miRaCL: a Novel miRNA target prediction
algorithm based on Chromosome Location
Various prediction algorithms are available for identifying putative miRNA target genes,
including MiRanda 4, PicTar 8, TargetScan 10, and DIANA micro-T 6 that are based on the
principles of base pairing as well as PITA 5 and rna22 12, which consider thermodynamic
stability of the miRNA-mRNA hybrid and accessibility of the 3ꞌ-UTR, respectively.
TargetScan 10, Pictar 8, and miRanda 4 are all based on 7-nt seed pairing, although they vary
in terms of stringency. Seed pairing is critical for the interaction between miRNAs and their
targets 2, 7, 9, 10. Searches for short seed sequence matches with computational tools generate
large numbers of putative targets, a significant fraction of which are false positives (in the
order of around 22%–34%) 11. To address this issue, other algorithms have been developed to
exclude false positive predictions and thus reduce the number of putative targets 1. For
example, computationally predicted miRNA target genes can be selected according to
evolutionary conservation across species 18 and multiplicity of target sites 15. However,
adding too many parameters can instead increase the false negative rate 11. As such, most
algorithms still predict anywhere from several hundred up to thousands of putative targets for
most miRNAs, making functional validation very difficult.
Chromosomes occupy discrete regions within the nucleus 14, 19. Recently, chromosome
painting by fluorescence in situ hybridization has revealed the arrangement of each
chromosome in cell nuclei. Chromatin is localized in interchromosomal domain
compartments 13 that are highly conserved across primates 16. Chromosome position plays an
important role in various biological processes 16, including gene regulation 16. It is now
known that nuclear architecture in the mammalian cell nucleus is compartmentalized into
chromosome territories (CTs), with an interchromatin compartment that contains
macromolecular complexes required for somatic homologous recombination, gene
conversion, and DNA replication and repair 3 .
Many miRNAs and their target genes are spatially associated with the same chromosomes 17.
However, to date there are no computational algorithms for predicting miRNA targets
based on CT theory. We speculated that spatial information of chromosomes is relevant the
regulatory interaction between miRNAs and their target genes. Based on this assumption, we
developed miRNA algorithm based on Chromosomal Location (miRaCL) to minimize false
positive identification of miRNA targets.
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