A scaling normalization method for differential expression analysis of RNA-seq data
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Corresponding authors: Mark D Robinson mrobinson@wehi.edu.au - Alicia Oshlack oshlack@wehi.edu.au
Genome Biology 2010, 11:R25 doi:10.1186/gb-2010-11-3-r25
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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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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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MicroRNAs and their isomiRs function cooperatively to target common biological pathways Nicole Cloonan, Shivangi Wani, Qinying Xu, Jian Gu, Kristi Lea, Sheila Heater, Catalin Barbacioru, Anita L Steptoe, Hilary C Martin, Ehsan Nourbakhsh, Keerthana Krishnan, Brooke Gardiner, Xiaohui Wang, Katia Nones, Jason A Steen, Nicholas A Matigian, David L Wood, Karin S Kassahn, Nic Waddell, Jill Shepherd, Clarence Lee, Jeff Ichikawa, Kevin McKernan, Kelli Bramlett, Scott Kuersten, Sean M Grimmond Genome Biology 2011, 12:R126 (30 December 2011) IsomiRs act cooperatively with canonical miRNAs to regulate gene expression
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Research article
Expression divergence measured by transcriptome sequencing of four yeast species Michele A Busby, Jesse M Gray, Allen M Costa, Chip Stewart, Michael P Stromberg, Derek Barnett, Jeffrey H Chuang, Michael Springer, Gabor T Marth BMC Genomics 2011, 12:635 (29 December 2011) |
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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
Monica F Poelchau, Julie A Reynolds, David L Denlinger, Christine G Elsik, Peter A Armbruster BMC Genomics 2011, 12:619 (20 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
Robert W Li, Manuela Rinaldi, Anthony V Capuco Veterinary Research 2011, 42:114 (30 November 2011) |
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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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Peter L Chang, Joseph P Dunham, Sergey V Nuzhdin, Michelle N Arbeitman BMC Genomics 2011, 12:364 (14 July 2011) |
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Research article
R Pitts, David C Rinker, Patrick L Jones, Antonis Rokas, Laurence J Zwiebel BMC Genomics 2011, 12:271 (27 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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