Unmasking Upstream Gene Expression Regulators with miRNA-corrected mRNA Data.
Expressed micro-RNA (miRNA) affects messenger RNA (mRNA) abundance, hindering the accuracy of upstream regulator analysis. Our objective was to provide an algorithm to correct such bias. Large mRNA and miRNA analyses were performed on RNA extracted from bovine liver and mammary tissue. Using four levels of target scores from TargetScan (all miRNA:mRNA target gene pairs or only the top 25%, 50%, or 75%). Using four levels of target scores from TargetScan (all miRNA:mRNA target gene pairs or only the top 25%, 50%, or 75%) and four levels of the magnitude of miRNA effect (ME) on mRNA expression (30%, 50%, 75%, and 83% mRNA reduction), we generated 17 different datasets (including the original dataset). For each dataset, we performed upstream regulator analysis using two bioinformatics tools. We detected an increased effect on the upstream regulator analysis with larger miRNA:mRNA pair bins and higher ME. The miRNA correction allowed identification of several upstream regulators not present in the analysis of the original dataset. Thus, the proposed algorithm improved the prediction of upstream regulators.
Department of Integrative Biology, Oregon State University, Corvallis, OR, USA.
Stephanie
S
Bollmann
State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.; CAAS-ICRAF Joint Laboratory on Agroforestry and Sustainable Animal Husbandry, East and Central Asia, World Agroforestry Centre, Beijing, China.
Dengpan
D
Bu
State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
Jiaqi
J
Wang
Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, OR, USA.
Massimo
M
Bionaz
liver; mammary; miRNA; transcription regulators; transcriptome