Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628 (2008).
Article CAS PubMed PubMed Central Google Scholar
Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
Article CAS PubMed PubMed Central Google Scholar
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
Article CAS PubMed PubMed Central Google Scholar
Robinson, M.D. & Smyth, G.K. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881–2887 (2007).
Article CAS PubMed Google Scholar
Robinson, M.D. & Smyth, G.K. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9, 321–332 (2008).
Article PubMed Google Scholar
Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Article CAS PubMed PubMed Central Google Scholar
McCarthy, D.J., Chen, Y. & Smyth, G.K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
Article CAS PubMed PubMed Central Google Scholar
Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).
Article PubMed PubMed Central Google Scholar
Zemach, A. et al. The Arabidopsis nucleosome remodeler DDM1 allows DNA methyltransferases to access H1-containing heterochromatin. Cell 153, 193–205 (2013).
Article CAS PubMed PubMed Central Google Scholar
Lam, M.T. et al. Rev-Erbs repress macrophage gene expression by inhibiting enhancer-directed transcription. Nature 498, 511–515 (2013).
Article CAS PubMed PubMed Central Google Scholar
Ross-Innes, C.S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).
Article CAS PubMed PubMed Central Google Scholar
Robinson, M.D. et al. Copy-number-aware differential analysis of quantitative DNA sequencing data. Genome Res. 22, 2489–2496 (2012).
Article CAS PubMed PubMed Central Google Scholar
Vanharanta, S. et al. Epigenetic expansion of VHL-HIF signal output drives multiorgan metastasis in renal cancer. Nat. Med. 19, 50–56 (2013).
Article CAS PubMed Google Scholar
Samstein, R.M. et al. Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification. Cell 151, 153–166 (2012).
Article CAS PubMed PubMed Central Google Scholar
Johnson, E.K. et al. Proteomic analysis reveals new cardiac-specific dystrophin-associated proteins. PloS ONE 7, e43515 (2012).
Article CAS PubMed PubMed Central Google Scholar
Fonseca, N.A., Rung, J., Brazma, A. & Marioni, J.C. Tools for mapping high-throughput sequencing data. Bioinformatics 28, 3169–3177 (2012).
Article CAS PubMed Google Scholar
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
Article CAS PubMed PubMed Central Google Scholar
Bullard, J.H., Purdom, E., Hansen, K.D. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMC Bioinform. 11, 94 (2010).
Article CAS Google Scholar
Grabherr, M.G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).
Article CAS PubMed PubMed Central Google Scholar
Siebert, S. et al. Differential gene expression in the siphonophore Nanomia bijuga (Cnidaria) assessed with multiple next-generation sequencing workflows. PLoS ONE 6, 12 (2011).
Article CAS Google Scholar
Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).
Article CAS PubMed PubMed Central Google Scholar
Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
Article CAS PubMed PubMed Central Google Scholar
Hardcastle, T.J. & Kelly, K.A. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinform. 11, 422 (2010).
Article Google Scholar
Zhou, Y.-H., Xia, K. & Wright, F.A. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 27, 2672–2678 (2011).
Article CAS PubMed PubMed Central Google Scholar
Tarazona, S., Garcia-Alcalde, F., Dopazo, J., Ferrer, A. & Conesa, A. Differential expression in RNA-seq: a matter of depth. Genome Res. 21, 2213–2223 (2011).
Article CAS PubMed PubMed Central Google Scholar
Lund, S.P., Nettleton, D., McCarthy, D.J. & Smyth, G.K. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat. Appl. Genet. Mol. Biol. 11, pii (2012).
Article CAS Google Scholar
Soneson, C. & Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform. 14, 91 (2013).
Article Google Scholar
Lareau, L.F., Inada, M., Green, R.E., Wengrod, J.C. & Brenner, S.E. Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature 446, 926–929 (2007).
Article CAS PubMed Google Scholar
Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012).
Article CAS PubMed PubMed Central Google Scholar
Glaus, P., Honkela, A. & Rattray, M. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics 28, 1721–1728 (2012).
Article CAS PubMed PubMed Central Google Scholar
Van De Wiel, M.A. et al. Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors. Biostatistics 14, 113–128 (2013).
Article PubMed Google Scholar
Blekhman, R., Marioni, J.C., Zumbo, P., Stephens, M. & Gilad, Y. Sex-specific and lineage-specific alternative splicing in primates. Genome Res. 20, 180–189 (2010).
Article CAS PubMed PubMed Central Google Scholar
Okoniewski, M.J. et al. Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage. Nucleic Acids Res. 40, e63 (2012).
Article CAS PubMed Google Scholar
Hansen, K.D., Wu, Z., Irizarry, R.A. & Leek, J.T. Sequencing technology does not eliminate biological variability. Nat. Biotechnol. 29, 572–573 (2011).
Article CAS PubMed PubMed Central Google Scholar
Leek, J.T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).
