The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (cross-linking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods.
Applying four different machine learning approaches to the CLASH data, researchers at the University of Central Florida identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, they developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, the researchers showed that TarPmiR predicted more than 74.2 % of true miRNA target sites in each dataset. Compared with three existing approaches, they demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision.
Four features selected by all four approaches. The m/e motif, the length of the target site, the length of the largest consecutive pairings, and the difference between the number of paired positions in the seed region and that in the miRNA 3’ end.
Availability – The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/