English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  PureCLIP: Capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data

Krakau, S., Richard, H., & Marsico, A. (2017). PureCLIP: Capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data. Genome Biology, 18(1): 1:240. doi:10.1186/s13059-017-1364-2.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0000-F6B0-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-F6B1-2
Genre: Journal Article

Files

show Files
hide Files
:
Krakau.pdf (Publisher version), 2MB
Name:
Krakau.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
© The Author(s) 2017

Locators

show

Creators

show
hide
 Creators:
Krakau, Sabrina, Author
Richard, Hugues, Author
Marsico, Annalisa1, Author              
Affiliations:
1RNA Bioinformatics (Annalisa Marsico), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2117285              

Content

show
hide
Free keywords: Crosslink sites; Hidden Markov model; Protein–RNA interaction; eCLIP-seq; iCLIP-seq
 Abstract: The iCLIP and eCLIP techniques facilitate the detection of protein-RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP ( https://github.com/skrakau/PureCLIP ), a hidden Markov model based approach, which simultaneously performs peak-calling and individual crosslink site detection. It explicitly incorporates a non-specific background signal and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates.

Details

show
hide
Language(s): eng - English
 Dates: 2017-11-242017-12-28
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1186/s13059-017-1364-2
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Genome Biology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: London : BioMed Central Ltd.
Pages: - Volume / Issue: 18 (1) Sequence Number: 1:240 Start / End Page: - Identifier: ISSN: 1465-6906
CoNE: /journals/resource/1000000000224390_1