Erratum: EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments (Bioinformatics (2013) 29:8 (1035-1043) DOI: 10.1093/bioinformatics/btt087);EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments

Bioinformatics

Volumn 29, Issue 8, 2013, Pages 1035-1043

  Leng, Ning ^a   Dawson, John A ^a   Thomson, James A ^b   Ruotti, Victor ^b   Rissman, Anna I ^c   Smits, Bart M G ^c   Haag, Jill D ^c   Gould, Michael N ^c   Stewart, Ron M ^b   Kendziorski, Christina ^d  

^a University of Wisconsin   (United States)

^b MORGRIDGE INSTITUTE FOR RESEARCH   (United States)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

^d UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA; RNA ISOFORM;

ARTICLE; BAYES THEOREM; CELL LINE; COMPUTER PROGRAM; EMBRYONIC STEM CELL; GENE EXPRESSION PROFILING; GENOME; METABOLISM; METHODOLOGY; SEQUENCE ANALYSIS; STATISTICAL MODEL;

BAYES THEOREM; CELL LINE; EMBRYONIC STEM CELLS; GENE EXPRESSION PROFILING; GENOME; MODELS, STATISTICAL; RNA ISOFORMS; RNA, MESSENGER; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 84876263777     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt337     Document Type: Erratum

References (32)

1. Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biol., 11, R106.
2. Anders, S. et al. (2012) Detecting differential usage ofexons from RNA-seq data. Genome Res., 22, 2008-2017.
3. Bock, C. et al. (2011) Reference maps of human ES and IPS cell variation enable high-throughput characterization ofpluripotent cell lines. Cell, 144, 439-452.
4. Bullard, J.H. et al. (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMCBioinformatics, 11, 94.
5. Consortium, M. (2006) The microarray quality control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol., 24, 1151-1161.
6. Dempster, A. et al. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc., 39, 1-38.
7. Dixon, W.J. (1950) Analysis of extreme values. Ann. Math. Stat., 21, 488.
8. Du, J. et al. (2012) IQSeq: integrated isoform quantification analysis based on next-generation sequencing. PLoS one, 7, e29175.
9. Glaus, P. et al. (2012) Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics, 28, 1721-1728.
10. Hardcastle, TJ. and Kelly, K.A. (2010) baySeq: empirical bayesian methods for iden- tifying differential expression in sequence count data. BMC Bioinformatics, 11, 422.
11. Howard, B.E. and Heber, S. (2010) Towards reliable isoform quantification using RNA-seq data. BMC Bioinformatics, 11(Suppl. 3), S6.
12. Jiang, H. and Wing, W.H. (2009) Statistical inferences for isoform expression in RNA-seq. Bioinformatics, 25, 1026-1032.
13. Katz, Y. et al. (2010) Analysis and design of RNA sequencing experiments for iden- tifying isoform regulation. Nat. Methods, 7, 1009-1015.
14. Langmead, B. et al. (2009) Ultrafast and memory-efficient alignment ofshort DNA sequences to the human genome. Genome Biol., 10, R25.
15. 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.
16. Li, B. et al. (2010) RNA-seq gene expression estimation with read mapping uncer- tainty. Bioinformatics, 26, 493-500.
17. Nicolae, M. et al. (2011) Estimation ofalternative splicing isoform frequencies from RNA-seq data. Algorithms Mol. Biol., 6, 9.
18. Ohi, Y. et al. (2011) Incomplete DNA methylation underlies a transcriptional memory ofsomatic cells in human IPS cells. Nat. Cell. Biol., 13, 541-549.
19. Phanstiel, H.P. etal. (2011) Proteomic and phosphoproteomic comparison of human ES and IPS cells. Nat. Methods, 8, 821-827.
20. R Development Core Team. (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
21. Robinson, M.D. and Oshlack, A. (2010) A scaling normalization method for differ- ential expression analysis of RNA-seq data. Genome Biol., 11, R25.
22. Robinson, M.D. and Smyth, G.K. (2007) Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 23, 2881-2887.
23. Sandmann, T. et al. (2011) The head-regeneration transcriptome of the planarian Schmidtea mediterranea. Genome Biol., 12, R76.
24. Singh, D. et al. (2011) FDM: a graph-based statistical method to detect differential transcription using RNA-seq data. Bioinformatics, 27, 2633-2640.
25. Smith, C.W. et al. (1989) Alternative splicing in the control ofgene expression. Annu. Rev. Genet., 23, 527-577.
26. Stamm, S. et al. (2005) Function of alternative splicing. Gene, 344, 1-20.
27. Trapnell, C. et al. (2009) Tophat: discovering splice junctions with RNA-seq. Bioinformatics, 25, 1105-1111.
28. Trapnell, C. et al. (2010) Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28, 211-215.
29. Trapnell, C. et al. (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc., 7, 562-578.
30. Trapnell, C. et al. (2013) Differential analysis ofgene regulation at transcript reso- lution with RNA-seq. Nat. Biotechnol., 31, 46-53.
31. Wang, E.T. et al. (2008) Alternative isoform regulation in human tissue transcrip- tomes. Nature, 456, 470-476.
32. Zhou, Y. et al. (2011) A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics, 27, 2672-2678.