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Volumn 31, Issue 17, 2015, Pages 2778-2784

Polyester: Simulating RNA-seq datasets with differential transcript expression

Author keywords

[No Author keywords available]

Indexed keywords

ISOPROTEIN; RNA;

EID: 84940775779     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv272     Document Type: Article
Times cited : (203)

References (33)
  • 1
    • 84887433583 scopus 로고    scopus 로고
    • Reproducibility of high-throughput mRNA, and small RNA sequencing across laboratories
    • AC't Hoen P., et al. (2013). Reproducibility of high-throughput mRNA, and small RNA sequencing across laboratories. Nat. Biotechnol, 31, 1015-1022
    • (2013) Nat. Biotechnol , vol.31 , pp. 1015-1022
    • Act Hoen, P.1
  • 2
    • 77958471357 scopus 로고    scopus 로고
    • Differential expression analysis for sequence count data
    • Anders S., and Huber W. (2010). Differential expression analysis for sequence count data. Genome Biol., 11, R106
    • (2010) Genome Biol , vol.11 , pp. R106
    • Anders, S.1    Huber, W.2
  • 3
    • 84861548193 scopus 로고    scopus 로고
    • Summarizing, and correcting the GC content bias in high-throughput sequencing
    • Benjamini Y., and Speed T.P. (2012). Summarizing, and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res., 40, e72
    • (2012) Nucleic Acids Res , vol.40 , pp. e72
    • Benjamini, Y.1    Speed, T.P.2
  • 4
    • 77949481052 scopus 로고    scopus 로고
    • Evaluation of statistical methods for normalization, and differential expression in mRNA-seq experiments
    • Bullard J.H., et al. (2010). Evaluation of statistical methods for normalization, and differential expression in mRNA-seq experiments. BMC Bioinformatics, 11, 94
    • (2010) BMC Bioinformatics , vol.11 , pp. 94
    • Bullard, J.H.1
  • 5
    • 84877602073 scopus 로고    scopus 로고
    • Accuracy of RNA-seq, and its dependence on sequencing depth
    • Cai G., et al. (2012). Accuracy of RNA-seq, and its dependence on sequencing depth. BMC Bioinformatics, 13(Suppl. 13), S5
    • (2012) BMC Bioinformatics , vol.13 , pp. S5
    • Cai, G.1
  • 6
    • 79955756836 scopus 로고    scopus 로고
    • Design, and validation issues in RNA-seq experiments
    • Fang Z., and Cui X. (2011). Design, and validation issues in RNA-seq experiments. Briefings in Bioinformatics, 12, 280-287
    • (2011) Briefings in Bioinformatics , vol.12 , pp. 280-287
    • Fang, Z.1    Cui, X.2
  • 7
    • 84924364363 scopus 로고    scopus 로고
    • Ballgown bridges the gap between transcriptome assembly, and expression analysis
    • Frazee A.C., et al. (2015). Ballgown bridges the gap between transcriptome assembly, and expression analysis. Nat. Biotechnol., 33, 243246
    • (2015) Nat. Biotechnol , vol.33 , pp. 243246
    • Frazee, A.C.1
  • 8
    • 80052745094 scopus 로고    scopus 로고
    • Comparative analysis of RNA-seq alignment algorithms, and the RNA-seq unified mapper (RUM
    • Grant G.R., et al. (2011). Comparative analysis of RNA-seq alignment algorithms, and the RNA-seq unified mapper (RUM). Bioinformatics, 27, 2518-2528
    • (2011) Bioinformatics , vol.27 , pp. 2518-2528
    • Grant, G.R.1
  • 9
    • 84869036699 scopus 로고    scopus 로고
    • Modelling, and simulating generic RNA-seq experiments with the flux simulator
    • Griebel T., et al. (2012). Modelling, and simulating generic RNA-seq experiments with the flux simulator. Nucleic Acids Res., 40, 10073-10083
    • (2012) Nucleic Acids Res , vol.40 , pp. 10073-10083
    • Griebel, T.1
  • 10
    • 77955883388 scopus 로고    scopus 로고
    • Biases in Illumina transcriptome sequencing caused by random hexamer priming
    • Hansen K.D., et al. (2010). Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res., 38, e131
    • (2010) Nucleic Acids Res , vol.38 , pp. e131
    • Hansen, K.D.1
  • 11
    • 84858068675 scopus 로고    scopus 로고
    • Removing technical variability in RNA-seq data using conditional quantile normalization
    • Hansen K.D., et al. (2012). Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics, 13, 204-216
    • (2012) Biostatistics , vol.13 , pp. 204-216
    • Hansen, K.D.1
  • 12
    • 56849101511 scopus 로고    scopus 로고
    • Handling overdispersion with negative binomial, and generalized poisson regression models
    • Citeseer
    • Ismail N., and Jemain A.A. (2007). Handling overdispersion with negative binomial, and generalized Poisson regression models. In: Casualty Actuarial Society Forum. Citeseer. pp.103-158
    • (2007) Casualty Actuarial Society Forum , pp. 103-158
    • Ismail, N.1    Jemain, A.A.2
  • 14
    • 0000930441 scopus 로고
    • Logspline density estimation for censored data
    • Kooperberg C., and Stone C.J. (1992). Logspline density estimation for censored data. J. Comput. Graph. Stat., 1, 301-328
    • (1992) J. Comput. Graph. Stat , vol.1 , pp. 301-328
    • Kooperberg, C.1    Stone, C.J.2
  • 15
    • 84911861819 scopus 로고    scopus 로고
    • IVT-seq reveals extreme bias in RNA-sequencing
    • Lahens N.F., et al. (2014). IVT-seq reveals extreme bias in RNA-sequencing. Genome Biol., 15, R86
    • (2014) Genome Biol , vol.15 , pp. R86
    • Lahens, N.F.1
  • 16
    • 84885645853 scopus 로고    scopus 로고
    • Transcriptome, and genome sequencing uncovers functional variation in humans
    • Lappalainen T., et al. (2013). Transcriptome, and genome sequencing uncovers functional variation in humans. Nature, 501, 506-511
    • (2013) Nature , vol.501 , pp. 506-511
    • Lappalainen, T.1
  • 17
    • 84988052086 scopus 로고
    • Negative binomial, and mixed poisson regression
    • Lawless J.F. (1987). Negative binomial, and mixed poisson regression. Can. J. Stat., 15, 209-225
    • (1987) Can. J. Stat , vol.15 , pp. 209-225
    • Lawless, J.F.1
  • 18
    • 84883368195 scopus 로고    scopus 로고
    • Software for computing, and annotating genomic ranges
    • Lawrence M., et al. (2013). Software for computing, and annotating genomic ranges. PLoS Comput. Biol., 9, e1003118
    • (2013) Plos Comput. Biol , vol.9 , pp. e1003118
    • Lawrence, M.1
  • 19
    • 79961123152 scopus 로고    scopus 로고
    • Rsem: Accurate transcript quantification from RNA-seq data with or without a reference genome
    • Li B., and Dewey C.N. (2011). RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics, 12, 323
    • (2011) BMC Bioinformatics , vol.12 , pp. 323
    • Li, B.1    Dewey, C.N.2
  • 20
    • 84869396421 scopus 로고    scopus 로고
    • Transcriptome assembly, and isoform expression level estimation from biased RNA-seq reads
    • Li W., and Jiang T. (2012). Transcriptome assembly, and isoform expression level estimation from biased RNA-seq reads. Bioinformatics, 28, 2914-2921
    • (2012) Bioinformatics , vol.28 , pp. 2914-2921
    • Li, W.1    Jiang, T.2
  • 21
    • 84856988681 scopus 로고    scopus 로고
    • Gemsim: General, error-model based simulator of next-generation sequencing data
    • McElroy K.E., et al. (2012). GemSIM: general, error-model based simulator of next-generation sequencing data. BMC Genomics, 13, 74
    • (2012) BMC Genomics , vol.13 , pp. 74
    • McElroy, K.E.1
  • 22
    • 46249106990 scopus 로고    scopus 로고
    • Mapping, and quantifying mammalian transcriptomes by RNA-seq
    • Mortazavi A., et al. (2008). Mapping, and quantifying mammalian transcriptomes by RNA-seq. Nature Methods, 5, 621-628
    • (2008) Nature Methods , vol.5 , pp. 621-628
    • Mortazavi, A.1
  • 23
    • 78650539308 scopus 로고    scopus 로고
    • From RNA-seq reads to differential expression results
    • Oshlack A., et al. (2010). From RNA-seq reads to differential expression results. Genome Biol., 11, 220
    • (2010) Genome Biol , vol.11 , pp. 220
    • Oshlack, A.1
  • 25
    • 84888865593 scopus 로고    scopus 로고
    • Differential abundance analysis for microbial marker-gene surveys
    • Paulson J.N., et al. (2013). Differential abundance analysis for microbial marker-gene surveys. Nature Methods, 10, 1200-1202
    • (2013) Nature Methods , vol.10 , pp. 1200-1202
    • Paulson, J.N.1
  • 26
    • 83455238345 scopus 로고    scopus 로고
    • GC-content normalization for RNA-seq data
    • Risso D., et al. (2011). GC-content normalization for RNA-seq data. BMC Bioinformatics, 12, 480
    • (2011) BMC Bioinformatics , vol.12 , pp. 480
    • Risso, D.1
  • 27
    • 75249087100 scopus 로고    scopus 로고
    • EdgeR: A Bioconductor package for differential expression analysis of digital gene expression data
    • Robinson M.D., et al. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140
    • (2010) Bioinformatics , vol.26 , pp. 139-140
    • Robinson, M.D.1
  • 29
    • 84861206073 scopus 로고    scopus 로고
    • Single read, and paired end mRNA-seq illumina libraries from 10 nanograms total RNA
    • Sengupta S., et al. (2011). Single read, and paired end mRNA-seq Illumina libraries from 10 nanograms total RNA. J. Visual. Exp, 56, e3340
    • (2011) J. Visual. Exp , vol.56 , pp. e3340
    • Sengupta, S.1
  • 31
    • 65449136284 scopus 로고    scopus 로고
    • TopHat: Discovering splice junctions with RNA-seq
    • Trapnell C., et al. (2009). TopHat: discovering splice junctions with RNA-seq. Bioinformatics, 25, 1105-1111
    • (2009) Bioinformatics , vol.25 , pp. 1105-1111
    • Trapnell, C.1
  • 32
    • 77952123055 scopus 로고    scopus 로고
    • Transcript assembly, and quantification by RNA-seq reveals unannotated transcripts, and isoform switching during cell differentiation
    • Trapnell C., et al. (2010). Transcript assembly, and quantification by RNA-seq reveals unannotated transcripts, and isoform switching during cell differentiation. Nature Biotechnol., 28, 511-515
    • (2010) Nature Biotechnol , vol.28 , pp. 511-515
    • Trapnell, C.1
  • 33
    • 84872198346 scopus 로고    scopus 로고
    • Differential analysis of gene regulation at transcript resolution with RNA-seq
    • Trapnell C., et al. (2013). Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature Biotechnol., 31, 46-53
    • (2013) Nature Biotechnol , vol.31 , pp. 46-53
    • Trapnell, C.1


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