A guide through present computational approaches for the identification of mammalian microRNA targets

By Praveen Sethupathy, Molly Megraw, Artemis G. Hatzigeorgiou. Nature Methods, 2006

614 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7a26e5e2090a6fafe5d8389a1d02075e6ff42a25

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Citations in the corpus

miRGen: a database for the study of animal microRNA genomic organization and function (2006, background): “We find that no one program can be considered as consistently superior to the rest; however, in many cases it may be helpful to use the intersection of the predictions of a subset of these programs (29).”
Desperately seeking microRNA targets (2010, result): “However, a few reviews have compared predictions against a 'benchmark' of experimentally validated or refuted miRNA target”
miRecords: an integrated resource for microRNA–target interactions (2008, result): “Comparative studies conducted with the earlier miRNA target prediction programs (including TargetScanS, PicTar, miRanda, DIANA-microT, RNAhybrid, MicroInspector and Rna22) suggested that no one program was consistently superior to all others (13,29).”
Targeted deletion reveals essential and overlapping functions of the miR-17 through 92 family of miRNA clusters. (2008, neutral): “Functional Cooperation between miR-17w92 and miR-106bw25 Given their sequence similarity, these three miRNA clusters are predicted to have a highly overlapping set of targets (Lewis et al., 2003, 2005; Sethupathy et al., 2006 ).”
starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data (2010, background): “However, because miRNA regulation of an animal mRNA requires base pairing with only few nucleotides of the 3 0 -UTR region of the target mRNA, different target prediction programs produce different results and have high false positive rates (4,17–19).”
Expression profiling of microRNAs by deep sequencing (2009, background): “Different algorithms may yield different microRNA:mRNA predictions, though most employ the same general criteria [28]: (i) complementarity between the microRNA sequence and the 30-UTR mRNA sequence (whether a perfect is required or one”
DIANA-microT web server: elucidating microRNA functions through target prediction (2009, neutral): “( 3 ) MiRNA names and corresponding links to miRBase (sequence information) and iHOP.”
The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations (2014, background): “An alternative approach would be to take the union (with the increase of sensitivity and loss of precision) or intersection (with the increase of precision at the cost of sensitivity) of multiple prediction programs (15,18).”
Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network (2007, background): “The assignments of miRs to targets are known to be of limited accuracy [21] .”
The database of experimentally supported targets: a functional update of TarBase (2008, background): “Although these programs are very important to guide wet lab experiments, they still lack in sensitivity and specificity (7,8).”
Identifying miRNAs, targets and functions (2012, background): “In most cases, these algorithms were developed in conjunction with a limited amount of empirical evidence from a few experimentally validated target sites for a small selection of miRNAs [42].”