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Determination of functions for poorly characterized genes is crucial forunderstanding biological processes and studying human diseases. Functionallyassociated genes are often gained and lost together through evolution.Therefore identifying co-evolution of genes can predict functional gene-geneassociations. We describe here the full statistical model and computationalstrategies underlying the original algorithm, CLustering by Inferred Models ofEvolution (CLIME 1.0) recently reported by us [Li et al., 2014]. CLIME 1.0employs a mixture of tree-structured hidden Markov models for gene evolutionprocess, and a Bayesian model-based clustering algorithm to detect gene moduleswith shared evolutionary histories (termed evolutionary conserved modules, orECMs). A Dirichlet process prior was adopted for estimating the number of geneclusters and a Gibbs sampler was developed for posterior sampling. We furtherdeveloped an extended version, CLIME 1.1, to incorporate the uncertainty on theevolutionary tree structure. By simulation studies and benchmarks on real datasets, we show that CLIME 1.0 and CLIME 1.1 outperform traditional methods thatuse simple metrics (e.g., the Hamming distance or Pearson correlation) tomeasure co-evolution between pairs of genes.

Bayesian Hidden Markov Tree Models for Clustering Genes with Shared Evolutionary History