Comparative studies of differential gene calling using RNA-Seq data

BMC Bioinformatics

Volumn 14, Issue SUPPL13, 2013, Pages

  Zheng, Ximeng ^a   Moriyama, Etsuko N ^a,b  

^a UNIVERSITY OF NEBRASKA   (United States)

^b UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPARATIVE STUDIES; DIFFERENTIALLY EXPRESSED GENE; GENE EXPRESSION DATA; MICROARRAY DATA; MICROARRAY DATA ANALYSIS; MICROARRAY EXPERIMENTS; NONPARAMETRIC METHODS; PARAMETRIC METHOD;

EXPERIMENTS; RNA;

GENE EXPRESSION;

ANIMAL; APHID; ARTICLE; CHLAMYDOMONAS REINHARDTII; COMPARATIVE STUDY; GENE EXPRESSION; GENETIC TRANSCRIPTION; GENETICS; HUMAN; MOLECULAR DYNAMICS; MOLECULAR GENETICS; PHYSIOLOGY; RNA SPLICING; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SPECIES DIFFERENCE; STATISTICAL MODEL; UTILIZATION REVIEW;

ANIMALS; APHIDS; CHLAMYDOMONAS REINHARDTII; GENE EXPRESSION; HUMANS; MODELS, STATISTICAL; MOLECULAR DYNAMICS SIMULATION; MOLECULAR SEQUENCE DATA; RNA SPLICE SITES; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, RNA; SPECIES SPECIFICITY; TRANSCRIPTION, GENETIC;

EID: 84886812970     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-S13-S7     Document Type: Article

References (25)

1. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10(1):57-63. 10.1038/nrg2484, 2949280, 19015660.
2. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 2010, 11:422. 10.1186/1471-2105-11-422, 2928208, 20698981.
3. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008, 5(7):621-628. 10.1038/nmeth.1226, 18516045.
4. Oshlack A, Wakefield MJ. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct 2009, 4:14. 10.1186/1745-6150-4-14, 2678084, 19371405.
5. Benjamini Y, Speed TP. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic acids research 2012, 40(10):e72. 10.1093/nar/gks001, 3378858, 22323520.
6. Hansen KD, Brenner SE, Dudoit S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res 2010, 38(12):e131. 10.1093/nar/gkq224, 2896536, 20395217.
7. Li J, Jiang H, Wong WH. Modeling non-uniformity in short-read rates in RNA-Seq data. Genome Biol 2010, 11(5):R50. 10.1186/gb-2010-11-5-r50, 2898062, 20459815.
8. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010, 11:94. 10.1186/1471-2105-11-94, 2838869, 20167110.
9. Wu Z, Jenkins BD, Rynearson TA, Dyhrman ST, Saito MA, Mercier M, Whitney LP. Empirical bayes analysis of sequencing-based transcriptional profiling without replicates. BMC Bioinformatics 2010, 11:564. 10.1186/1471-2105-11-564, 3098101, 21080965.
10. Smyth GK, Michaud J, Scott HS. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 2005, 21(9):2067-2075. 10.1093/bioinformatics/bti270, 15657102.
11. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010, 11(10):R106. 10.1186/gb-2010-11-10-r106, 3218662, 20979621.
12. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26(1):139-140. 10.1093/bioinformatics/btp616, 2796818, 19910308.
13. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 2012, 7(3):562-578. 10.1038/nprot.2012.016, 3334321, 22383036.
14. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010, 28(5):511-515. 10.1038/nbt.1621, 3146043, 20436464.
15. Auer PL, Doerge RW. A Two-Stage Poisson Model for Testing RNA-Seq Data. Statistical Applications in Genetics and Molecular Biology 2011, 10((1)).
16. Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome Res 2011, 21(12):2213-2223. 10.1101/gr.124321.111, 3227109, 21903743.
17. Glaus P, Honkela A, Rattray M. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics 2012, 28(13):1721-1728. 10.1093/bioinformatics/bts260, 3381971, 22563066.
18. Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics 2010, 185(2):405-416. 10.1534/genetics.110.114983, 2881125, 20439781.
19. McIntyre LM, Lopiano KK, Morse AM, Amin V, Oberg AL, Young LJ, Nuzhdin SV. RNA-seq: technical variability and sampling. BMC Genomics 2011, 12:293. 10.1186/1471-2164-12-293, 3141664, 21645359.
20. Kvam VM, Liu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot 2012, 99(2):248-256. 10.3732/ajb.1100340, 22268221.
21. Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 2009, 25(15):1966-1967. 10.1093/bioinformatics/btp336, 19497933.
22. DESeq. , http://www-huber.embl.de/users/anders/DESeq/
23. NOISeq website. , http://bioinfo.cipf.es/noiseq/doku.php?id=downloads
24. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008, 321(5891):956-960. 10.1126/science.1160342, 18599741.
25. Kall L, Storey JD, MacCoss MJ, Noble WS. Posterior error probabilities and false discovery rates: two sides of the same coin. J Proteome Res 2008, 7(1):40-44. 10.1021/pr700739d, 18052118.