Biology Direct

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Transcript length bias in RNA-seq data confounds systems biology

Alicia Oshlack* and Matthew J Wakefield

Biology Direct 2009, 4:14 doi:10.1186/1745-6150-4-14

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Proceedings   Open Access

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)

Research   Open Access Highly Accessed

A normalization strategy for comparing tag count data

Koji Kadota, Tomoaki Nishiyama, Kentaro Shimizu Algorithms for Molecular Biology 2012, 7:5 (5 April 2012)

Research article   Open Access Highly Accessed

Cutoffs and k-mers: Implications from a transcriptome study in allopolyploid plants

Nicole Gruenheit, Oliver Deusch, Christian Esser, Matthias Becker, Claudia Voelckel, Peter J. Lockhart BMC Genomics 2012, 13:92 (14 March 2012)

Research article   Open Access Highly Accessed

Identification and characterization of microRNAs in Phaseolus vulgaris by high-throughput sequencing

Pablo Pelaez, Minerva S Trejo, Luis P Iniguez, Georgina Estrada-Navarrete, Alejandra A Covarrubias, Jose L Reyes, Federico Sanchez BMC Genomics 2012, 13:83 (6 March 2012)

Research article   Open Access

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)

Research article   Open Access Highly Accessed

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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RNA-Seq and find: entering the RNA deep field

Adam Roberts, Lior Pachter Genome Medicine 2011, 3:74 (22 November 2011)

Lior Pachter and Adam Roberts discuss the advantages of a new method, RNA CaptureSeq, for the detection of low-abundance transcripts potentially important for disease, within the RNA "deep field".

Software   Open Access Highly Accessed

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

Research article   Open Access Highly Accessed

Bias detection and correction in RNA-Sequencing data

Wei Zheng, Lisa M Chung, Hongyu Zhao BMC Bioinformatics 2011, 12:290 (19 July 2011)

Software   Open Access Highly Accessed

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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Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays

Ashish Agarwal, David Koppstein, Joel Rozowsky, Andrea Sboner, Lukas Habegger, LaDeana W Hillier, Rajkumar Sasidharan, Valerie Reinke, Robert H Waterston, Mark Gerstein BMC Genomics 2010, 11:383 (17 June 2010)

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A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling

James R Bradford, Yvonne Hey, Tim Yates, Yaoyong Li, Stuart D Pepper, Crispin J Miller BMC Genomics 2010, 11:282 (5 May 2010)

Method   Open Access Highly Accessed

A scaling normalization method for differential expression analysis of RNA-seq data

Mark D Robinson, Alicia Oshlack Genome Biology 2010, 11:R25 (2 March 2010)

A novel and empirical method for normalization of RNA-seq data is presented

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Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

James H Bullard, Elizabeth Purdom, Kasper D Hansen, Sandrine Dudoit BMC Bioinformatics 2010, 11:94 (18 February 2010)

Method   Open Access Highly Accessed

Gene ontology analysis for RNA-seq: accounting for selection bias

Matthew D Young, Matthew J Wakefield, Gordon K Smyth, Alicia Oshlack Genome Biology 2010, 11:R14 (4 February 2010)

GOseq is a method for GO analysis of RNA-seq data that takes into account the length bias inherent in RNA-seq