Beiko dcsi2013

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Rob Beiko's keynote presentation at DCSI 2013 (http://dcsi.cs.dal.ca/DCSI2013/index.php)

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Beiko dcsi2013

  1. 1. Classifying biological information the promise and perils of DNA sequences Rob Beiko September 19 DCSI 2013 Norm MacDonald Donovan Parks 1
  2. 2. From Francis Crick’s letter to his son Michael, 1953 2
  3. 3. Your Genome and You 23 chromosomes 20,000 genes 3.1 billion nucleotides Mycobacterium tuberculosis 1 chromosome 4,000 genes 4.4 million nucleotides Tremblaya princeps 1 chromosome 110 genes 138,931 nucleotides Daphnia pulex 12 chromosomes 31,000 genes 200 million nucleotides Paris japonica ?? chromosomes ??? genes 150 billion nucleotides3
  4. 4. DNA Encodes the Business of the Cell Chromosome Chromosome region Gene GGATCCTATGGATGCATGCCGCCGTAGTATAAT… Protein Protein functions Copying the genome and the cell Transport into and out of the cell Energy production and storage Cellular defense etc… 4
  5. 5. Three key questions (1)What genes in an organism’s genome are responsible for its unique properties? For example: - Ability to withstand environmental challenges - Developmental “plan” - Sources of nutrients (2) How can we use properties of an organism’s genome as a “fingerprint” to identify that organism? (3) What mutations to an organism’s genome (including single base changes) are responsible for altered properties of that organism? 5
  6. 6. Microbes: hot or not? + ++ +++ +++++++ Strain 121 MacDonald, NJ and Beiko, RG (2010). Efficient learning of microbial genotype–phenotype association rules. Bioinformatics 26: 1834-1840. 6
  7. 7. Beating the heat Proteins tend to stop working at temperatures above 37-40° C Heat shock – “Things are getting uncomfortable here” Extreme heat shock – “Make it stop make it stop make it stop!!!!” What does an organism need to get by at higher temperatures? (1) Specific proteins that help keep everything working (2) Changes to all proteins that make them more heat tolerant (3) Various other things Proteins tend to stop working at temperatures above 37-40° C Heat shock – “Things are getting uncomfortable here” Extreme heat shock – “Make it stop make it stop make it stop!!!!” What does an organism need to get by at higher temperatures? (1) Specific proteins that help keep everything working (2) Changes to all proteins that make them more heat tolerant (3) Various other things 7
  8. 8. The “genotype-phenotype association” problem Genotype: An organism’s DNA sequence, somehow defined Phenotype: An organism’s physical properties In this case, “genotype” will refer to the presence of genes that are similar enough that they likely share the same function 8
  9. 9. The “genotype-phenotype association” problem Gene 1 Gene 2 Gene 3 Gene 4 Gene 5                   9
  10. 10. A suitable approach Problem: a typical dataset will contain between 50-500 genomes, and presence / absence data for >10,000 genes We need an approach that can detect interactions among genes, so the potential feature space is very large. Searching all 210,000 rule combinations is obviously not going to happen. ASSOCIATION RULE MINING (Agrawal et al 1993): Discover associative rules between items, e.g. {Milk, Eggs} -> {Flour} Classification Based on Predictive Association Rules (Yin and Han, 2003): iteratively generate rules to “cover” each subset of the data 10
  11. 11. 11 F F, Q F, Z A None above gain threshold Rules discovered: 1. F, Q -> POSITIVE 2. F, Z -> POSITIVE 3. A -> POSITIVE Covered samples get their weight reduced before the next iteration None above gain threshold None above gain threshold Classification based on Predictive Association Rules (CPAR)
  12. 12. CPAR results One example for now: THERMOPHILY – the ability of an organism to grow at temperatures above 42° C 427 genomes in the dataset: 376 mesophiles (negative set), 51 thermophiles (positive set) 26,290 genes to consider Use CPAR to learn rules, submit identified genes to SVM for classification. 10x 5-fold cross-validation CPAR accuracy: 84.3% (obtained in 10.6 seconds) Best competitor (NETCAR): 79.3% (obtained in 1250.9 seconds) 12
