Microbiome studies hold tremendous potential along with substantial computational challenges. I will present two computational approaches for microbiome data analysis. First, I will present SCRuB, a new method for in silico removal of contamination from microbiome data. We show that modeling the taxonomic composition of contamination sources, rather than trying to infer whether specific taxa are categorically contaminant, allows for more robust decontamination and improves downstream phenotypic prediction. Second, I will present copangraph, a new graph-based approach for representing genomic variability within and across microbiomes. We demonstrate a hybrid co-assembly approach that yields high-quality representation of the microbiome.
Tal Korem’s research program focuses on the development of computational methods that identify and interpret host-microbiome interactions in various clinical setting. The ultimate goal of his research is to translate microbiome findings to clinical care, with microbiome-based therapeutics and microbiome-informed clinical practices. He has developed several approaches for microbiome data analysis, inferring microbial growth rates, structural variants, and microbiome-metabolite interactions; and has applied these methods in diverse clinical and biological investigations, most notably for personalization of dietary treatment and predicting preterm birth. He is a member of Columbia’s Program for Mathematical Genomics (PMG), an Assistant Professor in the Departments of Systems Biology and Obstetrics & Gynecology, and was previously a CIFAR-Azrieli global scholar by the Canadian Institute for Advanced Research.
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