NDLI: Systematic evaluation of spliced alignment programs for RNA-seq data.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Engström, Pär G. Steijger, Tamara Sipos, Botond Grant, Gregory R. Kahles, André Rätsch, Gunnar Goldman, Nick Hubbard, Tim J. Harrow, Jennifer Guigó, Roderic Bertone, Paul
Organization	RGASP Consortium
Description	Author Affiliation: Engström PG ( 1] European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. [2].)
Abstract	High-throughput RNA sequencing is an increasingly accessible method for studying gene structure and activity on a genome-wide scale. A critical step in RNA-seq data analysis is the alignment of partial transcript reads to a reference genome sequence. To assess the performance of current mapping software, we invited developers of RNA-seq aligners to process four large human and mouse RNA-seq data sets. In total, we compared 26 mapping protocols based on 11 programs and pipelines and found major performance differences between methods on numerous benchmarks, including alignment yield, basewise accuracy, mismatch and gap placement, exon junction discovery and suitability of alignments for transcript reconstruction. We observed concordant results on real and simulated RNA-seq data, confirming the relevance of the metrics employed. Future developments in RNA-seq alignment methods would benefit from improved placement of multimapped reads, balanced utilization of existing gene annotation and a reduced false discovery rate for splice junctions.
File Format	HTM / HTML
ISSN	15487091
e-ISSN	15487105
DOI	10.1038/nmeth.2722
Journal	Nature Methods
Issue Number	12
Volume Number	10
Language	English
Publisher	Nature Publishing Group
Publisher Date	2013-12-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Clinical Laboratory Techniques Rna Splicing Sequence Alignment Sequence Analysis, Rna Animals Chromosome Mapping Computational Biology Exons False Positive Reactions High-throughput Nucleotide Sequencing K562 Cells Mice Rna, Messenger Metabolism Reproducibility Of Results Research Support, N.i.h., Extramural Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Cell Biology Biochemistry Molecular Biology Biotechnology

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