Flexible analysis of RNA-seq data using mixed effects models

Bioinformatics

Volumn 30, Issue 2, 2014, Pages 180-188

  Turro, Ernest ^a,b   Astle, William J ^c   Tavaré, Simon ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ISOPROTEIN;

ALGORITHM; ANIMAL; ARTICLE; BAYES THEOREM; C57BL MOUSE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; GENOME IMPRINTING; HIGH THROUGHPUT SEQUENCING; METHODOLOGY; MOUSE; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; BAYES THEOREM; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENOMIC IMPRINTING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; MICE; MICE, INBRED C57BL; MODELS, STATISTICAL; PROTEIN ISOFORMS;

EID: 84892702660     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt624     Document Type: Article

References (20)

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 of exons from RNA-seq data. Genome Res., 22, 2008-2017.
3. Brooks, A.N. et al. (2011) Conservation of an RNA regulatory map between Drosophila and mammals. Genome Res., 21, 193-202.
4. Carlin, B.P. and Chib, S. (1995) Bayesian model choice via Markov chain Monte Carlo methods. J. R. Stat. Soc. B Methodol., 57, 473-484.
5. Ferguson, T. (1996) A Course in Large Sample Theory. Chapman & Hall/CRC, London, UK.
6. Garcia-Blanco, M.A. et al. (2004) Alternative splicing in disease and therapy. Nat. Biotechnol., 22, 535-546.
7. Gelman, A. (2004) Bayesian Data Analysis. 2nd edn. Chapman & Hall/CRC, Boca Raton, FL.
8. Glaus, P. et al. (2012) Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics, 28, 1721-1728.
9. Goncalves, A. et al. (2012) Extensive compensatory cis-trans regulation in the evolution of mouse gene expression. Genome Res., 22, 2376-2384.
10. Hardcastle, T.J. and Kelly, K.A. (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422.
11. Hein, A.-M.K. and Richardson, S. (2006) A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates. BMC Bioinformatics, 7, 353.
12. Li, Y. and Sasaki, H. (2011) Genomic imprinting in mammals: its life cycle, molecular mechanisms and reprogramming. Cell Res., 21, 466-473.
13. Liu, X. et al. (2006) Probe-level measurement error improves accuracy in detecting differential gene expression. Bioinformatics, 22, 2107-2113.
14. Morison, I.M. et al. (2001) The imprinted gene and parent-of-origin effect database. Nucleic Acids Res., 29, 275-276.
15. Robinson, M.D. and Oshlack, A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25.
16. Schulz, R. et al. (2008) Wamidex: a web atlas of murine genomic imprinting and differential expression. Epigenetics, 3, 89-96.
17. Tarazona, S. et al. (2011) Differential expression in RNA-seq: a matter of depth. Genome Res., 21, 2213-2223.
18. Trapnell, C. et al. (2012) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol., 31, 46-53.
19. Turro, E. et al. (2011) Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol., 12, R13.
20. Wang, X. et al. (2010) Isoform abundance inference provides a more accurate estimation of gene expression levels in RNA-seq. J. Bioinform. Comput. Biol., 8 (Suppl. 1), 177-192.