Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis

Mallory A Freeberg; John K Kim

doi:10.1007/978-1-4939-3079-1_6

Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis

Methods Mol Biol. 2016:1361:91-104. doi: 10.1007/978-1-4939-3079-1_6.

Authors

Mallory A Freeberg^{1

2}, John K Kim^{3

4}

Affiliations

¹ Life Sciences Institute, University of Michigan, Ann Arbor, MI, 48109-2216, USA.
² Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2216, USA.
³ Life Sciences Institute, University of Michigan, Ann Arbor, MI, 48109-2216, USA. jnkim@jhu.edu.
⁴ Department of Biology, Johns Hopkins University, Baltimore, MD, 21211, USA. jnkim@jhu.edu.

PMID: 26483018
DOI: 10.1007/978-1-4939-3079-1_6

Abstract

Protein-RNA interactions are integral components of posttranscriptional gene regulatory processes including mRNA processing and assembly of cellular architectures. Dysregulation of RNA-binding protein (RBP) expression or disruptions in RBP-RNA interactions underlie a variety of human pathologies and genetic diseases including cancer and neurodegenerative diseases (reviewed in (Cooper et al., Cell 136(4):777-793, 2009; Darnell, Cancer Res Treat 42(3):125-129, 2010; Lukong et al., Trends Genet 24 (8):416-425, 2008)). Recent studies have uncovered only a small proportion of the extensive RBP-RNA interactome in any organism (Baltz et al., Mol Cell 46(5):674-690, 2012; Castello et al., Cell 149(6):1393-1406, 2012; Freeberg et al., Genome Biol 14(2):R13, 2013; Hogan et al., PLoS Biol 6(10):e255, 2008; Mitchell et al., Nat Struct Mol Biol 20(1):127-133, 2013; Tsvetanova et al. PLoS One 5(9): pii: e12671, 2010; Schueler et al., Genome Biol 15(1):R15, 2014; Silverman et al., Genome Biol 15(1):R3, 2014). To expand our understanding of how RBP-RNA interactions govern RNA-related processes, we developed gPAR-CLIP-seq (global photoactivatable-ribonucleoside-enhanced cross-linking and precipitation followed by deep sequencing) for capturing and sequencing all regions of the Saccharomyces cerevisiae transcriptome bound by RBPs (Freeberg et al., Genome Biol 14(2):R13, 2013). This chapter describes a pipeline for bioinformatic analysis of gPAR-CLIP-seq data. The first half of this pipeline can be implemented by running locally installed programs or by running the programs using the Galaxy platform (Blankenberg et al., Curr Protoc Mol Biol. Chapter 19:Unit 19 10 11-21, 2010; Giardine et al., Genome Res 15 (10):1451-1455, 2005; Goecks et al., Genome Biol 11(8):R86, 2010). The second half of this pipeline can be implemented by user-generated code in any language using the pseudocode provided as a template.

Keywords: Bioinformatics; Global PAR-CLIP-seq; High-throughput sequencing; Posttranscriptional gene regulation; RNA-binding proteins.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Binding Sites / genetics
Computational Biology / methods*
Eukaryotic Cells
Gene Expression Profiling / methods*
Gene Expression Regulation
Genome
High-Throughput Nucleotide Sequencing / methods*
Humans
RNA / genetics
RNA-Binding Proteins / biosynthesis
RNA-Binding Proteins / chemistry
RNA-Binding Proteins / genetics*
Saccharomyces cerevisiae / genetics
Transcriptome / genetics

Substances

RNA-Binding Proteins
RNA

Grants and funding

GM088565/GM/NIGMS NIH HHS/United States