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Transcript of Rachel Adams - SMBE Euks Meeting
![Page 1: Rachel Adams - SMBE Euks Meeting](https://reader034.fdocuments.in/reader034/viewer/2022042716/55a8dd391a28ab280d8b4619/html5/thumbnails/1.jpg)
Next-generational sequencing for microbial ecology:
alpha diversity, beta diversity, and biases in high-throughput sequencing
Rachel AdamsAndrew Rominger
Sara BrancoThomas Bruns
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Understudied but fundamental ecological habitat
Implications for human healthSick building syndrome
Metrics are practically absent: composition and quantitative characteristics
Need comparison of “typical” buildings
The microbiome of the built environment
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Understudied but fundamental ecological habitat
Implications for human healthSick building syndrome
Metrics are practically absent: composition and quantitative characteristics
Need comparison of “typical” buildings and high replication across settings to detect patterns
The microbiome of the built environment
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?
?
?
The What and Why of the indoor microbiome
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?
?
?Architecture
Ventilation
Building function
The What and Why of the indoor microbiome
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?
?
?Architecture
Ventilation
Building function Environmental setting
The What and Why of the indoor microbiome
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?
?
?Architecture
Ventilation
Building function Environmental setting
Residents
The What and Why of the indoor microbiome
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Fungi in the indoor microbiome, and beyond
Yeasts
Filaments
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Fungi in the indoor microbiome, and beyond
Yeasts
Filaments
Saprobes
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Fungi in the indoor microbiome, and beyond
Yeasts Saprobes
Symbionts
Parasites Mutualists
− +
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Assessing environmental fungi
1. Estimated that 5-20% of fungi grow in culture2. Identification requires a fungal taxonomist
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Assessing environmental fungi
SSU RNA (18S) (5.8S) LSU RNA (28S)
ITS1 ITS2
Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi - Schoch et al. 2012
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High-throughput sequencing has greatly expanded capabilities in microbial ecology
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ACGAGTGCGT
High-throughput sequencing has greatly expanded capabilities in microbial ecology
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ACGAGTGCGT
High-throughput sequencing has greatly expanded capabilities in microbial ecology
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ACGAGTGCGTACGCTCGACA AGACGCACTC AGCACTGTAG ATCAGACACG
104 – 107 sequence reads
High-throughput sequencing has greatly expanded capabilities in microbial ecology
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α1
β12
ϒ
α2 α3
β23
β13
alpha, beta, gamma diversity
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α1
α2 α3
alpha, beta, gamma diversity
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α1
β12
α2 α3
β23
β13
alpha, beta, gamma diversity
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α1
β12
ϒ
α2 α3
β23
β13
alpha, beta, gamma diversity
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Kunin et al. 2010
Groundtruthing high-throughput sequencing for alpha richness
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Kunin et al. 2010
αtrue < αest
Groundtruthing high-throughput sequencing for alpha richness
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Groundtruthing high-throughput sequencing
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True samples
Hig
h-th
roug
hput
seq
uenc
ing
Observed samples
α1
α2 α3
α1+
α2+ α3+
In terms of diversity, we know that α
can be elevated in high-throughput sequenced communities...
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True community
Observed community
β12 β13
β23
β12? β13?
β23?
α1
α2 α3
α1+
α2+ α3+
...but how does that change conclusions of ecological processes that are based on β diversity?
Hig
h-th
roug
hput
seq
uenc
ing
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A key component to community ecology: Linking processes to this compositional variation
Adams et al., ISME Journal, 2013
Beta diversity: the variation in species composition among sites
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Do errors that inflate alpha diversity bias conclusions on beta diversity between samples?
Why would it? • Particular taxa in one environment grouping do not amplify or
amplify in a way that skews relative abundance of all others*• Clustering incorrectly groups divergent taxa or splits identical
taxa
Hypothesis: No
While richness/diversity estimations will be off for any given sample, conclusions of beta-diversity will be robust to the errors
Question and hypotheses
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Do errors that inflate alpha diversity bias conclusions on beta diversity between samples?
