Literature DB >> 30527764

Practical considerations on performing and analyzing CLIP-seq experiments to identify transcriptomic-wide RNA-protein interactions.

Xiaoli Chen1, Sarah A Castro2, Qiuying Liu2, Wenqian Hu3, Shaojie Zhang4.   

Abstract

RNA-binding proteins are important players in post-transcriptional regulation, such as modulating mRNA splicing, translation, and degradation under diverse biological settings. Identifying and characterizing the RNA substrates is a critical step in deciphering the function and molecular mechanisms of the target RNA-binding proteins. High-throughput sequencing of the RNA fragments isolated by crosslinking immunoprecipitation (CLIP-seq) is one of the standard techniques to identify the in vivo transcriptome-wide binding sites of the target RNA-binding protein. This method is widely used in functional and mechanistic characterizations of RNA-binding proteins. In this review, we provide several practical considerations on performing and analyzing CLIP-seq experiments. Particularly, we focus on how to perform CLIP-seq experiments on endogenous RNA-binding proteins. In addition, we provide a practical summary on how to choose and use computational pipelines from an increasing number of computational methods and packages that are available for analyzing the sequencing datasets from the CLIP-seq experiments. We hope these practical considerations will facilitate experimental biologists in performing and analyzing CLIP-seq experiment to obtain biologically relevant mechanistic insights.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 30527764      PMCID: PMC6387833          DOI: 10.1016/j.ymeth.2018.12.002

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  83 in total

1.  Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

Authors:  Daniel Dominguez; Peter Freese; Maria S Alexis; Amanda Su; Myles Hochman; Tsultrim Palden; Cassandra Bazile; Nicole J Lambert; Eric L Van Nostrand; Gabriel A Pratt; Gene W Yeo; Brenton R Graveley; Christopher B Burge
Journal:  Mol Cell       Date:  2018-06-07       Impact factor: 17.970

2.  CLIP Tool Kit (CTK): a flexible and robust pipeline to analyze CLIP sequencing data.

Authors:  Ankeeta Shah; Yingzhi Qian; Sebastien M Weyn-Vanhentenryck; Chaolin Zhang
Journal:  Bioinformatics       Date:  2017-02-15       Impact factor: 6.937

3.  ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data.

Authors:  David Heller; Ralf Krestel; Uwe Ohler; Martin Vingron; Annalisa Marsico
Journal:  Nucleic Acids Res       Date:  2017-11-02       Impact factor: 16.971

4.  Cpeb4-mediated translational regulatory circuitry controls terminal erythroid differentiation.

Authors:  Wenqian Hu; Bingbing Yuan; Harvey F Lodish
Journal:  Dev Cell       Date:  2014-09-11       Impact factor: 12.270

Review 5.  Optimization of PAR-CLIP for transcriptome-wide identification of binding sites of RNA-binding proteins.

Authors:  Aitor Garzia; Cindy Meyer; Pavel Morozov; Marcin Sajek; Thomas Tuschl
Journal:  Methods       Date:  2016-10-17       Impact factor: 3.608

6.  CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data.

Authors:  Tsukasa Fukunaga; Haruka Ozaki; Goro Terai; Kiyoshi Asai; Wataru Iwasaki; Hisanori Kiryu
Journal:  Genome Biol       Date:  2014-01-21       Impact factor: 13.583

7.  GraphProt: modeling binding preferences of RNA-binding proteins.

Authors:  Daniel Maticzka; Sita J Lange; Fabrizio Costa; Rolf Backofen
Journal:  Genome Biol       Date:  2014-01-22       Impact factor: 13.583

8.  PureCLIP: capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data.

Authors:  Sabrina Krakau; Hugues Richard; Annalisa Marsico
Journal:  Genome Biol       Date:  2017-12-28       Impact factor: 13.583

9.  FastUniq: a fast de novo duplicates removal tool for paired short reads.

Authors:  Haibin Xu; Xiang Luo; Jun Qian; Xiaohui Pang; Jingyuan Song; Guangrui Qian; Jinhui Chen; Shilin Chen
Journal:  PLoS One       Date:  2012-12-20       Impact factor: 3.240

Review 10.  Advances and challenges in the detection of transcriptome-wide protein-RNA interactions.

Authors:  Emily C Wheeler; Eric L Van Nostrand; Gene W Yeo
Journal:  Wiley Interdiscip Rev RNA       Date:  2017-08-29       Impact factor: 9.957

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  1 in total

1.  Spatial correlation statistics enable transcriptome-wide characterization of RNA structure binding.

Authors:  Veronica F Busa; Alexander V Favorov; Elana J Fertig; Anthony K L Leung
Journal:  Cell Rep Methods       Date:  2021-10-01
  1 in total

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