PROTEOMICSBioinformaticsPeer-reviewed journal article
ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies
Anna Pamela CalinawaniD, Xiaoyu SongiD, Jiayi JiiD, Saravana Mohan DhanasekaraniD, Francesca PetraliaiD, Pei WangiD, Boris A. RevaiD
Abstract
The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we built a prototype user-friendly web application called "ProTrack" with the CPTAC clear cell renal cell carcinoma (ccRCC) data set (http://ccrcc.cptac-data-view.org). Here we describe the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data. Statement of SignificanceThe CPTAC initiative (https://proteomics.cancer.gov/) has generated multi-omics data for multiple cancer types to understand the proteogenomic aberrations of these malignancies. Collectively this effort has so far produced a large data resource for the research community, including high-throughput profiles for proteome, phosphoproteome, whole exome, whole genome, transcriptome, and DNA methylome. To make this valuable data-resource useful to the larger research community, there is a pressing need for development of user-friendly, readily accessible, and easily shared analytic and visualization tools for aligning multi-omics data and exploring alterations in key cancer genes, to drive and support new biological hypotheses. To bridge this gap, we have developed CPTAC ProTrack, an interactive web application which uses a multilayered, client-server architecture in order to deliver an interactive web experience to any user of a modern web-browser. This tool is intentionally designed accessible for researchers, biologists, and clinicians who are interested in multi-omic data without any need to code.
- organism: human
- assay: proteomics
- assay: DNA methylation
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- research focus: database / resource
- approach: experimental assay
- Citations
- 15
- FWCI
- 1.15
- Field percentile
- top 28%
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