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

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

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dc.contributor.author Tarazona Campos, Sonia es_ES
dc.contributor.author Furió Tarí, Pedro es_ES
dc.contributor.author Turrà, David es_ES
dc.contributor.author Di Pietro, Antonio es_ES
dc.contributor.author Nueda, María José es_ES
dc.contributor.author Ferrer, Alberto es_ES
dc.contributor.author Conesa, Ana es_ES
dc.date.accessioned 2017-02-17T16:39:18Z
dc.date.available 2017-02-17T16:39:18Z
dc.date.issued 2015-12-02
dc.identifier.issn 0305-1048
dc.identifier.uri http://hdl.handle.net/10251/78008
dc.description 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. es_ES
dc.description.abstract [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. es_ES
dc.description.sponsorship 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. en_EN
dc.language Inglés es_ES
dc.publisher Oxford University Press (OUP) es_ES
dc.relation.ispartof Nucleic Acids Research es_ES
dc.rights Reconocimiento - No comercial (by-nc) es_ES
dc.subject Differential expression es_ES
dc.subject Bioinformatics es_ES
dc.subject NOISeq es_ES
dc.subject Bioconductor es_ES
dc.subject RNA-seq es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1093/nar/gkv711
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/2007-2013, 306000/EU/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//BIO2008-04638-E/ES/PATHOGENOMICS - REDES TRANSCRIPCIONALES CONTROLADORAS DE VIRULENCIA EN HONGOS FILAMENTOSOS PATOGENOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2008-06880-C03-03/ES/TECNICAS ESTADISTICAS MULTIVARIANTES PARA EL CONOCIMIENTO, MONITORIZACION Y OPTIMIZACION DE BIOPROCESOS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.contributor.affiliation Universitat Politècnica de València. Facultad de Administración y Dirección de Empresas - Facultat d'Administració i Direcció d'Empreses es_ES
dc.description.bibliographicCitation 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/gkv711 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1093/nar/gkv711 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 43 es_ES
dc.description.issue 21 es_ES
dc.relation.senia 293282 es_ES
dc.identifier.eissn 1362-4962
dc.identifier.pmid 26184878 en_EN
dc.identifier.pmcid PMC4666377 en_EN
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.description.references Malone, J. H., & Oliver, B. (2011). Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biology, 9(1). doi:10.1186/1741-7007-9-34 es_ES
dc.description.references Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M. (2012). Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics, 13(1), 484. doi:10.1186/1471-2164-13-484 es_ES
dc.description.references Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008). RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 18(9), 1509-1517. doi:10.1101/gr.079558.108 es_ES
dc.description.references Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5(7), 621-628. doi:10.1038/nmeth.1226 es_ES
dc.description.references Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57-63. doi:10.1038/nrg2484 es_ES
dc.description.references Wagner, J. R., Ge, B., Pokholok, D., Gunderson, K. L., Pastinen, T., & Blanchette, M. (2010). Computational Analysis of Whole-Genome Differential Allelic Expression Data in Human. PLoS Computational Biology, 6(7), e1000849. doi:10.1371/journal.pcbi.1000849 es_ES
dc.description.references Bell, G. D. M., Kane, N. C., Rieseberg, L. H., & Adams, K. L. (2013). RNA-Seq Analysis of Allele-Specific Expression, Hybrid Effects, and Regulatory Divergence in Hybrids Compared with Their Parents from Natural Populations. Genome Biology and Evolution, 5(7), 1309-1323. doi:10.1093/gbe/evt072 es_ES
dc.description.references Łabaj, P. P., Leparc, G. G., Linggi, B. E., Markillie, L. M., Wiley, H. S., & Kreil, D. P. (2011). Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics, 27(13), i383-i391. doi:10.1093/bioinformatics/btr247 es_ES
dc.description.references Auer, P. L., & Doerge, R. W. (2010). Statistical Design and Analysis of RNA Sequencing Data. Genetics, 185(2), 405-416. doi:10.1534/genetics.110.114983 es_ES
dc.description.references Busby, M. A., Stewart, C., Miller, C. A., Grzeda, K. R., & Marth, G. T. (2013). Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics, 29(5), 656-657. doi:10.1093/bioinformatics/btt015 es_ES
dc.description.references Cai, G., Li, H., Lu, Y., Huang, X., Lee, J., Müller, P., … Liang, S. (2012). Accuracy of RNA-Seq and its dependence on sequencing depth. BMC Bioinformatics, 13(Suppl 13), S5. doi:10.1186/1471-2105-13-s13-s5 es_ES
dc.description.references Oshlack, A., & Wakefield, M. J. (2009). Transcript length bias in RNA-seq data confounds systems biology. Biology Direct, 4(1), 14. doi:10.1186/1745-6150-4-14 es_ES
