riboviz: analysis and visualization of ribosome profiling datasets

Background Using high-throughput sequencing to monitor translation in vivo, ribosome profiling can provide critical insights into the dynamics and regulation of protein synthesis in a cell. Since its introduction in 2009, this technique has played a key role in driving biological discovery, and yet...

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Published in:BMC bioinformatics Vol. 18; no. 1; pp. 461 - 4
Main Authors: Carja, Oana, Xing, Tongji, Wallace, Edward W. J., Plotkin, Joshua B., Shah, Premal
Format: Journal Article
Language:English
Published: London BioMed Central 25.10.2017
BioMed Central Ltd
Springer Nature B.V
BMC
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ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Using high-throughput sequencing to monitor translation in vivo, ribosome profiling can provide critical insights into the dynamics and regulation of protein synthesis in a cell. Since its introduction in 2009, this technique has played a key role in driving biological discovery, and yet it requires a rigorous computational toolkit for widespread adoption. Description We have developed a database and a browser-based visualization tool, riboviz , that enables exploration and analysis of riboseq datasets. In implementation, riboviz consists of a comprehensive and flexible computational pipeline that allows the user to analyze private, unpublished datasets, along with a web application for comparison with published yeast datasets. Source code and detailed documentation are freely available from https://github.com/shahpr/RiboViz . The web-application is live at www.riboviz.org. Conclusions riboviz provides a comprehensive database and analysis and visualization tool to enable comparative analyses of ribosome-profiling datasets. This toolkit will enable both the community of systems biologists who study genome-wide ribosome profiling data and also research groups focused on individual genes to identify patterns of transcriptional and translational regulation across different organisms and conditions.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-017-1873-8