{"id":28321652,"url":"https://github.com/felixernst/rnamodr","last_synced_at":"2025-07-29T02:04:52.228Z","repository":{"id":73649334,"uuid":"53844127","full_name":"FelixErnst/RNAmodR","owner":"FelixErnst","description":":package: RNAmodR: detection of post-transcriptional RNA modifications based on HTS data","archived":false,"fork":false,"pushed_at":"2024-03-26T21:46:06.000Z","size":96458,"stargazers_count":3,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"devel","last_synced_at":"2025-07-27T14:48:40.400Z","etag":null,"topics":["alkanilineseq","bioconductor","modifications","r","ribomethseq","rna","rnamodr"],"latest_commit_sha":null,"homepage":"https://felixernst.github.io/RNAmodR/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/FelixErnst.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2016-03-14T09:40:42.000Z","updated_at":"2024-06-09T13:22:48.000Z","dependencies_parsed_at":"2024-03-26T21:43:22.959Z","dependency_job_id":"a19779ff-f914-45d3-85ed-1766ae71cf52","html_url":"https://github.com/FelixErnst/RNAmodR","commit_stats":{"total_commits":432,"total_committers":4,"mean_commits":108.0,"dds":"0.22916666666666663","last_synced_commit":"114a9f8f781a896205e573c3a87f437978dfe03f"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/FelixErnst/RNAmodR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixErnst%2FRNAmodR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixErnst%2FRNAmodR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixErnst%2FRNAmodR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixErnst%2FRNAmodR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/FelixErnst","download_url":"https://codeload.github.com/FelixErnst/RNAmodR/tar.gz/refs/heads/devel","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixErnst%2FRNAmodR/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267617643,"owners_count":24116208,"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":"2025-07-29T02:00:12.549Z","response_time":2574,"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":["alkanilineseq","bioconductor","modifications","r","ribomethseq","rna","rnamodr"],"created_at":"2025-05-25T12:15:14.298Z","updated_at":"2025-07-29T02:04:52.216Z","avatar_url":"https://github.com/FelixErnst.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RNAmodR \u003cimg src=\"https://raw.githubusercontent.com/Bioconductor/BiocStickers/devel/RNAmodR/RNAmodR.png\" height=\"300\" align=\"right\"\u003e\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/FelixErnst/RNAmodR/workflows/R-CMD-check-bioc-devel/badge.svg)](https://github.com/FelixErnst/RNAmodR/actions/)\n[![BioC Build](https://bioconductor.org/shields/build/devel/bioc/RNAmodR.svg)](http://bioconductor.org/checkResults/devel/bioc-LATEST/RNAmodR/)\n[![codecov](https://codecov.io/gh/FelixErnst/RNAmodR/branch/devel/graph/badge.svg)](https://codecov.io/gh/FelixErnst/RNAmodR)\n[![BioC Years](https://bioconductor.org/shields/years-in-bioc/RNAmodR.svg)](https://doi.org/doi:10.18129/B9.bioc.RNAmodR)\n\u003c!-- badges: end --\u003e\n\nPost-transcriptional modifications can be found abundantly in rRNA and tRNA and\ncan be detected classically via several strategies. However, difficulties arise\nif the identity and the position of the modified nucleotides is to be determined\nat the same time. Classically, a primer extension, a form of reverse\ntranscription (RT), would allow certain modifications to be accessed by blocks\nduring the RT or changes in the cDNA sequences. Other modification would\nneed to be selectively treated by chemical reactions to influence the outcome of\nthe reverse transcription.\n\nWith the increased availability of high throughput sequencing, these classical\nmethods were adapted to high throughput methods allowing more RNA molecules to\nbe accessed at the same time. With these advances post-transcriptional\nmodifications were also detected on mRNA. Among these high throughput techniques\nare for example Pseudo-Seq ([Carlile et al. 2014](#Literature)), RiboMethSeq\n([Birkedal et al. 2015](#Literature)) and AlkAnilineSeq \n([Marchand et al. 2018](#Literature)) each able to detect a specific type of \nmodification from footprints in RNA-Seq data prepared with the selected methods.\n\nSince similar pattern can be observed from some of these techniques, overlaps of\nthe bioinformatical pipeline already are and will become more frequent with new\nemerging sequencing techniques.\n\n# Installation\n\nThe current version of the `RNAmodR` package is available from Bioconductor.\n\n```\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\n\nBiocManager::install(\"RNAmodR\")\nlibrary(RNAmodR)\n```\n\n# Introduction\n\n`RNAmodR` implements classes and a workflow to detect post-transcriptional RNA\nmodifications in high throughput sequencing data. It can be easily adapted for\nnew methods and can help during the phase of initial method development as well\nas more complex screenings.\n\nBriefly, from the `SequenceData` and `SequenceDataFrame` classes, specific \nsubclasses are derived for accessing specific aspects of aligned reads, e.g. \n5’-end positions or pileup data. With these, a `Modifier` class can be used to \ndetect specific patterns for individual types of modifications. The \n`SequenceData` classes can be shared by different `Modifier` classes enabling \neasy adaptation to new methods and benefiting from other efforts in method\ndevelopment.\n\nWhereas, the `SequenceData` classes are used to hold the data, `Modifier`\nclasses are used to detect certain features within high throughput sequencing\ndata to assign the presence of specific modifications for an established\npattern. The `Modifier` class is virtual and can be adapted for individual\nmethods, whereas the type of nucleotide under investigation is specified by\ninheriting from the virtual `RNAModifier` and `DNAModifier` classes. To fix \nthe data processing and detection strategy, for each type of sequencing method \na `Modifier` class can be developed alongside to detect  modifications.\n\nFor further details have a look at the vignette or the additional packages for\nimplementation examples:\n- [RNAmodR.RiboMethSeq](https://doi.org/doi:10.18129/B9.bioc.RNAmodR.RiboMethSeq)\n- [RNAmodR.AlkAnilineSeq](https://doi.org/doi:10.18129/B9.bioc.RNAmodR.AlkAnilineSeq)\n- [RNAmodR.ML](https://doi.org/doi:10.18129/B9.bioc.RNAmodR.ML) for classes \nfascilitating machine learning approaches\n\n# Literature\n\n- Carlile, Thomas M., Maria F. Rojas-Duran, Boris Zinshteyn, Hakyung Shin,\nKristen M. Bartoli, and Wendy V. Gilbert (2014): “Pseudouridine Profiling Reveals\nRegulated mRNA Pseudouridylation in Yeast and Human Cells.” Nature 515 (7525):\n143–46.\n\n- Birkedal, Ulf, Mikkel Christensen-Dalsgaard, Nicolai Krogh, Radhakrishnan\nSabarinathan, Jan Gorodkin, and Henrik Nielsen (2015): “Profiling of Ribose\nMethylations in Rna by High-Throughput Sequencing.” Angewandte Chemie\n(International Ed. In English) 54 (2): 451–55.\nhttps://doi.org/10.1002/anie.201408362.\n\n- Marchand, Virginie, Lilia Ayadi, __Felix G. M. Ernst__, Jasmin Hertler,\nValérie Bourguignon-Igel, Adeline Galvanin, Annika Kotter, Mark Helm, \n__Denis L. J. Lafontaine__, and Yuri Motorin (2018): “AlkAniline-Seq: Profiling \nof m7G and m3C Rna Modifications at Single Nucleotide Resolution.” Angewandte \nChemie International Edition 57 (51): 16785–90. \nhttps://doi.org/10.1002/anie.201810946.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixernst%2Frnamodr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffelixernst%2Frnamodr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixernst%2Frnamodr/lists"}