Bioinformatics

A new user-friendly software package named RNA-Seq by Expectation Maximization (RSEM), which works on RNA-seq data has been developed under Department of Computer Sciences, University of Wisconsin-Madison by Bo Li and Colin N Dewey. RNA-Seq is a powerful technology for

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analyzing transcriptomes that is predicted to replace microarrays. Leveraging recent advances in sequencing technology, RNA-Seq experiments produce millions of relatively short reads from the ends of cDNAs derived from fragments of sample RNA. The reads produced can be used for a number of transcriptome analyses, including transcript quantification, differential expression testing, reference-based gene annotation and de novo transcript assembly. After sequencing, the quantification task typically involves two steps: (1) the mapping of reads to a reference genome or transcript set, and (2) the estimation of gene and isoform abundances based on the read mappings.

A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments.

RSEM is software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. A key feature unique to RSEM is the lack of the requirement of a reference genome. Instead, it only requires the user to provide a set of reference transcript sequences, such as one produced by a de novo transcriptome assembler. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM’s ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene.

Authors: Bo Li and Colin N Dewey

Link: http://www.biomedcentral.com/1471-2105/12/323