  13. 13. CPAR Results Aeropyrum_pernix_K1 YES 0 Archaeoglobus_fulgidus_DSM_4304 YES 0 Caldicellulosiruptor_saccharolyticus_DSM_8903YES 0 Fervidobacterium_nodosum_Rt17-B1 YES 0 Hyperthermus_butylicus_DSM_5456 YES 0 Ignicoccus_hospitalis_KIN4/I YES 0 Metallosphaera_sedula_DSM_5348 YES 0 Pyrobaculum_arsenaticum_DSM_13514 YES 0 Pyrobaculum_calidifontis_JCM_11548 YES 0 Pyrobaculum_islandicum_DSM_4184 YES 0 Pyrococcus_abyssi_GE5 YES 0 Pyrococcus_furiosus_DSM_3638 YES 0 Pyrococcus_horikoshii_OT3 YES 0 Staphylothermus_marinus_F1 YES 0 Sulfolobus_acidocaldarius_DSM_639 YES 0 Sulfolobus_solfataricus_P2 YES 0 Thermoanaerobacter_tengcongensis_MB4 YES 0 Thermofilum_pendens_Hrk_5 YES 0 Thermotoga_maritima_MSB8 YES 0 Thermotoga_petrophila_RKU-1 YES 0 Thermus_thermophilus_HB27 YES 0 Thermus_thermophilus_HB8 YES 0 Roseiflexus_castenholzii_DSM_13941 YES 0 Thermosipho_melanesiensis_BI429 YES 0 Roseiflexus_sp._RS-1 YES 0 Moorella_thermoacetica_ATCC_39073 YES 0 Streptococcus_thermophilus_LMG_18311 YES 0 Thermoplasma_volcanium_GSS1 YES 1 Methanosaeta_thermophila_PT YES 1 Nanoarchaeum_equitans_Kin4-M YES 1 Thermoplasma_acidophilum_DSM_1728 YES 1 Picrophilus_torridus_DSM_9790 YES 1 Carboxydothermus_hydrogenoformans_Z-2901YES 1 Streptococcus_thermophilus_CNRZ1066 YES 1 Aquifex_aeolicus_VF5 YES 2 Methanopyrus_kandleri_AV19 YES 2 Pelotomaculum_thermopropionicum_SI YES 2 Rubrobacter_xylanophilus_DSM_9941 YES 3 Geobacillus_kaustophilus_HTA426 YES 3 Nitratiruptor_sp._SB155-2 YES 4 Synechococcus_sp._JA-3-3Ab YES 6 Geobacillus_thermodenitrificans_NG80-2 YES 7 Methanocaldococcus_jannaschii_DSM_2661 YES 8 Acidothermus_cellulolyticus_11B YES 8 Deinococcus_geothermalis_DSM_11300 YES 9 Clostridium_thermocellum_ATCC_27405 YES 9 Thermosynechococcus_elongatus_BP-1 YES 9 Sulfurovum_sp._NBC37-1 YES 10 Thermobifida_fusca_YX YES 10 Chlorobium_tepidum_TLS YES 10 Symbiobacterium_thermophilum_IAM_14863YES 10 # of misclassifications in 10 replicate runs Sulfurovum_sp._NBC37-1 YES 10 The classifier is right; the database is wrong!! 13
  14. 14. A complication Organisms are not independent observations! They share common ancestry Gene 1 Gene 2 Gene 3 Gene 4 Gene 5                   14
  15. 15. What to do? MUTUAL INFORMATION: CONDITIONAL MUTUAL INFORMATION: Weight CMI by total MI – CONDITIONAL WEIGHTED MUTUAL INFORMATION (CWMI) Reweight CPAR rules to reflect MI or CWMI: what patterns emerge? 15
  16. 16. What genes are identified? 16 Highlighted boxes: genes identified in “A DNA repair system specific for thermophilic Archaea and bacteria predicted by genomic context analysis” (Makarova et al., Nucleic Acids Research, 2002, 30 (2) , 482-496) Top CWMITop MI
  17. 17. Wrong, but in different ways 1717 Organism CPAR MI CWMI Streptococcus thermophilus LMG 18311 0 10 10 Streptococcus thermophilus CNRZ1066 1 10 10 Carboxydothermus hydrogenoformans Z-2901 1 8 5 Geobacillus kaustophilus HTA426 3 10 9 Synechococcus sp. JA-3-3Ab 6 8 2 Methanocaldococcus jannaschii DSM 2661 8 0 0 Acidothermus cellulolyticus 11B 8 9 6 Deinococcus geothermalis DSM 11300 9 8 5 Clostridium thermocellum ATCC 27405 9 10 4 Chlorobium tepidum TLS 10 10 8
  18. 18. Summary 18 Misclassifications (10 replicates) 18 - CPAR is FAST and fairly accurate, but the problem is challenging: no “magic” set of genes that automatically make you a thermophile - But we can investigate what pops up in the rules to find out which genes are most likely associated with heat tolerance - The hardest organisms to classify are from weird groups, with few or no close relatives that are also thermophilic - Different weighting schemes, especially those that consider the confounding effects of taxonomy, have different strengths and can identify different candidate genes
  19. 19. What’s next? 1919 - Much larger microbial datasets with much broader taxonomic coverage are now available - Will give us more precise models of what genes make a thermophile, pathogen, etc. - Consider other lines of evidence: variation WITHIN genes in addition to gene presence/absence - Apply to emerging pathogen data: classify outbreak isolates based on antibiotic resistance, virulence and other properties (SFU, BCCDC, National Microbiology Laboratory) Jie (Jessie) Ning
