VisRseq: R-based visual framework for analysis of sequencing data.

Hamid Younesy,Mohammad M Karimi,Matthew C Lorincz,Torsten Möller,Steven Jm Jones

doi:10.1186/1471-2105-16-s11-s2

Abstract

BackgroundSeveral tools have been developed to enable biologists to perform initial browsing and exploration of sequencing data. However the computational tool set for further analyses often requires significant computational expertise to use and many of the biologists with the knowledge needed to interpret these data must rely on programming experts.ResultsWe present VisRseq, a framework for analysis of sequencing datasets that provides a computationally rich and accessible framework for integrative and interactive analyses without requiring programming expertise. We achieve this aim by providing R apps, which offer a semi-auto generated and unified graphical user interface for computational packages in R and repositories such as Bioconductor. To address the interactivity limitation inherent in R libraries, our framework includes several native apps that provide exploration and brushing operations as well as an integrated genome browser. The apps can be chained together to create more powerful analysis workflows.ConclusionsTo validate the usability of VisRseq for analysis of sequencing data, we present two case studies performed by our collaborators and report their workflow and insights.

Highlights

Several tools have been developed to enable biologists to perform initial browsing and exploration of sequencing data
Biologists are often interested in studying these data in specific regions of interest
We present the R apps framework, which offers a semi-auto generated and unified graphical user interface for computational R packages and repositories such as Bioconductor [1]

Summary

Introduction

Several tools have been developed to enable biologists to perform initial browsing and exploration of sequencing data. Sequencing data is the generic name for the datasets acquired using high-throughput nucleic acid sequencing techniques. This technology can be used to measure the biochemical states of cells such as the expression levels of genes or binding sites of proteins in DNA. RNA sequencing (RNA-seq) measures the presence and quantity of total RNA in a cell at a given moment in time and is widely used in gene expression analysis. Another example is ChIP-sequencing (ChIP-seq) which is used to analyze protein interactions with DNA.

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