Towards a comprehensive human protein-protein interaction network
Abstract
Obtaining a reliable interaction data set describing the human interactome is a
milestone yet to be reached. The past few years has seen tremendous progress in
elucidating the yeast interactome. Experimental approaches for obtaining large-scale
protein interaction data coupled with powerful computational methods for combining
these data sets and for predicting functional relations between genes have been successful
in tackling the yeast interactome. The concerted development of visualization techniques
and the progress in the field of network biology has provided us with tools to evaluate,
analyze, and interpret the interactome.
Although techniques are being scaled to tackle mammalian genomes, as witnessed
by the first protein interaction networks for fly and worm we are far from a complete map
of the human interactome. Human genes create additional challenges due to molecular
complexity, tissue specificity, and alternate splicing. It therefore becomes important to
build well-annotated benchmarks and accuracy measures to evaluate new data.
Here, we describe three methods that provide a framework to build a
comprehensive human interactome. We have developed a novel algorithm for predicting
protein interaction partners based on comparing the position of proteins in their
respective phylogenetic trees. We establish two tests of the accuracy of human protein
interaction data sets and integrate the small-scale human interaction data sets using a Log
likelihood framework. The benchmarks and the consolidated interaction set will provide a
basis for determining the quality of future large-scale human protein interaction assays.
Lastly, based on patterns of conserved co-expression of human gene pairs and their
orthologs from 5 different organisms (A. thaliana, M. musculus, D. melanogaster, C.
elegans, and Yeast) we predict protein interactions, and test them against the benchmarks
established by us. By combining the existing interaction data sets, we build a network of
61,974 interactions between 9,642 human proteins and cluster the network to show
examples representative of the quality of the interactions in the network.
The methods, benchmarks and the Log likelihood framework, we hope, would
enable us to build a comprehensive human interactome.
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