Active

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs.

Immunoprecipitation of RNA binding proteins (RBPs) after in vivo crosslinking, coupled with sequencing of associated RNA footprints (HITS-CLIP, CLIP-seq), is a method of choice for the identification of RNA targets and binding sites for RBPs. Compared with RNA-seq, CLIP-seq analysis is widely diverse and depending on the RBPs that are analyzed, the approaches vary significantly, necessitating the development of flexible and efficient informatics tools.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate transcriptional processes via binding to the target gene mRNA. In animals, this binding is imperfect, which makes the computational prediction of animal miRNA targets a challenging task. The accuracy of miRNA target prediction can be improved with the use of machine learning methods. Previous work has described methods using supervised learning, but they suffer from the lack of adequate training examples, a common problem in miRNA target identification, which often leads to deficient generalization ability.

Identification of microRNAs (miRNAs) is an important step toward understanding post-transcriptional gene regulation and miRNA-related pathology. Difficulties in identifying miRNAs through experimental techniques combined with the huge amount of data from new sequencing technologies have made in silico discrimination of bona fide miRNA precursors from non-miRNA hairpin-like structures an important topic in bioinformatics. Among various techniques developed for this classification problem, machine learning approaches have proved to be the most promising.