Viewing RNA-seq data on the entire human genome.

Eric M Weitz,Ben Busby,Jingzhi Zhu,Lorena Pantano,Bennett Upton

doi:10.12688/f1000research.9762.1

Abstract

RNA-Seq Viewer is a web application that enables users to visualize genome-wide expression data from NCBI's Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) databases. The application prototype was created by a small team during a three-day hackathon facilitated by NCBI at Brandeis University. The backend data pipeline was developed and deployed on a shared AWS EC2 instance. Source code is available at https://github.com/NCBI-Hackathons/rnaseqview.

Highlights

Interactive visualizations can yield insights from the deluge of gene expression data brought about by RNA-seq technology
In this paper we describe RNA-Seq Viewer, a web application that enables users to visualize genome-wide expression data from the National Center for Biotechnology Information’s (NCBI) Sequence Read Archive (SRA) (Kodama et al, 2012) and Gene Expression Omnibus (GEO) (Barrett et al, 2013) databases
The data pipeline, developed by a small team of software engineers in a three-day NCBI hackathon at Brandeis University, extracts aligned RNA-seq data from SRA or GEO and transforms it into a format used by Ideogram

Summary

Introduction

Interactive visualizations can yield insights from the deluge of gene expression data brought about by RNA-seq technology. Several genome browsers enable users to see such data conveniently plotted within a single chromosome in a web application (Broad Institute, 2014; Kent et al, 2002; National Center for Biotechnology Information: Genome Data Viewer (2016)). While such singlechromosome views excel at displaying local features, depicting RNA-seq data across all chromosomes in a genome, i.e. in an ideogram, has the potential to intuitively highlight global patterns of gene expression (such as in Figure 2a in Parker et al, 2016). Ideogram displays the distribution of genes in chromosome context across the entire human genome and enables users to filter those genes by gene type or expression levels in the given SRA/GEO sample

Methods

Results

Discussion