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conservation pattern across species is functional and has been maintained
by selection in one or more species, as opposed to being maintained in
the absence of selection pressure in any of the species. Additionally,
ElMMo infers the strength of selection pressure on target sites for a given
miRNA along each branch of the evolutionary tree, thus enabling one to
analyze the dynamics of miRNA target site evolution.
As was the case with miRNA gene prediction, new methods for
miRNA target prediction continue to emerge. Among those that use the
most distinct set of features are
TargetBoost 133 — uses an adaptation of a genetic programming
algorithm to define motifs that presumably capture the character-
istics of interaction between miRNAs and targets;
miTarget 134 — uses an SVM based on local and global structural
and thermodynamic features;
GenMir 135 — uses miRNA and mRNA expression profiles to identify
sets of genes whose expression changes from sample to sample in
a way that is consistent with the miRNA expression;
a linear model that takes into account features computed over the con-
text of target sites: A/U content, base pairing with nucleotides 13-17
of the miRNA, and distance to the nearest 3
UTR boundary 117 ; and
Pita 136 — predicts target sites based on the energy of interaction
between miRNA and target sites, taking into account both the
energetic cost of unfolding the target site and the energy gained
through the hybridization of the miRNA with the target.
8. Conclusions
The abundance and the functions of non-protein-coding RNAs in regu-
lating gene expression have only just begun to be explored.
Computational approaches play a prominent role in this field, yet they
face the problem that, with the relatively poor understanding of the con-
straints on particular types of molecules or interactions, accurate, predic-
tive models are difficult to construct. Nonetheless, computational biology
of small regulatory RNAs is a field in which combined computational
and experimental approaches are common, and are generally successful.
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