This document discusses methods for comparing metagenomic profiles between environments, including calculating distances between taxonomic and functional profiles, clustering profiles to identify similar environments, and comparing profiles using k-mer frequencies. It also describes using cross-assembly of sequencing reads from multiple metagenomes to identify shared genetic material between samples. The document evaluates these methods on human microbiome data and simulated metagenomes.

1. Comparative metagenomics
quantifying 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 water
100
90 crAss
80 BlastN
70
60
50
40
30
20
10
0
Dutilh et al. submitted

15. Simulated metagenomes
0% 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