Presenter: Irene Kaplow, PhD<\/p>\n
Carnegie Mellon University<\/p>\n
Talk Title: Relating enhancer genetic variation across mammals to complex phenotypes using machine learning<\/p>\n
Lecture abstract: Advances in the genome sequencing have provided a comprehensive view of cross-species conservation across small segments of nucleotides.\u00a0 These conservation measures have proven invaluable for associating phenotypic variation, both within and across species, to variation in genotype at protein-coding genes or highly conserved enhancers.\u00a0 However, these approaches cannot be applied to the vast majority of enhancers, where the conservation levels of individual nucleotides are often low even when enhancer function is conserved and where activity is tissue- or cell type-specific.\u00a0 To overcome these limitations, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT), in which convolutional neural network models learn the regulatory code connecting genome sequence to open chromatin in a tissue of interest, allowing us to accurately predict cases where differences in genotype are associated with differences in open chromatin in that tissue at candidate enhancer regions.\u00a0 We established a new set of evaluation criteria for machine learning models developed for this task and used these criteria to compare our models to models trained using different negative sets and to conservation scores.\u00a0 We then developed a framework for connecting these predictions to phenotypes in a way that accounts for the phylogenetic tree.\u00a0 When applying our framework to motor cortex, we identified dozens of new candidate enhancers associated with the evolution of brain size and vocal learning.<\/p>\n
Bio.: Irene Kaplow received her B.S. in Mathematics with a minor in Biology from the Massachusetts Institute of Technology in 2010. There, she began her career as a computational biologist while doing research with Bonnie Berger. She then went to graduate school at Stanford University, where she received her Ph.D. in Computer Science in 2017. At Stanford, she worked in the Hunter Fraser and Anshul Kundaje’s labs to develop methods to analyze novel high-throughput sequencing datasets to better understand the roles of DNA methylation and Cys2-His2 zinc finger transcription factor binding in gene expression regulation. Irene is now a Lane Postdoctoral Fellow in Andreas Pfenning’s lab in the Computational Biology Department at Carnegie Mellon University, where she is developing methods to identify enhancers involved in the evolution of neurological characteristics that have evolved through gene expression.<\/p>\n","protected":false},"excerpt":{"rendered":"