Demography inference

Reconstructing the demographic history of populations and species is a fundamental task in evolutionary analysis. We develop methods for inferring past demography through analysis of small numbers of complete individual genomes. This is done by utilizing the ancestral information encoded in numerous loosely-linked genomic loci, and using a genealogy sampler to accommodate for uncertainty in the local ancestry at each locus. The Generalized Phylogenetic Coalescent Sampler (G-PhoCS) based on this approach was introduced in a paper we published in Nature Genetics in 2011, where we used this approach to infer ancient human demography by analyzing the complete seuenced genomes of six human individuals. We are currently interested in two specific applications of this approach: (1) studying the dynamics leading to species divergence and speciation through analysis of genomes sequenced from closely related species (see 2014 study on origins of domestic dogs); and (2) investigating the demography and evolution of early human populations in Africa through analysis of ancient DNA and the genomes of individuals from divergent human populations.

Integration of Functional and Population Genomic Data

A central challenge in genomics is to find constructive ways to integrate diverse types of genomic data. We are interested in methods for integrating sequence vairation data (e.g., individual genome sequences) with functional genomic data (e.g., ChIP-seq, DNase-seq, RNA-seq). Through this we hope to gain insights on the interplay between biochemical functions of the genome (e.g., transcription, protein binding) and forces of natural selection acting on the DNA sequence. In a paper published in MBE in 2013 we present INSIGHT - a method for infering natural selection in short interspersed genomic lements, and in a companion paper in Nature Genetics, we use this method to study how natural selection has shaped the DNA sequence at 1.4 million binding sites of 78 transcription factors. We have recently scaled this approach genome-wide using diverse types of functional genomic data and a simple clustering technique (see paper on fitCons).

Phylogeny Inference

Despite being a classic problem in computational biology, nearly half a century old, the fundamental task of reconstructing evolutionary trees (phylogenies) from short sequences still poses interesting theoretical challenges. Most of our work in this area focuses on the distance-based approach for phylogenetic reconstruction. We study and develop algorithms that have provable reconstruction guarantees ( Gronau, et. al., 2012) as well as methods for computing more statistically robust evolutionary distances ( Gronau, et. al., 2009; Doerr, et. al., 2009).


Ilan Gronau    

  • + 972 - 9 - 952 - 7907
  • Room 127, Computer Science and Communications Building
  • The Interdisciplinary Center (IDC)
    P.O.Box 167,   Kanfei Nesharim St.
    Herzliya 46150,   Israel
CS & Comm building

GronauLab @ IDC

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