Microbiome Data Analytics
Master microbiome analysis in a two-day boot camp: learn planning, generating, and analyzing 16S rRNA gene sequencing datasets through seminars and hands-on sessions.
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Course Description
The Microbiome Data Analytics Boot Camp is a two-day intensive training of seminars and hands-on analytical sessions to provide an overview of 16S rRNA gene sequencing surveys including planning, generating and analyzing sequencing datasets.
- Learn the theoretical foundations of 16S rRNA investigations and methodologies for generating sequence data.
- Master quality control techniques and infer amplicon sequence variants for taxonomic annotation.
- Gain expertise in biodiversity estimation, Principal Component Analysis, and taxon-covariate correlation modeling.
- Acquire introductory skills in machine learning and phylogenetic analysis for microbiome data interpretation.
Course Prerequisites
Each participant must have an introductory background in statistics, must be familiar with R, and must have a computer with either Chrome or Firefox installed (latest update).
What You Will Learn
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
Instructors
Dr. David’s laboratory studies gut-brain interactions, and aim to unravel how the gut microbiota can impact our behavior, specifically in Autism Spectrum Disorder and Anxiety Disorders. Her team uses a crowd-sourced approach to collect lifestyle information, dietary habits, and microbiome samples. Her laboratory also works on identifying bottlenecks in microbiome data exploration and has been developing new biocomputing methods to improve sequencing data annotation and analysis. Her interest lies in using machine learning algorithms to extract meaningful information from massive datasets already publicly available such as the Human Microbiome Project.
Dr. Sharpton applies systems biology tools to understand how nutritional and chemical exposure impacts the structure and function of the vertebrate gut microbiome, and how these impacts affect human health. He has published over 50 manuscripts pertaining to the bioinformatic analysis of microbial genomic and metagenomic data and directs the Oregon State University Microbiome Initiative. His current research includes defining how the microbiome modulates environmental exposure over lifespan, mining the gut microbiome for novel pharmaceuticals, and developing analytical resources to define how the microbiome links to health and disease.