1. Accurate estimation of microbial communities using 16S tags
Julien Tremblay, PhD

2. 16S rRNA as phylogenetic marker gene
Escherichia coli
16S rRNA
Primary and Secondary Structure
70S Ribosome
subunits
30S
50S
34 proteins
21 proteins
5S rRNA
23S rRNA
16S rRNA
highly conserved between different species of bacteria and archaea
Falk Warnecke

3. 16S rRNA in environmental microbiology (Sanger clone libraries)
bp length
Falk Warnecke

4. Next generation sequencing (NGS)
454
Illumina
0.5M 450bp reads $$
10-350M 150bp reads/lane $
Read length
Throughput and cost

5. Illumina tags (itags) 454 = “1” read
ACGTGGTACTACGTGAT….
~400 bp
ACGTGGTACTACGTGATAGTGTAT
~250 bp
454 = “1” read
Illumina = “2” reads => have to be assembled
Both reads need to be of good quality

6. Game plan to survey microbial diversity
16S rRNA
Generate amplicons of a
given variable region
from bacterial community
(many millions of sequences)
Why amplicon tags ?
Deeper, cheaper, faster
Reduce dataset by
dereplication/clustering
X 10,000
X 800
X 1,200
X 200
X 2,000
X 1,000
X 1
X 10
Identification
(BLAST, RDP classifier)

7. Rare biosphere High sequencing depth of NGS  reveals “rare” OTUs
Lots of reads only present once in sample…
Abundance
Rare biosphere
Rank
Low abundance
High abundance
Sequencing error? Chimeras? Background noise?

8. Rare bias sphere? Is rare biosphere an artifact of the NGS error?
Control experiment: estimate rare biosphere
in a single strain of E.coli
V1 & V2 V8
27F
342R
1114F
1392R
Should not be a sequencing artifact, if relatively stringent
clustering parameters are applied
Subject to controversy – Is rare always real?
Kunin et al., (2009), Environ. Microbiol.
Quince et al., (2009), Nat. Methods

9. Illumina tags (itags) Typical 454 run  450,000 – 500,000 reads
“Typical” Illumina run:
GAIIx  10,000,000 – 40,000,000 reads/lane
Hiseq   ~ 350,000,000 reads/lane
Miseq (new)  ~8,000,000 reads/lane
Move 16S tags sequencing to Illumina platform
HiSeq = huge output compared to 454 (suitable for big projects indexes(barcodes)/libraries
MiSeq = moderatly high throughput (More suitable)
throughput more efficient clustering algorithm (SeqObs).

10. 16S tags clustering
Edward Kirton, JGI

11. Number of reads >> number of clusters30 25 20 15
Number of reads (millions) 10
Clustering happens here!
~100,000 clusters 5
Edward Kirton, JGI

12. Validation of 16S tags on MiSeq
Quality is superior in MiSeq
454
MiSeq reads 1 and 2 separately
MiSeq reads 1 and 2 assembled

13. MiSeq validation Exploratory experiments using 11 wetlands samples.
Validate reproducibility between runs

14. MiSeq validation
Beta diversity (UniFrac Distances)
Run 1
Run 2

15. MiSeq validation
What are the gains using MiSeq assembled paired-end reads over 454 reads?
Average bootstrap value for all clusters at every tax level.
454 clusters shows higher confidence than MiSeq clusters
Better quality in MiSeq reads, but lower read lengths
Average bootstrap value
Taxonomic level

16. Comparing 454 with MiSeq
What are the gains using MiSeq assembled paired-end reads over 454?
Clusters having > 0.50 bootstrap value
For instance, ~310,000 reads
made it to the class level
MiSeq outperforms 454 in terms of read depth

17. itags – rare OTUs MiSeq wetlands test samples
Low abundant reads consistently shows low confidence in
Classification.
Low abundant reads = errors, artifacts?
Low abundant reads are underrepresented in databases?

18. Comparing 454 with illumina
Compare runs of 454 and MiSeq of same sample
Although challenge to compare V4 with V6-8 region.
515
806
926
1392
~291 bp
~466 bp

19. Comparing 454 with illumina
Primer pair of variable region is likely to affect outcome of results.
In silico PCR on 16S Greengenes database.

20. PyroTagger (for 454 amplicons)
Unzip, validate
Remove low-quality reads
Redundancy removal
PyroClust & Uclust
Remove chimeras
Samples comparison,
post-processing
pyrotagger.jgi-psf.org

21. Classification and barcode separation
Sequences of cluster (OTU) representatives
Blast vs GreenGenes and Silva databases, dereplicated at 99.5%
Distribution of microbial phyla in the dataset
Also see the Qiime pipeline

22. Challenges Short size of amplicon
What filtering parameters to use (stringency level)?
 balance between stringency filter and keeping as much data as we can
Whole new dimension for rare biosphere?
Handling large numbers of sample (tens of thousand magnitude)
Sequencing run is fast, but library preparation time is long.
Cost of barcoded primers (will need lots of barcodes), handling.
Huge ammount of samples  statistics models…

23. Acknowledgments Susannah Tringe Edward Kirton Feng Chen Kanwar Singh
Alison Fern
And many others!
Thanks!

24. 16S rRNA
Dangl lab, UNC