Supplementary data: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

By Doron Betel, Anjali J. Koppal, Phaedra Agius, Christopher Sander, Christina S. Leslie. 2010

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Citations in the corpus

Predicting effective microRNA target sites in mammalian mRNAs (2015, background): “1 (September 2013) (Vejnar and Zdobnov, 2012), mirSVR (August 2010) (Betel et al., 2010), miRTarget2 (from miRDB v4.”
miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology (2020, background): “To facilitate miRNA research in species important for agriculture and veterinary medicine, we have added support for cows, pigs and chickens following well-established protocols (18).”
mirDIP 4.1—integrative database of human microRNA target predictions (2017, background): “org 2008 2010 yes yes yes yes yes yes (39)”
Tools for Sequence-Based miRNA Target Prediction: What to Choose? (2016, methodology): “incorporated the miRNA support vector regression algorithm (mirSVR) for scoring and ranking the efficiency of miRanda-predicted miRNA target sites, considering the mRNA expression changes [64].”
starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data (2013, methodology): “Genomic coordinates of these conserved miRNAs target sites predicted by TargetScan (25), miRanda/mirSVR (27), PITA (28), Pictar 2.0 (19) and RNA22 (29) were collected and converted to hg19, mm9/ mm10 and ce6/ce10 assemblies using LiftOver, respectively.”
miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes (2011, background): “In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miRNAs [5,16–26].”
miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis (2016, methodology): “mansoni only), users can specify a minimum cut-off value based on the probability score from the mirSVR algorithm (27).”
Identifying miRNAs, targets and functions (2012, methodology): “miRNA-TARGET PREDICTION In the absence of high-throughput biological approaches to identify miRNA targets, many computational methods, such as miRanda [33], mirSVR [34], PicTar [35], TargetScan [36], TargetScanS [37], RNA22 [38], PITA [39], RNAhybird [40] and DIANA-microT [41], were developed relati”