Microbiome Data Analysis: Methods, Impac

This course aims to provide a comprehensive understanding of computational approaches in the microbiome field. Through the discussion of state-of-the-art methods and algorithms, we will review key methodological challenges in microbiome data analysis, such as taxonomic inference, compositional data analysis, reference-free metagenomic reconstructions, and applications of machine learning. To understand the role of these challenges and methods in the wider context of biological research in the field, we will discuss major high-impact controversies among researchers as well as impactful areas of clinical and biological investigations. The course will comprise of lectures, short weekly assignments, a midterm, and a final presentation. Each week will comprise a lecture on computational methods and another on clinical impact and controversies. By the end of the course, students will have a deep understanding of the current state of microbiome research and potential future directions, and will be able to dissect and analyze different computational approaches for their advantages and disadvantages in the context of progress in the field.

This course is suitable for: biology-oriented students who wish to obtain a better understanding of computational challenges in the field; CS/engineering students who wish to get exposure to applications of computational methods in biology; microbiome enthusiasts.

Prerequisites: A prior introductory class in mathematics or statistics.