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Items: 1 to 20 of 90

1.

Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.

Lund SP, Nettleton D, McCarthy DJ, Smyth GK.

Stat Appl Genet Mol Biol. 2012 Oct 22;11(5). pii: /j/sagmb.2012.11.issue-5/1544-6115.1826/1544-6115.1826.xml. doi: 10.1515/1544-6115.1826.

PMID:
23104842
2.

Comparing next-generation sequencing and microarray technologies in a toxicological study of the effects of aristolochic acid on rat kidneys.

Su Z, Li Z, Chen T, Li QZ, Fang H, Ding D, Ge W, Ning B, Hong H, Perkins RG, Tong W, Shi L.

Chem Res Toxicol. 2011 Sep 19;24(9):1486-93. doi: 10.1021/tx200103b. Epub 2011 Aug 23.

PMID:
21834575
3.

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.

4.

Dispersion estimation and its effect on test performance in RNA-seq data analysis: a simulation-based comparison of methods.

Landau WM, Liu P.

PLoS One. 2013 Dec 9;8(12):e81415. doi: 10.1371/journal.pone.0081415. eCollection 2013.

5.

Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.

Van De Wiel MA, Leday GG, Pardo L, Rue H, Van Der Vaart AW, Van Wieringen WN.

Biostatistics. 2013 Jan;14(1):113-28. doi: 10.1093/biostatistics/kxs031. Epub 2012 Sep 17.

PMID:
22988280
6.

RNA-Seq Atlas--a reference database for gene expression profiling in normal tissue by next-generation sequencing.

Krupp M, Marquardt JU, Sahin U, Galle PR, Castle J, Teufel A.

Bioinformatics. 2012 Apr 15;28(8):1184-5. doi: 10.1093/bioinformatics/bts084. Epub 2012 Feb 17.

PMID:
22345621
7.

A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data.

Kvam VM, Liu P, Si Y.

Am J Bot. 2012 Feb;99(2):248-56. doi: 10.3732/ajb.1100340. Epub 2012 Jan 20. Review.

8.

Bias detection and correction in RNA-Sequencing data.

Zheng W, Chung LM, Zhao H.

BMC Bioinformatics. 2011 Jul 19;12:290. doi: 10.1186/1471-2105-12-290.

9.

Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation.

Li JJ, Jiang CR, Brown JB, Huang H, Bickel PJ.

Proc Natl Acad Sci U S A. 2011 Dec 13;108(50):19867-72. doi: 10.1073/pnas.1113972108. Epub 2011 Dec 1.

10.

GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data.

Feng J, Meyer CA, Wang Q, Liu JS, Shirley Liu X, Zhang Y.

Bioinformatics. 2012 Nov 1;28(21):2782-8. doi: 10.1093/bioinformatics/bts515. Epub 2012 Aug 24.

PMID:
22923299
11.

Statistical detection of differentially expressed genes based on RNA-seq: from biological to phylogenetic replicates.

Gu X.

Brief Bioinform. 2016 Mar;17(2):243-8. doi: 10.1093/bib/bbv035. Epub 2015 Jun 24. Review.

PMID:
26108230
12.

Robust detection and genotyping of single feature polymorphisms from gene expression data.

Wang M, Hu X, Li G, Leach LJ, Potokina E, Druka A, Waugh R, Kearsey MJ, Luo Z.

PLoS Comput Biol. 2009 Mar;5(3):e1000317. doi: 10.1371/journal.pcbi.1000317. Epub 2009 Mar 13.

13.

Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: applications to differential gene expression.

Montazeri Z, Yanofsky CM, Bickel DR.

Stat Appl Genet Mol Biol. 2010;9:Article23. doi: 10.2202/1544-6115.1504. Epub 2010 Jun 8.

PMID:
20597849
14.

Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads.

Chen HI, Liu Y, Zou Y, Lai Z, Sarkar D, Huang Y, Chen Y.

BMC Genomics. 2015;16 Suppl 7:S14. doi: 10.1186/1471-2164-16-S7-S14. Epub 2015 Jun 11.

15.

Mapping and quantifying mammalian transcriptomes by RNA-Seq.

Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B.

Nat Methods. 2008 Jul;5(7):621-8. doi: 10.1038/nmeth.1226. Epub 2008 May 30.

PMID:
18516045
16.

Estimating accuracy of RNA-Seq and microarrays with proteomics.

Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, Menzel C, Chen W, Li Y, Zeng R, Khaitovich P.

BMC Genomics. 2009 Apr 16;10:161. doi: 10.1186/1471-2164-10-161.

17.

A two-parameter generalized Poisson model to improve the analysis of RNA-seq data.

Srivastava S, Chen L.

Nucleic Acids Res. 2010 Sep;38(17):e170. doi: 10.1093/nar/gkq670. Epub 2010 Jul 29.

18.

Next generation sequencing of microbial transcriptomes: challenges and opportunities.

van Vliet AH.

FEMS Microbiol Lett. 2010 Jan;302(1):1-7. doi: 10.1111/j.1574-6968.2009.01767.x. Epub 2009 Aug 21. Review.

19.

It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR.

Lun AT, Chen Y, Smyth GK.

Methods Mol Biol. 2016;1418:391-416. doi: 10.1007/978-1-4939-3578-9_19.

PMID:
27008025
20.

Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing.

Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM.

BMC Genomics. 2012 Sep 17;13:484. doi: 10.1186/1471-2164-13-484.

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