article Open AccessTop 1% cited
GC-Content Normalization for RNA-Seq Data
BMC Bioinformatics · 2011 · Vol. 12(1) · pp. 480–480
Davide Risso(University of California, Berkeley)Katja Schwartz(Stanford University)Gavin Sherlock(Stanford University)Sandrine Dudoit✉(University of California, Berkeley)
Abstract
Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.
Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsRNA Research and SplicingNormalization (sociology)RNA-SeqDNA microarrayBioconductorInferenceTranscriptomeComputer scienceComputational biologyGene expression profilingGene expression
MeSH terms
Base CompositionSaccharomyces cerevisiaeSequence Analysis, RNAGene Expression ProfilingTranscriptome
Funding
- Università degli Studi di Padova
- National Institutes of Health
- National Human Genome Research Institute
Citations
939
FWCI
13.31
field-weighted impact
References
35
Percentile
99%
vs. same field & year
Citations per year
Cited by
New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx
PLoS Computational Biology · 2019 · 593 citations
A survey of best practices for RNA-seq data analysis
Genome biology · 2016 · 2,822 citations
RNA sequencing: the teenage years
Nature Reviews Genetics · 2019 · 1,988 citations
variancePartition: interpreting drivers of variation in complex gene expression studies
BMC Bioinformatics · 2016 · 848 citations
RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types
Cell Reports · 2019 · 1,006 citations
Normalization of RNA-seq data using factor analysis of control genes or samples
Nature Biotechnology · 2014 · 2,258 citations
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Genome biology · 2014 · 97,164 citations
References
RNA-Seq: a revolutionary tool for transcriptomics
Nature Reviews Genetics · 2008 · 13,223 citations
The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements
Nature Biotechnology · 2006 · 2,162 citations
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Journal of the American Statistical Association · 1988 · 5,429 citations
A scaling normalization method for differential expression analysis of RNA-seq data
Genome biology · 2010 · 8,395 citations
Gene ontology analysis for RNA-seq: accounting for selection bias
Genome biology · 2010 · 7,740 citations
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1995 · 106,483 citations
<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
Bioinformatics · 2009 · 43,721 citations
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Genome Research · 2008 · 2,821 citations
Related articles
A scaling normalization method for differential expression analysis of RNA-seq data
Genome biology · 2010 · 8,395 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.