Journal
NUCLEIC ACIDS RESEARCH
Volume 43, Issue 21, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkv711
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Funding
- European Union [306000]
- Spanish Ministry of Science and Innovation [MICINN], in the framework of ERA-Net Pathogenomics [BIO2008-04638-E]
- MICINN [DPI2008-06880-C03-03/DPI]
- University of Florida, Publication Funds
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As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the nonparametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.
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