Why would it? • Particular taxa in one environment grouping do not amplify or
amplify in a way that skews relative abundance of all others*• Clustering incorrectly groups divergent taxa or splits identical
taxa
Hypothesis: No
While richness/diversity estimations will be off for any given sample, conclusions of beta-diversity will be robust to the errors
Question and hypotheses
![Page 29: Rachel Adams - SMBE Euks Meeting](https://reader034.fdocuments.in/reader034/viewer/2022042716/55a8dd391a28ab280d8b4619/html5/thumbnails/29.jpg)
Do errors that inflate alpha diversity bias conclusions on beta diversity between samples?
Why would it? • Particular taxa in one environment grouping do not amplify or
amplify in a way that skews relative abundance of all others*• Clustering incorrectly groups divergent taxa or splits identical
taxa
While richness/diversity estimations will be off for any given sample, conclusions of beta-diversity will be robust to the errors
Question and hypotheses
![Page 30: Rachel Adams - SMBE Euks Meeting](https://reader034.fdocuments.in/reader034/viewer/2022042716/55a8dd391a28ab280d8b4619/html5/thumbnails/30.jpg)
Simulation process
Initial community
Simulated community
OTU1 OTU2 … OTUj
Sample 1
Sample 2
…
Sample i
OTU1 OTU2 … OTUk
Sample 1
Sample 2
…
Sample i
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Simulation process
Expected relative abundance of OTUs
Initial communities
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Simulation process
Biased relative abundance
Variation in taxon-specific amplification
Initial communities
Expected relative abundance of OTUs
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Simulation process
Biased relative abundance
Variation in taxon-specific amplification
Biased relative abundance + error
Sequence error
Initial communities
Expected relative abundance of OTUs
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Simulation process
Biased relative abundance
Variation in taxon-specific amplification
Biased relative abundance + error
Sequence error
Clustering OTUs
Initial communities
Biased relative abundance + error + clustering
Expected relative abundance of OTUs
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Simulation process
Biased relative abundance
Variation in taxon-specific amplification
Biased relative abundance + error
Sequence error
Biased relative abundance + error + clusteringClustering OTUs
Simulated communities
Initial communities
Expected relative abundance of OTUs
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Model summary – 2 types of errors
1. Create group differences that aren’t there (Type I error)
-0.5 0.0 0.5
-0.4
-0.2
0.0
0.2
0.4
True
NMDS1
NM
DS
2
-0.5 0.0 0.5
-0.4
-0.2
0.0
0.2
0.4
Perceived
NMDS1
NM
DS
2
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Model summary – 2 types of errors
2. Loose groups differences that are there (Type II error)
-0.5 0.0 0.5
-0.4
-0.2
0.0
0.2
0.4
True
NMDS1
NM
DS
2
-0.5 0.0 0.5
-0.4
-0.2
0.0
0.2
0.4
Perceived
NMDS1
NM
DS
2
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Model summary output
1. Presence of bias: Statistical categorical differences
Groups R2 p-value
Location 0.02 0.34
Season 0.20 0.001
2. Degree of bias: percentage difference between true and simulated communities
(Simulated – True) True
= Normalized bias
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Model summary output
1. Presence of bias: Statistical categorical differences
2. Degree of bias: percentage difference between true and simulated communities
(Simulated distance – True distance)True distance
= Normalized error
Morisita-Horn distance metric
Groups R2 p-value
Location 0.02 0.34
Season 0.20 0.001
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Categorical differences are robust to high-throughput sequencing errors in alpha diversity, regardless of the underlying patterns of beta-diversity
The degree of bias is not affected by the underlying patterns of beta-diversity but dependent on community characteristics
Model findings
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Model findings
Categorical differences are robust to high-throughput sequencing errors in alpha diversity, regardless of the underlying patterns of beta-diversity
The degree of bias is not affected by the underlying patterns of beta-diversity but dependent on community characteristics
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True Simulated True Simulated
