SISG INTEGRATIVE GENOMICS MODULE IG1

This new module starts the integrative genomics stream by providing participants with an overview of the statistical genetic methods used to analyze both the microbiome and the metabolome. We treat them together because of the important interactions between the domains, with microbial metabolism thought to be a driver of inflammatory disease, and conversely metabolic variation impacting microbial diversity. While human biology will form the background for much of the treatment, the concepts covered apply to analyses where host species interact with microbes.

The course is designed to introduce participants to fundamental concepts and practical tools for analyzing microbiome sequencing data and metabolome data. This course combines theoretical foundations with hands-on practice, allowing participants to gain confidence in processing and interpreting datasets.

Learning Objectives: After attending this module, participants will be able to: 

1. Understand core concepts in microbiome science including microbial community composition, study design and sample collection, and the relationship between microbiota and their hosts, building a strong foundation for downstream analytical approaches
2. Master essential bioinformatics workflows for microbiome data processing, including quality control of raw sequencing reads, denoising methods, taxonomic classification, and the creation of ASV/OTU tables using modern tools like dada2
3. Develop proficiency in analyzing microbial community composition through alpha and beta diversity metrics, and identifying differentially abundant taxa between experimental conditions using statistical methods appropriate for compositional data
4. Learn advanced approaches for integrating microbiome data with host genetic data and other omics data types
5. Learn about various experimental approaches in the field of metabolomics from sample preparation methods to metabolomic data generation with an emphasis on best practices for designing metabolomics studies.
6. Learn the principles of metabolomic data annotation, normalization techniques, unsupervised and supervised high-dimensional metabolomic data analyses and pathway enrichment analysis with tools such as MetaboAnalyst to uncover patterns and relationships in metabolomics datasets.
7. Develop an understanding of the approaches to integrate metabolomics data with other omics data.

Ran Blekhman is an Associate Professor in the Section of Genetic Medicine at the University of Chicago. He studies how host genomic factors control and interact with the microbiome, employing computational, statistical, network-theory, data mining, and population genetic analytical approaches. Ran’s work is equally motivated by biomedical concerns, and understanding the evolution of host-microbe interactions. Access his publications here.

Youssef Idaghdour is also an Associate Professor of Biology, at the Abu Dhabi campus of New York University. He is interested in how genotype-environment interactions shape health and disease, particularly with respect to malaria susceptibility in sub-Saharan Africa. A native of Morocco, Youssef also studies genetics across North Africa, often using metabolomic methods. He has hosted two editions of the Winter Institute in Statistical Genetics in Abu Dhabi. Learn more about Youssef’s work here.