Computational methods for learning from (meta)-genomes

Alice McHardy
Max-Planck-Institut für Informatik

Next generation sequencing allows to extensively survey the genome-wide genetic diversity of microbial communities, as well as populations from all domains of life. A major challenge is the development of computational methods for the analysis of these large-scale data sets. I will present an overview of several methods for the computational analysis of metagenomic and population-level genomic data sets. In metagenomics we are working on fast methods for taxonomic assignment of metagenome sequence fragments and inference of functional and phenotypic relationships between protein families. We are furthermore seeking to understand and detect the imprint of selection from population-level genomics data.

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