Metagenomics, the study of genetic materials recovered directly from environmental samples without isolating and culturing organisms, has become one of the principal tools of “meta-omic” analysis. It can be used to explore the diversity, function, and ecology of whole microbial ecosystems. The broad field may also be referred to as environmental genomics, ecogenomics or community genomics. While traditional microbiology and and genomics rely on cultivated clonal cultures, early environmental gene sequencing cloned specific marker genes (often the 16S/18S rRNA gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of microbial diversity had been missed by cultivation-based methods. Recent studies use either shotgun (WGS) or amplicon (16S/18S) sequencing to get largely unbiased samples from all the members of the sampled communities. Shotgun metagenomics (also known as quantitative metagenomics) is more expensive but with a much higher resolution. This course will cover all the steps from sampling to data analysis.
|R: this group will make extensive use of the R programming language. If you are not familiar with R, you are required to follow this free DataCamp class: https://www.datacamp.com/courses/free-introduction-to-r.|
- Definition of Metagenomics
- Technologies: 16S rRNA motivation and WGS motivation
- NGS data preprocessing (QC, assembly, alignment, abundance counting)
- Whole community analysis
- Assembly, Binning, Analytical Pipelines.
- Exploratory data analysis, Clustering and Multiple testing
- Differential abundance testing, sequencing depth, rarefaction curves
- Longitudinal analyses
- Annotation and Functional analysis
- Analyzing a metagenome and assessing metagenomics sequence quality
- Comparing metagenomes: Big data analytics for metagenomics