Dataset Information

ProteoViz: a tool for the analysis and interactive visualization of phosphoproteomics data.

ABSTRACT: Quantitative proteomics generates large datasets with increasing depth and quantitative information. With the advance of mass spectrometry and increasingly larger data sets, streamlined methodologies and tools for analysis and visualization of phosphoproteomics are needed both at the protein and modified peptide levels. To assist in addressing this need, we developed ProteoViz, which includes a set of R scripts that perform normalization and differential expression analysis of both the proteins and enriched phosphorylated peptides, and identify sequence motifs, kinases, and gene set enrichment pathways. The tool generates interactive visualization plots that allow users to interact with the phosphoproteomics results and quickly identify proteins and phosphorylated peptides of interest for their biological study. The tool also links significant phosphosites with sequence motifs and pathways that will help explain the experimental conditions and guide future experiments. Here, we present the workflow and demonstrate its functionality by analyzing a phosphoproteomic data set from two lymphoma cell lines treated with kinase inhibitors. The scripts and data are freely available at and via the ProteomeXchange with identifier PXD015606.

SUBMITTER: Storey AJ

PROVIDER: S-EPMC7423749 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSequencing 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.ResultsWe 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.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.

Dataset Information

ProteoViz: a tool for the analysis and interactive visualization of phosphoproteomics data.

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