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

BMC Bioinformatics

Research

Steven JM Jones¹  Matthew C Lorincz²  Mohammad M Karimi³  Hamid Younesy⁴  Torsten Möller⁵ 

[1] Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada;Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada;Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada;Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada;Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada;Biomedical Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada;Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada;Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada;Biomedical Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada;Graphics Usability and Visualization Lab, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada;Research Group on Visualization and Data Analysis, Faculty of Computing Science, University of Vienna, Vienna, Austria;

关键词: Interactive Visualization;    Sequencing Data;    R-project;   

DOI  :  10.1186/1471-2105-16-S11-S2

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Younesy et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.