RNASEQR—a streamlined and accurate RNA-seq sequence analysis program

Chen, Leslie Y.; Wei, Kuo-Chen; Huang, Abner C.-Y.; Wang, Kai; Huang, Chiung-Yin; Yi, Danielle; Tang, Chuan Yi; Galas, David J.; Hood, Leroy E.

doi:10.1093/nar/gkr1248

Reference : RNASEQR—a streamlined and accurate RNA-seq sequence analysis program


	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/26506

Title :	RNASEQR—a streamlined and accurate RNA-seq sequence analysis program
Language :	English
Author, co-author :	Chen, Leslie Y. []
	Wei, Kuo-Chen []
	Huang, Abner C.-Y. []
	Wang, Kai []
	Huang, Chiung-Yin []
	Yi, Danielle []
	Tang, Chuan Yi []
	Galas, David J. [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Hood, Leroy E. []
Publication date :	2012
Journal title :	Nucleic Acids Research
Publisher :	Oxford University Press
Volume :	40
Issue/season :	6
Pages :	e42-
Peer reviewed :	Yes (verified by ORBi^lu)
ISSN :	0305-1048
e-ISSN :	1362-4962
City :	Oxford
Country :	United Kingdom
Abstract :	[en] Next-generation sequencing (NGS) technologiesbased transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. Toward this goal, we have developed RNASEQR to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient. Our results showed that RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs). RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)
Permalink :	http://hdl.handle.net/10993/26506
DOI :	10.1093/nar/gkr1248

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