article Open AccessTop 1% cited
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
BMC Bioinformatics · 2010 · Vol. 11(1) · pp. 94–94
James Bullard✉(University of California, Berkeley)Elizabeth Purdom(University of California, Berkeley)Kasper D. Hansen(University of California, Berkeley)Sandrine Dudoit(University of California, Berkeley)
Abstract
Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.
Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsGenomics and Phylogenetic StudiesNormalization (sociology)Computational biologyComputer scienceDNA microarrayStatistical inferenceStatistical hypothesis testingRNA-SeqData miningDatabase normalizationInference
MeSH terms
RNA, MessengerSequence Analysis, RNAComputational BiologyDatabases, Genetic
Funding
- National Science Foundation
- National Institutes of Health
Citations
1,770
FWCI
43.14
field-weighted impact
References
23
Percentile
100%
vs. same field & year
Citations per year
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References
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