Universität zu Köln

Huessler, Eva-Maria, Schaefer, Martin, Schwender, Holger and Landgraf, Pablo (2019). BayMAP: a Bayesian hierarchical model for the analysis of PAR-CLIP data. Bioinformatics, 35 (12). S. 1992 - 2001. OXFORD: OXFORD UNIV PRESS. ISSN 1460-2059

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Abstract

Motivation Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) is a biochemical method for detecting interaction sites of proteins with mRNA. This method introduces T-to-C substitutions at sequenced cDNA that help to detect binding sites on mRNA. However, T-to-C substitutions can also occur due to other reasons such as mismatches or SNPs. Only few statistical procedures exist for detecting binding sites in PAR-CLIP data. Most of these methods do not account for other types of substitutions than those induced by PAR-CLIP, and therefore, also report positions with high T-to-C substitution rates, e.g. SNPs, as binding sites. Moreover, none of these procedures allow to include additional information, e.g. the type of mRNA region, relevant for the biology of microRNA-binding sites. Results We have developed BayMAP, a procedure based on a fully Bayesian hierarchical model that takes other sources of substitutions into account. Furthermore, this model enables the incorporation of additional information into the analysis of PAR-CLIP data. This incorporation does not only permit a better detection of binding sites, but also a better understanding of the data and the biology of binding sites. In applications to simulated PAR-CLIP data, BayMAP distinguishes binding sites from noise better than existing methods. Additionally, it yields good estimates of the influence of the additional information. We here demonstrate BayMAP's usability for real datasets even when noisy data is present. Availability and implementation BayMAP is freely available as an R package at http://stat.math.uni-duesseldorf.de/baymap. Supplementary information Supplementary data are available at Bioinformatics online.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Huessler, Eva-Maria UNSPECIFIED UNSPECIFIED UNSPECIFIED Schaefer, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Schwender, Holger UNSPECIFIED UNSPECIFIED UNSPECIFIED Landgraf, Pablo UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-137710
DOI:	10.1093/bioinformatics/bty904
Journal or Publication Title:	Bioinformatics
Volume:	35
Number:	12
Page Range:	S. 1992 - 2001
Date:	2019
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1460-2059
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language MICRORNAS; PROTEIN; SITES; IDENTIFICATION; RNAS Multiple languages Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/13771