Evolutionary Genomics @ Columbia University

We are a group of scientists with a common interest in the genetic basis of variation within and between species.
Work in our groups spans the fields of phylogenetics, epigenetics, functional genomics, population and quantitative genetics, human genetics, neurogenetics, animal behavior, experimental evolution, and comparative and evolutionary genomics.

• Who we are and what we do
• Events
• Evolution Courses at Columbia

• Back

People and interests

Peter Andolfatto

Biology

• Lab website

• Email
• Twitter
• Google Scholar

To what extent are adaptive evolutionary paths “predictable”? What factors limit the rate of adaptive evolution? We address these fundamental questions at a variety of scales, from the dissection of repeated adaptations in single proteins to the characterization of changes in gene regulatory networks underlying complex phenotypes.

Andrés Bendesky

Zuckerman Mind Brain Behavior Institute
Ecology, Evolution and Environmental Biology

• Lab website

• Email
• Twitter
• Google Scholar

The diversity of animal behavior is fascinating, yet we know little about how behavior evolves. Our research occupies a niche at the intersection of genomics and neuroscience; we leverage natural variation in behavior in multiple vertebrates to uncover fundamental principles about the evolution of behavior and function of the brain, with an emphasis on behaviors that are significant in nature and of great relevance to humans.

Hilary Callahan

Barnard Biology

• Lab website

• Email
• Twitter
• Google Scholar

The lab focuses on the ecology and evolution of plant-gene-environment interactions, often working with Arabidopsis thaliana. We are part of a multi-campus effort to phenotype a comprehensive mutant library in Arabidopsis, examining what proportion and what types of mutations alter whole-plant phenotypes. As a lab at Barnard, we prioritize undergraduate research, and integrating research with teaching.

Deren Eaton

Ecology, Evolution and Environmental Biology

• Lab website

• Email
• Twitter
• Google Scholar

Why is hybridization more common in some taxonomic groups? How does it affect rates of speciation and genome organization? And how can we properly reconstruct evolutionary history in the presence of horizontal gene transfer? We combine fieldwork and experimentation with computational genomics and comparative methods to investigate these questions in plants within an ecological and evolutionary context.

Laura Landweber

Biochemistry and Molecular Biophysics
Biology
Systems Biology

• Lab website

• Email

We study the evolutionary genomics of microbial eukaryotes with complex genome architectures. We use both functional genomic experiments that manipulate chromosome structure and comparative genomics to understand differences in chromosome organization, epigenetic marks, and DNA sequence. The model organisms we use have two nuclei, which also permits comparative genomics within a single cell: one genome gives rise to the other during programmed genome reorganization, and we study how this system arose by transposon coevolution.

Itzik Pe'er

Computer Science

• Lab website

• Email

How is it best to measure, describe and quantify differences between individual DNA sequences? How does sequence variation affect biological processes? How can we use it to understand and influence human disease? All these questions pose complex analytical challenges, with direct impact on medical research.

Molly Przeworski

Biology
Systems Biology

• Lab website

• Email
• Twitter
• Google Scholar

We are interested in the genetic and evolutionary mechanisms that give rise to heritable variation among humans and across species. Our research combines genomic data analysis, statistical modeling, and some data collection. Currently, we are primarily focused on understanding adaptation in humans and the evolution of mutation and recombination among vertebrates.

Raúl Rabadán

Systems Biology
Biomedical Informatics

• Lab website

• Email
• Google Scholar

The amount of high throughput data in biological and clinical systems—from next-generation sequencing experiments to electronic health records—is increasing dramatically, allowing for the development of a quantitative understanding of these complex systems. Our lab is an interdisciplinary team interested in developing mathematical and computational tools to extract useful biological information from large data sets.

Dustin Rubenstein

Ecology, Evolution and Environmental Biology

• Lab website

• Email
• Twitter
• Google Scholar

We take an integrative approach to understand why complex animal societies form and how organisms cope with environmental change through studies that combine behavior, ecology and evolution with those of the underlying molecular and neuroendocrine mechanisms. We work on a variety of organisms, including insects, crustaceans, and birds in both the lab and field, including in Asia, Africa, Central America, the Caribbean, and North America.

Guy Sella

Biology
Systems Biology

• Lab website

• Email
• Google Scholar

We study the evolutionary processes that give rise to genetic and phenotypic differences between individuals, populations and closely related species. To these ends, we use mathematical models to better understand these processes, and statistical analyses to identify their footprints in data and make inferences about them. Our current work focuses primarily on the evolutionary causes of adaptation and disease, but we also study a variety of other topics.

