OmicsView: Omics data analysis through interactive visual analytics

Fergal Casey; Soumya Negi; Jing Zhu; Yu H Sun; Maria Zavodszky; Derrick Cheng; Dongdong Lin; Sally John; Michelle A Penny; David Sexton; Baohong Zhang

doi:10.1016/j.csbj.2022.02.022

OmicsView: Omics data analysis through interactive visual analytics

Comput Struct Biotechnol J. 2022 Mar 10:20:1277-1285. doi: 10.1016/j.csbj.2022.02.022. eCollection 2022.

Authors

Affiliations

¹ Translational Biology, Research Development, Biogen, Inc., Cambridge, MA 02142, USA.
² BioInfoRx, Inc., 510 Charmany Dr, Suite 275A, Madison, WI 53719, USA.

Abstract

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000 s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohn's disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

Keywords: DEG, Differentially Expressed Gene; Expression databases; Expression profiling; Meta-analysis; NGS, Next Generation Sequencing; RNASeq, RNA Sequencing; RNAseq; Visual analytics.