title: Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets.
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.
authors.affiliation: Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.
authors.forename: Diana
authors.initials: D
authors.lastname: Tichy
authors.affiliation: German Cancer Research Center, Heidelberg, Germany.
authors.forename: Julia Maria Anna
authors.initials: JMA
authors.lastname: Pickl
authors.affiliation: Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Benner
authors.affiliation: German Cancer Research Center, Heidelberg, Germany.
authors.forename: Holger
authors.initials: H
authors.lastname: Sültmann
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D047468:Immunoprecipitation / Q000379:methods / Q000706:statistics & numerical data; D016014:Linear Models; D008297:Male; D035683:MicroRNAs / Q000235:genetics*; D000078722:PC-3 Cells; D011471:Prostatic Neoplasms / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software
chemical_list: D014408:Biomarkers, Tumor; C540933:MIRN375 microRNA, human; D035683:MicroRNAs