Research
Our research is devoted to the development of computational methods for analyzing data generated by high throughput biological experiments. This work involves constructing mathematical models for biological processes, designing efficient inferernce algorithms under these models, implementing these algorithms into public software tools, and most importantly . . . analyzing real data. We are particularly interested in capitalizing on recent progress in DNA sequencing technologies to advance the study of evolution and population genetics.
Education
We are devoted to education through various courses and student projects given at the Efi Arazi School of Computer Science. The courses foucs on formal modeling of complex problems and data sets and development of efficient inference algorithms for these models.
Select Publications
- Kuhlwilm M*, Gronau I*, Hubisz MJ, de Filippo C, Prado-Martinez J, et al. (2016) Ancient gene flow from modern humans into Siberian Neanderthals . Nature 530, 429-433 PubMed ReadCube public view Dedicated page
- Campagna L, Gronau I, Silveira LF, Siepel A, Lovette IJ. (2015) Distinguishing noise from signal in patterns of genomic divergence in a highly polymorphic avian radiation. Molecular Ecology 24:4238-4251 PubMed
- Gulko B, Hubisz MJ, Gronau I*, Siepel, A*. (2015) Probabilities of fitness consequences for point mutations across the human genome. Nature Genetics 47:276-283 PubMed
- Freedman A, Gronau I, Schweizer RM, Han E, Ortega-Del Vecchyo D, et al. (2014) Genome Sequencing Highlights the Dynamic Early History of Dogs. PLoS Genetics 10(8), e1004631 PubMed
- Gronau I, Arbiza L, Mohammed J, Siepel A. (2013) Inference of Natural Selection from Interspersed Genomic Elements Based on Polymorphism and Divergence. Molecular Biology and Evolution 30(5):1159-1171 PubMed
- Gronau I, Hubisz MJ, Gulko B, Danko CG, Siepel A. (2011) Bayesian inference of ancient human demography from individual genome sequences. Nature Genetics 43:1031-1034 PubMed
- Gronau I, Moran S, Yavneh I. (2009) Towards Optimal Distance Functions for Stochastic Substitutions Models. Journal of Theoretical Biology 260:294-307 PubMed
Software
A key part of our mission is to provide open source software for the methods we develop. We strive to expand the user base of our methods by continuously improving the software based on feedback from users.
