Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.[EN] 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 non-parametric 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.European Union Seventh Framework Programme [FP7/2007-2013, 306000]; Spanish Ministry of Science and Innovation [MICINN, BIO2008-04638-E], in the framework of ERA-Net Pathogenomics; MICINN [DPI2008-06880-C03-03/DPI]. Funding for open access charge: University of Florida, Publication Funds.Tarazona Campos, S.; Furió Tarí, P.; Turrà, D.; Di Pietro, A.; Nueda, MJ.; Ferrer, A.; Conesa, A. (2015). Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Research. 43(21):1-13. https://doi.org/10.1093/nar/gkv711S113432