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

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This was presented at iEvoBio 2012 in Ottawa.

This was presented at iEvoBio 2012 in Ottawa.

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  • 1. Morgan LangilleDalhousie 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 genomeMarker Gene Information • SEED NCBI • Etc. Etc.
  • 9. PICRUST Phylogenetic Investigation of Communities by Reconstruction of Unobserved STates http://picrust.sourceforge.net
  • 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. 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 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. 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 prediction1. Pretend a genome has not been sequenced2. Predict genome composition using PICRUST3. Compare predictions to real data4. Repeat for all genomes
  • 18. Accuracy depends on distance toclosest 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 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. Acknowledgements Rob Beiko Curtis Huttenhower Rob Knight Jesse Zaneveld Greg Caporaso Joshua Reyes Dan Knights Daniel McDonald

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