- -

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

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

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

Show full item record

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/78008

Files in this item

Item Metadata

Title: Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package
Author: Tarazona Campos, Sonia Furió Tarí, Pedro Turrà, David Di Pietro, Antonio Nueda, María José Ferrer, Alberto Conesa, Ana
UPV Unit: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials
Universitat Politècnica de València. Facultad de Administración y Dirección de Empresas - Facultat d'Administració i Direcció d'Empreses
Issued date:
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 ...[+]
Subjects: Differential expression , Bioinformatics , Noiseq , Bioconductor , Rna-seq
Copyrigths: Reconocimiento - No comercial (by-nc)
Source:
Nucleic Acids Research. (issn: 0305-1048 ) (eissn: 1362-4962 )
DOI: 10.1093/nar/gkv711
Publisher:
Oxford University Press (OUP)
Publisher version: https://doi.org/10.1093/nar/gkv711
Project ID:
info:eu-repo/grantAgreement/EC/FP7/2007-2013, 306000/EU/
info:eu-repo/grantAgreement/MICINN//BIO2008-04638-E/ES/PATHOGENOMICS - REDES TRANSCRIPCIONALES CONTROLADORAS DE VIRULENCIA EN HONGOS FILAMENTOSOS PATOGENOS/
info:eu-repo/grantAgreement/MICINN//DPI2008-06880-C03-03/ES/TECNICAS ESTADISTICAS MULTIVARIANTES PARA EL CONOCIMIENTO, MONITORIZACION Y OPTIMIZACION DE BIOPROCESOS/
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.
Thanks:
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 ...[+]
Type: Artículo

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

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

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 [+]
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Russo, F., & Angelini, C. (2014). RNASeqGUI: a GUI for analysing RNA-Seq data. Bioinformatics, 30(17), 2514-2516. doi:10.1093/bioinformatics/btu308

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(2012). An integrated encyclopedia of DNA elements in the human genome. Nature, 489(7414), 57-74. doi:10.1038/nature11247

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

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

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

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

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

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

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

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

Wang, L., Wang, S., & Li, W. (2012). RSeQC: quality control of RNA-seq experiments. Bioinformatics, 28(16), 2184-2185. doi:10.1093/bioinformatics/bts356

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

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

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

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

(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

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record