1. Metabarcoding 16S RNA targeted sequencing
Peter Tsai
Bioinformatics Institute, University of Auckland

2. Overview What’s metagenomics and metabarcoding?
Next generation sequencing and metabarcoding
How NGS changes metagenomics
Analysis approach
Taxonomic dependent and independent analysis
Study example
NZ vine yeast biogeography by pyrosequencing

3. Metagenomics Metagenomics
Study of metagenomes, genetic materials directly from environmental samples.
Shotgun metagenomics
Randomly shears DNA, sequence many different species in environment and attempts to reconstruct multiple genomes.
Metabarcoding
Subset of metagenomics.
Study of one or more marker gene.
Gene specific primers to ‘barcode’ that gene, i.e. 16S, ITS or CO1
Aim is often to identify different species and compare different community

4. Not all microbes can be cultured
NGS and metagenomics
Accelerated by NGS, predominately 454 sequencing because of the longer read length, now more with Illumina based chemistry.
Organism no longer needs to be cultivated and cloned — Culture independent insight
Direct sequencing from environment as a “community”
You can pool multiple samples together
Not all microbes can be cultured

5. Analysis approach

6. Analysis approach Taxonomy independent analysis
Reads are group into operational taxonomic units (OTU) based on a specified sequence variation.
Taxonomy dependent analysis
Assignment at the level of domain, phylum, class, order, family, genus, and species
Require a reference database

7. Taxonomy independent analysis
Group reads into OTU based on certain imposed similarity threshold
In study of bacteria, 97% seems like a good starting point
Species dependent, genes dependent, threshold may vary
1 OTU = 1 organism
Extract a OTU representative sequence
Most common sequence
Sequence that has minimum difference to all other sequences in the same OTU

8. Taxonomy dependent analysis
Classify sequences
BLAST
Simply BLAST what you have
 Online RDP classifier (Ribosomal Database Project )
RDP (Release 10, Update 26 consists of 1,613,063 aligned and annotated 16S rRNA sequences
Limited by number of reads you can submit
Online Greengenes classifier based on NAST alignment
Require pre-aligned dataset

11. NZ vine yeast biogeography by pyrosequencing
M. W. Taylor, N. Anfang, A. H. Thrimawithana, P. Tsai, H. Ross and M. R. Goddard
School of Biological Sciences, University of Auckland

12. NZ vine yeast biogeography by pyrosequencing
Yeasts are the agents responsible for fermentation of fruits into wine
Yeasts naturally associated with vines and wines are reasonably well characterised
Microbes have an effect on both vine and fruit development (as some are pathogens), as well as the resulting wine quality and style
Investigations into the ecology of these organisms is lacking.
Vitis vinifera

13. NZ vine yeast biogeography by pyrosequencing
6 distinct vineyards in each of four major and distinct wine-producing regions
West Auckland (WA)
Hawke’s Bay (HB)
Marlborough (MB)
Central Otago (CO)
26S RNA gene from DNA directly extracted from microbial communities associated with ripe Chardonnay fruit

14. NZ vine yeast biogeography by pyrosequencing
Quality checks
Remove short reads
Remove reads containing ambiguity
Trim off low quality regions
Taxonomy independent analysis
No well established reference database for eukaryotic 26S
Clustering into 98% OTU
ANOSIM for statistical test between regions
Limited classification rely upon NCBI Taxonomy DB

15. NZ vine yeast biogeography by pyrosequencing
2,000 species were found using deep sequencing across all regions.
Culture based analysis recovered 7 species from West Auckland and Hawke’s Bay
Deep sequencing identified ~700 from the same West Auckland and Hawke’s Bay sample.
All 7 species were found in pyrosequencing dataset
The culture-based may miss ~99% of the community

17. Marlborough
Hawke’s Bay
Central Otago
West Auckland

18. Geographic patterns for yeast communities
Central Otago harbours the most distinct community
Different communities associated with Chardonnay vines in different areas of NZ
Community similarity significantly decays with distance and temperature
Different regions harbour different communities, may, in part, contribute to the distinctiveness of wines deriving from that area.

19. Key questions associated with Metagenomics
Number of reads needed
Statistical power
Over estimating due to sequencing error
Results in large number of OTUs
Multiple copies of 16S rRNA gene in some species
Lead to overrepresentation
Accuracy of taxonomic classification
Not all rRNA genes amplify equally well with the same “universal” primers

20. Summary
Basic introduction, basic method, one of many ways of analysing metabarcoded dataset.
Increasingly popular way of extracting the genomes of micro- organisms.
Direct insight into communities without the need of culturing
Culture based and sequencing based method may recover different proportion of organisms