Our major research strengths in Computational Biology are in comparative, evolutionary, and population genomics.
Specific problems of interest include the detection of genomic regions underlying complex traits, inference of ancestry from genome sequences, the detection of positive selection, the evolutionary genomics of plant and animal domestication, the discovery of new human genes, and the identification and characterization of functional noncoding elements in mammals.
Our faculty are key members of a strong, active community at Cornell in comparative, evolutionary, and population genomics that spans departments and colleges.
Our research themes
Behavior & Neuroethology
Researchers in Computational Biology are working to understand how humans and other animals construct adaptive sequences of behavior in the complex settings we encounter in the world around us. This work fuses new experimental technologies with algorithmic and computational modeling.
Complex Trait Genetics
Researchers in Computational Biology are studying genomic data to understand the genetics of complex traits. We develop computational/statistical methods to analyze genome-wide data collected for a diversity of organisms with measured phenotypes, and we apply these methods to study the genetics of traits important for conservation, evolution, agriculture, and medicine.
Multi-scale imaging techniques, able to capture brain structure and function from the single synapse to the whole brain, are opening a window to unprecedented neuro-scientific insights. This big imaging data also necessitates sophisticated computational tools, including mathematical/statistical modeling and machine learning, to allow testing of mechanisms and theories.
Researchers in Computational Biology are applying and extending tools from Artificial Intelligence to transform how data are collected from earth's ecosystems. These new data drive models that are helping scientists understand nutrient fluxes, species interactions, and ecosystem tipping points.
Epigenomics & Gene Regulation
Devising methods for analysis of genome-scale probes of chromatin states and the rules that establish their connection with regulation of gene expression. Testing the role of vertical transmission of epigenetic modifications. Learning how chromatin states and their effects on gene regulation are shaped by evolution, and how they change across populations and in disease.
Human Admixture & Ancestry
Admixture is ubiquitous in sexual organisms, leading to mosaicism in ancestry within a genome. We are developing scalable and accurate methods for inferring population admixture and ancestry with the goal of shedding light on population history, natural selection, and complex traits.
Phylogenetics & Phylodynamics
Phylogenies reconstructed from molecular sequence data provide information about the evolutionary history of populations and species. By viewing the observed phylogeny as a realization of a birth–death-sampling process or a coalescent process, we develop phylodynamic methods for studying population dynamics. The main applications include in the field of epidemiology and cell biology.
Understanding patterns of genetic variation in populations and the evolutionary processes that underlie them constitutes a major research focus of the department. We develop sophisticated computational approaches for the analysis of population genomic data that allow us to make inferences about a population's history and the evolutionary forces it experienced.
Structural Proteomics & Network Biology
Multi-scale modeling of proteins, protein interactions, and macromolecule complexes using biophysical and machine learning (especially deep learning) approaches. Understanding the impact of mutations and relationships among genes within diverse biological networks to prioritize disease-associated mutations and genes.