The University of Arizona

MetaGen: Reference-Free Learning with Multiple Metagenomic Samples

MetaGen: Reference-Free Learning with Multiple Metagenomic Samples

Series: Statistics GIDP Colloquium
Location: ENR2 S395
Presenter: Wenxuan Zhong, Georgia State University

Abstract:  A major goal of metagenomics is to identify and study the entire collection of microbial species in a set of targeted samples. In this talk, I will present a novel statistical metagenomic algorithm that simultaneously identifies microbial species and estimates their abundances without using reference genomes. Compared to reference-free methods based primarily on k-mer distributions or coverage information, the proposed approach achieves a higher species binning accuracy and is particularly powerful when sequencing coverage is low. I will demonstrate the performance of this new method through both simulation and real metagenomic studies. The MetaGen software is available at is external).

(Refreshments will be served.)

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