title: Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms.
abstract: We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).
authors.affiliation: Department of Computer and Information Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
authors.forename: Ola
authors.initials: O
authors.lastname: Saetrom
authors.forename: Ola
authors.initials: O
authors.lastname: Snøve
authors.forename: Pål
authors.initials: P
authors.lastname: Saetrom
mesh_terms: D020121:5' Untranslated Regions; D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D001665:Binding Sites; D019521:Body Patterning / Q000235:genetics; D017173:Caenorhabditis elegans / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D004330:Drosophila / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D019143:Evolution, Molecular; D017238:Genes, Helminth; D017344:Genes, Insect; D020407:Internet; D035683:MicroRNAs / Q000737:chemistry* / Q000235:genetics* / Q000378:metabolism; D008969:Molecular Sequence Data; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013816:Thermodynamics
chemical_list: D020121:5' Untranslated Regions; D035683:MicroRNAs