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The discovery that microRNAs (miRNAs) are synthesized as hairpin-con-taining precursors and share many features has stimulated the development of several computational approaches for identifying new miRNA genes in various animal species. | MINIREVIEW A study of microRNAs in silico and in vivo bioinformatics approaches to microRNA discovery and target identification Malik Yousef1 2 Louise Showe3 and Michael Showe3 1 The Galilee Society Institute of Applied Research Israel 2 Al-Qasemi Academic College Baqa Algharbiya Israel 3 Systems Biology Division The Wistar Institute Philadelphia PA USA Keywords bioinformatics machine learning microRNA microRNA target Correspondence M. Yousef Research Development Center The Galilee Society P.O. Box 437 Shefa-Amr 20200 Israel Fax 972 4 95044525 Tel 972 4 9504523 4 E-mail yousef@gal-soc.org Received 27 August 2008 revised 9 January 2009 accepted 22 January 2009 The discovery that microRNAs miRNAs are synthesized as hairpin-containing precursors and share many features has stimulated the development of several computational approaches for identifying new miRNA genes in various animal species. Many of these approaches rely heavily on conservation of sequence within and between species whereas others emphasize machine-learning methods to screen hairpin candidates for structural features shared with known miRNA precursors. The identification of animal miRNA targets is a particularly difficult problem because an exact match to the target sequence is not required. We discuss the most recently devised algorithms for miRNA and target discovery. We do not discuss plant miRNAs because their varying sizes and structural characteristics pose different problems of identification and target selection. doi 10.1111 j.1742-4658.2009.06933.x Machine-learning approaches to microRNA discovery Methods derived from the machine-learning field have recently been applied to microRNA miRNA discovery with good success. Machine learning depends on the development of algorithms and methods that allow a specific computer program to learn from data already collected on verified miRNAs. These algorithms require a training set for the learning process that consists of positive examples which define
