target prediction

Despite experiments showing that the number of microRNA (miRNA) target sites is critical for miRNA targeting, most existing methods focus on identifying individual miRNA target sites and do not model contributions of multiple target sites to miRNA regulation. Recently, scientists at the Norwegian University of Science and Technology developed a miRNA target prediction model that recognizes the individual characteristics of functional binding sites and the global characteristics of miRNA-targeted mRNAs.

Benchmark experiments showed that this two-step model generally had a higher overall performance than other established miRNA target prediction algorithms and that the model was especially suited to identify true miRNA targets among genes that all contain conserved target sites.  The most critical factors for the model’s performance of predicting targets were mRNA-level features that characterized the number and strength of individual target sites within the mRNA, and the model’s lack of reliance on conservation.

The model is available for online predictions or as pre-computed predictions on the human genome.

http://tare.medisin.ntnu.no/mirna_target

Saito T, Saetrom P. (2011) A two-step site and mRNA-level model for predicting microRNA targets. BMC Bioinformatics 11(1), 612. [abstract]

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A critical step in elucidating miRNA function is identifying potential miRNA targets and, although many computational tools have been developed for predicting animal miRNA targets, few tools are available for identifying plant miRNA targets.

There are currently three tools [miRU (Zhang, 2005), Helper tools (Moxon, et al., 2008), and TAPIR (Bonnet, et al., 2010)]

Because previous studies have demonstrated that most plant miRNAs cleave targets by perfectly or near-perfectly binding to their target, these 3 currently available tools predict plant miRNA targets based on very strictly limited criteria.

However, recent studies show that some miRNAs may inhibit translation by non-perfectly binding to target mRNAs with more potential target sites requiring a new computational program with more flexible criteria.

Target-align  is available at: http://www.leonxie.com/targetAlign.php.

Xie F, Zhang B. (2010) Target-align: a tool for plant microRNA target identification. Bioinformatics [Epub ahead of print]. [abstract]

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MicroRNAs bind to partially complementary sites in the 3′UTR of target mRNAs. This process generally results in repression of multiple targets by a particular microRNA. There is substantial interest in methods designed to predict the microRNA targets and effect of single nucleotide polymorphisms (SNPs) on microRNA binding, given the impact of microRNA on posttranscriptional regulation and its potential relation to complex diseases.

MicroSNiPer is a new web tool predicts the impact of a SNP on putative microRNA targets. This application interrogates the 3′-untranslated region and predicts if a SNP within the target site will disrupt/eliminate or enhance/create a microRNA binding site. MicroSNiPer computes these sites and examines the effects of SNPs in real time. MicroSNiPer is a user-friendly web-based tool. Its advantages include ease of use, flexibility and straightforward graphical representation of the results.

It is freely accessible at http://cbdb.nimh.nih.gov/microsniper.

Barenboim M, Zoltick BJ, Guo Y, Weinberger DR. (2010) MicroSNiPer: a web tool for prediction of SNP effects on putative microRNA targets. Hum Mutat [Epub ahead of print]. [abstract]

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A new regression method for predicting likelihood of target mRNA down-regulation from sequence and structure features in microRNA/mRNA predicted target sites.

MirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

mirSVR scores are available at www.microRNA.org.

Betel D, Koppal A, Agius P, Sander C, Leslie C. (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biology  11, R90. [article]

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microRNA Target Prediction Tools

by Chris on November 16, 2009

in Web Based Tools

miRecords is resource for animal miRNA-target interactions developed at the University of Minnesota. miRecords consists of two components. The Validated Targets component is a large, high-quality database of experimentally validated miRNA targets resulting from meticulous literature curation. The Predicted Targets component of miRecords is an integration of predicted miRNA targets produced by 11 established miRNA target prediction programs

PicTar is an algorithm for the identification of microRNA targets. This searchable website provides details (3′ UTR alignments with predicted sites, links to various public databases etc) regarding microRNA target predictions in vertebrates, several Drosophila species, and C. elegans.

miRanda is an algorithm for finding genomic targets for microRNAs. This algorithm has been written in C and is available as an open-source method under the GPL. MiRanda was developed at the Computational Biology Center of Memorial Sloan-Kettering Cancer Center. This software will be further developed under the open source model, coordinated by Anton Enright and Chris Sander (miranda@cbio.mskcc.org).

TargetScan: Prediction of microRNA targets – These are the most recent TargetScanS predictions (April 2005). They are essentially the 3′UTR targets reported in the Lewis et al., 2005 paper, with a few changes arising from updated gene boundary definitions from the April 2005 UCSC genome browser mapping of RefSeq mRNAs to the hg17 human genome assembly. To avoid difficulties in browser display, the few predictions spanning splice junctions are excluded.   [click to continue…]

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