TargetSpy: a supervised machine learning approach for microRNA target prediction

Background: Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results: We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Martin Sturm, Michael Hackenberg, David Langenberger, Dmitrij Frishman
• Keywords: BMCBI 2010, Match Requirement, Seed Match, Target Sites