Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
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* Corresponding author: James H Bullard bullard@berkeley.edu
- † Equal contributors
BMC Bioinformatics 2010, 11:94 doi:10.1186/1471-2105-11-94
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BioMed Central: 14 citations
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Proceedings
MGMR: leveraging RNA-Seq population data to optimize expression estimation Roye Rozov, Eran Halperin, Ron Shamir BMC Bioinformatics 2012, 13(Suppl 6):S2 (19 April 2012) |
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Research
A normalization strategy for comparing tag count data Koji Kadota, Tomoaki Nishiyama, Kentaro Shimizu Algorithms for Molecular Biology 2012, 7:5 (5 April 2012) |
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Research article
Statistical methods on detecting differentially expressed genes for RNA-seq data Zhongxue Chen, Jianzhong Liu, Hon Ng, Saralees Nadarajah, Howard L Kaufman, Jack Y Yang, Youping Deng BMC Systems Biology 2011, 5(Suppl 3):S1 (23 December 2011) |
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Research article
Alessandro Guida, Claudia Lindstädt, Sarah L Maguire, Chen Ding, Desmond G Higgins, Nicola J Corton, Matthew Berriman, Geraldine Butler BMC Genomics 2011, 12:628 (22 December 2011) |
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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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Research
The transcriptional landscape of Chlamydia pneumoniae Marco Albrecht, Cynthia M Sharma, Marcus T Dittrich, Tobias Müller, Richard Reinhardt, Jörg Vogel, Thomas Rudel Genome Biology 2011, 12:R98 (11 October 2011) The life cycle of the obligate intracellular human pathogen C. pneumoniae has a dynamic transcriptional landscape
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Research article
Transcriptomes of Frankia sp. strain CcI3 in growth transitions Derek M Bickhart, David R Benson BMC Microbiology 2011, 11:192 (25 August 2011) |
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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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Software
ExpressionPlot: a web-based framework for analysis of RNA-Seq and microarray gene expression data Brad A Friedman, Tom Maniatis Genome Biology 2011, 12:R69 (28 July 2011) A web-based RNA-seq and microarray analysis tool
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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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Research article
Koji Kadota, Kentaro Shimizu BMC Bioinformatics 2011, 12:227 (6 June 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) |
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Software
Cloud-scale RNA-sequencing differential expression analysis with Myrna Ben Langmead, Kasper D Hansen, Jeffrey T Leek Genome Biology 2010, 11:R83 (11 August 2010) This article is part of a collection on Cloud computing tools and... Myrna is a software pipeline for calculating differential gene expression from large RNA-seq data sets in the cloud.
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