1. The Computational Biology of Genetically Diverse Assemblages Allen Rodrigo 1, Frederic Bertels 1, Mehul Rathod 2, Sean Irvine 2, John Cleary 2,3, Peter Tsai 1 1 The Allan Wilson Centre for Molecular Ecology and Evolution and the Bioinformatics Institute New Zealand, University of Auckland 2 NetValue Ltd 3 Department of Computer Science, University of Waikato

2. Metagenomics The study of the genetics of diverse assemblages of (micro)organisms from natural environments is called metagenomics. Metagenomic studies… – Utilise new high-throughput sequencing technologies – Typically include unknown organisms and novel genes – Will generate large amounts of genetic data – Can be performed in a range of environments – Requires significant computational resources and new algorithms – Have the potential to revolutionize the way we think about the genetic makeup of the environment

3. The New Icons

4. Source: J. Craig Venter Institute www.jvci.org

5. Preliminary Results of the GOS Study 2000 new protein “types” – Many viral proteins – New occurrences of proteins in previously unrecorded taxonomic groups >6000 new open reading frames (potential protein coding sequences)

6. Metagenomics of Communities at Neighbouring Thermal Vents Rarefaction Curves Higher taxa Species Huber et al, 2007, Science 318: 97 - 100

7. The Marine Viromes Project

8. Angly FE, Felts B, Breitbart M, Salamon P, Edwards RA, et al. (2006) The Marine Viromes of Four Oceanic Regions. PLoS Biol 4(11): e368 doi:10.1371/journal.pbio.0040368

9. Community Comparisons If the primary purpose is to relate community structure to environment, space or time then: – We need to quantify the similarities between different communities – So that we can relate these similarities to the environmental, temporal or spatial similarities.

10. Community Comparisons The bottleneck in these analyses is the identification of each sequence in the sample. Sequences may be amplicons of single loci or environmental shotgun sequences.

11. New Sequencing Technologies Roche, Illumina, and Applied Biosystems have released next-generation sequencers that produce large quantities of sequence information. – Millions of shotgun fragments, each between 25nt-250nt long – 10 6 - 10 9 nt in a single run (within days) Other technologies will follow.

12. Community Comparisons The bottleneck in these analyses is the identification of each sequence in the sample. The challenge is to either Find algorithms that can speed up this process Free ourselves of the process

13. Identifying The Species Present Using BLAST takes time. However, new tools are presently available. Used SLIMSearch (www.slimsearch.com)www.slimsearch.com – Proprietory search algorithm based on word matching – Disclosure: I am on the SAB!

14. Identifying The Species Present Simulations: Select random 60 genomes from the set of 546 fully- sequenced bacterial genomes Compute the number of reads for each genome in the 60 following the log normal distribution as above – 250nt reads, 0.7x coverage (distributed over 60 genomes using a log normal distribution mean = 2, standard deviation = 3.3) – Approx. 600,000 reads Set error at 0.5% – generated by random selection from the genome and appropriate mutation(90% indels 10% substitutions) Time SLIMSearch and BLASTN with each set as query against 546 genomes

15. Identifying The Species Present BLASTN (sec) -- 247127.56 = 68.6hrs SLIMSearch (sec) -- 384.53 = 6 mins computer configuration – TAHI 2 x Dual core opteron 2212 (2.0 GHz), 8 GB RAM, 1 TB (2 x 500GB), Debian AMD64 4.0(Etch), DELL Poweredge 1435

16. What About Identifying The Species Present?

18. Community Comparisons The bottleneck in these analyses is the identification of each sequence in the sample. Sequences may be amplicons of single loci or environmental shotgun sequences. The challenge is to either Find algorithms that can speed up this process Free ourselves of the process

19. Identification-Free Comparisons We have chosen to explore the use of alignment-free methods. These can be classed into 2 broad types: Similarity of word frequency spectra Compression-type procedures

20. Similarity of Word Frequency Spectra Define a word-length, k. For each taxon/sequence, identify the frequencies of all possible k-words. Compare frequency spectra between pairs using an appropriate distance metric. – Metrics tend to differ based on how they normalise word frequencies, the distances used, and how expected frequencies are calculated. Dates back to Blaisdell (1986).

21. Compression-based Methods Some sophisticated maths, but a very simple idea. What is the “compressibility” of two datasets when they are combined, relative to the sum of their individual “compressibilities”? – How much shared information is there between two datasets? Previous work has shown some nice phylogenetic properties.

22. Alignment-free Comparisons We applied word frequency and compression algorithms to datasets consisting of: – 16S complete rDNA sequences of 35 bacteria spanning a wide range of phyla and with a range of GC-contents from the Ribosomal Database Project (Maidak et al, 1997). – the same 16S rDNA sequences, cut into random short fragments of length 250 (+/-50) each with 3X coverage, using the program READSIM (source: http://www-ab.informatik.uni-tuebingen.de/software/ readsim/welcome.html) with a relatively high error rate of approximately 4%http://www-ab.informatik.uni-tuebingen.de/software/ readsim/welcome.html – full genomes of the same bacteria as in (a).

23. Alignment-free Comparisons Pairwise ML distances between the original sequences were obtained with PAUP* using models of substitution obtained with ModelTest. 22 compression algorithms used – Ferragina et al. (2007) http://www.math.unipa.it/~raffaele/kolmogorov/http://www.math.unipa.it/~raffaele/kolmogorov/ – Distances computed using Universal Compression Dissimilarity distance: Frequencies of k-words ( ) were compared using Manhattan or Euclidean distances.

24. Compression Algorithms: Distance comparisons with complete 16S rDNA

25. Word Algorithms: Distance comparisons with complete 16S rDNA A) Manhattan word length 4 B) Euclidean word length 4 C) Euclidean word length 6 D) Manhattan word length 6 E) Manhattan word length 8 F) Euclidean word length 8 G) Manhattan word length 7 H) Euclidean word length 5 I) Manhattan word length 5 J) Euclidean word length 7

26. Compression Algorithms: Distance comparisons with short-read 16S rDNA

27. Word Algorithms: Distance comparisons with short-read 16S rDNA A) Manhattan word length 4 B) Euclidean word length 4 C) Euclidean word length 6 D) Manhattan word length 6 E) Manhattan word length 8 F) Euclidean word length 8 G) Manhattan word length 7 H) Euclidean word length 5 I) Manhattan word length 5 J) Euclidean word length 7

28. Compression Algorithms: Distance comparisons with complete genomes

29. Word Algorithms: Distance comparisons with complete genomes A) Manhattan word length 4 B) Euclidean word length 4 C) Euclidean word length 6 D) Manhattan word length 6 E) Manhattan word length 8 F) Euclidean word length 8 G) Manhattan word length 7 H) Euclidean word length 5 I) Manhattan word length 5 J) Euclidean word length 7

30. Problems and Challenges It appears that we are able to use compression and word-frequency methods with a single locus. With whole genomes, these methods break down. – Lateral gene transfer – GC content differences across the genome – Numbers of repeats

31. Can we use alignment-free methods to quantify the similarity of communities for which only a single locus has been sequenced? Simulations – 100 communities – Each with 10 randomly-selected bacterial species’ 16SrRNA – Log-normal species frequency distribution

32. Alignment-free Community Comparisons

33. Provisional Conclusions Alignment-free methods hold promise for the rapid estimation of pairwise distances between amplicons and NGS from single species or communities They work less well with whole genomes. Advancements in search/identification strategies may negate the necessity for these fast methods.

34. Acknowledgements NZ-France Dumont D’Urville Fund