Computational prediction of novel miRNAs from genome-wide data

Abstract

The computational prediction of novel microRNAs (miRNAs) within a full genome involves identifying sequences having the highest chance of being bona fide miRNA precursors (pre-miRNAs). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed. Although the selection of positive labeled examples is straightforward, it is very difficult to build a set of negative examples in order to obtain a good set of training samples for a supervised method. In this chapter we describe an approach to this problem, based on the unsupervised clustering of unlabeled sequences from genome-wide data, and the well-known miRNA precursors for the organism under study. Therefore, the protocol developed allows for quick identification of the best candidates to miRNA as those sequences clustered together with known precursors.