Gregor Rot, Arne Wehling, Roland Schmucki, Nikolaos Berntenis, Jitao David Zhang, Martin Ebeling
{"title":"<i>splicekit</i>: an integrative toolkit for splicing analysis from short-read RNA-seq.","authors":"Gregor Rot, Arne Wehling, Roland Schmucki, Nikolaos Berntenis, Jitao David Zhang, Martin Ebeling","doi":"10.1093/bioadv/vbae121","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong>Analysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. To the best of our knowledge, there is currently no integrative open-source software dedicated to this task.</p><p><strong>Results: </strong>Here, we introduce <i>splicekit</i>, a Python package that provides and integrates a set of existing and novel splicing analysis tools for conducting splicing analysis.</p><p><strong>Availability and implementation: </strong>The software <i>splicekit</i> is open-source and available at Github (https://github.com/bedapub/splicekit) and <i>via</i> the Python Package Index.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364168/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Motivation: Analysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. To the best of our knowledge, there is currently no integrative open-source software dedicated to this task.
Results: Here, we introduce splicekit, a Python package that provides and integrates a set of existing and novel splicing analysis tools for conducting splicing analysis.
Availability and implementation: The software splicekit is open-source and available at Github (https://github.com/bedapub/splicekit) and via the Python Package Index.