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dc.contributor.authorKucukural, Alper
dc.contributor.authorYukselen, Onur
dc.contributor.authorOzata, Deniz M.
dc.contributor.authorMoore, Melissa J.
dc.contributor.authorGarber, Manuel
dc.date2022-08-11T08:07:59.000
dc.date.accessioned2022-08-23T15:38:01Z
dc.date.available2022-08-23T15:38:01Z
dc.date.issued2019-01-05
dc.date.submitted2019-01-09
dc.identifier.citation<p>BMC Genomics. 2019 Jan 5;20(1):6. doi: 10.1186/s12864-018-5362-x. <a href="https://doi.org/10.1186/s12864-018-5362-x">Link to article on publisher's site</a></p>
dc.identifier.issn1471-2164 (Linking)
dc.identifier.doi10.1186/s12864-018-5362-x
dc.identifier.pmid30611200
dc.identifier.urihttp://hdl.handle.net/20.500.14038/25845
dc.description.abstractBACKGROUND: Sequencing data has become a standard measure of diverse cellular activities. For example, gene expression is accurately measured by RNA sequencing (RNA-Seq) libraries, protein-DNA interactions are captured by chromatin immunoprecipitation sequencing (ChIP-Seq), protein-RNA interactions by crosslinking immunoprecipitation sequencing (CLIP-Seq) or RNA immunoprecipitation (RIP-Seq) sequencing, DNA accessibility by assay for transposase-accessible chromatin (ATAC-Seq), DNase or MNase sequencing libraries. The processing of these sequencing techniques involves library-specific approaches. However, in all cases, once the sequencing libraries are processed, the result is a count table specifying the estimated number of reads originating from each genomic locus. Differential analysis to determine which loci have different cellular activity under different conditions starts with the count table and iterates through a cycle of data assessment, preparation and analysis. Such complex analysis often relies on multiple programs and is therefore a challenge for those without programming skills. RESULTS: We developed DEBrowser as an R bioconductor project to interactively visualize every step of the differential analysis, without programming. The application provides a rich and interactive web based graphical user interface built on R's shiny infrastructure. DEBrowser allows users to visualize data with various types of graphs that can be explored further by selecting and re-plotting any desired subset of data. Using the visualization approaches provided, users can determine and correct technical variations such as batch effects and sequencing depth that affect differential analysis. We show DEBrowser's ease of use by reproducing the analysis of two previously published data sets. CONCLUSIONS: DEBrowser is a flexible, intuitive, web-based analysis platform that enables an iterative and interactive analysis of count data without any requirement of programming knowledge.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=30611200&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rights© The Author(s). 2019. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectData visualization
dc.subjectDifferential expression
dc.subjectInteractive data analysis
dc.subjectUMCCTS funding
dc.subjectAmino Acids, Peptides, and Proteins
dc.subjectBiochemistry, Biophysics, and Structural Biology
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectGenetic Phenomena
dc.subjectGenomics
dc.subjectNucleic Acids, Nucleotides, and Nucleosides
dc.titleDEBrowser: interactive differential expression analysis and visualization tool for count data
dc.typeArticle
dc.source.journaltitleBMC genomics
dc.source.volume20
dc.source.issue1
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1147&amp;context=bioinformatics_pubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/bioinformatics_pubs/138
dc.identifier.contextkey13591437
refterms.dateFOA2022-08-23T15:38:01Z
html.description.abstract<p>BACKGROUND: Sequencing data has become a standard measure of diverse cellular activities. For example, gene expression is accurately measured by RNA sequencing (RNA-Seq) libraries, protein-DNA interactions are captured by chromatin immunoprecipitation sequencing (ChIP-Seq), protein-RNA interactions by crosslinking immunoprecipitation sequencing (CLIP-Seq) or RNA immunoprecipitation (RIP-Seq) sequencing, DNA accessibility by assay for transposase-accessible chromatin (ATAC-Seq), DNase or MNase sequencing libraries. The processing of these sequencing techniques involves library-specific approaches. However, in all cases, once the sequencing libraries are processed, the result is a count table specifying the estimated number of reads originating from each genomic locus. Differential analysis to determine which loci have different cellular activity under different conditions starts with the count table and iterates through a cycle of data assessment, preparation and analysis. Such complex analysis often relies on multiple programs and is therefore a challenge for those without programming skills.</p> <p>RESULTS: We developed DEBrowser as an R bioconductor project to interactively visualize every step of the differential analysis, without programming. The application provides a rich and interactive web based graphical user interface built on R's shiny infrastructure. DEBrowser allows users to visualize data with various types of graphs that can be explored further by selecting and re-plotting any desired subset of data. Using the visualization approaches provided, users can determine and correct technical variations such as batch effects and sequencing depth that affect differential analysis. We show DEBrowser's ease of use by reproducing the analysis of two previously published data sets.</p> <p>CONCLUSIONS: DEBrowser is a flexible, intuitive, web-based analysis platform that enables an iterative and interactive analysis of count data without any requirement of programming knowledge.</p>
dc.identifier.submissionpathbioinformatics_pubs/138
dc.contributor.departmentGarber Lab
dc.contributor.departmentRNA Therapeutics Institute
dc.contributor.departmentProgram in Molecular Medicine
dc.contributor.departmentBioinformatics Core
dc.source.pages6


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© The Author(s). 2019. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as © The Author(s). 2019. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.