Prediction of novel microRNA genes in cancer-associated genomic regions--a combined computational and experimental approach.

ABSTRACT: The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set

SUBMITTER: Oulas A 

PROVIDER: S-EPMC2691815 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature