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Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)
Author(s) -
Gregory R. Grant,
Michael H. Farkas,
Angel Pizarro,
Nicholas F. Lahens,
Jonathan Schug,
Brian P. Brunk,
Christian J. Stoeckert,
John B. Hogenesch,
Eric A. Pierce
Publication year - 2011
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btr427
Subject(s) - rna seq , computer science , algorithm , computational biology , rna splicing , pipeline (software) , rna , ensembl , data mining , genomics , gene , biology , genome , genetics , transcriptome , gene expression , programming language
A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously.

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