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Items: 1 to 20 of 222

1.

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae.

Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlén M, Nielsen J.

Nucleic Acids Res. 2012 Nov 1;40(20):10084-97. doi: 10.1093/nar/gks804. Epub 2012 Sep 10.

2.

Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays.

Bottomly D, Walter NA, Hunter JE, Darakjian P, Kawane S, Buck KJ, Searles RP, Mooney M, McWeeney SK, Hitzemann R.

PLoS One. 2011 Mar 24;6(3):e17820. doi: 10.1371/journal.pone.0017820.

3.

Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets.

Xu X, Zhang Y, Williams J, Antoniou E, McCombie WR, Wu S, Zhu W, Davidson NO, Denoya P, Li E.

BMC Bioinformatics. 2013;14 Suppl 9:S1. doi: 10.1186/1471-2105-14-S9-S1. Epub 2013 Jun 28.

4.

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications.

Otero JM, Vongsangnak W, Asadollahi MA, Olivares-Hernandes R, Maury J, Farinelli L, Barlocher L, Osterås M, Schalk M, Clark A, Nielsen J.

BMC Genomics. 2010 Dec 22;11:723. doi: 10.1186/1471-2164-11-723.

5.

Next-generation sequencing facilitates quantitative analysis of wild-type and Nrl(-/-) retinal transcriptomes.

Brooks MJ, Rajasimha HK, Roger JE, Swaroop A.

Mol Vis. 2011;17:3034-54. Epub 2011 Nov 23.

6.

A comparison of next generation sequencing technologies for transcriptome assembly and utility for RNA-Seq in a non-model bird.

Finseth FR, Harrison RG.

PLoS One. 2014 Oct 3;9(10):e108550. doi: 10.1371/journal.pone.0108550. eCollection 2014.

7.

Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.

Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X.

PLoS One. 2014 Jan 16;9(1):e78644. doi: 10.1371/journal.pone.0078644. eCollection 2014.

8.

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.

Li P, Piao Y, Shon HS, Ryu KH.

BMC Bioinformatics. 2015 Oct 28;16:347. doi: 10.1186/s12859-015-0778-7.

9.

Comparative transcriptome analysis of epithelial and fiber cells in newborn mouse lenses with RNA sequencing.

Hoang TV, Kumar PK, Sutharzan S, Tsonis PA, Liang C, Robinson ML.

Mol Vis. 2014 Nov 4;20:1491-517. eCollection 2014.

10.

Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments.

Vijay N, Poelstra JW, Künstner A, Wolf JB.

Mol Ecol. 2013 Feb;22(3):620-34. doi: 10.1111/mec.12014. Epub 2012 Sep 24.

PMID:
22998089
11.

Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis.

Li W, Turner A, Aggarwal P, Matter A, Storvick E, Arnett DK, Broeckel U.

BMC Genomics. 2015 Dec 16;16:1069. doi: 10.1186/s12864-015-2270-1.

12.

Analysis of RNA-Seq Data Using TopHat and Cufflinks.

Ghosh S, Chan CK.

Methods Mol Biol. 2016;1374:339-61. doi: 10.1007/978-1-4939-3167-5_18.

PMID:
26519415
13.

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment.

Gierliński M, Cole C, Schofield P, Schurch NJ, Sherstnev A, Singh V, Wrobel N, Gharbi K, Simpson G, Owen-Hughes T, Blaxter M, Barton GJ.

Bioinformatics. 2015 Nov 15;31(22):3625-30. doi: 10.1093/bioinformatics/btv425. Epub 2015 Jul 23.

14.

Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster.

Lin Y, Golovnina K, Chen ZX, Lee HN, Negron YL, Sultana H, Oliver B, Harbison ST.

BMC Genomics. 2016 Jan 5;17:28. doi: 10.1186/s12864-015-2353-z.

15.

Analysis of RNA-Seq data with TopHat and Cufflinks for genome-wide expression analysis of jasmonate-treated plants and plant cultures.

Pollier J, Rombauts S, Goossens A.

Methods Mol Biol. 2013;1011:305-15. doi: 10.1007/978-1-62703-414-2_24.

PMID:
23616006
16.

Power analysis and sample size estimation for RNA-Seq differential expression.

Ching T, Huang S, Garmire LX.

RNA. 2014 Nov;20(11):1684-96. doi: 10.1261/rna.046011.114. Epub 2014 Sep 22.

17.

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.

18.

Efficient assembly and annotation of the transcriptome of catfish by RNA-Seq analysis of a doubled haploid homozygote.

Liu S, Zhang Y, Zhou Z, Waldbieser G, Sun F, Lu J, Zhang J, Jiang Y, Zhang H, Wang X, Rajendran KV, Khoo L, Kucuktas H, Peatman E, Liu Z.

BMC Genomics. 2012 Nov 5;13:595. doi: 10.1186/1471-2164-13-595.

19.

Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology.

Fumagalli D, Blanchet-Cohen A, Brown D, Desmedt C, Gacquer D, Michiels S, Rothé F, Majjaj S, Salgado R, Larsimont D, Ignatiadis M, Maetens M, Piccart M, Detours V, Sotiriou C, Haibe-Kains B.

BMC Genomics. 2014 Nov 21;15:1008. doi: 10.1186/1471-2164-15-1008.

20.

Evaluation of the coverage and depth of transcriptome by RNA-Seq in chickens.

Wang Y, Ghaffari N, Johnson CD, Braga-Neto UM, Wang H, Chen R, Zhou H.

BMC Bioinformatics. 2011 Oct 18;12 Suppl 10:S5. doi: 10.1186/1471-2105-12-S10-S5.

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