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 0 end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3 0 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/.)

MicroRNAs and their regulatory roles in animals and plants

“…This suggests a powerful strategy to predict miRNA targets by computational approaches. Based on this characteristic, several laboratories have developed different computational strategies to predict miRNA targets in available genome database, and successfully identified 100s of miRNA targets for given miRNAs Enright et al, 2003;Lewis et al, 2003;Stark et al, 2003;Axton, 2004;Bonnet et al, 2004;John et al, 2004;JonesRhoades and Bartel, 2004;Kiriakidou et al, 2004;Lai, 2004;Rajewsky and Socci, 2004;Rehmsmeier et al, 2004;Wang et al, 2004;Axtell and Bartel, 2005;Bentwich, 2005;Brennecke et al, 2005;Brown and Sanseau, 2005;Burgler and Macdonald, 2005;Grun et al, 2005;Hariharan et al, 2005;Kawasaki and Taira, 2005;Krek et al, 2005;Legendre et al, 2005;Lewis et al, 2005;Li and Zhang, 2005;Nakahara et al, 2005;Robins et al, 2005;Saetrom et al, 2005;Williams et al, 2005a;Xie et al, 2005;Yoon and De Micheli, 2005;Zhang, 2005). These computer software programs include TargetScan (Lewis et al, 2003), TargetScanS (Lewis et al, 2005), miRanda (Enright et al, 2003;John et al, 2004), MovingTargets (Burgler and Macdonald, 2005), PicTar …”

Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies

“…Similarly, PicTar (Krek et al, 2005) is based on a statistical method using genome-wide alignments of related species. TargetBoost (Saetrom et al, 2005) uses machine learning based on sequence information to create weighted sequence motifs that extract a profile describing binding characteristics between miRNAs and their targets. Likewise, SungKyu et al (Kim et al, 2005) and Yan, X., et al (Yan et al, 2007), used machine learning algorithms (SVM and ensemble learning, respectively) to predict miRNA-mRNA duplexes.…”

“…(Lewis et al, 2005) miRanda http://www.microrna.org (John et al, 2004) PicTar http://pictar.mdc-berlin.de/ (Krek et al, 2005) RNAhybrid http://bibiserv.techfak.unibielefeld.de/rnahybrid (Krüger and Rehmsmeier, 2006) DianamicroT http://diana.imis.athenainnovation.gr/DianaTools/index.php (Kiriakidou et al, 2004) Target Boost http://www.interagon.com/demo (Saetrom et al, 2005) (Yousef et al, 2007) miRecords http://mirecords.umn.edu/miRecords/ (Xiao et al, 2009) …”

MicroRNA Identification Based on Bioinformatics Approaches

“…This is likely to be a conservative estimate due to the incomplete input data. TargetBoost [76] is a machine learning algorithm for miRNA target prediction using only sequence information to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. The authors suggest that TargetBoost is stable and identifies more of the already verified true targets than do other existing algorithms.…”