riboviz: analysis and visualization of ribosome profiling datasets
- PMID: 29070028
- PMCID: PMC5657068
- DOI: 10.1186/s12859-017-1873-8
riboviz: analysis and visualization of ribosome profiling datasets
Abstract
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riboviz consists of a comprehensive and flexible computational pipeline that allows the user to analyze private, unpublished datasets, along with a web application for comparison with published yeast datasets.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.
Keywords: Database; Ribosome profiling; Translation quantification; Visualization and comparison tool-kit.
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References
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- Csárdi G. Franks A. Choi DS. Airoldi EM. Drummond DA Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast. PLoS Genet. 2015;11(5):e1005206. doi: 10.1371/journal.pgen.1005206. - DOI - PMC - PubMed
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