Article CAS PubMed Google Scholar
Auer, P.L. & Doerge, R.W. Statistical design and analysis of RNA sequencing data. Genetics 185, 405–416 (2010).
Article CAS PubMed PubMed Central Google Scholar
Gagnon-Bartsch, J.A. & Speed, T.P. Using control genes to correct for unwanted variation in microarray data. Biostatistics 13, 539–552 (2011).
Article PubMed Google Scholar
Leek, J.T. & Storey, J.D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).
Article CAS PubMed Google Scholar
Myers, R.M. Classical and Modern Regression with Applications 2nd edn. (Duxbury Classic Series, 2000).
Gentleman, R. Reproducible research: a bioinformatics case study. Stat. Appl. Genet. Mol. Biol. 4, Article2 (2005).
Article PubMed Google Scholar
Trapnell, C. & Salzberg, S.L. How to map billions of short reads onto genomes. Nat. Biotechnol. 27, 455–457 (2009).
Article CAS PubMed PubMed Central Google Scholar
Wu, T.D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).
Article CAS PubMed PubMed Central Google Scholar
Wang, K. et al. MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).
Article CAS PubMed PubMed Central Google Scholar
Liao, Y., Smyth, G.K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).
Article CAS PubMed PubMed Central Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Article CAS Google Scholar
Thorvaldsdóttir, H., Robinson, J.T. & Mesirov, J.P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).
Article CAS PubMed PubMed Central Google Scholar
Fiume, M., Williams, V., Brook, A. & Brudno, M. Savant: genome browser for high-throughput sequencing data. Bioinformatics 26, 1938–1944 (2010).
Article CAS PubMed PubMed Central Google Scholar
Fiume, M. et al. Savant genome browser 2: visualization and analysis for population-scale genomics. Nucleic Acids Res. 40, 1–7 (2012).
Article CAS Google Scholar
Morgan, M. et al. ShortRead: a Bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 2607–2608 (2009).
Article CAS PubMed PubMed Central Google Scholar
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Article CAS PubMed PubMed Central Google Scholar
Brooks, A.N. et al. Conservation of an RNA regulatory map between Drosophila and mammals. Genome Res. 21, 193–202 (2011).
Article CAS PubMed PubMed Central Google Scholar
Edgar, R., Domrachev, M. & Lash, A.E. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).
Article CAS PubMed PubMed Central Google Scholar
Cox, D.R. & Reid, N. Parameter orthogonality and approximate conditional inference. J. Roy. Stat. Soc. Ser. B Method. 49, 1–39 (1987).
Google Scholar
Dudoit, S., Yang, Y.H., Callow, M.J. & Speed, T.P. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat. Sinica 12, 111–139 (2002).
Google Scholar
Bourgon, R., Gentleman, R. & Huber, W. Independent filtering increases detection power for high-throughput experiments. Proc. Natl. Acad. Sci. USA 107, 9546–9551 (2010).
Article PubMed Google Scholar
Robinson, M.D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
Article CAS PubMed PubMed Central Google Scholar
Cappiello, C., Francalanci, C. & Pernici, B. Data quality assessment from the user's perspective. Architecture 22, 68–73 (2004).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
Google Scholar
Wu, H., Wang, C. & Wu, Z. A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics 14, 232–243 (2012).
Article PubMed PubMed Central Google Scholar
Smyth, G.K. Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds. Gentleman, R. et al.) 397–420 (Springer, 2005).
Nookaew, I. et al. 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 Res. 40, 10084–10097 (2012).
Article CAS PubMed PubMed Central Google Scholar
Rapaport, F. et al. Comprehensive evaluation of differential expression analysis methods for RNA-seq data http://arXiv.org/abs/1301.5277v2 (23 January 2013).
Hansen, K.D., Irizarry, R.A. & Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13, 204–216 (2012).
Article PubMed PubMed Central Google Scholar
Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-seq data. BMC Bioinform. 12, 480 (2011).
Article CAS Google Scholar
Delhomme, N., Padioleau, I., Furlong, E.E. & Steinmetz, L. easyRNASeq: a Bioconductor package for processing RNA-seq data. Bioinformatics 28, 2532–2533 (2012).
Article CAS PubMed PubMed Central Google Scholar
Leisch, F. Sweave: dynamic generation of statistical reports using literate data analysis. In Compstat 2002 Proceedings in Computational Statistics Vol. 69 (eds. Härdle, W. & Rönz, B.) 575–580. Institut für Statistik und Wahrscheinlichkeitstheorie, Technische Universität Wien (Physica Verlag, 2002).
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Source: https://www.nature.com/articles/nprot.2013.099?error=cookies_not_supported&code=150f1d84-d4e9-4aa3-93d9-118240637883
0 Response to "Bioinformatics and Computational Biology Solutions Using R and Bioconductor"
Post a Comment