Journal Article

RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data

Nicole Cloonan, Qinying Xu, Geoffrey J. Faulkner, Darrin F. Taylor, Dave T. P. Tang, Gabriel Kolle and Sean M. Grimmond

in Bioinformatics

Volume 25, issue 19, pages 2615-2616
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp459

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Summary: Mapping of next-generation sequencing data derived from RNA samples (RNAseq) presents different genome mapping challenges than data derived from DNA. For example, tags that cross exon-junction boundaries will often not map to a reference genome, and the strand specificity of the data needs to be retained. Here we present RNA-MATE, a computational pipeline based on a recursive mapping strategy for placing strand specific RNAseq data onto a reference genome. Maximizing the mappable tags can provide significant savings in the cost of sequencing experiments. This pipeline provides an automatic and integrated way to align color-space sequencing data, collate this information and generate files for examining gene-expression data in a genomic context.

Availability: Executables, source code, and exon-junction libraries are available from http://grimmond.imb.uq.edu.au/RNA-MATE/

Contact: n.cloonan@imb.uq.edu.au

Supplementary information: Supplementary data are available at Bioinformatics Online.

Journal Article.  1342 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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