Abstract SY36-01: Decoding the biochemical, regulatory, and clinical impact of genomic mutations

Abstract

A key challenge in precision medicine lies in decoding the complexity of the functional effects of genome variation to determine which mutations are functional and what are their biochemical and phenotypic consequences. This question is especially difficult for the 98% of the genome that is outside of exomes. To address this challenge we developed deep learning-based methods, DeepSEA and SeqWeaver, that predict the transcriptional and post-transcriptional effects of noncoding variants with single-nucleotide sensitivity. These methods not only classify mutations as likely to be associated with disease, they also provide specific biochemical consequences of each mutation, including on transcription factor and RBP binding, histone modification, and DNA accessibility. Building upon these findings, we created a deep learning-based framework, ExPecto, that can accurately predict, ab initio from DNA sequence alone, the tissue-specific transcriptional effects of mutations. These methods thus provide a platform for predicting and characterizing specific mutational impact for any genomic mutation, including those that are rare or that have not been observed. Applying these methods (available at hb.flatironinstitute.org) to whole genomes in a number of diseases, including cancer, autism, and heart disease, we demonstrate the contribution of de novo noncoding mutations and analyze the functional landscape of noncoding cancer-associated mutations. Citation Format: Olga Troyanskaya. Decoding the biochemical, regulatory, and clinical impact of genomic mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY36-01.