Literature DB >> 28224499

Finding RNA-Protein Interaction Sites Using HMMs.

Tao Wang1, Jonghyun Yun2, Yang Xie1, Guanghua Xiao3.   

Abstract

RNA-binding proteins play important roles in the various stages of RNA maturation through binding to its target RNAs. Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Several Hidden Markov model-based (HMM) approaches have been suggested to identify protein-RNA binding sites from CLIP-Seq datasets. In this chapter, we describe how HMM can be applied to analyze CLIP-Seq datasets, including the bioinformatics preprocessing steps to extract count information from the sequencing data before HMM and the downstream analysis steps following peak-calling.

Entities:  

Keywords:  Hidden Markov models; Interaction sites; RNA-binding proteins

Mesh:

Substances:

Year:  2017        PMID: 28224499      PMCID: PMC5568642          DOI: 10.1007/978-1-4939-6753-7_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  16 in total

1.  CLIP: a method for identifying protein-RNA interaction sites in living cells.

Authors:  Jernej Ule; Kirk Jensen; Aldo Mele; Robert B Darnell
Journal:  Methods       Date:  2005-12       Impact factor: 3.608

Review 2.  RNA regulons: coordination of post-transcriptional events.

Authors:  Jack D Keene
Journal:  Nat Rev Genet       Date:  2007-07       Impact factor: 53.242

3.  Identification of protein binding sites on U3 snoRNA and pre-rRNA by UV cross-linking and high-throughput analysis of cDNAs.

Authors:  Sander Granneman; Grzegorz Kudla; Elisabeth Petfalski; David Tollervey
Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-29       Impact factor: 11.205

4.  Transcriptome-wide discovery of microRNA binding sites in human brain.

Authors:  Ryan L Boudreau; Peng Jiang; Brian L Gilmore; Ryan M Spengler; Rebecca Tirabassi; Jay A Nelson; Christopher A Ross; Yi Xing; Beverly L Davidson
Journal:  Neuron       Date:  2014-01-02       Impact factor: 17.173

5.  FET proteins TAF15 and EWS are selective markers that distinguish FTLD with FUS pathology from amyotrophic lateral sclerosis with FUS mutations.

Authors:  Manuela Neumann; Eva Bentmann; Dorothee Dormann; Ali Jawaid; Mariely DeJesus-Hernandez; Olaf Ansorge; Sigrun Roeber; Hans A Kretzschmar; David G Munoz; Hirofumi Kusaka; Osamu Yokota; Lee-Cyn Ang; Juan Bilbao; Rosa Rademakers; Christian Haass; Ian R A Mackenzie
Journal:  Brain       Date:  2011-08-19       Impact factor: 13.501

6.  Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data.

Authors:  Chaolin Zhang; Robert B Darnell
Journal:  Nat Biotechnol       Date:  2011-06-01       Impact factor: 54.908

7.  PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data.

Authors:  David L Corcoran; Stoyan Georgiev; Neelanjan Mukherjee; Eva Gottwein; Rebecca L Skalsky; Jack D Keene; Uwe Ohler
Journal:  Genome Biol       Date:  2011-08-18       Impact factor: 13.583

8.  dCLIP: a computational approach for comparative CLIP-seq analyses.

Authors:  Tao Wang; Yang Xie; Guanghua Xiao
Journal:  Genome Biol       Date:  2014-01-07       Impact factor: 13.583

9.  Neuronal Elav-like (Hu) proteins regulate RNA splicing and abundance to control glutamate levels and neuronal excitability.

Authors:  Gulayse Ince-Dunn; Hirotaka J Okano; Kirk B Jensen; Woong-Yang Park; Ru Zhong; Jernej Ule; Aldo Mele; John J Fak; Chingwen Yang; Chaolin Zhang; Jong Yoo; Margaret Herre; Hideyuki Okano; Jeffrey L Noebels; Robert B Darnell
Journal:  Neuron       Date:  2012-09-20       Impact factor: 17.173

10.  iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution.

Authors:  Julian König; Kathi Zarnack; Gregor Rot; Tomaz Curk; Melis Kayikci; Blaz Zupan; Daniel J Turner; Nicholas M Luscombe; Jernej Ule
Journal:  Nat Struct Mol Biol       Date:  2010-07-04       Impact factor: 15.369

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  2 in total

1.  iAPSL-IF: Identification of Apoptosis Protein Subcellular Location Using Integrative Features Captured from Amino Acid Sequences.

Authors:  Yadong Tang; Lu Xie; Lanming Chen
Journal:  Int J Mol Sci       Date:  2018-04-13       Impact factor: 5.923

2.  Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix.

Authors:  Xiaoli Ruan; Dongming Zhou; Rencan Nie; Yanbu Guo
Journal:  Biomed Res Int       Date:  2020-01-14       Impact factor: 3.411

  2 in total

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