Computational methods for ribosome profiling data analysis

Kiniry, Stephen J.; Michel, Audrey M.; Baranov, Pavel V.

Computational methods for ribosome profiling data analysis

dc.check.info	Access to this article is restricted until 12 months after publication by request of the publisher.	en
dc.contributor.author	Kiniry, Stephen J.
dc.contributor.author	Michel, Audrey M.
dc.contributor.author	Baranov, Pavel V.
dc.contributor.funder	Wellcome Trust	en
dc.contributor.funder	Science Foundation Ireland	en
dc.contributor.funder	Health Research Board	en
dc.date.accessioned	2019-12-18T11:25:46Z
dc.date.available	2019-12-18T11:25:46Z
dc.date.issued	2019-11-24
dc.date.updated	2019-12-18T10:10:49Z
dc.description.abstract	Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis.	en
dc.description.status	Peer reviewed	en
dc.description.version	Accepted Version	en
dc.format.mimetype	application/pdf	en
dc.identifier.articleid	e1577	en
dc.identifier.citation	Kiniry, S. J., Michel, A. M. and Baranov, P. V. (2019) 'Computational methods for ribosome profiling data analysis', Wiley Interdisciplinary Reviews RNA, e1577. doi: 10.1002/wrna.1577	en
dc.identifier.doi	10.1002/wrna.1577	en
dc.identifier.issn	1757-7004
dc.identifier.issn	2573-9468
dc.identifier.journaltitle	Wiley Interdisciplinary Reviews RNA	en
dc.identifier.uri	https://hdl.handle.net/10468/9435
dc.language.iso	en	en
dc.publisher	John Wiley & Sons, Inc.	en
dc.relation.uri	https://onlinelibrary.wiley.com/doi/abs/10.1002/wrna.1577
dc.rights	© 2019, Wiley Periodicals, Inc. All rights reserved. This is the peer reviewed version of the following article: Kiniry, S. J., Michel, A. M. and Baranov, P. V. (2019) 'Computational methods for ribosome profiling data analysis', Wiley Interdisciplinary Reviews RNA, e1577, doi: 10.1002/wrna.1577, which has been published in final form at https://doi.org/10.1002/wrna.1577 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.	en
dc.subject	mRNA translation	en
dc.subject	Protein synthesis	en
dc.subject	Ribosome profiling	en
dc.subject	Translatome	en
dc.subject	Ribo‐Seq	en
dc.title	Computational methods for ribosome profiling data analysis	en
dc.type	Article (peer-reviewed)	en