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Items: 1 to 20 of 86

1.

The most informative spacing test effectively discovers biologically relevant outliers or multiple modes in expression.

Pawlikowska I, Wu G, Edmonson M, Liu Z, Gruber T, Zhang J, Pounds S.

Bioinformatics. 2014 May 15;30(10):1400-8. doi: 10.1093/bioinformatics/btu039. Epub 2014 Jan 22.

2.

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. Epub 2014 May 9.

3.

RNASeqGUI: a GUI for analysing RNA-Seq data.

Russo F, Angelini C.

Bioinformatics. 2014 Sep 1;30(17):2514-6. doi: 10.1093/bioinformatics/btu308. Epub 2014 May 7.

4.

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables.

Pounds S, Cheng C, Cao X, Crews KR, Plunkett W, Gandhi V, Rubnitz J, Ribeiro RC, Downing JR, Lamba J.

Bioinformatics. 2009 Aug 15;25(16):2013-9. doi: 10.1093/bioinformatics/btp357. Epub 2009 Jun 15.

5.

A powerful and flexible approach to the analysis of RNA sequence count data.

Zhou YH, Xia K, Wright FA.

Bioinformatics. 2011 Oct 1;27(19):2672-8. doi: 10.1093/bioinformatics/btr449. Epub 2011 Aug 2.

6.

RNAseqViewer: visualization tool for RNA-Seq data.

Rogé X, Zhang X.

Bioinformatics. 2014 Mar 15;30(6):891-2. doi: 10.1093/bioinformatics/btt649. Epub 2013 Nov 8.

7.

A new approach to bias correction in RNA-Seq.

Jones DC, Ruzzo WL, Peng X, Katze MG.

Bioinformatics. 2012 Apr 1;28(7):921-8. doi: 10.1093/bioinformatics/bts055. Epub 2012 Jan 28.

8.

SpliceSeq: a resource for analysis and visualization of RNA-Seq data on alternative splicing and its functional impacts.

Ryan MC, Cleland J, Kim R, Wong WC, Weinstein JN.

Bioinformatics. 2012 Sep 15;28(18):2385-7. doi: 10.1093/bioinformatics/bts452. Epub 2012 Jul 20.

9.

Sensitive gene fusion detection using ambiguously mapping RNA-Seq read pairs.

Kinsella M, Harismendy O, Nakano M, Frazer KA, Bafna V.

Bioinformatics. 2011 Apr 15;27(8):1068-75. doi: 10.1093/bioinformatics/btr085. Epub 2011 Feb 16.

10.

A novel spliced fusion of MLL with CT45A2 in a pediatric biphenotypic acute leukemia.

Cerveira N, Meyer C, Santos J, Torres L, Lisboa S, Pinheiro M, Bizarro S, Correia C, Norton L, Marschalek R, Teixeira MR.

BMC Cancer. 2010 Sep 29;10:518. doi: 10.1186/1471-2407-10-518.

11.

A genomic random interval model for statistical analysis of genomic lesion data.

Pounds S, Cheng C, Li S, Liu Z, Zhang J, Mullighan C.

Bioinformatics. 2013 Sep 1;29(17):2088-95. doi: 10.1093/bioinformatics/btt372. Epub 2013 Jul 10.

12.

Gene expression profiling identifies a subset of adult T-cell acute lymphoblastic leukemia with myeloid-like gene features and over-expression of miR-223.

Chiaretti S, Messina M, Tavolaro S, Zardo G, Elia L, Vitale A, Fatica A, Gorello P, Piciocchi A, Scappucci G, Bozzoni I, Fozza C, Candoni A, Guarini A, Foà R.

Haematologica. 2010 Jul;95(7):1114-21. doi: 10.3324/haematol.2009.015099. Epub 2010 Apr 23.

13.

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. Epub 2011 Aug 8.

14.

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. Epub 2013 Feb 21. Erratum in: Bioinformatics. 2013 Aug 15;29(16):2073.

15.

A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.

Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR.

BMC Bioinformatics. 2013 Aug 21;14:254. doi: 10.1186/1471-2105-14-254.

16.

A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease.

Raghavachari N, Barb J, Yang Y, Liu P, Woodhouse K, Levy D, O'Donnell CJ, Munson PJ, Kato GJ.

BMC Med Genomics. 2012 Jun 29;5:28. doi: 10.1186/1755-8794-5-28.

17.

Overview of available methods for diverse RNA-Seq data analyses.

Chen G, Wang C, Shi T.

Sci China Life Sci. 2011 Dec;54(12):1121-8. doi: 10.1007/s11427-011-4255-x. Epub 2012 Jan 7. Review.

PMID:
22227904
18.

Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM).

Grant GR, Farkas MH, Pizarro AD, Lahens NF, Schug J, Brunk BP, Stoeckert CJ, Hogenesch JB, Pierce EA.

Bioinformatics. 2011 Sep 15;27(18):2518-28. doi: 10.1093/bioinformatics/btr427. Epub 2011 Jul 19.

19.

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. Epub 2012 Oct 11.

20.

DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data.

Gong T, Szustakowski JD.

Bioinformatics. 2013 Apr 15;29(8):1083-5. doi: 10.1093/bioinformatics/btt090. Epub 2013 Feb 21.

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