Systematic evaluation of spliced alignment programs for RNA-seq data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12

AUTHORS

Pär G Engström, Tamara Steijger, Botond Sipos, Gregory R Grant, André Kahles, The RGASP Consortium, Tyler Alioto, Jonas Behr, Paul Bertone, Regina Bohnert, Davide Campagna, Carrie A Davis, Alexander Dobin, Thomas R Gingeras, Nick Goldman, Roderic Guigó, Jennifer Harrow, Tim J Hubbard, Géraldine Jean, Peter Kosarev, Sheng Li, Jinze Liu, Christopher E Mason, Vladimir Molodtsov, Zemin Ning, Hannes Ponstingl, Jan F Prins, Gunnar Rätsch, Paolo Ribeca, Igor Seledtsov, Victor Solovyev, Giorgio Valle, Nicola Vitulo, Kai Wang, Thomas D Wu, Georg Zeller

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. More... »

PAGES

1185-1191

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth.2722

DOI

http://dx.doi.org/10.1038/nmeth.2722

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PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24185836


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