CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing

Pradipta Ray, Suyash Shringarpure, Mladen Kolar, Eric P. Xing

Abstract: Author SummaryFunctional turnover of transcription factor binding sites (TFBSs), such as whole-motif loss or gain, are common events during genome evolution, and play a major role in shaping the genome and regulatory circuitry of contemporary species. Conventional methods for searching non-conserved motifs across evolutionarily related species have little or no probabilistic machinery to explicitly model this important evolutionary process; therefore, they offer little insight into the mechanism and dynamics of TFBS turnover and have limited power in finding motif patterns shaped by such processes. In this paper, we propose a new method: Conditional Shadowing via Multi-resolution Evolutionary Trees, or CSMET, which uses a mathematically elegant and computationally efficient way to model biological sequence evolution at both nucleotide level at each individual site, and functional level of a whole TFBS. CSMET offers the first principled way to take into consideration lineage-specific evolution of TFBSs and CRMs during motif detection, and offers a readily computable analytical form of the posterior distribution of motifs under TFBS turnover. Its performance improves upon current state-of-the-art programs. It represents an initial foray into the problem of statistical inference of functional evolution of TFBS, and offers a well-founded mathematical basis for the development of more realistic and informative models.