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Items: 1 to 20 of 100

1.

Time series expression analyses using RNA-seq: a statistical approach.

Oh S, Song S, Grabowski G, Zhao H, Noonan JP.

Biomed Res Int. 2013;2013:203681. doi: 10.1155/2013/203681. Epub 2013 Mar 24.

2.

EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments.

Leng N, Li Y, McIntosh BE, Nguyen BK, Duffin B, Tian S, Thomson JA, Dewey CN, Stewart R, Kendziorski C.

Bioinformatics. 2015 Aug 15;31(16):2614-22. doi: 10.1093/bioinformatics/btv193. Epub 2015 Apr 5.

3.

RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.

Sîrbu A, Kerr G, Crane M, Ruskin HJ.

PLoS One. 2012;7(12):e50986. doi: 10.1371/journal.pone.0050986. Epub 2012 Dec 10.

4.

A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data.

Lin Z, Li M, Sestan N, Zhao H.

Stat Appl Genet Mol Biol. 2016 Apr;15(2):139-50. doi: 10.1515/sagmb-2015-0070.

5.

A statistical framework for eQTL mapping using RNA-seq data.

Sun W.

Biometrics. 2012 Mar;68(1):1-11. doi: 10.1111/j.1541-0420.2011.01654.x. Epub 2011 Aug 12.

6.

EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering.

Lee S, Seo CH, Alver BH, Lee S, Park PJ.

BMC Bioinformatics. 2015 Sep 3;16:278. doi: 10.1186/s12859-015-0704-z.

7.

Identifying differentially expressed transcripts from RNA-seq data with biological variation.

Glaus P, Honkela A, Rattray M.

Bioinformatics. 2012 Jul 1;28(13):1721-8. doi: 10.1093/bioinformatics/bts260. Epub 2012 May 3.

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. Epub 2015 Jan 21.

9.

Dynamic expression of 3' UTRs revealed by Poisson hidden Markov modeling of RNA-Seq: implications in gene expression profiling.

Lu J, Bushel PR.

Gene. 2013 Sep 25;527(2):616-23. doi: 10.1016/j.gene.2013.06.052. Epub 2013 Jul 9.

10.

A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.

Ye M, Wang Z, Wang Y, Wu R.

Brief Bioinform. 2015 Mar;16(2):205-15. doi: 10.1093/bib/bbu013. Epub 2014 May 10.

PMID:
24817567
11.

Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments.

Black MB, Parks BB, Pluta L, Chu TM, Allen BC, Wolfinger RD, Thomas RS.

Toxicol Sci. 2014 Feb;137(2):385-403. doi: 10.1093/toxsci/kft249. Epub 2013 Nov 5.

PMID:
24194394
12.

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.

13.

Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq.

Wu Z, Wang X, Zhang X.

Bioinformatics. 2011 Feb 15;27(4):502-8. doi: 10.1093/bioinformatics/btq696. Epub 2010 Dec 17.

PMID:
21169371
14.

Seq-ing improved gene expression estimates from microarrays using machine learning.

Korir PK, Geeleher P, Seoighe C.

BMC Bioinformatics. 2015 Sep 4;16:286. doi: 10.1186/s12859-015-0712-z.

15.

HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data.

Cui X, Meng J, Rao MK, Chen Y, Huang Y.

BMC Genomics. 2015;16 Suppl 4:S2. doi: 10.1186/1471-2164-16-S4-S2. Epub 2015 Apr 21.

16.

NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data.

Bi Y, Davuluri RV.

BMC Bioinformatics. 2013 Aug 27;14:262. doi: 10.1186/1471-2105-14-262.

17.

Joint modeling of RNase footprint sequencing profiles for genome-wide inference of RNA structure.

Zou C, Ouyang Z.

Nucleic Acids Res. 2015 Oct 30;43(19):9187-97. doi: 10.1093/nar/gkv950. Epub 2015 Sep 22.

18.

Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling.

Zhao W, He X, Hoadley KA, Parker JS, Hayes DN, Perou CM.

BMC Genomics. 2014 Jun 2;15:419. doi: 10.1186/1471-2164-15-419.

19.

deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies.

Chu C, Fang Z, Hua X, Yang Y, Chen E, Cowley AW Jr, Liang M, Liu P, Lu Y.

BMC Genomics. 2015 Jun 13;16:455. doi: 10.1186/s12864-015-1676-0.

20.

RNA-Seq: a revolutionary tool for transcriptomics.

Wang Z, Gerstein M, Snyder M.

Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/nrg2484. Review.

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