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Items: 1 to 20 of 88

1.

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

Cumbie JS, Kimbrel JA, Di Y, Schafer DW, Wilhelm LJ, Fox SE, Sullivan CM, Curzon AD, Carrington JC, Mockler TC, Chang JH.

PLoS One. 2011;6(10):e25279. doi: 10.1371/journal.pone.0025279. Epub 2011 Oct 6.

2.

High-throughput RNA-seq for allelic or locus-specific expression analysis in Arabidopsis-related species, hybrids, and allotetraploids.

Ng DW, Shi X, Nah G, Chen ZJ.

Methods Mol Biol. 2014;1112:33-48. doi: 10.1007/978-1-62703-773-0_3.

PMID:
24478006
3.

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
4.

From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data.

Li J, Hou J, Sun L, Wilkins JM, Lu Y, Niederhuth CE, Merideth BR, Mawhinney TP, Mossine VV, Greenlief CM, Walker JC, Folk WR, Hannink M, Lubahn DB, Birchler JA, Cheng J.

PLoS One. 2015 Apr 22;10(4):e0125000. doi: 10.1371/journal.pone.0125000. eCollection 2015.

5.

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.

6.

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.

7.

The characteristic direction: a geometrical approach to identify differentially expressed genes.

Clark NR, Hu KS, Feldmann AS, Kou Y, Chen EY, Duan Q, Ma'ayan A.

BMC Bioinformatics. 2014 Mar 21;15:79. doi: 10.1186/1471-2105-15-79.

8.

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
9.

An automated RNA-Seq analysis pipeline to identify and visualize differentially expressed genes and pathways in CHO cells.

Chen C, Le H, Goudar CT.

Biotechnol Prog. 2015 Sep-Oct;31(5):1150-62. doi: 10.1002/btpr.2137. Epub 2015 Jul 22.

PMID:
26150012
10.

How to analyze gene expression using RNA-sequencing data.

Ramsköld D, Kavak E, Sandberg R.

Methods Mol Biol. 2012;802:259-74. doi: 10.1007/978-1-61779-400-1_17.

PMID:
22130886
11.

Comparative study of RNA-seq- and microarray-derived coexpression networks in Arabidopsis thaliana.

Giorgi FM, Del Fabbro C, Licausi F.

Bioinformatics. 2013 Mar 15;29(6):717-24. doi: 10.1093/bioinformatics/btt053. Epub 2013 Feb 1.

PMID:
23376351
12.

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.

13.

Detection of generic differential RNA processing events from RNA-seq data.

Tran Vdu T, Souiai O, Romero-Barrios N, Crespi M, Gautheret D.

RNA Biol. 2016;13(1):59-67. doi: 10.1080/15476286.2015.1118604.

14.

SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data.

Varet H, Brillet-Guéguen L, Coppée JY, Dillies MA.

PLoS One. 2016 Jun 9;11(6):e0157022. doi: 10.1371/journal.pone.0157022. eCollection 2016.

15.

A comprehensive metatranscriptome analysis pipeline and its validation using human small intestine microbiota datasets.

Leimena MM, Ramiro-Garcia J, Davids M, van den Bogert B, Smidt H, Smid EJ, Boekhorst J, Zoetendal EG, Schaap PJ, Kleerebezem M.

BMC Genomics. 2013 Aug 2;14:530. doi: 10.1186/1471-2164-14-530.

16.

Cloud-scale RNA-sequencing differential expression analysis with Myrna.

Langmead B, Hansen KD, Leek JT.

Genome Biol. 2010;11(8):R83. doi: 10.1186/gb-2010-11-8-r83. Epub 2010 Aug 11.

17.

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.

18.

Grape RNA-Seq analysis pipeline environment.

Knowles DG, Röder M, Merkel A, Guigó R.

Bioinformatics. 2013 Mar 1;29(5):614-21. doi: 10.1093/bioinformatics/btt016. Epub 2013 Jan 17.

19.

ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.

Yang W, Rosenstiel PC, Schulenburg H.

BMC Genomics. 2016 Aug 4;17:541. doi: 10.1186/s12864-016-2848-2.

20.

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists.

Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z.

BMC Bioinformatics. 2009 Feb 3;10:48. doi: 10.1186/1471-2105-10-48.

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