dc.description.references Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011). GC-Content Normalization for RNA-Seq Data. BMC Bioinformatics, 12(1), 480. doi:10.1186/1471-2105-12-480 es_ES
dc.description.references Robinson, M. D., & Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology, 11(3), R25. doi:10.1186/gb-2010-11-3-r25 es_ES
dc.description.references Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010). Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics, 11(1). doi:10.1186/1471-2105-11-94 es_ES
dc.description.references Dillies, M.-A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., … Servant, N. (2012). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics, 14(6), 671-683. doi:10.1093/bib/bbs046 es_ES
dc.description.references Zheng, W., Chung, L. M., & Zhao, H. (2011). Bias detection and correction in RNA-Sequencing data. BMC Bioinformatics, 12(1), 290. doi:10.1186/1471-2105-12-290 es_ES
dc.description.references Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616 es_ES
dc.description.references Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12). doi:10.1186/s13059-014-0550-8 es_ES
dc.description.references Soneson, C., & Delorenzi, M. (2013). A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics, 14(1). doi:10.1186/1471-2105-14-91 es_ES
dc.description.references Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010). Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology, 11(2), R14. doi:10.1186/gb-2010-11-2-r14 es_ES
dc.description.references Bashir, A., Bansal, V., & Bafna, V. (2010). Designing deep sequencing experiments: detecting structural variation and estimating transcript abundance. BMC Genomics, 11(1), 385. doi:10.1186/1471-2164-11-385 es_ES
dc.description.references Russo, F., & Angelini, C. (2014). RNASeqGUI: a GUI for analysing RNA-Seq data. Bioinformatics, 30(17), 2514-2516. doi:10.1093/bioinformatics/btu308 es_ES
dc.description.references Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., … Zhang, J. (2004). Genome Biology, 5(10), R80. doi:10.1186/gb-2004-5-10-r80 es_ES
dc.description.references Tarazona, S., Garcia-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011). Differential expression in RNA-seq: A matter of depth. Genome Research, 21(12), 2213-2223. doi:10.1101/gr.124321.111 es_ES
dc.description.references Ferreira, P. G., Patalano, S., Chauhan, R., Ffrench-Constant, R., Gabaldón, T., Guigó, R., & Sumner, S. (2013). Transcriptome analyses of primitively eusocial wasps reveal novel insights into the evolution of sociality and the origin of alternative phenotypes. Genome Biology, 14(2), R20. doi:10.1186/gb-2013-14-2-r20 es_ES
dc.description.references Nascimento, R., Queiroz, F., Rocha, A., Ren, T. I., Mello, V., & Peixoto, A. (2012). Computer-assisted coloring and illuminating based on a region-tree structure. SpringerPlus, 1(1). doi:10.1186/2193-1801-1-1 es_ES
dc.description.references Zhu, Q.-H., Stephen, S., Kazan, K., Jin, G., Fan, L., Taylor, J., … Wang, M.-B. (2013). Characterization of the defense transcriptome responsive to Fusarium oxysporum-infection in Arabidopsis using RNA-seq. Gene, 512(2), 259-266. doi:10.1016/j.gene.2012.10.036 es_ES
dc.description.references Shearman, J. R., Jantasuriyarat, C., Sangsrakru, D., Yoocha, T., Vannavichit, A., Tragoonrung, S., & Tangphatsornruang, S. (2013). Transcriptome analysis of normal and mantled developing oil palm flower and fruit. Genomics, 101(5), 306-312. doi:10.1016/j.ygeno.2013.02.012 es_ES
dc.description.references Chen, G., Chen, J., Shi, C., Shi, L., Tong, W., & Shi, T. (2013). Dissecting the Characteristics and Dynamics of Human Protein Complexes at Transcriptome Cascade Using RNA-Seq Data. PLoS ONE, 8(6), e66521. doi:10.1371/journal.pone.0066521 es_ES
dc.description.references Durban, J., Pérez, A., Sanz, L., Gómez, A., Bonilla, F., Rodríguez, S., … Calvete, J. J. (2013). Integrated «omics» profiling indicates that miRNAs are modulators of the ontogenetic venom composition shift in the Central American rattlesnake, Crotalus simus simus. BMC Genomics, 14(1), 234. doi:10.1186/1471-2164-14-234 es_ES
dc.description.references Liu, W.-Y., Chang, Y.-M., Chen, S. C.-C., Lu, C.-H., Wu, Y.-H., Lu, M.-Y. J., … Li, W.-H. (2013). Anatomical and transcriptional dynamics of maize embryonic leaves during seed germination. Proceedings of the National Academy of Sciences, 110(10), 3979-3984. doi:10.1073/pnas.1301009110 es_ES
dc.description.references Su, L., Zhou, L., Liu, J., Cen, Z., Wu, C., Wang, T., … Liu, C. (2014). Phenotypic, genomic, transcriptomic and proteomic changes in Bacillus cereus after a short-term space flight. Advances in Space Research, 53(1), 18-29. doi:10.1016/j.asr.2013.08.001 es_ES
dc.description.references Xia, J. H., Liu, P., Liu, F., Lin, G., Sun, F., Tu, R., & Yue, G. H. (2013). Analysis of Stress-Responsive Transcriptome in the Intestine of Asian Seabass (Lates calcarifer) using RNA-Seq. DNA Research, 20(5), 449-460. doi:10.1093/dnares/dst022 es_ES
