Nathan Schaefer

Background

About Nathan Schaefer

Bioinformatician with expertise in single cell multi-omic analysis, statistics, tool development, population genomics, ancient DNA, and genome assembly, and an interest in human-specific features of brain development, and how their perturbation can lead to disease. In my current role, I investigate the functional consequences of human-specific genetic mutations: how they alter gene regulation and influence human-specific features of brain development. I also draw on my background in population genomics to explore when they arose and how they were shaped by natural selection. I have a strong interest in quality control and methods development. To help enable pooled culture and/or pooled processing in single-cell experiments, which improves experimental scalability and mitigates batch effects, I wrote a set of computational tools (CellBouncer) for demultiplexing, QC, and analysis. As a graduate student, I developed a fast, heuristic ancestral recombination graph inference algorithm to enable new types of population genomic analyses and to highlight genomic regions targeted by recent human-specific evolution. I am interested in using neural network-based tools to predict the functional impact of mutations, both coding and regulatory, extinct and extant. I believe that integrating the wealth of single cell functional genomics data with information about allele frequency history and functional constraint will provide new insights into human-specific biology as well as disease vulnerabilities.