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

TitlePrediction of novel microRNA genes in cancer-associated genomic regions - A combined computational and experimental approach
Publication TypeJournal Article
Year of Publication2009
AuthorsOulas, A, Boutla A, Gkirtzou K, Reczko M, Kalantidis K, Poirazi P
JournalNucleic Acids Research
Volume37
Issue10
Pages3276 - 3287
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 of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.

URLhttp://www.scopus.com/inward/record.url?eid=2-s2.0-67249093200&partnerID=40&md5=8a9f358253b1a60147e10a37a6bfc3ef

User login