A survey of statistical software for analysing RNA-seq data

被引：0
作者：
Gao D. [1 ,5 ]
Kim J. [2 ]
Kim H. [4 ]
Phang T.L. [3 ]
Selby H. [2 ]
Tan A.C. [2 ,5 ]
Tong T. [6 ]
机构：
[1] Department of Pediatrics, University of Colorado School of Medicine, Aurora
[2] Division of Medical Oncology, University of Colorado School of Medicine, Aurora
[3] Division of Critical Care and Pulmonary Medicine, University of Colorado School of Medicine, Aurora
[4] Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora
[5] Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora
[6] Department of Applied Mathematics, University of Colorado, Boulder
来源：
Human Genomics | / 5卷 / 1期
关键词：
normalisation; RNA-sequencing analysis; sequencing data; statistical software;
D O I：
10.1186/1479-7364-5-1-56
中图分类号：
学科分类号：
摘要：
High-throughput RNA sequencing is rapidly emerging as a favourite method for gene expression studies. We review three software packages - edgeR, DEGseq and baySeq - from Bioconductor http://bioconductor.org for analysing RNA-sequencing data. We focus on three aspects: normalisation, statistical models and the testing employed on these methods. We also discuss the advantages and limitations of these software packages.
引用
收藏
页码：56 / 60
页数：4
相关论文
共 54 条
[1]  
Marioni JC(2008)RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays Genome Res 18 1509-1517
[2]  
Mason CE(2010)Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments BMC Bioinformatics 11 94-332
[3]  
Mane SM(2008)Small-sample estimation of negative binomial dispersion, with applications to SAGE data Biostatistics 9 321-628
[4]  
Stephens M(2008)Mapping and quantifying mammalian transcriptomes by RNA-Seq Nat Methods 5 621-138
[5]  
Bullard JH(2004)Bayesian model accounting for within-class biological variability in serial analysis of gene expression (SAGE) BMC Bioinformatics 5 119-1872
[6]  
Purdom EA(2009)Transcript length bias in RNA-seq data confounds system biology Biol Direct 4 14-2887
[7]  
Hansen KD(2010)A scaling normalization method for differential expression analysis of RNA-seq data Genome Biol 11 R25-140
[8]  
Durinck S(2004)Overdispersed logistic regression for sage: Modelling multiple groups and covariates BMC Bioinformatics 5 144-300
[9]  
Robinson MD(2010)DEGseq: An R package for identifying differentially expressed genes from RNA-seq data Bioinformatics 26 136-5121
[10]  
Smyth GK(1999)Dynamics of gene expression revealed by comparison of serial analysis of gene expression transcript profiles from yeast grown on two different carbon sources Mol Biol Cell 10 1859-1032
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