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Cell. 2014 Jul 3;158(1):213-25. doi: 10.1016/j.cell.2014.05.034.

Expansion of biological pathways based on evolutionary inference.

Author information

1
Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
2
Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute, Cambridge, MA 02141, USA.
3
Department of Biostatistics, Brown University, Providence, RI 02912, USA.
4
Department of Statistics, Harvard University, Cambridge, MA 02138, USA. Electronic address: jliu@stat.harvard.edu.
5
Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute, Cambridge, MA 02141, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: vamsi@hms.harvard.edu.

Abstract

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.

PMID:
24995987
PMCID:
PMC4171950
DOI:
10.1016/j.cell.2014.05.034
[Indexed for MEDLINE]
Free PMC Article

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