cinnetcrash/qiime2-session

Material for Trainning session with QIIME2 for Microbial diversity analysis using 16S rRNA gene

title	author	date	output
Microbial diversity analysis using 16S rRNA gene- Trainning session with QIIME2	Belén Carbonetto	March 26, 2018	html_document

Session overview

This session will introduce state-of-the art analysis of 16s rRNA amplicons to study microbial diversity. Participants will learn how to use QIIME2 analysis package to asses microbial community diversity and composition.

Target audience

PhD students and post-doctoral researchers who are planning to use Illumina based 16S rRNA gene amplicon sequencing to study microbial diversity.

Learning objectives

• Basic concepts of Microbial diversity analysis.

• Do quality filtering, denoising and picking of features and representative sequences with DADA2

• Assign taxonomy to features with trained classifiers.

• Align sequences and infer phylogeny.

• Calculate alpha and beta diversity.

• Test for differential abundances between groups of samples using ANCOM.

Learning outcomes

LO1- Understand core concepts in diversity analysis

LO1.1- Explain what alpha diversity is. What are the components of alpha diversity?

LO1.2- How can you measure beta diversity? What are the differences between distances?

LO1.3- List advantages of 16S rRNA subunit gene as marker for diversity analysis.

LO2- List steps in 16S rRNA amplicon based microbial diveristy analysis

LO3- Prepare a mapping file for QIIME2

LO4- Import data into QIIME2

LO4.1- List the options for importing data

LO4.2- Explore quality report and plot and decide on the quality filters to apply next

LO5- Perform quality control and cluster reads into features

LO5.1- Perform quality filtering by phred score and explore results

LO5.2- Name the main differences between subOTUs and OTUs

LO5.3- Create summaries

LO6- Perform alignment and a phylogenetic tree

LO6.1- Perform a multiple sequence alignment of the sequences

LO6.2- Mask the alignment

LO6.3- Create a phylogenetic tree

LO7- Perform diveristy analysis

LO7.1- List the options of alpha and beta diversity metrics that can be measured

LO7.2- Decide on the subsampling depth to use for diversity calculations. How many samples are excluded?

LO7.3- Run diverstiy analysis and explore results.

     7.3.1 - Can you observe any community structure defined by metadata categories? Is this grouping statistically   significant? Can you observe the same results for all calculated distances?
     7.3.2 - Which categories in metadata are most strongly associated with the differences in microbial community richness? Are these differences statistically significant? Can you observe the same results for all diveristy metrics?

LO8- Perform taxonomic classification and analysis

LO8.1- Describe the classification method available. What is needed to train the classifiers?

LO8.2- Perform differential abundance analysis using ANCOM

     8.2.1 - Which features differ in abundance across the factor selected for the analysis?