Lost in translation: an assessment and perspective for computational microRNA target identification

By Panagiotis Alexiou, Manolis Maragkakis, Giorgos L. Papadopoulos, Martin Reczko, Artemis G. Hatzigeorgiou. Bioinform., 2009

353 citations recorded by the source corpus.

UNLABELLED MicroRNAs (miRNAs) are a class of short endogenously expressed RNA molecules that regulate gene expression by binding directly to the messenger RNA of protein coding genes. They have been found to confer a novel layer of genetic regulation in a wide range of biological processes. Computational miRNA target prediction remains one of the key means used to decipher the role of miRNAs in development and disease. Here we introduce the basic idea behind the experimental identification of miRNA targets and present some of the most widely used computational miRNA target identification programs. The review includes an assessment of the prediction quality of these programs and their combinations. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b6d1e2b63e5290510dbcde7a62909c15c142cbba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

De novo identification of differentially methylated regions in the human genome (2015, background): “overwhelming proportion of the genome is evaluated as true negative in the confusion matrix, precision–recall statistics can be a preferred option for genomic region prediction [57,58].”
The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations (2014, methodology): “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).”
MicroRNAs in stress signaling and human disease (2012, background): “Many different algorithms exist for the bioinformatic prediction ofmiRNA targets and all generally predict hundreds of targets for each miRNA (Alexiou et al., 2009).”
Desperately seeking microRNA targets (2010, background): “However, these algorithms typically predict hundreds to thousands of target genes for each miRNA, and most predicted genes are not bona fide target”
Functional microRNA targets in protein coding sequences (2012, neutral): “Most miRNA target prediction programs nevertheless limit their search for MREs only within the 3′-UTR (Alexiou et al., 2009). Here we describe an algorithm for the prediction of miRNAtargets in both 3′-UTRs and CDSs that are trained on a positive and a negative set of MREs defined by PAR-CLIP data o”
Identifying miRNAs, targets and functions (2012, neutral): “[51] who compared the performance for eight”
DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways (2012, background): “Gene target prediction applications were some of the first sectors to receive intense research focus (10).”