dc.description.references Nookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M., & Nielsen, J. (2012). A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Research, 40(20), 10084-10097. doi:10.1093/nar/gks804 es_ES
dc.description.references Bi, Y., & Davuluri, R. V. (2013). NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data. BMC Bioinformatics, 14(1). doi:10.1186/1471-2105-14-262 es_ES
dc.description.references Klambauer, G., Unterthiner, T., & Hochreiter, S. (2013). DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions. Nucleic Acids Research, 41(21), e198-e198. doi:10.1093/nar/gkt834 es_ES
dc.description.references Li, J., & Tibshirani, R. (2011). Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Statistical Methods in Medical Research, 22(5), 519-536. doi:10.1177/0962280211428386 es_ES
dc.description.references Lin, B., Zhang, L.-F., & Chen, X. (2014). LFCseq: a nonparametric approach for differential expression analysis of RNA-seq data. BMC Genomics, 15(Suppl 10), S7. doi:10.1186/1471-2164-15-s10-s7 es_ES
dc.description.references Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M., Smyth, G. K., Huber, W., & Robinson, M. D. (2013). Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nature Protocols, 8(9), 1765-1786. doi:10.1038/nprot.2013.099 es_ES
dc.description.references Nueda, M. j., Ferrer, A., & Conesa, A. (2011). ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics, 13(3), 553-566. doi:10.1093/biostatistics/kxr042 es_ES
dc.description.references Efron, B., Tibshirani, R., Storey, J. D., & Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment. Journal of the American Statistical Association, 96(456), 1151-1160. doi:10.1198/016214501753382129 es_ES
dc.description.references (2012). An integrated encyclopedia of DNA elements in the human genome. Nature, 489(7414), 57-74. doi:10.1038/nature11247 es_ES
dc.description.references Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., … Hubbard, T. J. (2012). GENCODE: The reference human genome annotation for The ENCODE Project. Genome Research, 22(9), 1760-1774. doi:10.1101/gr.135350.111 es_ES
dc.description.references Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 14(4), R36. doi:10.1186/gb-2013-14-4-r36 es_ES
dc.description.references Anders, S., Pyl, P. T., & Huber, W. (2014). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics, 31(2), 166-169. doi:10.1093/bioinformatics/btu638 es_ES
dc.description.references López-Berges, M. S., Capilla, J., Turrà, D., Schafferer, L., Matthijs, S., Jöchl, C., … Di Pietro, A. (2012). HapX-Mediated Iron Homeostasis Is Essential for Rhizosphere Competence and Virulence of the Soilborne Pathogen Fusarium oxysporum. The Plant Cell, 24(9), 3805-3822. doi:10.1105/tpc.112.098624 es_ES
dc.description.references Flicek, P., Amode, M. R., Barrell, D., Beal, K., Brent, S., Carvalho-Silva, D., … Fitzgerald, S. (2011). Ensembl 2012. Nucleic Acids Research, 40(D1), D84-D90. doi:10.1093/nar/gkr991 es_ES
dc.description.references Ren, S., Peng, Z., Mao, J.-H., Yu, Y., Yin, C., Gao, X., … Sun, Y. (2012). RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell Research, 22(5), 806-821. doi:10.1038/cr.2012.30 es_ES
dc.description.references Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25(9), 1105-1111. doi:10.1093/bioinformatics/btp120 es_ES
dc.description.references Li, S., Łabaj, P. P., Zumbo, P., Sykacek, P., Shi, W., Shi, L., … Mason, C. E. (2014). Detecting and correcting systematic variation in large-scale RNA sequencing data. Nature Biotechnology, 32(9), 888-895. doi:10.1038/nbt.3000 es_ES
dc.description.references Wang, L., Wang, S., & Li, W. (2012). RSeQC: quality control of RNA-seq experiments. Bioinformatics, 28(16), 2184-2185. doi:10.1093/bioinformatics/bts356 es_ES
dc.description.references DeLuca, D. S., Levin, J. Z., Sivachenko, A., Fennell, T., Nazaire, M.-D., Williams, C., … Getz, G. (2012). RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics, 28(11), 1530-1532. doi:10.1093/bioinformatics/bts196 es_ES
dc.description.references García-Alcalde, F., Okonechnikov, K., Carbonell, J., Cruz, L. M., Götz, S., Tarazona, S., … Conesa, A. (2012). Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics, 28(20), 2678-2679. doi:10.1093/bioinformatics/bts503 es_ES
dc.description.references Liu, Y., Zhou, J., & White, K. P. (2013). RNA-seq differential expression studies: more sequence or more replication? Bioinformatics, 30(3), 301-304. doi:10.1093/bioinformatics/btt688 es_ES
dc.description.references Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., … Betel, D. (2013). Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biology, 14(9), R95. doi:10.1186/gb-2013-14-9-r95 es_ES
dc.description.references (2014). A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature Biotechnology, 32(9), 903-914. doi:10.1038/nbt.2957 es_ES


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