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Items: 1 to 20 of 214

1.

Updating RNA-Seq analyses after re-annotation.

Roberts A, Schaeffer L, Pachter L.

Bioinformatics. 2013 Jul 1;29(13):1631-7. doi: 10.1093/bioinformatics/btt197.

2.

Identification of novel transcripts in annotated genomes using RNA-Seq.

Roberts A, Pimentel H, Trapnell C, Pachter L.

Bioinformatics. 2011 Sep 1;27(17):2325-9. doi: 10.1093/bioinformatics/btr355.

3.

TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference.

Nariai N, Hirose O, Kojima K, Nagasaki M.

Bioinformatics. 2013 Sep 15;29(18):2292-9. doi: 10.1093/bioinformatics/btt381.

4.

Computational approaches for isoform detection and estimation: good and bad news.

Angelini C, De Canditiis D, De Feis I.

BMC Bioinformatics. 2014 May 9;15:135. doi: 10.1186/1471-2105-15-135.

5.

Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data.

Hayer KE, Pizarro A, Lahens NF, Hogenesch JB, Grant GR.

Bioinformatics. 2015 Dec 15;31(24):3938-45. doi: 10.1093/bioinformatics/btv488.

6.

SSP: an interval integer linear programming for de novo transcriptome assembly and isoform discovery of RNA-seq reads.

Safikhani Z, Sadeghi M, Pezeshk H, Eslahchi C.

Genomics. 2013 Nov-Dec;102(5-6):507-14. doi: 10.1016/j.ygeno.2013.10.003.

7.

Efficient RNA isoform identification and quantification from RNA-Seq data with network flows.

Bernard E, Jacob L, Mairal J, Vert JP.

Bioinformatics. 2014 Sep 1;30(17):2447-55. doi: 10.1093/bioinformatics/btu317.

8.

Accurate inference of isoforms from multiple sample RNA-Seq data.

Tasnim M, Ma S, Yang EW, Jiang T, Li W.

BMC Genomics. 2015;16 Suppl 2:S15. doi: 10.1186/1471-2164-16-S2-S15.

9.

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.

Li B, Dewey CN.

BMC Bioinformatics. 2011 Aug 4;12:323. doi: 10.1186/1471-2105-12-323.

10.

NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data.

Ma X, Zhang X.

BMC Bioinformatics. 2013 Jul 10;14:220. doi: 10.1186/1471-2105-14-220.

11.

GeneScissors: a comprehensive approach to detecting and correcting spurious transcriptome inference owing to RNA-seq reads misalignment.

Zhang Z, Huang S, Wang J, Zhang X, Pardo Manuel de Villena F, McMillan L, Wang W.

Bioinformatics. 2013 Jul 1;29(13):i291-9. doi: 10.1093/bioinformatics/btt216.

12.

ORMAN: optimal resolution of ambiguous RNA-Seq multimappings in the presence of novel isoforms.

Dao P, Numanagić I, Lin YY, Hach F, Karakoc E, Donmez N, Collins C, Eichler EE, Sahinalp SC.

Bioinformatics. 2014 Mar 1;30(5):644-51. doi: 10.1093/bioinformatics/btt591.

13.

Assessing the impact of human genome annotation choice on RNA-seq expression estimates.

Wu PY, Phan JH, Wang MD.

BMC Bioinformatics. 2013;14 Suppl 11:S8. doi: 10.1186/1471-2105-14-S11-S8.

14.

Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data.

Kanitz A, Gypas F, Gruber AJ, Gruber AR, Martin G, Zavolan M.

Genome Biol. 2015 Jul 23;16:150. doi: 10.1186/s13059-015-0702-5.

15.

TopHat: discovering splice junctions with RNA-Seq.

Trapnell C, Pachter L, Salzberg SL.

Bioinformatics. 2009 May 1;25(9):1105-11. doi: 10.1093/bioinformatics/btp120.

16.

Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms.

Patro R, Mount SM, Kingsford C.

Nat Biotechnol. 2014 May;32(5):462-4. doi: 10.1038/nbt.2862.

17.

Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads.

Li W, Jiang T.

Bioinformatics. 2012 Nov 15;28(22):2914-21. doi: 10.1093/bioinformatics/bts559.

18.

RNA-Skim: a rapid method for RNA-Seq quantification at transcript level.

Zhang Z, Wang W.

Bioinformatics. 2014 Jun 15;30(12):i283-i292. doi: 10.1093/bioinformatics/btu288.

19.

FDM: a graph-based statistical method to detect differential transcription using RNA-seq data.

Singh D, Orellana CF, Hu Y, Jones CD, Liu Y, Chiang DY, Liu J, Prins JF.

Bioinformatics. 2011 Oct 1;27(19):2633-40. doi: 10.1093/bioinformatics/btr458.

20.

EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.

Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, Haag JD, Gould MN, Stewart RM, Kendziorski C.

Bioinformatics. 2013 Apr 15;29(8):1035-43. doi: 10.1093/bioinformatics/btt087. Erratum in: Bioinformatics. 2013 Aug 15;29(16):2073.

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