Professor Christophe Béroud (PharmD, PhD) has 27 years' experience in research laboratories and is an expert in bioinformatics and molecular biology. He is the creator of the UMD system as well as multiple databases and software, including Human Splicing Finder (HSF) and UMD-Predictor, to process and analyse NGS data. He has published 130 international papers and has 5 patents. He is/was involved in multiple national and international committees and is/was the INSERM scientific team leader of various EU projects including RD-Connect.

He is full professor of Human Genetics in the Molecular Genetics Laboratory of the Aix Marseille University (AMU) and the APHM Hospital, head of the "Genetics and Bioinformatics" research team (INSERM UMR_S910), expert for the French Ministry of Research and Health and the European Commission, member of the Scientific committee of the Human Genome Variation Society (HGVS), member of the CSS2 at INSERM, communicating editor of the Human Mutation journal and member of the "Big data" committee at AMU.

He has contributed to oncogenetics with the development of international reference databases for TP53, BRCA1/2 and MMR genes; prenatal diagnosis of rare diseases with the development of the ISET technology to isolate and study circulating foetal cells; identification of disease-causing mutations in the NGS context with the creation of the most efficient in silico pathogenicity prediction systems; and to the diagnosis of rare disease (national expert centre for the diagnosis of congenital muscular dystrophies and dystonia).

After graduating from Harvard summa cum laude with a A.B. in physics in 1989, Prof. Mark Gerstein earned a doctorate in theoretical chemistry and biophysics from Cambridge University in 1993. He did postdoctoral research in bioinformatics at Stanford University from 1993 to 1996. He came to Yale in 1997 as an assistant professor in the Department of Molecular Biophysics and Biochemistry, and since 1999, in the Computer Science Department. He was named an associate professor in 2001, and the following year became co-director of the Yale Computational Biology and Bioinformatics Program. Gerstein has published appreciably in the scientific literature, with >400 publications in total, including a number of them in prominent venues, such as Science, Nature, and Scientific American. His research is focused on bioinformatics, and he is particularly interested in data science & data mining, macromolecular geometry & simulation, and human genome annotation & cancer genomics.

Associate Professor
The William R. Brody Faculty Scholar
Institute for Computational Medicine

Education

Ph.D., Computer Science, University of California, Santa Cruz, 2003
M.S., Computer Science, University of California, Santa Cruz, 2000
B.S., Computer Engineering, University of California, Santa Cruz, 1998

Research Interests

Genetic variation is critical to our susceptibility to diseases and response to medications. Yet the functional consequences of most genetic variants are unknown. We are working to predict these consequences using computation, by integrating information from molecular modeling and sequence analysis with clinical patient data and in vitro functional studies, through collaborations with physicians, genetic counselors, and experimental biologists. We are particularly interested in inherited cancer susceptibilities and gain of function mutations in tumor genomes.

Rachel is also the Diversity Champion representing BME.

Dr. Sieh’s research focuses on identifying genetic and epidemiologic factors underlying cancer susceptibility and outcomes.  Current interests include: 
1) Genetic and environmental determinants of mammographic breast density; relationship of breast density and other image features to breast cancer risk.
2) Genomic risk prediction using machine learning methods.
3) Risk and prognosis of hormone-related cancers, including cancers of the breast, ovary and prostate.

Education

BA Biology, Harvard University

MD Medicine, McGill University

MS Biostatistics, University of Washington

PhD Epidemiology, University of Washington

Postdoc Statistical Genetics, University of Washington

Research Description

Research in my lab concentrates on two areas of genetic susceptibility to cancer. The first is identification and characterization of intermediate-risk and high-risk cancer susceptibility genes. The second is analysis of unclassified variants that are observed during the clinical testing of established high-risk cancer susceptibility genes.

Historically, most of the known high-risk cancer susceptibility genes were found either by linkage analysis/ positional cloning or by mutation screening of established high-risk susceptibility genes' biochemical pathway "nearest-neighbors". While the linkage analysis/ positional cloning approach is nearly obsolete, next-generation sequencing enables a number of new strategies for gene identification. On of these is whole-exome mutation screening in pedigrees as a method to identify relatively high-risk susceptibility genes. Another is biochemical pathway-based mutation screening in a case-control format as a method to identify intermediate-risk susceptibility genes. We are pursuing breast cancer genetics projects in both of these areas and are likely to expand to prostate cancer or colon cancer projects in the near future.

Clinical mutation screening of high-risk cancer susceptibility genes such as BRCA1, BRCA2, MLH1, and MSH2 will often find clearly pathogenic mutations, providing very useful information for the clinical management of high-risk patients and their close relatives. However, about 10% of patients who undergo mutation screening are found to carry an unclassified sequence variant (UV). Observations of UVs are problematic for clinical mutation screening services, for clinical cancer genetics, and for the patients. We have developed a bioinformatics method, called the "integrated evaluation", for analysis and eventual classification of UVs. Currently, the method is applicable to UVs in the breast cancer susceptibility genes BRCA1 and BRCA2. We are working to improve the method, to extend it to other susceptibility genes, and to create databases that will disseminate classification results to clinical cancer geneticists throughout the world.

Research Keywords:

Cancer genetics, bioinformatics, next-generation sequencing, mutation screening, unclassified variants