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Abstract
The recent increase in accessibility of sequencing has facilitated a rise in precision medicine efforts focused on the interpretation of the effects of individual-specific genome variation. Evaluation of variants in terms of their functional contributions to molecular mechanisms holds promise for both a better understanding of the said mechanisms, as well as of drivers of disease and drug discovery/optimization. Many computational tools have been developed to evaluate the functional effects of non-synonymous variants, but most are not informative of the type of effect they annotate. Synonymous variants have often been dismissed altogether as “silent”, although they can affect biological functions via multiple mechanisms. Finally, the interpretation of the effects of the entire collection of genome variants is sorely lacking. We developed machine learning-based tools for the analysis of synonymous and non-synonymous variants. Importantly, we find that molecular effects of variation are often underestimated. We also propose a means for interpretation of the whole exome collection of variant effects in light of disease to reveal disease-associated pathways. As a technological advance, we further argue that this model can be useful in the validation of newly-developed computational variant assessment techniques.
Speaker Bio
Dr. Yana Bromberg is an an associate professor at the Department of Biochemistry and Microbiology, Rutgers University. She also holds an adjunct position at the Department of Genetics at Rutgers University and is a fellow of the Institute for Advanced Study at the Technical University of Munich, Germany. Dr. Bromberg is the vice-president of the Board of Directors of the International Society for Computational Biology and actively participates in the organization of the ISMB/ECCB conferences. Dr. Bromberg received her Bachelor degrees in Biology and Computer Sciences from the State University of New York at Stony Brook and a Ph.D. in Biomedical Informatics from Columbia University, New York. She is known for her seminal work on a machine learning-based method for screening for effects of genetic variation (SNAP). This work has led to Dr. Bromberg’s current interests in the analyses of human genomes and associated microbial metagenomes for disease predisposition. Broadly, research in the Bromberg lab is focused on the molecular functional annotation of genes, genomes, and metagenomes in the context of specific environments and diseases. The lab also studies evolution of life’s electron transfer reactions in Earth’s history and as potentially applicable to other planets. The newest lab contribution to science is Ava,Dx, a machine learning-based method for analyzing Crohn’s Disease genomic determinants. The lab’s microbiome analysis tool, mi-faser, is also recent and quickly becoming popular with over 19,000 hits since its 2017 publication. Dr. Bromberg's work has been recognized by several awards, including the NSF CAREER award, the Rutgers Board of Trustees Research fellowship for Scholarly Excellence, the PhRMA foundation young investigator research starter award, and the Hans-Fischer award for outstanding early career scientists. This work has been funded by various agencies including the NSF, NIH, NASA, and a number of private foundations. Dr. Bromberg is frequently invited to talk about her research in conferences all over the world and has, to-date, co-authored over 70 peer reviewed scientific articles.