Workshops and tutorials on microbiome and genomics
Week 1 - Wet lab
Week 2 - Microbiome bioinformatics
SLIDES
References for microbiome
- Johnson, J.S., Spakowicz, D.J., Hong, BY. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun 10, 5029 (2019). doi: 10.1038/s41467-019-13036-1
- Pollock J, Glendinning L, Wisedchanwet T, Watson M. The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies. Appl Environ Microbiol. 2018;84(7):e02627-17. doi: 10.1128/AEM.02627-17
- Nilakanta H, Drews KL, Firrell S, Foulkes MA, Jablonski KA. A review of software for analyzing molecular sequences. BMC Res Notes. 2014;7:830. doi: 10.1186/1756-0500-7-830
- Roumpeka DD, Wallace RJ, Escalettes F, Fotheringham I, Watson M. A Review of Bioinformatics Tools for Bio-Prospecting from Metagenomic Sequence Data. Front Genet. 2017;8:23. doi: 10.3389/fgene.2017.00023
- Schriefer AE, Cliften PF, Hibberd MC, Sawyer C, Brown-Kennerly V, Burcea L, Klotz E, Crosby SD, Gordon JI, Head RD. A multi-amplicon 16S rRNA sequencing and analysis method for improved taxonomic profiling of bacterial communities. J Microbiol Methods. 2018;154:6-13. doi: 10.1016/j.mimet.2018.09.019
- Bharti R, Grimm DG. Current challenges and best-practice protocols for microbiome analysis. Brief Bioinform. 2021;22(1):178-193. doi: 10.1093/bib/bbz155
- Allaband C, McDonald D, Vázquez-Baeza Y, et al. Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians. Clin Gastroenterol Hepatol. 2019;17(2):218-230. doi: 10.1016/j.cgh.2018.09.017
- Lepage P, Leclerc MC, Joossens M, et al. A metagenomic insight into our gut’s microbiome. Gut 2013;62:146-158. doi: 10.1136/gutjnl-2011-301805
- Ref: Liu, YX., Qin, Y., Chen, T. et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell (2020). 10.1007/s13238-020-00724-8
Links
learning R and misc. coding related tools
For more detail on RStudio and lots of links see my research website here.
The bioinformatics page also has extra links related to RStudio and microbiome analysis.
Workshops/groups for learning
Keep an eye on the Rezbaz website here. It was cancelled in 2020 but might be coming back this year.
AMSI BioInfoSummer is a week-long workshop/conference that is tailored around bioinformatics - mainly genomics but also other ’omic data as well. As with all things it went virtual in 2020 so likely will be in 2021.
Biocommons is a group that brings together bioinformaticians. They help provide and facilitate workshops and training relating to biological data and informatics.
The BioCommons is committed to enabling this critical workforce transition, by building upon training activities that we developed as part of the EMBL-ABR training programme:
See also Murdoch uni workshops and drop in sessions available here.
Learning outcomes
- Wet lab
- Understand the process of DNA extraction
- Identify potential sample collection and preparation that can impact the microbiome and describes possible measures to control these
- Be familiar with optimization methods for low biomass samples
- Bioinformatics
- Understand the different ways data is stored during the sequencing and analysis process
- Understand post-sequence processing steps involved and there importance in downstream analysis
- Explain the differences in diversity measures used in microbiome analysis
- Be familar with analysis of amplicon 16S bacteria analysis using the RStudio environment
Glossary
- Microbiome - the microorganisms of a specific habitat and surrounding environment. Sometimes specific for bacteria, but also can be used more broadly for microscopic organisms (e.g. viruses, single-cell eukaryotes, bacteria and sometimes parasites).
- Metagenomics - all the genetic material recovered directly from environmental samples.
- OTUs - Operation taxonomic units. Generally considered to be clustered at 97% similar level - species level.
- ASVs - Amplicon sequence variants. Denoised sequence variants. Equivalent to zero radius operational taxonomic units (zOTU)
- 16S - 16S ribosomal RNA gene, small subunit of a prokaryotic ribosome (SSU rRNA).
- Hypervariable region - portions in the genome of a taxa with much higher levels of variation than other similar areas,
- Cluster - algorithms that attempt to group related biological sequences, generally at a set threshold, for example: species level = 97% (e.g. OTUs).
- Denoise - a computational method for removing sequence errors and identifying correct true biological sequences in the reads. These approaches provide improved resolution and result in unique biological sequences (e.g. ASVs, ZOTUs).
- OTU table - also known as count data, contains the list of OTU/ASVs and number of sequences per sample.
In this example each row is a sample and a column is the OTU/ASV.
- Taxonomy table - spreadsheet containing OTU/ASV and taxonomic identify, generally as 7 columns (Kingdom, Phylum, Class, Order, Family, Genus, Species).
- Sample data - contains metadata associated with samples.
- Phyloseq object - multi-component data set merging OTU table, taxonomy table, sample data, sequences and phylogenetic table. Part of the phyloseq R package.