0.0
0.2
0.4
0.6
0.8
1.0
p v
alu
esNo groups Two groups
Model summary – Type I & II error
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True Simulated True Simulated
0.0
0.2
0.4
0.6
0.8
1.0
p v
alu
esNo groups Two groups
Model summary – Type I & II error
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True Simulated True Simulated
0.0
0.2
0.4
0.6
0.8
1.0
p v
alu
esNo groups Two groups
Model summary – Type I & II error
Whether groups are different or the same will not be biased by inflated alpha diversity
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Model summary – Degree of bias
Degree of bias will be affected by - the error rate of the platform and OTU- clustering- the gamma diversity of the environment- the precise shape of the species abundance
distribution
But not the relationship among samples
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Increasing probability of sequencing error and over-splitting OTUs increases bias
1e-04 0.0334 0.0667 0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
No groups
Nor
mal
ized
err
or
1e-04 0.0334 0.0667 0.1
Two groups
Probability of splitting
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Increasing OTU richness decreases bias
100 600 1100
0.0
0.2
0.4
0.6
0.8
Number of OTUs
Nor
mal
ized
err
or
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Shape of species abundance distribution (SAD) affects bias
0 200 400 600 800 1000 1200
01
00
02
000
30
00
40
005
00
0
Rank
Ab
und
an
ce
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Shape of species abundance distribution (SAD) affects bias
1.5 2.5 3.5
0.0
0.2
0.4
0.6
0.8
Increasing SAD variance
No
rmal
ized
err
or
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As true community distance increases, degree of error decreases
0.65 0.70 0.75 0.80
0.2
0.3
0.4
0.5
0.6
True distance
No
rma
lize
d e
rro
r
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Clustering is the main error-producing step
True Amplified Split
0.0
0.1
0.2
0.3
0.4
0.5
R^2
va
lue
sTwo groups
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Simulation overview
Categorical analysis very robust to errors in high-throughput biases
Degree of bias will be affected by error rate of the sequencing platform and OTU-clustering, the gamma diversity of the environment, the precise shape of the species abundance distribution
High-throughput error leads to an over-estimation of the difference between groups
Mean bias is ~20-40%Incorrect OTU clustering is most of that
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Steps
1. In silico: Add further complexity to simulations
2. In vitro: Empirically test artificially-created microbial communities
![Page 54: Rachel Adams - SMBE Euks Meeting](https://reader034.fdocuments.in/reader034/viewer/2022042716/55a8dd391a28ab280d8b4619/html5/thumbnails/54.jpg)
Do errors that inflate alpha diversity bias conclusions on beta diversity between samples?
Why would it?
• Particular taxa in one environment grouping do not amplify or amplify in a way that skews relative abundance of all others*
• Clustering incorrectly groups divergent taxa or splits identical taxa
Hypothesis: No
While richness/diversity estimations will be off for any given sample, conclusions of beta-diversity will be robust to the errors
Question and hypotheses
![Page 55: Rachel Adams - SMBE Euks Meeting](https://reader034.fdocuments.in/reader034/viewer/2022042716/55a8dd391a28ab280d8b4619/html5/thumbnails/55.jpg)
Air samples in a mycology classroom: a unique source distorts perceived species richness
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Air samples in a mycology classroom: a unique source distorts perceived species richness
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Mycology classroom appears to be less rich than other classrooms…
0 2000 4000 6000 8000
02
0040
060
080
010
00B
AC
D
E
Individuals
Cha
o E
stim
ated
Ric
hne
ss
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… but has higher biomass
A B C D E
050
100
15
02
00
Classroom
Pe
nic
illiu
m s
pore
eq
uiva
lent
s
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Composition of non-mycology classrooms are similar
AB
CD
E
Proportion
Cla
ssro
om
0 20 40 60 80 100
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Mycology classroom dominated by a few taxa
AB
CD
E
Proportion
Cla
ssro
om
0 20 40 60 80 100
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xxPuffballs dominate mycology classroom
