RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study-Reference-Cited by-同舟云学术

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RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study

Published:2017-09-05 Issue:1 Volume:10 Page:
ISSN:1756-0381
Container-title:BioData Mining
language:en
Short-container-title:BioData Mining

Author:

Berghoff Bork A.,Karlsson Torgny,Källman Thomas,Wagner E. Gerhart H.,Grabherr Manfred G.^ORCID

Funder

Svenska Forskningsrådet Formas

European Molecular Biology Organization

DFG

Vetenskapsrådet

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Genetics,Molecular Biology,Biochemistry

Link

http://link.springer.com/content/pdf/10.1186/s13040-017-0150-8.pdf

Reference50 articles.

1. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–8.

2. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010;11:94.

3. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

4. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.

5. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2012;14:671–83.

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