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Genome Biol. 2019 Jan 22;20(1):18. doi: 10.1186/s13059-019-1629-z.

Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data.

Author information

1
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA.
2
Nawgen LLC, St. Louis, MO, USA.
3
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA. xiaowei.wang@wustl.edu.

Abstract

We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB ( http://mirdb.org ).

KEYWORDS:

CLIP-seq; MicroRNA; RNA-seq; Target prediction

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