Welcome to the Computational Comparative Genomics Lab led by Jian Ma in the Department of Bioengineering at the University of Illinois at Urbana-Champaign.

The success of the Human Genome Project has ushered in a new era in which biology has become an information science. More recently, the rapid advancement in ultra high-throughput DNA sequencing technologies underlines the urgent need for more innovative computational research in modern biology, for manipulating, analyzing and understanding massive amount of data to develop and evaluate biological hypotheses.

Our general research interest is Computational Biology. Our interdisciplinary work combines approaches from computer science and engineering, statistics, biology, and medical sciences to understand how the phenotype is generated from the genotype and to shed new light on disease mechanisms, such as cancer. We develop novel computational methods to explore the human genome, integrating comparative genomics data to elucidate cross-species differences and within-species variation and their associations with disease. We are also working on cancer genomics, studying cancer genomes using high-throughput next-generation sequencing technologies.

What's New

12/19/2011. Yang's TrueSight paper has been accepted by RECOMB 2012.
12/01/2011. Jian is named one of "Tomorrow's PIs" by Genome Technology magazine [cover].
05/13/2011. We received Arnold O. Beckman Award from the University to study genome aberration in cancer.
03/12/2011. Peter's work has been selected for oral presentation at the 2011 Undergrad Research Symposium.
12/06/2010. Jaebum's PSAR paper has been accepted by RECOMB 2011.
11/16/2010. We will receive NSF CAREER Award to study large-scale genomic changes in mammalian genomes.
08/16/2009. Our lab started officially.

Open Position

We are looking for graduate and undergraduate students who have interest in computational biology to join our group. Students with the following two types of background are particularly welcome: 1) Students with strong computational or quantitative background. Students are expected to solve challenging computational problems raised in practice and to design algorithms and tools to facilitate biological and biomedical discoveries. 2) Students with solid background in biology and an enormous enthusiasm to use computational methods to answer exciting questions.