Improving RNA-Seq expression estimates by correcting for fragment bias
-
* Corresponding author: Lior Pachter lpachter@math.berkeley.edu
Genome Biology 2011, 12:R22 doi:10.1186/gb-2011-12-3-r22
Accesses
- Last 30 days: 1059 accesses
- Last 365 days: 18919 accesses
- All time: 26449 accesses
Cited by
BioMed Central: 7 citations
|
Research article
Ashley M Driver, Francisco Penagaricano, Wen Huang, Khawaja R Ahmad, Katie S Hackbart, Milo C Witlbank, Hasan Khatib BMC Genomics 2012, 13:118 (28 March 2012) |
|
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.
|
|
Research highlight
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".
|
|
Research article
Identification and correction of systematic error in high-throughput sequence data Frazer Meacham, Dario Boffelli, Joseph Dhahbi, David IK Martin, Meromit Singer, Lior Pachter BMC Bioinformatics 2011, 12:451 (21 November 2011) |
|
Oral presentation
Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data Sebastian J Schultheiss, Géraldine Jean, Jonas Behr, Regina Bohnert, Philipp Drewe, Nico Görnitz, André Kahles, Pramod Mudrakarta, Vipin T Sreedharan, Georg Zeller, Gunnar Rätsch BMC Bioinformatics 2011, 12(Suppl 11):A7 (21 November 2011) |
|
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
|
|
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) |