Modeling non-uniformity in short-read rates in RNA-Seq data
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* Corresponding author: Wing H Wong whwong@stanford.edu
Genome Biology 2010, 11:R50 doi:10.1186/gb-2010-11-5-r50
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BioMed Central: 5 citations
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Research article
GC-Content Normalization for RNA-Seq Data Davide Risso, Katja Schwartz, Gavin Sherlock, Sandrine Dudoit BMC Bioinformatics 2011, 12:480 (17 December 2011) The combination of three different strategies for GC-content normalization of RNA-seq data leads to more accurate estimations of gene expression levels and fold-changes, making statistical inference of differential expression less prone to false discoveries.
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Software
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome Bo Li, Colin N Dewey BMC Bioinformatics 2011, 12:323 (4 August 2011) RSEM is a new user-friendly software tool for quantifying transcript abundance from RNA-seq data that does not rely on a reference genome and is particularly useful for quantification with de novo transcriptome assemblies
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Research article
Bias detection and correction in RNA-Sequencing data Wei Zheng, Lisa M Chung, Hongyu Zhao BMC Bioinformatics 2011, 12:290 (19 July 2011) |
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Review
Microarrays, deep sequencing and the true measure of the transcriptome John H Malone, Brian Oliver BMC Biology 2011, 9:34 (31 May 2011) Global measures of gene expression can now be extracted either from microarrays or from RNA-seq, which do not always seem to give the same answer. Malone and Oliver review the advantages and limitations of each and conclude that, with some important exceptions, they tell the same story.
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Software
rnaSeqMap: a Bioconductor package for RNA sequencing data exploration Anna Leśniewska, Michał J Okoniewski BMC Bioinformatics 2011, 12:200 (25 May 2011) |