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DEBrowser: interactive differential expression analysis and visualization tool for count data
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6321710/ https://www.ncbi.nlm.nih.gov/pubmed/30611200 http://dx.doi.org/10.1186/s12864-018-5362-x |
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author | Kucukural, Alper Yukselen, Onur Ozata, Deniz M. Moore, Melissa J. Garber, Manuel |
author_facet | Kucukural, Alper Yukselen, Onur Ozata, Deniz M. Moore, Melissa J. Garber, Manuel |
author_sort | Kucukural, Alper |
collection | PubMed |
description | 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. 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5362-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6321710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63217102019-01-09 DEBrowser: interactive differential expression analysis and visualization tool for count data Kucukural, Alper Yukselen, Onur Ozata, Deniz M. Moore, Melissa J. Garber, Manuel BMC Genomics Software 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. 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5362-x) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-05 /pmc/articles/PMC6321710/ /pubmed/30611200 http://dx.doi.org/10.1186/s12864-018-5362-x Text en © The Author(s). 2019 Open AccessThis 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. |
spellingShingle | Software Kucukural, Alper Yukselen, Onur Ozata, Deniz M. Moore, Melissa J. Garber, Manuel DEBrowser: interactive differential expression analysis and visualization tool for count data |
title | DEBrowser: interactive differential expression analysis and visualization tool for count data |
title_full | DEBrowser: interactive differential expression analysis and visualization tool for count data |
title_fullStr | DEBrowser: interactive differential expression analysis and visualization tool for count data |
title_full_unstemmed | DEBrowser: interactive differential expression analysis and visualization tool for count data |
title_short | DEBrowser: interactive differential expression analysis and visualization tool for count data |
title_sort | debrowser: interactive differential expression analysis and visualization tool for count data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6321710/ https://www.ncbi.nlm.nih.gov/pubmed/30611200 http://dx.doi.org/10.1186/s12864-018-5362-x |
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