Amplicon Analysis Workshop 2019
Monday 23rd September – Friday 27 September, 2019
CSIRO Hobart, Castray Esplanade, Battery Point, Tasmania
Overview
The use of amplicons generated by high throughput sequencing methods is now common place, and many avenues exist to learn specific workflows using explicit platforms. There are, however, fewer opportunities to learn the concepts behind amplicon sequence analysis in a platform agnostic way. We are offering an introductory course to amplicon sequence analysis. The course is aimed primarily at newcomers to sequence (amplicon) analysis and will cover the basics from experimental design to basic multivariate analysis of ecological data.
The course will comprise short lectures and hands on exercises. Course notes will be made available. There are no pre-requisites. Students will be expected to install a course virtual machine (supplied prior) to their computers before the beginning of the course.
Instructors:
- Andrew Bisset
- Jeff Powell
- Simon Jarman
Contact Andrew Bissett for registration and more information.
Course Outline:
| Monday | Compute set up/introductions |
| Introduction to jupyter notebook | |
| NGS/multiplexed Amplicon sequencing | |
| Experimental Design | |
| What does sequence data look like and how do I check it out? • file formats |
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| Review/questions | |
| Tuesday | Initial sequence processing • QC and paired end merging |
| Error correction / denoising • When, why, how |
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| Wednesday | Clustering • When, why, how |
| Classification | |
| Dealing with large data tables | |
| Python • Summary statistics and plots • Review and Questions |
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| Thursday | Data curation – trying our best to avoid catastrophic mistakes |
| Alpha diversity • Coverage • Diversity indices • Rarefaction • Linear models – fitting, diagnostics, predictions, multiple comparisons |
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| Ordination • Choice of approach (PCA/CA/PCoA/NMDS) • Data standardisation (binary/counts/relabund/Hellinger/CoDa) • Interpreting output (loadings, associations) • Plotting |
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| Friday | Constrained ordination • Choice of approach (RDA/CCA/CAP) • Data standardisation (scaling/transformation/categorical) • Choosing predictors (vif/envfit/ordistep) • Interpreting output • Plotting |
| Variation partitioning • Estimates and hypothesis tests • Spatial predictors |
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| Experimental frameworks • PerMANOVA • Dispersion • Plotting |
For further information or to register, please contact Andrew Bisset.