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Beiko hpcs
- 1. (an example of) Computing the Microbial World Rob Beiko June 25, 2014
- 3. Siddique et al. (2014) Front Microbiol
- 4. Lawley et al., PLoS Genet (2012)
- 6. The Breakfast Organisms "Bacon Fields" Author: Michael DeForge
- 7. 240M “pieces”, each 150 nucleotides long 3.6 x 1010 nucleotides ~40 GB Hundreds of “species” Genomes between 1.5M – 6M nucleotides
- 8. 150 nt x 150 nt We know this And this But not this
- 9. who is doing what? Marker genes WHO Environmental “Shotgun” WHAT The challenge of METAGENOME CLASSIFICATION
- 10. Clues – Sequence similarity (homology) 150 nt x 150 nt Reference genes Take the WHOLE SEQUENCE Best Worst
- 11. Clues – composition 150 nt x 150 nt Reference genome k-mer profiles Genome #1: 20% G & C 30% A & T Genome #2: 24% G & C 26% A & T Best Worst Take a K-MER FREQUENCY DECOMPOSITION
- 12. Homology >> Composition * GGCTGGACCA 1 GACTGGACCA 2 GGCCGGACTA But homology evidence can mislead or be absent Homology + Composition > Homology alone
- 13. GGCTGGACCA GCCTGGTCCA GCCAGGTGCA GCCTGTCCA NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN Query: Subject: Exact string search? NO BLAST? OK, but SLOW!
- 14. A compromise: UBLAST • BLAST seeks out very similar “anchor points” between a pair of sequences before doing a more thorough search • Typically, a query is compared against all candidate DB sequences, but most will return no hits UBLAST: GGCTGGACCA GCCTGTCCA NNNNNNNNNN NNNNNNNNNN GCCAGGTGCA NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN GCCTGGTCCA NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN (1) Query, DB sequences GGCTGGACCA GCCTGGTCCA GCCAGGTGCA GCCTGTCCA NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN (3) Rank DB based on k-mer matching GGCTGGACCA GCCTGGTCCA GCCAGGTGCA GCCTGTCCA NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN NNNNNNNNNN (4) Do detailed search until there is no more point X (2) k-mer table
- 15. Compositional models • Interpolated Markov models: adaptively generate frequency models based on extending k-mers with sufficiently high frequencies • One model per genome • Evaluate probability of each k-mer in query sequence, given shorter k-mers in sequence • Model construction can take a while k = 4 k = 5 k = 6 k = 7 PhymmBL: Brady and Salzberg (2009) Nat Methods
- 16. An alternative: Naïve Bayes • Just compute the frequency of each k-mer for a fixed length k • Build one frequency model for each genome • FAST • Assumes conditional independence – may not matter Probability of a query Fragment originating from genome Gi For all k-mers in the fragment… The frequency of that k-mer in Gi Parks et al. (2011) BMC Bioinformatics
- 17. RITA: Rapid Identification of Taxonomic Assignments UBLAST filter MacDonald et al. (2012) Nucleic Acids Res
- 18. Evaluation set • “Fake metagenome”: take sequences from known genomes, randomly sample fragments of 50, 100, 200 and 1000 nt in different trials • Build reference models from other genomes – can leave close relatives out of reference model • Leave out other strains within the same species – not so hard • Leave out other classes in the same phylum - HARD
- 20. But does it work? Full RITA Best class (homology and composition agree) DNA sequence length50 Predicting genus from different species Predicting phylum from different class
- 21. Conclusions • Careful attention needs to be paid to the choice of approach – simple is better • RITA illustrates two key points in (microbial) bioinformatics: 1. Homology: How heuristic are you willing to go? 2. Naïve Bayes: Keep it simple until told otherwise • Technological change means that many bioinformatics algorithms will be irrelevant in 5 years
- 22. FIN