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Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)

by: Gregory R. Grant, Michael H. Farkas, Angel D. Pizarro, Nicholas F. Lahens, Jonathan Schug, Brian P. Brunk, Christian J. Stoeckert, John B. Hogenesch, Eric A. Pierce
Bioinformatics, Vol. 27, No. 18. (15 September 2011), pp. 2518-2528, doi:10.1093/bioinformatics/btr427  Key: citeulike:9567543

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Abstract

Motivation: 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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