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Cell DOI:10.1016/j.cell.2014.05.034

Expansion of biological pathways based on evolutionary inference.

Publication TypeJournal Article
Year of Publication2014
AuthorsLi, Y, Calvo, SE, Gutman, R, Liu, JS, Mootha, VK
Date Published2014 Jul 03
KeywordsAlgorithms, Cluster Analysis, Humans, Mitochondria, Phylogeny, Plasmodium falciparum, Proteome, Rhodophyta, Signal Transduction, Yeasts

The availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a shared history but rather consist of distinct "evolutionary modules." We introduce a computational algorithm, clustering by inferred models of evolution (CLIME), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest. CLIME partitions the gene set into disjoint evolutionary modules, simultaneously learning the number of modules and a tree-based evolutionary history that defines each module. CLIME then expands each module by scanning the genome for new components that likely arose under the inferred evolutionary model. Application of CLIME to ∼1,000 annotated human pathways and to the proteomes of yeast, red algae, and malaria reveals unanticipated evolutionary modularity and coevolving components. CLIME is freely available and should become increasingly powerful with the growing wealth of eukaryotic genomes.


Alternate JournalCell
PubMed ID24995987
PubMed Central IDPMC4171950
Grant ListR01 GM077465 / GM / NIGMS NIH HHS / United States
GM0077465 / GM / NIGMS NIH HHS / United States
/ / Howard Hughes Medical Institute / United States