Ryan Bressler, Jake Lin, Andrea Eakin, Thomas Robinson, Richard Kreisberg, Hector Rovira, Theo Knijnenburg, John Boyle, Ilya Shmulevich
{"title":"Fastbreak: a tool for analysis and visualization of structural variations in genomic data.","authors":"Ryan Bressler, Jake Lin, Andrea Eakin, Thomas Robinson, Richard Kreisberg, Hector Rovira, Theo Knijnenburg, John Boyle, Ilya Shmulevich","doi":"10.1186/1687-4153-2012-15","DOIUrl":null,"url":null,"abstract":"<p><p>Genomic studies are now being undertaken on thousands of samples requiring new computational tools that can rapidly analyze data to identify clinically important features. Inferring structural variations in cancer genomes from mate-paired reads is a combinatorially difficult problem. We introduce Fastbreak, a fast and scalable toolkit that enables the analysis and visualization of large amounts of data from projects such as The Cancer Genome Atlas.</p>","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":"2012 1","pages":"15"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1687-4153-2012-15","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURASIP journal on bioinformatics & systems biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/1687-4153-2012-15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Genomic studies are now being undertaken on thousands of samples requiring new computational tools that can rapidly analyze data to identify clinically important features. Inferring structural variations in cancer genomes from mate-paired reads is a combinatorially difficult problem. We introduce Fastbreak, a fast and scalable toolkit that enables the analysis and visualization of large amounts of data from projects such as The Cancer Genome Atlas.