DEBrowser: Interactive Differential Expression Analysis and Visualization Tool for Count Data

Abstract

BackgroundSequencing 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 transposaseaccessible 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. ResultsWe 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. ConclusionsDEBrowser is a flexible, intuitive, web-based analysis platform that enables an iterative and interactive analysis of count data without any requirement of programming knowledge.

DolphinNext: A graphical user interface for creating, deploying and executing Nextflow pipelines

“…DolphinNext offers a highly modular platform. Though this manuscript only focuses on the power of DolphinNext for data processing, we have also integrated two different downstream analysis tools (DEBrower (30) and Rmarkdown) that take the count matrices output from the data processing steps directly into analysis and data exploration steps. Furthermore, DolphinNext's modular architecture allows for easy integration of any custom data analysis and visualization tool.…”

“…Process designers can define any number of secondary outputs, which the DolphinNext report module can process and present in a user-friendly format. While there is an integrated table viewer for tabular data, these data can also be seamlessly loaded and analyzed using an embedded DEBrowser, an interactive visualization tool for count data (30). PDF and HTML files can be visualized within DolphinNext or downloaded for offline analysis (Figure 5).…”

“…Finally, the RNA-Seq pipeline returns a quantification matrix that includes the estimated counts and transcripts per million (TPM) based on RSEM (39) or by simply counting reads using featureCounts (57) for each gene and/or for each annotated isoform. These matrices are used as inputs for differential gene expression analysis and can be uploaded directly to an embedded instance of our DEBrowser (30) software, which allows interactive exploration of the resulting data(30).…”

“…Data processing involves taking raw sequencing data to produce a count matrix in which features (genes, peaks or any genomic annotation) are scored across samples. Data analysis uses this count matrix to perform comparisons across conditions (differential analysis), unbiased analysis (clustering, principal component analysis), or supervised analysis (data modeling, classifier building) (30). An informative count matrix requires systematic data processing steps that are consistent across samples and even projects.…”

OCTAD: an open workplace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

“…Comparing case and control groups can be interactively performed which results in an interactive plot of differentially expressed genes. DEBrowser is an R shiny based package that creates an interactive dashboard to facilitate differential expression analysis and visualization of RNA count data 34 . Users can upload their own data and the application allows for multiple QC steps and visualization.…”