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Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012
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Comparative metagenomics: quantifying similarities between environments, CMBI, NCMLS, Radboud University Nijmegen Medical Centre, Nijmegen, Bas E. Dutilh, Copenhagenomics 2012

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Comparative metagenomics: quantifying similarities between environments

Comparative metagenomics: quantifying similarities between environments

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  • 1. Comparative metagenomicsquantifying similarities between environments Bas E. Dutilh CPHx, Copenhagen, Denmark June 14th 2012
  • 2. Metagenomic analysis tools
  • 3. Taxonomic or functional profiles Trindade-Silva et al. PLoS ONE 2012 Kip et al. Env. Microbiol. Rep. 2011 Boleij et al. Mol. Cell. Proteomics 2012
  • 4. Clustering profiles• Calculate pairwise distances – Manhattan distance – Correlation between profiles • High correlation ↔ similar environment • Low correlation ↔ dissimilar environment frequency → – Angle between vectors in n-dimensional space • Small angle ↔ similar environment • Large angle ↔ dissimilar environment taxa / functions → freq taxon 1 → freq taxon 1 → freq taxon 2 → freq taxon 2 → ... • Wootters distance between profiles• Create cladogram Wootters Phys. Rev. D 1981
  • 5. Microbiomes of water animals- BlastN reads against Genbank- Taxonomic profiles including parent clades- Wootters distance formula- BioNJ cladogram Trindade-Silva et al. PLoS ONE 2012
  • 6. Many unknowns in viral metagenomes Mokili et al. Curr. Opin. Virology 2012
  • 7. Highly divergent samples 100 % reads used (BlastN mapping to Genbank) 90 80 70 60 50 40 30 20 10 0 - BlastN reads against Genbank - Taxonomic profiles including parent clades human water - Distance = 1 minus correlation - BioNJ cladogram Dutilh et al. submitted
  • 8. Human microbiota well characterized* * The terrestrial hot spring metagenomes consist of 99.8% reads from Synechococcus
  • 9. Reference-independent methods• k-mer profiles GATGGATGAC 0 AAAA ... → GATG 1 ATGA ATGG 1 ATGG TGGA → 2 GATG GGAT 1 GGAT GATG 1 TGAC ATGA ... TGAC 0 TTTT – 4k/2 entries (in this case 44/2 = 128) – Calculate profile similarities• Enhance with habitat k-mer signatures (HabiSign)• Advantages – Very fast to calculate• Disadvantages – A lot of information is lost – Biologically (rather) meaningless Ghosh et al. BMC Bioinformatics 2011
  • 10. Cross-assembly• Combine sequencing reads from different metagenomes in a single assembly – Use your favorite assembly tool• Cross-contigs contain reads from more than 1 sample – Directly represent shared entities between samples• The number of reads assembled into cross-contigs determines the degree of overlap between samples Dutilh et al. submitted
  • 11. http://edwards.sdsu.edu/crass/ Dutilh et al. submitted
  • 12. 2 or 3 samples compared Dutilh et al. submitted
  • 13. 4 or more samples compared• Clustering: – Calculate distance measure • Correct for metagenome size • Correct for contig length water – Create cladogram human Dutilh et al. submitted
  • 14. Similar numbers of utilized reads human water10090 crAss80 BlastN70605040302010 0 Dutilh et al. submitted
  • 15. Simulated metagenomes0% 30% 60% 90%10% 40% 70% 100%20% 50% 80% Dutilh et al. submitted
  • 16. Acknowledgements• Robert Schmieder• Jim Nulton• Ben Felts• Peter Salamon• Robert A. Edwards• John L. Mokili

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