DEBrowser: Interactive Differential Expression Analysis and Visualization Tool for Count Data
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Abstract
Background Sequencing data has become a standard measure for studying 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 (CLIP-Seq) or RNA immunoprecipitation (RIP-Seq) sequencing, DNA accessibility by assay for transposase-accessible chromatin (ATAC-Seq), and DNase or MNase sequencing libraries. Analysis of these sequencing techniques involve 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 a 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 iterative approach 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 each step of the differential analysis of count data, without any requirement for programming expertise. The application presents a rich and interactive web based graphical user interface based on R’s shiny infrastructure. We use shiny’s reactive programming interface for a dynamic webpage that responds to user input and integrates its visualization widgets at each stage of the analysis. In this way, every step of the analysis can be displayed in one application that combines many approaches and multiple results. We show DEBrowser’s capabilities 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.
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