RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study.
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Berghoff BA, Karlsson T, Källman T, Wagner EGH, Grabherr MG
BioData Min 10 (-) 30 [2017-09-05; online 2017-09-05]
Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes. Existing methods thus either normalize on selected known reference genes that are invariant in expression across the experiment, assume that the majority of genes are invariant, or that the effects of up- and down-regulated genes cancel each other out during the normalization. Here, we present a novel method, The proposed RNA-seq normalization method,
PubMed 28878825
DOI 10.1186/s13040-017-0150-8
Crossref 10.1186/s13040-017-0150-8
pii: 150
pmc: PMC5584328