{"id":20829366,"url":"https://github.com/johnguoy/imatools","last_synced_at":"2025-10-09T21:05:20.344Z","repository":{"id":37412887,"uuid":"450757702","full_name":"JohnGuoy/iMATools","owner":"JohnGuoy","description":"An integrated methylation pattern region identification and annotation platform iMATools based on long-read sequencing data is used to process the coding, identification and feature annotation of methylation pattern regions in ultra-large-scale long-read methylation information, with a view to providing methylation Methylation researchers provide specialized methylation analysis and visualization tools for long-read sequencing to precisely reveal DNA methylation patterns at the cellular and read levels.","archived":false,"fork":false,"pushed_at":"2022-06-21T08:59:11.000Z","size":466,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-12T07:45:38.266Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Perl","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JohnGuoy.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-01-22T08:17:21.000Z","updated_at":"2024-11-14T18:18:25.000Z","dependencies_parsed_at":"2022-08-18T04:10:14.197Z","dependency_job_id":null,"html_url":"https://github.com/JohnGuoy/iMATools","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/JohnGuoy/iMATools","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnGuoy%2FiMATools","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnGuoy%2FiMATools/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnGuoy%2FiMATools/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnGuoy%2FiMATools/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JohnGuoy","download_url":"https://codeload.github.com/JohnGuoy/iMATools/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnGuoy%2FiMATools/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279002068,"owners_count":26083285,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-09T02:00:07.460Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-17T23:20:04.510Z","updated_at":"2025-10-09T21:05:20.311Z","avatar_url":"https://github.com/JohnGuoy.png","language":"Perl","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n## iMATools\n\u0026emsp;\u0026emsp;We developed an integrated DNA methylation pattern region identification and annotation platform iMATools (integrated Methylation Analysis Tools) based on long-read sequencing data, which is used for encoding processing, format conversion, methylation in ultra-large-scale long-read methylation information It aims to provide methylation researchers with professional methylation analysis and visualization tools for long-read sequencing, and accurately reveal DNA methylation patterns at the cellular and read levels.\n\n\u0026emsp;\u0026emsp;There are 3 software tools in iMATools platform:\n* **towig** - methylation level file to Wiggle format\n* **pattern** - identification of methylation patterns  of genomic reigons\n* **mrv** - visualization of CpG sites in specific regions of the genome\n\n### Workflow\n\n### Install\nYou first need to install Python v3.8+ and Perl v5.16+, then iMATools can be used directly after decompressing. \n```\nunzip iMATools-main.zip\n```\n\nFor the mrv tool software, you need to install some dependency packages first:\n```\npip install portion\npip install tqdm\npip install matplotlib\n```\n\n### Manual\n\nThese are simple examples.\n\n__Usage:__ Convert \"H1_bismark.cov\" into \"wig\" format. Methy counts is in col 5,unmethy counts is in col 6.\n```shell\ntowig -t bismark -n H1 -r hg19.fa -i H1_bt2.bismark.cov -o H1_wig\n```\n__Usage:__ Identify methylation patterns from \"H1_wig/\".\n```shell\npattern -i H1_wig/ -o H1_pattern/ -n H1\n```\n__Usage:__ Visualizing methylation of CpG sites in specific regions of Y chromosome third-generation sequencing data\n```shell\npython mrv.py --data-file ./Y10895.txt --chromosome Y --cpg-range [10084283,10090100]\n```\nOutput `Y_10084283_10087018_visualization.svg` file, you can open this file with Chrome browser, where black dots represent methylated CpG sites and white dots represent unmethylated CpG sites:\n![ CpG ranges [10084283,10087018] of Y chromosome ](https://github.com/JohnGuoy/iMATools/blob/main/test_data/Y_10084283_10087018_visualization.jpg)\n\n### Using Tips\n\n1 If you use PBS(Portable Batch System) in your cluster server, **avoid to appoint relative path** for `-o,--outdir` and other parameters which need to assign path because workspace will be changed when pbs file is submitted. \n\n2 wiggle format\nMore detail information in [UCSC Genome Browser: Wiggle Track Format (WIG)](http://genome.ucsc.edu/goldenPath/help/wiggle.html).\n\n3 construction information\niMATools is contructed in `Python v3.8` and `Perl v5.16.3`. \nand iMATools is tested in Python v3.8 and Perl v5.16.3. \n\n4 dependence relationship\ntowig is independent. Input could come from `BSMAP`,`Bismark` or ENCODE, Roadmap, TCGA.\npattern is independent. \nmrv requires libraries of shutil, portion, matplotlib and tqdm. \n\n### A work we did with iMATools platform\nWe used the towig and pattern tool software in the iMATools platform to mine the intermediate methylation pattern regions in the ONT sequencing data of normal breast cells. The average methylation level of this methylation pattern region is about 0.5, which can be found on the IGV Genome Browser. It is intuitive to see that the methylation level of this region is at an intermediate level.\n\nWe also used the mrv tool software in the iMATools platform to visualize and analyze the intermediate methylation pattern regions of imprinted genes and non-imprinted genes at the read level, and found the CpG sites of the reads in the pattern region. Whether or not they are methylated varies greatly in overall form, and we speculate that imprinted and non-imprinted genes differ in the formation of intermediate methylation pattern regions.\n\nVisualization of the intermediate methylation pattern region (IMR) and its reads adjacent to the imprinted gene **FAM50B**:\n![FAM50B](https://github.com/JohnGuoy/iMATools/blob/main/test_data/FAM50B.png)\n\nIt can be seen intuitively that the multiple reads in this region can be clearly divided into two categories. One category of reads has almost all methylated CpG sites (the black dots in the figure represent methylated reads). CpG sites, white dots represent unmethylated CpG sites), while another class of reads has almost no CpG sites unmethylated. This is the obvious feature of allele-specific methylation regions, and we speculate that the intermediate methylation pattern regions of imprinted genes are caused by their allelic differential methylation. As for the reason why a CpG site on a read in the visualization is not fully methylated or not methylated at all, it may be due to errors in third-generation sequencing.\n\nVisualization of intermediate methylation pattern regions (IMRs) and their reads adjacent to the non-imprinted gene **FRMD6**:\n![FRMD6](https://github.com/JohnGuoy/iMATools/blob/main/test_data/FRMD6.png)\n\nThe intermediate methylation pattern regions of non-imprinted genes cannot be clearly divided into two categories. We therefore speculate that the intermediate methylation pattern regions of common genes are phenotypes of intermediate states in their demethylation processes, rather than caused by allelic differential methylation.\n\nStudies have shown that FAM50B (family with sequence similarity 50 member B) is a protein-coding gene. Diseases associated with the FAM50B gene include Temple syndrome and gestational trophoblastic tumor. The function of the FAM50B gene may be regulated by allele-specific methylation, and the method used in this section provides an alternative tool for the analysis and validation of this hypothesis.\n\nThe ONT sequencing data file of normal breast cells used in this work, R143-0107.filt.fq.gz, can be obtained from the person in charge of the Institute of Biomedical Big Data, Eye Hospital Affiliated to Wenzhou Medical University.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjohnguoy%2Fimatools","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjohnguoy%2Fimatools","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjohnguoy%2Fimatools/lists"}