Statistical modeling of isoform splicing dynamics from RNA-seq time series data

Bioinformatics

Volumn 32, Issue 19, 2016, Pages 2965-2972

  Huang, Yuanhua ^a   Sanguinetti, Guido ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ISOPROTEIN; RNA;

ANIMAL; GENE EXPRESSION PROFILING; HUMAN; RNA SPLICING; SEQUENCE ANALYSIS; STATISTICAL MODEL;

ANIMALS; GENE EXPRESSION PROFILING; HUMANS; MODELS, STATISTICAL; PROTEIN ISOFORMS; RNA; RNA SPLICING; SEQUENCE ANALYSIS, RNA;

EID: 84990985984     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw364     Document Type: Article

References (29)

1. Aijö, T. et al. (2014) Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation. Bioinformatics, 30, i113-i120.
2. Bar-Joseph, Z. et al. (2012) Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet., 13, 552-564.
3. Barrass, J.D. et al. (2015) Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling. Genome Biol., 16, 17.
4. Blencowe, B.J. (2006) Alternative splicing: new insights from global analyses. Cell, 126, 37-47.
5. de Pretis, S. et al. (2015) INSPEcT: a Computational Tool to Infer mRNA Synthesis, Processing and Degradation Dynamics from RNA-and 4sU-seq Time Course Experiments. Bioinformatics, 31, 2829-2935
6. Diedenhofen, B. and Musch, J. (2015) Cocor: a comprehensive solution for the statistical comparison of correlations. PloS One, 10, e0121945.
7. Eser, P. et al. (2016) Determinants of RNA metabolism in the Schizosaccharomyces pombe genome. Mol. Syst. Biol., 12, 857.
8. Fuchs, G. et al. (2014) 4sUDRB-seq: measuring genomewide transcriptional elongation rates and initiation frequencies within cells. Genome Biol., 15, R69.
9. Geweke, J. (1991) Evaluating the Accuracy of sampling-Based Approaches to the Calculation of Posterior Moments, vol. 196. Federal Reserve Bank of Minneapolis, Research Department Minneapolis, MN, USA.
10. Glaus, P. et al. (2012) Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics, 28, 1721-1728.
11. Graveley, B.R. (2001) Alternative splicing: increasing diversity in the prote-omic world. Trends Genet., 17, 100-107.
12. Honkela, A. et al. (2015) Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays. Proc. Natl. Acad. Sci. U. S. A., 112, 13115-13120.
13. Kanitz, A. et al. (2015) Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data. Genome Biol., 16, 1-26.
14. Katz, Y. et al. (2010) Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods, 7, 1009-1015.
15. Kim, D. et al. (2015) HISAT: a fast spliced aligner with low memory requirements. Nat. Methods, 12, 357-360.
16. Lawrence, N.D. et al. (2006). Modelling transcriptional regulation using Gaussian processes. In: Advances in Neural Information Processing Systems, pp. 785-792.
17. Nicolae, M. et al. (2011) Estimation of alternative splicing isoform frequencies from RNA-Seq data. Algorithms Mol. Biol., 6, 9.
18. Rasmussen, C.E. and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, USA.
19. Roberts, A. et al. (2011) Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol., 12, R22.
20. Scotti, M.M. and Swanson, M.S. (2016) RNA mis-splicing in disease. Nat. Rev. Genet., 17, 19-32.
21. Stegle, O. et al. (2010) A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. J. Comput. Biol., 17, 355-367.
22. Sturgill, D. et al. (2013) Design of RNA splicing analysis null models for post hoc filtering of Drosophila head RNA-Seq data with the splicing analysis kit (Spanki). BMC Bioinformatics, 14, 320.
23. Trapnell, C. et al. (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28, 511-515.
24. Tuomela, S. et al. (2012) Identification of early gene expression changes during human Th17 cell differentiation. Blood, 119, e151-e160.
25. Veloso, A. et al. (2014) Rate of elongation by RNA polymerase II is associated with specific gene features and epigenetic modifications. Genome Res., 24, 896-905.
26. Wang, E.T. et al. (2008) Alternative isoform regulation in human tissue tran-scriptomes. Nature, 456, 470-476.
27. Wang, Z. et al. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57-63.
28. Windhager, L. et al. (2012) Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution. Genome Res., 22, 2031-2042.
29. Zhang, R. et al. (2014) A circadian gene expression atlas in mammals: implications for biology and medicine. Proc. Natl. Acad. Sci. U. S. A., 111, 16219-16224.