Journal Article

DKFZ-2018-02374

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets.

Tichy, D. (First author)DKFZ* ; Pickl, J. M. A. (First author)DKFZ* ; Benner, A. (Last author)DKFZ* ; Sültmann, H. (Last author)DKFZ*

2018
Oxford University Press Oxford [u.a.]

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Please use a persistent id in citations: doi:10.1093/bib/bbx032

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.

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Contributing Institute(s):

1. C060 Biostatistik (C060)
2. B063 Krebsgenomforschung (B063)

Research Program(s):

1. 312 - Functional and structural genomics (POF3-312) (POF3-312)

Appears in the scientific report 2018

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Database coverage:
; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; Nationallizenz ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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