to claim 7 or 10, wherein, Dividing the genome into regions also includes: Count the number of target methylation sites contained within the genomic region; and, Remove genomic regions where the number of target methylation sites does not meet a predetermined threshold.

13. The method according to claim 9, further comprising: Imprinted genes were identified based on the differentially methylated regions between haplotypes.

14. The method according to claim 13, wherein, Imprinted gene identification includes: Based on the identified haplotype differential methylation regions, haplotype differential methylation genes associated with the haplotype differential methylation regions are determined; and, Haplotype differentially methylated genes existing in known imprinted gene databases were identified as candidate imprinted genes.

15. The method according to claim 1 or 9, further comprising: Location annotation was performed on the identified differentially methylated regions; and, Optionally, functional enrichment analysis or pathway enrichment analysis may be performed on differentially methylated genes within the identified differentially methylated regions.

16. A computing device, wherein, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, cause the computing device to perform the steps of the method according to any one of claims 1 to 15.

17. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 15.

18. A computer program product comprising a computer program, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 15.

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