Columbia University: Microbiome Data Analytics Boot Camp: Planning, generating, and analyzing 16S rRNA gene sequencing surveys

Date/Time: Jun 12, 2025, 09:00 AM to Jun 13, 2025, 04:00 PM

Location: Livestream, virtual training

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Amplicon sequencing of taxonomic marker genes such as the 16S rRNA gene in bacteria has been used over the last two decades to survey the microbiota of myriad environments. From soil to aquatic to human systems, 16S rRNA amplicon sequences are widely used to characterize the diversity and composition of the gut microbiome, discover novel microbes, and define how specific microbes link to environmental or host traits of interest. As a result, 16S rRNA amplicon surveys have proven invaluable in efforts designed to uncover the potential contribution of microbiomes to critical ecological and health processes.

This two-day intensive workshop will provide a rigorous introduction to the theory and methodology underlying the design, generation, and analysis of Amplicon Sequence Variant (ASV) based investigations of microbial communities. The workshop will introduce state-of-the-art techniques using the R language and environment. A team of leading experts in microbiome data analytics and statistics will offer a hands-on experience in learning how to implement these techniques by integrating publicly available data and R packages to explore and understand some of the pitfalls and information drawn from 16S rRNA data analysis. This workshop specifically trains participants in the use of the R programming environment for the analysis of microbiome sequence data, including the implementation of the DADA2 and phyloseq software packages. It will also introduce introductory concepts and methods in the generation and analysis of shotgun metagenomic data.

By the end of the workshop, participants will be familiar with the following topics:

The theoretical basis underpinning 16S rRNA investigations

Methodologies for generating 16S rRNA sequence data

16S sequence data quality control

Amplicon sequence variant inference

Taxonomic annotation

Biodiversity estimation

Principal Component Analysis and PERMANOVA

Taxon-covariate correlation and regression modeling

Introduction to machine learning in microbiome data

Phylogenetic analysis

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