Computational Biology, Bioinformatics, and Genomics (CBBG) 

Image from Dr. Najib El-Sayed's lab. CBBG Faculty Current CBBG Students

Image from Dr. Najib El-Sayed's lab.

CBBG Faculty
Current CBBG Students

Students from diverse backgrounds, including biological sciences and chemistry, computer science, mathematics, physics, statistics, and related areas, are encouraged to apply.

The massive and ever-increasing influx of diverse biological data, spurred by unprecedented advances in sequencing technologies, has resulted in a paradigm shift in the approaches to biomedical research. The need to integrate and analyze complex data has rendered the field of Computational Biology, Bioinformatics and Genomics critical to biomedical research. Becoming a proficient biomedical researcher thus requires an integrative training in multiple fields spanning life science, computer science, mathematics, statistics, and engineering.  The CBBG concentration area aims to foster the next generation of scientists via multi-disciplinary training and research opportunities in different  aspects of computational biology and genomics. Interests and expertise of CBBG faculty cover a broad range of topics including Next generation sequencing data analysis, Genome assembly, Metagenomics, Molecular evolution, Functional genomics, Proteomics, Transcriptional and post-transcriptional regulation, Epigenomics, protein structure, determination and analysis of protein-protein interaction networks, population genomics, and systems biology.

Assistant Professor of Computer Science Héctor Corrada Bravo uses high-throughput genomics and other methods to better understand molecular changes that can lead to cancer and other diseases. Corrada Bravo recently co-authored a paper that describes a new way to classify tumor samples using computational biology. In this video, Corrada Bravo talks about his research within the Center for Bioinformatics and Computational Biology (CBCB) at the University of Maryland.

Sridhar Hannenhalli, professor of cell biology and molecular genetics, uses computational biology to understand how specific genes turn on and off, and how those changes could lead to cancer.