filmov
tv
Eighth IBSE Colloquium

Показать описание
Are DNA variants bugs or references? - Talk by Dr. Yana Bromberg, Dept. of Biochemistry and Microbiology, Rutgers, USA
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.
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.