Improved data-driven likelihood factorizations for transcript abundance estimation

Bioinformatics

Volumn 33, Issue 14, 2017, Pages i142-i151

  Zakeri, Mohsen ^a   Srivastava, Avi ^a   Almodaresi, Fatemeh ^a   Patro, Rob ^a  

^a Stony Brook University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGICAL MODEL; BIOLOGY; GENE EXPRESSION PROFILING; HUMAN; PROCEDURES; SEQUENCE ANALYSIS; SOFTWARE; STATISTICAL MODEL;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, BIOLOGICAL; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 85024483286     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx262     Document Type: Conference Paper

References (19)

1. Bray, N.L. et al. (2016) Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol., 34, 525-527.
2. Consortium, S.-I. et al. (2014) A comprehensive assessment of rna-seq accuracy, reproducibility and information content by the sequencing quality control consortium. Nat. Biotechnol., 32, 903-914.
3. Frazee, A.C. et al. (2015) Polyester: simulating RNA-seq datasets with differential transcript expression. Bioinformatics, 31, 2778-2784.
4. Glaus, P. et al. (2012) Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics, 28, 1721-1728.
5. Hensman, J. et al. (2015) Fast and accurate approximate inference of transcript expression from RNA-seq data. Bioinformatics, 31, 3881-3889.
6. Lappalainen, T. et al. (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 501, 506-511.
7. Li, B. and Dewey, C.N. (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12, 1.
8. Li, B. et al. (2010) RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics, 26, 493-500.
9. Nariai, N. et al. (2013) TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference. Bioinformatics, btt381.
10. Nariai, N. et al. (2014) TIGAR2: sensitive and accurate estimation of transcript isoform expression with longer RNA-Seq readsonline. BMC Genomics, 15, S5.
11. Nicolae, M. et al. (2011) Estimation of alternative splicing isoform frequencies from RNA-Seq data. Algorithms Mol. Biol., 6, 9.
12. Patro, R. et al. (2014) Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat. Biotechnol., 32, 462-464.
13. Patro, R. et al. (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods, 14, 417-419.
14. Roberts, A. and Pachter, L. (2013) Streaming fragment assignment for realtime analysis of sequencing experiments. Nat. Methods, 10, 71-73.
15. Salzman, J. et al. (2011) Statistical modeling of RNA-Seq data. Stat. Sci. Rev. J. Inst. Math. Stat., 26, 62-83.
16. Srivastava, A. et al. (2016) RapMap: A rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes. Bioinformatics, 32, i192-i200.
17. 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.
18. Turro, E. et al. (2011) Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol., 12, 1.
19. Yates, A. et al. (2015) Ensembl 2016. Nucleic Acids Res., gkv1157.