Pisolithus, aka dog turd fungus Battarrea, tall stiltball
Lycoperdon, common puffball
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Mycology classroom dominated by a few taxa
AB
CD
E
Proportion
Cla
ssro
om
0 20 40 60 80 100
* * **
Adams et al., in review
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Beta diversity of mycology classroom: distinct communities
-1.5 -1.0 -0.5 0.0 0.5
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
NMDS1
NM
DS
2Observed
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Beta diversity of mycology classroom: distinct communities
-1.5 -1.0 -0.5 0.0 0.5
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
NMDS1
NM
DS
2ObservedTaxonomy reassigned
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Beta diversity of mycology classroom: distinct communities
-1.5 -1.0 -0.5 0.0 0.5
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
NMDS1
NM
DS
2ObservedTaxonomy reassignedAbundance reassigned
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Conclusions
• While deciphering alpha diversity is problematic:- Inflated alpha due to sequence error & clustering- Deflated alpha due to unevenness
beta diversity calculations are robust to these errors in high-throughput sequencing
• Empirical test will be used to corroborate conclusions of in silico simulations
• High-throughput sequencing will continue to be a promising tool for microbial ecologists
![Page 67: Rachel Adams - SMBE Euks Meeting](https://reader034.fdocuments.in/reader034/viewer/2022042716/55a8dd391a28ab280d8b4619/html5/thumbnails/67.jpg)
Conclusions
• While deciphering alpha diversity is problematic:- Inflated alpha due to sequence error & clustering- Deflated alpha due to unevenness
beta diversity calculations are robust to these errors in high-throughput sequencing
• Empirical test will be used to corroborate conclusions of in silico simulations
• High-throughput sequencing will continue to be a promising tool for microbial ecologists
![Page 68: Rachel Adams - SMBE Euks Meeting](https://reader034.fdocuments.in/reader034/viewer/2022042716/55a8dd391a28ab280d8b4619/html5/thumbnails/68.jpg)
Conclusions
• While deciphering alpha diversity is problematic:- Inflated alpha due to sequence error & clustering- Deflated alpha due to unevenness
beta diversity calculations are robust to these errors in high-throughput sequencing
• Empirical test will be used to corroborate conclusions of in silico simulations
• High-throughput sequencing will continue to be a promising tool for microbial ecologists
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References – potential biases in high-throughput sequencingDNA extraction: Frostegard et al Appl Environ Microbiol 1999; DeSantis et al FEMS Microbiology 2005; Feinsten et al Appl Environ Microbiol 2009; Morgan et al PLoS ONE 2010; Delmont et al Appl Environ Microbiol 2011
PCR amplification/Relative abundance: Amend et al Mol Ecol 2010; Engelbrektson et al ISME Journal 2010; Bellemain et al BMC Microbiol 2010; Schloss et al PLoS ONE 2011; Pinto & Raskin PLoS ONE 2012; Klindworth et al Nucleic Acids Res 2013
Sequencing error/Chimeras/OTU clustering: Huse et al Genome Biol 2007; Huse et al Environ Microbiol 2010; Kunin et al Environ Microbiol 2010; Quince et al BMC Bioinformatics 2010; Lee et al PLoS ONE 2012; Pinto & Raskin PLoS ONE 2012; Bachy et al ISME Journal 2013
Sequencing platform/protocol: Morgan et al PLoS ONE 2010; Luo et al PLoS ONE 2012
Even sampling depth: Schloss et al PLoS ONE 2011; Gihring et al Environ Microbiol 2012
Denoising: Gasper & Thomas PLoS ONE 2013;
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Empirical test of simulation results
100 600 1100
0.0
0.2
0.4
0.6
0.8
Number of OTUs
Nor
mal
ized
err
or
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PCR bias
-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2
0.0
0.5
1.0
1.5
2.0
PCR bias: beta distribution a=0.5, beta=1.0
Scatter around line of true abundance versus amplified abundance
Den
sity
0 200 400 600 800 1000 1200
020
04
006
0080
010
00
1200
1400
True abundance
Am
plifi
ed a
bund
anc
e
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OTU splitting bias
0 5 10 15 20
0.0
0.1
0.2
0.3
0.4
Split bias: binomial distribution with n=100
Number of splits
Den
sity
p=0.001
p=0.0667
p=0.0334
p=0.0001
0.0 0.5 1.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Split location: beta distribution with a=b=0.5
Location of split
Den
sity