Transcript 32. Accurate Estimation of Microbial Communities Using Pyrotags

Accurate estimation of microbial
communities using 16S tags
Julien Tremblay, PhD
[email protected]
16S rRNA as phylogenetic marker gene
21 proteins
16S rRNA
30S
70S Ribosome
subunits
50S
34 proteins
5S rRNA
23S rRNA
highly conserved between different species
of bacteria and archaea
Escherichia coli
16S rRNA
Primary and Secondary Structure
Falk Warnecke
16S rRNA in environmental microbiology
(Sanger clone libraries)
900-1100 bp length
Falk Warnecke
Next generation sequencing (NGS)
Illumina
0.5M 450bp reads
10-350M 150bp reads/lane
$$
$
Read length
454
Throughput and cost
Illumina tags (itags)
454
Illumina
~400 bp
ACGTGGTACTACGTGATAGTGTAT
~250 bp
• 454 = “1” read
• Illumina = “2” reads => have to be assembled
• Both reads need to be of good quality
Game plan to survey microbial diversity
V1
V2
V3
V4
V5
V6
V7
V8
V9
16S rRNA
Generate amplicons of a
given variable region
from bacterial community
(many millions of sequences)
Reduce dataset by
dereplication/clustering
X 10
X1
X 1,000
X 2,000
X 200
X 1,200
X 800
X 10,000
Why amplicon tags ?
Deeper, cheaper, faster
Identification
(BLAST, RDP classifier)
Rare biosphere
Abundance
High sequencing depth of NGS  reveals “rare” OTUs
Lots of reads only present once in sample…
Rare biosphere
Rank
Sequencing error? Chimeras? Background noise?
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
27F
342R
V8
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
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 1000+ indexes(barcodes)/libraries
• MiSeq = moderatly high throughput (More suitable)
• throughput more efficient clustering algorithm
(SeqObs).
16S tags clustering
Edward Kirton, JGI
Number of reads >> number of clusters
Illumina rRNA Amplicon Sequencing
30
30000000
25
20
15000000
15
10000000
10
5
5000000
00
RAW
BARCODE
OVERLAP
ASSEM
~100,000 clusters
20000000
Clustering happens here!
Number of Sequences
Number of reads (millions)
25000000
CLUSTERS
Edward Kirton, JGI
Validation of 16S tags on MiSeq
• Quality is superior in MiSeq
454
MiSeq reads 1 and 2 separately
MiSeq reads 1 and 2 assembled
MiSeq validation
• Exploratory experiments using 11 wetlands
samples.
• Validate reproducibility between runs
MiSeq validation
• Beta diversity (UniFrac Distances)
Run 1
Run 2
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.
Average bootstrap value
•
454 clusters shows higher
confidence than MiSeq clusters
Better quality in MiSeq reads, but
lower read lengths
Taxonomic level
Comparing 454 with MiSeq
• What are the gains using MiSeq assembled pairedend 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
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?
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
~291 bp
1392
~466 bp
Comparing 454 with illumina
• Primer pair of variable region is likely to
affect outcome of results.
In silico PCR on 16S Greengenes database.
PyroTagger (for 454 amplicons)
Unzip, validate
Remove low-quality reads
Redundancy removal
PyroClust & Uclust
Remove chimeras
Samples comparison,
post-processing
pyrotagger.jgi-psf.org
Classification and barcode separation
• Sequences of cluster (OTU) representatives
100%
90%
80%
70%
60%
• Blast vs GreenGenes and Silva databases,
dereplicated at 99.5%
50%
40%
30%
20%
10%
0%
• Distribution of microbial phyla in the dataset
C lus ter1
C lus ter2
C lus ter3
C lus ter4
C lus ter5
C lus ter7
C lus ter8
C lus ter9
C lus ter1 0
C lus ter1 3
C lus ter1 5
C lus ter1 7
C lus ter1 8
C lus ter1 9
C lus ter2 0
C lus ter2 1
C lus ter2 2
C lus ter2 3
C lus ter2 4
C lus ter2 9
C lus ter3 1
C lus ter3 3
C lus ter3 4
C lus ter3 5
C lus ter4 1
C lus ter4 4
C lus ter5 0
C lus ter5 3
C lus ter6 7
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• Also see the Qiime pipeline
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…
Acknowledgments
•
•
•
•
•
•
Susannah Tringe
Edward Kirton
Feng Chen
Kanwar Singh
Alison Fern
And many others!
Thanks!
16S rRNA
Dangl lab, UNC