Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Saetrom:2005:RNA,
-
author = "Ola Saetrom and Ola {Snove, Jr.} and Pal Saetrom",
-
title = "Weighted sequence motifs as an improved seeding step
in microRNA target prediction algorithms",
-
journal = "RNA",
-
year = "2005",
-
volume = "1",
-
number = "7",
-
pages = "995--1003",
-
month = jul,
-
keywords = "genetic algorithms, genetic programming, miRNA target
prediction, boosting, machine learning",
-
DOI = "doi:10.1261/rna.7290705",
-
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' end and their targets; (2)
relatively high thermodynamic duplex stability; (3)
multiple target sites in the target's 3' 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/.)",
-
notes = "PMID:",
- }
Genetic Programming entries for
Ola Saetrom
Ola Snove Jr
Pal Saetrom
Citations