Using FunCoup to predict disease genes
Eric Sonnhammer *
Build networks combining all biological data PPI, coexpression, phylogenetic profiles (for co-evolution), domain interactions, subcellular co-localisation, TF binding sites, miRNA targetting.
Uses orthology to relate between organisms. Data is compared vs "training sets" in a Bayesian Framework. Using Gold Standard datasets HPRD for PPI, KEGG metabolic and signalling pathways. Uses log odds ratios. Networks cut at a confidence of 0.2 number of interactions grows exponentially with confidence level (why does this worry me?). Lots of support for interations comes from orthologous species.
http://FunCoup.sbc.su.se
Query from a few starting genes. Searches across species show orthologues. Examples for Parkinsons and Alzheimers from his paper.
http://genome.cshlp.org/content/19/6/1107.long
The methods can only predict associations to disease if the genes are part of the Large Central Component - isolated genes are not within the network.
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