Morgan LangilleDalhousie University      July 10, 2012
16S rRNA gene   Standard marker gene for bacterial and    archaeal species identification   Recent widespread use in met...
Using 16S anonymously 16S reads often clustered into OTUs Alpha diversity Beta diversity Rarefaction Biogeography    ...
What is in a name?   Real names vs OTU1234                            Lee et al. 2010
What is in a name?   Real names vs OTU1234   Haloferax                            Lee et al. 2010
What is in a name?   Real names vs OTU1234   Haloferax                            Lee et al. 2010   Prochlorococcus
What is in a name?   Real names vs OTU1234   Haloferax                            Lee et al. 2010   Prochlorococcus   ...
Extending 16S to functions     Metagenomics: “What are they doing?”       Requires WGS sequencing       More costly   ...
PICRUST   Phylogenetic Investigation of    Communities by Reconstruction of    Unobserved STates   http://picrust.source...
PICRUST: Predicting genomesReference 16S        Genome Trait     Tree                Table(Green Genes)      (e.g. KEGG, 1...
PICRUST: Predicting metagenomes                  16S Copy Number          Functional Trait                     Predictions...
Ancestral State Reconstruction   Needs to accept continuous data   Must run fast! (8000 traits across 3500    genomes) ...
Accuracy for metagenome prediction1.   Obtain metagenomic projects with both     WGS and 16S only sequencing2.   Make func...
ASR methods on metagenomics                         Wagner Parsimony         ACE PIC   HMP Mock          R2= 0.92        ...
Accuracy on metagenomes
Accuracy across various HMP sites
Accuracy for genome prediction1.   Pretend a genome has not been sequenced2.   Predict genome composition using PICRUST3. ...
Accuracy depends on distance toclosest sequenced genome                       R2=-0.72
Accuracy across the TOL                               Staphylococcus aerues      E. coli                http://itol.embl.d...
Accuracy depends on type of functional category                               PICRUST Accuracy
Possible applications1.       16S only microbiome studies          Make hypotheses about the functions they encode2.     ...
Acknowledgements Rob Beiko Curtis Huttenhower Rob Knight Jesse Zaneveld Greg Caporaso Joshua Reyes Dan Knights Dan...
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Leveraging ancestral state reconstruction to infer community function from a single marker gene

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Leveraging ancestral state reconstruction to infer community function from a single marker gene

  1. 1. Morgan LangilleDalhousie University July 10, 2012
  2. 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. 3. Using 16S anonymously 16S reads often clustered into OTUs Alpha diversity Beta diversity Rarefaction Biogeography Bik et al., 2012
  4. 4. What is in a name? Real names vs OTU1234 Lee et al. 2010
  5. 5. What is in a name? Real names vs OTU1234 Haloferax Lee et al. 2010
  6. 6. What is in a name? Real names vs OTU1234 Haloferax Lee et al. 2010 Prochlorococcus
  7. 7. What is in a name? Real names vs OTU1234 Haloferax Lee et al. 2010 Prochlorococcus Bacillus
  8. 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 genomeMarker Gene Information • SEED NCBI • Etc. Etc.
  9. 9. PICRUST Phylogenetic Investigation of Communities by Reconstruction of Unobserved STates http://picrust.sourceforge.net
  10. 10. PICRUST: Predicting genomesReference 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. 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. 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. 13. Accuracy for metagenome prediction1. Obtain metagenomic projects with both WGS and 16S only sequencing2. Make functional predictions using PICRUST with 16S only data3. Compare predictions with WGS data
  14. 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. 15. Accuracy on metagenomes
  16. 16. Accuracy across various HMP sites
  17. 17. Accuracy for genome prediction1. Pretend a genome has not been sequenced2. Predict genome composition using PICRUST3. Compare predictions to real data4. Repeat for all genomes
  18. 18. Accuracy depends on distance toclosest sequenced genome R2=-0.72
  19. 19. Accuracy across the TOL Staphylococcus aerues E. coli http://itol.embl.de/shared/mlangill
  20. 20. Accuracy depends on type of functional category PICRUST Accuracy
  21. 21. Possible applications1. 16S only microbiome studies  Make hypotheses about the functions they encode2. Complete metagenomic studies  Compare functions we “observe” to what we would expect based on species present3. Aid other metagenomic computational methods  Binning  Metabolic reconstruction4. Insight into correlation between species & function  For different taxonomic groups  For different functional classes
  22. 22. Acknowledgements Rob Beiko Curtis Huttenhower Rob Knight Jesse Zaneveld Greg Caporaso Joshua Reyes Dan Knights Daniel McDonald

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