BIOL 647 - Data Analysis for Disease Ecology

1 Credit(s) | Laboratory | Graded
Course can be counted for credit once

Description:
Next-generation sequencing data are becoming an integral part of disease ecology. For targeted amplicon sequencing to transcriptomics and metagenomics, these robust datasets can transform our understanding of host-pathogen interactions in complex environments. In particular, microbiomes are increasingly being studied for their symbiotic relationships with hosts, influence on host-pathogen dynamics, and their role in ecosystem processes. Analysis of these data requires the use of a suite of bioinformatics pipelines and analysis tools. In this workshop style class students will gain hands-on experience with next generation datasets. The focus will be on analysis of microbiome data, but can be applied in broader contexts to other data from next-generation sequencing from targeted amplicon sequencing to shotgun transcriptomics and metagenomics. Special topics will range from calculation and analysis of alpha and beta diversity to co-occurrence analysis, and from differential abundance analysis to complex multivariate statistics for time series data. Students will be given the opportunity to work with their own data as well as instructor-provided tutorial data and gain experience with QIIME2, R as well as other command line tools. This lab will be taught alongside the optional graduate seminar BIOL 646. Students can sign up for either course separately or both together.

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