This document describes PICRUSt, a computational approach for predicting metagenome functional content from 16S rRNA gene surveys. PICRUSt uses ancestral state reconstruction to infer the presence or absence of gene families or functional traits in unobserved genomes based on the phylogenetic relationships between observed microbes and their genomic content. The document evaluates PICRUSt's accuracy on predicting metagenomes from mock communities and human microbiome project samples, finding good prediction across various body sites. Potential applications of PICRUSt include generating hypotheses about microbial community functions and aiding other metagenomic analyses.

1. Morgan Langille
Dalhousie University
July 10, 2012

2. 16S rRNA gene
 Standard marker gene for bacterial and
archaeal species identification
 Recent widespread use in metagenomic
microbiome surveys
 Limited to telling us: “who is there?”

3. Using 16S anonymously
 16S reads often clustered into OTUs
 Alpha diversity
 Beta diversity
 Rarefaction
 Biogeography
Bik et al., 2012

4. What is in a name?
 Real names vs OTU1234
Lee et al. 2010

5. What is in a name?
 Real names vs OTU1234
 Haloferax
Lee et al. 2010

6. What is in a name?
 Real names vs OTU1234
 Haloferax
Lee et al. 2010
 Prochlorococcus

7. What is in a name?
 Real names vs OTU1234
 Haloferax
Lee et al. 2010
 Prochlorococcus
 Bacillus

8. Extending 16S to functions
 Metagenomics: “What are they doing?”
 Requires WGS sequencing
 More costly
 Use microbial databases
 ~3500 genomes
• KEGG
• 16S gene IMG • PFAM
• Or Other Functional • EC
Find genome
Marker Gene Information • SEED
NCBI
• Etc.
Etc.

9. PICRUST
 Phylogenetic Investigation of
Communities by Reconstruction of
Unobserved STates
 http://picrust.sourceforge.net

10. PICRUST: Predicting genomes
Reference 16S Genome Trait
Tree Table
(Green Genes) (e.g. KEGG, 16S
copy number)
Prune taxa with
no genome
information
Infer Predict
ancestral genome
genome traits compositions

11. PICRUST: Predicting metagenomes
16S Copy Number Functional Trait
Predictions Predictions
(per genome) (per genome)
OTU Table Predict Metagenome Functions by
Normalize OTU Table Sample
(16S by Sample) Functional Traits

12. Ancestral State Reconstruction
 Needs to accept continuous data
 Must run fast! (8000 traits across 3500
genomes)
 Wagner Parsimony (Count software; Csuos, 2010)
 ACE (APE R Library; Paradis, 2004)
 PIC
 ML
 REML

13. Accuracy for metagenome prediction
1. Obtain metagenomic projects with both
WGS and 16S only sequencing
2. Make functional predictions using
PICRUST with 16S only data
3. Compare predictions with WGS data

14. ASR methods on metagenomics
Wagner Parsimony ACE PIC
 HMP Mock R2= 0.92 R2= 0.91
Community
(known
organisms
sequenced)
 All methods
give similar ACE REML ACE ML
results except R2= 0.92 R2= 0.72
for “ACE ML”
 known problem
and recently
added “REML”
method solves
problem

15. Accuracy on metagenomes

16. Accuracy across various HMP sites

17. Accuracy for genome prediction
1. Pretend a genome has not been sequenced
2. Predict genome composition using PICRUST
3. Compare predictions to real data
4. Repeat for all genomes

18. Accuracy depends on distance to
closest sequenced genome
R2=-0.72

19. Accuracy across the TOL
Staphylococcus aerues
E. coli
http://itol.embl.de/shared/mlangill

20. Accuracy depends on type of functional category
PICRUST Accuracy

21. Possible applications
1. 16S only microbiome studies
 Make hypotheses about the functions they encode
2. Complete metagenomic studies
 Compare functions we “observe” to what we would expect
based on species present
3. Aid other metagenomic computational methods
 Binning
 Metabolic reconstruction
4. Insight into correlation between species & function
 For different taxonomic groups
 For different functional classes

22. Acknowledgements
 Rob Beiko
 Curtis Huttenhower
 Rob Knight
 Jesse Zaneveld
 Greg Caporaso
 Joshua Reyes
 Dan Knights
 Daniel McDonald