Alexander Kramer

UC Santa Cruz

“Scalable bioinformatics approaches for enormous pathogen datasets”

My research focuses on pathogen genomics and phylogenetic inference with huge biomedical datasets. I develop methods for high-throughput evolutionary genomics targeting microbial genomes like SARS-CoV-2 and the computational challenges associated with pandemic scale data analysis. The COVID-19 pandemic has produced a vast genomic dataset that stressed or exceeded the capacities of most bioinformatics platforms, and similar datasets will soon be available for all major human pathogens. I work on methods for precise and accurate analysis of huge genomic datasets. For example, Treenome Browser (cov2tree.org) is a genome and phylogeny browser for 7+ million pathogen samples. I hope to contribute scalable, useful tools for microbial evolutionary genomics with a broader goal of assisting readiness and response to current and future health crises.

ABSTRACT

I develop novel methods and tools for studying human pathogens using huge genomic datasets. During the SARS-CoV-2 pandemic, over 14 million complete viral genomes have been collected and sequenced. Much of my work so far has focused on constructing and visualizing phylogenetic trees from this global dataset and those of other pathogens. My current research aims to devise new ways of building such trees while eliminating a major source of error in the inference process. Specifically, these methods avoid the current standard practice of comparing new samples to a single, fixed reference genome.
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