Sample of our projects:
A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations
Using protein domains as a framework to compare variant information, this study demonstrates the utility of a multi-species analysis in understanding the relationship between genetic mutations and phenotypic changes.
Peterson, T.A., D. Park, and M.G. Kann, A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations. BMC Genomics, 2013. 14 Suppl 3: p. S5.
Domain Significance Score (DS-Score)
The Domain Significance Score (DS-Score) is a statistical method developed to assess the significance of disease mutation clusters on protein domain positions and incorporates domain functional annotation to assist in the functional characterization of novel variants. By aggregating variants with known disease association at the domain level, the method was able to identify positions within protein domains that are enriched with multiple occurances of deleterious variants.
Peterson, T.A., et al., Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer. J Am Med Inform Assoc, 2012. 19(2): p. 275-83.
Domain Landscapes of Somatic Cancer Mutations
The Domain Landscapes project identifies somatic mutations with high potential to drive cancer development by utilizing a method that encapsulates the inherent modularity of proteins. In this work, somatic mutations are aggregated from all genes containing a specific protein domain, enabling the identification of variants that, while rare the gene level, occur frequently within protein domains. Mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.
Nehrt, N.L., et al., Domain landscapes of somatic mutations in cancer. BMC Genomics, 2012. 13 Suppl 4: p. S9.
Domain Mappings of Disease Mutations: http://bioinf.umbc.edu/dmdm
Domain Mapping of Disease Mutations (DMDM) is a database in which each disease mutation can be displayed by its gene, protein, or domain location. DMDM provides a unique domain-level view where all human coding mutations are mapped on the protein domain.
Thomas A. Peterson, Asa Adadey, Ivette Santana-Cruz, Yanan Sun, Andrew Winder, and Maricel G. Kann. DMDM: Domain Mapping of Disease Mutations. Bioinformatics, 26 (19), 2458-2459.
E. Doughty, A. Kertesz-Farkas, O. Bodenreider, G. Thompson, A. Adadey, T. Peterson, and M. G. Kann. (2010) Toward an automatic method for extracting cancer and other disease-related point mutations from biomedical literature 27 408-415.
See Emily’s interview featured at the UMBC undergraduate researchers site:
Understanding relationships among proteins is crucial to understand the molecular machinery of the cell. Computational tools to predict domain-domain interactions provide a detailed molecular view of the protein interactions and complements expensive and laborious experimental techniques to identify such interactions. The evolutionary distances of interacting proteins often display a higher level of similarity than those of non-interacting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitute the basis of a method to predict protein interactions known as mirrortree. In a recent publication, we showed that binding neighborhoods of interacting proteins have, on average, higher co-evolutionary signal compared to the regions outside binding sites; although when the binding neighborhood was removed, the remaining domain sequence still contained some co-evolutionary signal. We have several projects focusing on the investigation of the role of compensatory mutations in protein co-evolution and which are shading light on the process of co-evolution of interacting proteins.
Kann, MG. Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Briefings in bioinformatics 11, 96-110 (2010).
Kann, MG, Shoemaker, BA, Panchenko, AR & Przytycka, TM. Correlated evolution of interacting proteins: looking behind the mirrortree. Journal of molecular biology 385, 91-98 (2009).
Kann, MG, Jothi, R, Cherukuri, PF & Przytycka, TM. Predicting protein domain interactions from coevolution of conserved regions. Proteins 67, 811-820 (2007).
Coming up: PINTT (Protein INteraction Text-mining Tool)
The goal of the PINTT project is to develop computational tools to extract protein interactions from literature. PINTT will be modeled after EMU: teams of undergraduate students from Biology, Computer Sciences and Bioinformatics will work together developing the software and methods to perform the task and creating manually curated databases to use as gold standard to benchmark the methods.PINTT is a collaboration with Dr. Graciela Gonzalez from Arizona State University (http://bmi.asu.edu/directory/869038). If you would like to join the PINTT project, please contact email@example.com
Bioinformatics PhD Student
Bioinformatics PhD Student
Biology and Computer Science Major, Bioinformatics Minor
Bachelor of Science in Biology and Computer Science, minor in Bioinformatics, UMBC. Currently a graduate student at MIT.
Bachelor of Science in Biology and Computer Science, minor in Bioinformatics, UMBC. Currently a graduate student at Stanford.
B.S. in Bioinformatics
Bachelor of Science in Bioinformatics and Computational Biology, UMBC, cum laude, Fall 2007. Bachelor of Arts in Modern Languages and Linguistics, UMBC, cum laude, Fall 2007. Graduate of Bioinformatics in the Dept. of Biological Sciences, College of Natural and Mathematics, UMBC, 2007. MARC U* STAR scholar, UMBC, Fall 2005 – Spring 2007. Meyerhoff Scholar, UMBC, Fall 2005 – December 2007. Currently at the University of Maryland Institute of Genome Sciences.
Dr. Mileidy Gonzalez currently holds a position at the National Institutes of Health.
B.S. in Bioinformatics
Graduated from the bioinformatics department. Minor in statistics and computer science. Currently a Ph.D student in Duke University.
B.S. in Computer Sciences
Graduated from the Computer Science Department. Andrew enjoys football, baseball, and the beach. Currently works for USA Football as a software engineer.
MS Information Systems
Biology and Bioinformatic Major
Double Major: Biology and Bioinformatics. Outstanding Bioinformatics Student Award