title: Construction of microRNA functional families by a mixture model of position weight matrices.
abstract: MicroRNAs (miRNAs) are small regulatory molecules that repress the translational processes of their target genes by binding to their 3' untranslated regions (3' UTRs). Because the target genes are predominantly determined by their sequence complementarity to the miRNA seed regions (nucleotides 2-7) which are evolutionarily conserved, it is inferred that the target relationships and functions of the miRNA family members are conserved across many species. Therefore, detecting the relevant miRNA families with confidence would help to clarify the conserved miRNA functions, and elucidate miRNA-mediated biological processes. We present a mixture model of position weight matrices for constructing miRNA functional families. This model systematically finds not only evolutionarily conserved miRNA family members but also functionally related miRNAs, as it simultaneously generates position weight matrices representing the conserved sequences. Using mammalian miRNA sequences, in our experiments, we identified potential miRNA groups characterized by similar sequence patterns that have common functions. We validated our results using score measures and by the analysis of the conserved targets. Our method would provide a way to comprehensively identify conserved miRNA functions.
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul , Korea.
authors.forename: Je-Keun
authors.initials: JK
authors.lastname: Rhee
authors.forename: Soo-Yong
authors.initials: SY
authors.lastname: Shin
authors.forename: Byoung-Tak
authors.initials: BT
authors.lastname: Zhang
keywords: EM algorithm; Machine learning; MicroRNA; Mixture model; Position weight matrix; Sequence analysis