ABSTRACT
The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years; however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking.Here, we investigate the experimental design for Ago-RIP-Seq and examine biostatistical methods to identify de novo miRNA target genes. Statistical approaches considered are either based on a negative binomial model fit to the read count data or applied to transformed data using a normal distribution-based generalized linear model. We compare them by a real data simulation study using plasmode data sets and evaluate the suitability of the approaches to detect true miRNA targets by sensitivity and false discovery rates. Our results suggest that simple approaches like linear regression models on (appropriately) transformed read count data are preferable.
Subject(s)
High-Throughput Nucleotide Sequencing/methods , MicroRNAs/genetics , Sequence Analysis, RNA/methods , Algorithms , Biomarkers, Tumor/genetics , Computational Biology/methods , Computer Simulation , Data Interpretation, Statistical , High-Throughput Nucleotide Sequencing/statistics & numerical data , Humans , Immunoprecipitation/methods , Immunoprecipitation/statistics & numerical data , Linear Models , Male , PC-3 Cells , Prostatic Neoplasms/genetics , Sequence Analysis, RNA/statistics & numerical data , SoftwareABSTRACT
Cytochrome P450 family member CYP51A1 is a key enzyme in cholesterol biosynthesis whose deregulation is implicated in numerous diseases, including retinal degeneration. Here we describe that HAdV-37 infection leads to downregulation of CYP51A1 expression and overexpression of its antisense non-coding Alu element (AluCYP51A1) in retinal pigment epithelium (RPE) cells. This change in gene expression is associated with a reversed accumulation of a positive histone mark at the CYP51A1 and AluCYP51A1 promoters. Further, transient AluCYP51A1 RNA overexpression correlates with reduced CYP51A1 mRNA accumulation. Collectively, our data suggest that AluCYP51A1 might control CYP51A1 gene expression in HAdV-37-infected RPE cells.
