{"title":"motifbreakR v2:扩展的变异分析,包括嵌合和来自转录因子结合数据库的综合证据。","authors":"Simon G Coetzee, Dennis J Hazelett","doi":"10.1093/bioadv/vbae162","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong><i>motifbreakR</i> scans genetic variants against position weight matrices of transcription factors (TFs) to determine the potential for the disruption of binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to query a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single-nucleotide variants on TF binding sites, in <i>motifbreakR</i> v2, we have updated the functionality.</p><p><strong>Results: </strong>New features include the ability to query other types of complex genetic variants, such as short insertions and deletions. This capability allows modeling a more extensive array of variants that may have significant effects on TF binding. Additionally, predictions based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, <i>motifbreakR can directly query</i> the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in <i>motifbreakR</i>, in addition to the existing interface, we implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.</p><p><strong>Availability and implementation: </strong><i>motifbreakR</i> is implemented in R. Source code, documentation, and tutorials are available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and GitHub at https://github.com/Simon-Coetzee/motifBreakR.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae162"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520234/pdf/","citationCount":"0","resultStr":"{\"title\":\"<i>motifbreakR</i> v2: expanded variant analysis including indels and integrated evidence from transcription factor binding databases.\",\"authors\":\"Simon G Coetzee, Dennis J Hazelett\",\"doi\":\"10.1093/bioadv/vbae162\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Motivation: </strong><i>motifbreakR</i> scans genetic variants against position weight matrices of transcription factors (TFs) to determine the potential for the disruption of binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to query a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single-nucleotide variants on TF binding sites, in <i>motifbreakR</i> v2, we have updated the functionality.</p><p><strong>Results: </strong>New features include the ability to query other types of complex genetic variants, such as short insertions and deletions. This capability allows modeling a more extensive array of variants that may have significant effects on TF binding. Additionally, predictions based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, <i>motifbreakR can directly query</i> the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in <i>motifbreakR</i>, in addition to the existing interface, we implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.</p><p><strong>Availability and implementation: </strong><i>motifbreakR</i> is implemented in R. Source code, documentation, and tutorials are available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and GitHub at https://github.com/Simon-Coetzee/motifBreakR.</p>\",\"PeriodicalId\":72368,\"journal\":{\"name\":\"Bioinformatics advances\",\"volume\":\"4 1\",\"pages\":\"vbae162\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520234/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/bioadv/vbae162\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae162","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}
motifbreakR v2: expanded variant analysis including indels and integrated evidence from transcription factor binding databases.
Motivation: motifbreakR scans genetic variants against position weight matrices of transcription factors (TFs) to determine the potential for the disruption of binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to query a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single-nucleotide variants on TF binding sites, in motifbreakR v2, we have updated the functionality.
Results: New features include the ability to query other types of complex genetic variants, such as short insertions and deletions. This capability allows modeling a more extensive array of variants that may have significant effects on TF binding. Additionally, predictions based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR can directly query the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.
Availability and implementation: motifbreakR is implemented in R. Source code, documentation, and tutorials are available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and GitHub at https://github.com/Simon-Coetzee/motifBreakR.