Rob csm2018

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beiko

Rob's keynote presentation at the 2018 Canadian Society of Microbiologists meeting (Winnipeg). Out with OTUs!

Wissenschaft

Evolutionary and
Temporal Views of
Microbial Diversity
Rob Beiko
19 June 2018


2
http://seuss.wikia.com/wiki/Thing_One_and_Thing_Two

Langille et al., Nat Biotechnol (2013)
Predicting function from marker genes - PICRUSt
4

Loss?
Misannotation?
Lateral gene transfer?
5

6
Adapted from Martiny et al., ISME J (2013)
High conservation
Low conservation

Units should depend on function and ecological context
Do they?
7

The tyranny of the (16S)
Operational Taxonomic Unit
8
99%97% 100%

https://en.wikipedia.org/wiki/File:Amit_Yadav_Phytoplasma_rRNA_operon.jpg
giphy.com 9

Martiny et al., Env Microbiol (2009) 10
The “100% OTU"

There are better methods for sequence
aggregation (e.g., amplicon sequence variants)
But this doesn’t solve the grouping problem
We need to look beyond arbitrary gene (and
genome) similarity to define units
11

13
Ning and Beiko, Microbiome (2015)
Machine learning of microbial community signatures

14
?
?
?
?
?
Ning and Beiko, Microbiome (2015)

16
Accuracy:
Clades (80%)
> 16S OTUs (78%)
> PICRUSt predictions (76%)

18
Hall et al., PeerJ (2017)
Time-series
Dissimilarity
Ananke: temporal clustering of unique sequences
Sequence abundance
profiles
Similarity-based
grouping

19
97% OTU #1:
Dorea
97% OTU #2:
Ruminococcus gnavus
Hall et al., PeerJ (2017)

20
Same time-series cluster: Bacteroides + Synechococcus

21
Same OTU, different temporal patterns

23
Liu et al. (Accepted pending minor revision)
Phylogenetic profiles
Genomes
Gene presence / absence

24
Liu et al. (Accepted pending minor revision)
Genomes
“Module” – genes with similar distributions

25
Genome 1
Genome 2
“Thingness” depends on specific modules
= trait-based ecology

OTUs are things only by accident
Sequence similarity needs to be augmented with
other types of information
26

Acknowledgments
PICRUSt: Langille, Zaneveld, McDonald, Knights,
Reyes, Clemente, Burkepile, Vega Thurber, Knight,
Huttenhower
Clade-based classification: Ning
Ananke: Hall, Rohwer, Perrie, McMahon
Phylogenetic profiling: Liu, Wright, Allen-Vercoe
27

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1. Evolutionary and Temporal Views of Microbial Diversity Rob Beiko 19 June 2018 

2. 2 http://seuss.wikia.com/wiki/Thing_One_and_Thing_Two

3. 3

4. Langille et al., Nat Biotechnol (2013) Predicting function from marker genes - PICRUSt 4

5. Loss? Misannotation? Lateral gene transfer? 5

6. 6 Adapted from Martiny et al., ISME J (2013) High conservation Low conservation

7. Units should depend on function and ecological context Do they? 7

8. The tyranny of the (16S) Operational Taxonomic Unit 8 99%97% 100%

9. https://en.wikipedia.org/wiki/File:Amit_Yadav_Phytoplasma_rRNA_operon.jpg giphy.com 9

10. Martiny et al., Env Microbiol (2009) 10 The “100% OTU"

11. There are better methods for sequence aggregation (e.g., amplicon sequence variants) But this doesn’t solve the grouping problem We need to look beyond arbitrary gene (and genome) similarity to define units 11

12. 12OTUs plus Evolution

13. 13 Ning and Beiko, Microbiome (2015) Machine learning of microbial community signatures

14. 14 ? ? ? ? ? Ning and Beiko, Microbiome (2015)

15. 15 Ning and Beiko, Microbiome (2015)

16. 16 Accuracy: Clades (80%) > 16S OTUs (78%) > PICRUSt predictions (76%)

17. 17Time and tide

18. 18 Hall et al., PeerJ (2017) Time-series Dissimilarity Ananke: temporal clustering of unique sequences Sequence abundance profiles Similarity-based grouping

19. 19 97% OTU #1: Dorea 97% OTU #2: Ruminococcus gnavus Hall et al., PeerJ (2017)

20. 20 Same time-series cluster: Bacteroides + Synechococcus

21. 21 Same OTU, different temporal patterns

22. 22 Cohesion

23. 23 Liu et al. (Accepted pending minor revision) Phylogenetic profiles Genomes Gene presence / absence

24. 24 Liu et al. (Accepted pending minor revision) Genomes “Module” – genes with similar distributions

25. 25 Genome 1 Genome 2 “Thingness” depends on specific modules = trait-based ecology

26. OTUs are things only by accident Sequence similarity needs to be augmented with other types of information 26

27. Acknowledgments PICRUSt: Langille, Zaneveld, McDonald, Knights, Reyes, Clemente, Burkepile, Vega Thurber, Knight, Huttenhower Clade-based classification: Ning Ananke: Hall, Rohwer, Perrie, McMahon Phylogenetic profiling: Liu, Wright, Allen-Vercoe 27

28. Fin 28

Rob csm2018

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