| Summary: | Identifying functionally significant microRNAs (miRs) and their correspondingly
most important messenger RNA targets (mRNAs) in specific biological contexts is
a critical task to improve our understanding of molecular mechanisms underlying
organismal development, physiology and disease. However, current
miR–mRNA target prediction platforms rank miR targets based on estimated
strength of physical interactions and lack the ability to rank interactants as a
function of their potential to impact a given biological system. To address
this, we have developed ToppMiR (http://toppmir.cchmc.org),
a web-based analytical workbench that allows miRs and mRNAs to be co-analyzed
via biologically centered approaches in which gene function associated
annotations are used to train a machine learning-based analysis engine. ToppMiR
learns about biological contexts based on gene associated information from
expression data or from a user-specified set of genes that relate to
context-relevant knowledge or hypotheses. Within the biological framework
established by the genes in the training set, its associated information content
is then used to calculate a features association matrix composed of biological
functions, protein interactions and other features. This scoring matrix is then
used to jointly rank both the test/candidate miRs and mRNAs. Results of these
analyses are provided as downloadable tables or network file formats usable in
Cytoscape.
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