Campo Grande, September 1, 2014
Follow us on Twitter Subscribe via  RSS Feed

Maricel Kann

University of Maryland/Baltimore, USA

 


 

Dr. Maricel Kann is an Assistant Professor at the University of Maryland,Baltimore County. She received a B. Sc. degree in Chemistry and a graduate degree in Pharmaceutical Chemistry from the Universidad de la Republica in Montevideo (Uruguay), where she was a research assistant in the Quantum Chemistry Department. In 2001, she obtained a doctoral degree from the University of Michigan in Chemistry. Her thesis work under the guidance of Dr. Richard A. Goldstein focused on the theory, statistics and methods for protein sequence alignment. After completing her Ph.D., Dr. Kann joined the Structure group at the National Center for Biotechnology Information (NIH) as a postdoctoral fellow. In August 2007, she joined the Department of Biological Sciences at UMBC as an Assistant Professor. Dr. Kann’s research focuses on computational approaches to annotate the human genome with the goal of revealing the molecular underpinning of human diseases. One of the crucial steps after sequencing the genome is to classify and assign function to gene-encoded proteins.  Dr. Kann’s work addresses these challenges studying new computational methodologies to align, classify and predict interactions of proteins as well as to identify the role of certain mutations in the disease mechanisms. Dr. Kann is one of the leading experts in the area of translational Bioinformatics, she is the editor of the textbook "Translational Bioinformatics and has chaired several international conference sessions at the Pacific Symposium on Biocomputing (PSB), the Intelligent Systems and Molecular Biology (ISMB) and the American Medical Informatics Association (AMIA) Summit in Bioinformatics. She is a member of AMIA, the American Association for the Advancement of Science and the International Society of Computational Biology. Dr. Kann is part of the editorial boards of the Journal of Biomedical Informatics and the International Journal of Computational Models and Algorithms in Medicine and she is an advisory board member of the PubMedCentral National Committee.

Title

Protein Domain-Centric Approach to Study Cancer Somatic Mutations from High-throughput sequencing studies

Abstract

The fight against cancer has been hindered by its highly heterogeneous nature. Recent genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare cancer somatic mutations present only in a small fraction of lesions. For instance, the genome of a colorectal cancer in one patient can have somewhere between 50 to 100 somatic mutations, but might share only 2 or 3 mutated genes with colorectal tumor genomes from other patients. Somatic mutations that are frequently found in tumor genomes often play a significant role in tumor development and are thus classified as cancer driver mutations. However, efforts to correlate somatic mutations found in one or few individual tumor genomes with critical functional roles in tumor development have so far been unsuccessful. In this paper, we analyze cancer somatic mutations from lung cancer patients using a new approach based on aggregation of mutational data at the protein domain level. Our preliminary analysis confirms that our approach creates a framework for leveraging structural genomics and evolution into the analysis of somatic cancer mutations. We found that by incorporating information about classification of proteins and protein sites we are able to detect novel clusters of lung cancer somatic mutations. 

Organization

  • Faculdade de Computação - UFMS
  • UFMS
  • Embrapa Gado de Corte
  • Fundect
  • Museu das Culturas Dom Bosco

Promotion

  • SBC