Simon Tavaré

Biology
Statistics

• Email
• Google Scholar

My lab is interested in evolutionary genomics in the cancer setting. We develop and apply methods motivated by population genetics approaches to understand aspects of tumor evolution, particularly tumor heterogeneity and its consequences for treatment. In this world, the individuals are cells; we have recently been working on methods for molecularly annotating tumors in three+ dimensions by exploiting microscopy methods such as STPT and merFISH.

Saeed Tavazoie

Biochemistry and Molecular Biophysics
Systems Biology

• Lab website

• Email

We use laboratory experimental evolution to study how cells adapt to the challenges imposed by severe changes to the external environment. Genetic, genomic, and physiological characterization of adapted cells is revealing how perturbations to signaling and regulatory networks enable cells to rapidly adapt to novel or extreme environments. We also utilize comparative sequence analysis to infer the presence and functional roles of transcriptional and post-transcriptional regulatory elements.

• Back

Upcoming Seminars

Please subscribe to our Evolutionary Biology calendar by clicking on the '+' in the calendar below.You may receive information on upcoming Evolution Supergroup meetings by signing up to our mailing list here.

• Back

Evolutionary Biology Courses

BIOL UN3560 Evolution in the Age of Genomics

This course introduces basic concepts in evolutionary biology, from speciation to natural selection. While the lectures incorporate a historical perspective, the main goal of the class is to familiarize students with topics and tools of evolutionary genetics as practiced today, in the era of genomics. Thus, the focus will be on evidence from molecular evolution and genetics and exercises will assume a basic background in genetics. Examples will be drawn from across the tree of life, but with a primary focus on humans.

EEEB GR6110 Fundamentals of Evolution

Lectures cover principal topics in evolutionary biology including genetics, genome organization, population and quantitative genetics, the history of evolutionary theory, systematics, speciation and species concepts, co-evolution, and biogeography.

EEEB UN1010 Human Origins and Evolution

This is an introductory course in human evolution. Building on a foundation of evolutionary theory, students explore primate behavioral morphology and then trace the last 65 million years of primate evolution from the earliest Paleocene forms to the fossil remains of earliest humans and human relatives. Along with Behavioral Biology of the Living Primates this serves as a core required class for the EBHS program.

G4017 Deep Sequencing

Next-generation sequencing (NGS) has become ubiquitous in biomedical research with numerous applications. This course, which has been offered for three consecutive years, will provide an in-depth introduction to principles of modern sequencing, key computational algorithms and statistical models, and applications in disease genetics, cancer, and fundamental biology. It will cover genome, exome, and transcriptome sequencing approaches. Emphasis will be placed on understanding the interplay between experimental design, data acquisition, and data analysis so that students can apply these powerful tools in their own research.

GU4055 Principles and Applications of Modern DNA Sequencing Technologies

Genome sequencing, the technology used to translate DNA into data, is now a fundamental tool in biological and biomedical research, and is expected to revolutionize many related fields and industries in coming years as the technology becomes faster, smaller, and less expensive. Learning to use and interpret genomic information, however, remains challenging for many students, as it requires synthesizing knowledge from a range of disciplines, including genetics, molecular biology, and bioinformatics. Although genomics is of broad interest to many fields—such as ecology, evolutionary biology, genetics, medicine, and computer science—students in these areas often lack sufficient background training to take a genomics course. This course bridges this gap, by teaching skills in modern genomic technologies that will allow students to innovate and effectively apply these tools in novel applications across disciplines. To achieve this, we implement an active learning approach to emphasize genomics as a data science, and use this organizing principle to structure the course around computational exercises, lab-based activities using state-of-the-art sequencing instruments, case studies, and field work. Together, this approach will introduce students to the principles of genomics by allowing them to generate, analyze, and interpret data hands-on while using the most cutting-edge genomic technologies of today in a stimulating and engaging learning experience.

W6560 Human Evolutionary Genetics

The course brings together population genetics theory, empirical studies and genetic models of disease to provide an integrated perspective on the evolutionary forces that shape human variation and in particular disease risk. Our goals are to provide you with a basic toolbox with which to approach human variation data and in parallel, to expose you to cutting-edge research and to the forefront of knowledge in human population genetics. To this end, the course includes in-depth discussions of classic papers in these fields coupled with recent findings employing new technologies and approaches.

COMS4761 Introduction to Computational Genomics

Technology for obtaining DNA sequences have been consistently improving faster than Moore's law. This has opened a wealth of computational challenges in weaving the heaps of straw of DNA sequence data into gold of biological insight. The class serves as an introduction to computational genomics, explaining the basic challenges and teaching the general computer-science tools to tackle them. This course is intended to introduce students of both computational and bio-medical skill sets to current quantitative understanding of genomics and prepare them to computational research or industrial development in the field.