  20. 20. METAGENOMICS: Because one genome at a time is too easy MacDonald NJ, Parks DH, and Beiko, RG (2012). Rapid identification of high-confidence taxonomic assignments for metagenomic data. Nucleic Acids Research 40: e111. Parks DH, MacDonald NJ, and Beiko, RG (2011). Classifying short genomic fragments from novel lineages using composition and homology. BMC Bioinformatics 12: 328. 20
  21. 21. The microbial community problem - Microbes almost never act alone; samples will typically contain dozens or hundreds of different species - How can we answer the following questions: - What microbes are present in a given sample? - What functions do they carry out? - How do they interact with one another? 21
  22. 22. Metagenomics Sample Extract DNA Sequence DNA Assign sequences GATAA ? ? ?? 22
  23. 23. The species assignment problem GATAAATCTGG ? ? ?? - UNSUPERVISED (clustering-ish) and SUPERVISED approaches - For supervised classification, we need a set of known genomes - Two attributes provide key clues: (i) Genomic composition of k- mers (aka n-grams) (ii) Comparison with known gene sequences 23
  24. 24. The species assignment problem GATAAATCTGG 24 Mystery sequence Where did I come from? COMPOSITION (k-mers) k-mer frequency AA 2/10 AC 0/10 AG 0/10 AT 1/10 k-mer frequency AA 2/10 AC 0/10 AG 0/10 AT 1/10 k-mer frequency AA 2/10 AC 0/10 AG 0/10 AT 1/10 k-mer frequency AA 2/10 AC 0/10 AG 0/10 AT 1/10 k-mer frequency AA 2/10 AC 0/10 AG 0/10 AT 1/10 Genome models SIMILARITY GATAAATCTGG GATAAGTCTGG GACCAATCTGG GATAAACTTAG CAAGGATAAGC Sequences from reference genomes Sequence from metagenome
  25. 25. Metagenomes - the first few years 25 Cost of DNA sequencing (note log scale) Study Author, Year # of nucleotides Size of each “read” Acid mine drainage Tyson et al, 2004 7.62 x 107 737 nt Obese / Lean twins Turnbaugh et al, 2009 1.83 x 109 341 nt Human gut “catalogue” Qin et al, 2010 5.77 x 1011 75 nt
  26. 26. Summary of challenges 26 - Datasets are already huge, and getting bigger and more numerous - DNA sequences that we need to classify are SHORT: unstable estimates of composition and similarity - Our predictions depend on the coverage in our reference database - We need to combine different lines of evidence into a coherent prediction scheme
  27. 27. Two approaches 27 PhymmBL: Brady and Salzberg, 2010 - Similarity of sequences assessed through the BLAST algorithm - Composition assessed using interpolated context models - Predictions are combined using a formula RITA: MacDonald, Parks and Beiko, 2012 - Similarity of sequences assessed using UBLAST and BLAST - Composition assessed using naïve Bayes approach - Look for agreement between predictors; if no agreement, decide based on best evidence
  28. 28. The naïve Bayes approach 28 - Build k-mer profiles for each reference genome - The probability that a given DNA sequence fragment F originated from a given genome Gi is: - (that is, the combined frequencies of all k-mers from F in genome Gi) - Note that naïve Bayes assumes INDEPENDENCE, which is a bit funny with overlapping k-mers (But We Did It Anyway) M j iji GwPGFP 1 || AGGCTTGTCAA
  29. 29. Naïve Bayes in action 29 Build fake metagenomes by chopping up real sequenced genomes into pieces of length 200 Build a reference database that excludes the chopped up genomes AND Their close relatives (leave-one-out) How accurate is the classifier, for different values of k? k Average proportion of sequences correctly classified
  30. 30. Composition versus Similarity 30 Similarity (three right-hand sets) are more accurate (and slower) than composition approaches NB and P 1000 nt 200 nt
  31. 31. RITA: Rapid Identification of Taxonomic Assignments 31 Query DNA sequence fragment Run naïve Bayes classifier UBLAST filter (fast, imprecise) BLAST comparisons (slower, better) Is there a BLAST match? Is there a strong naïve Bayes preference? Do BLAST and naïve Bayes agree? Is there a strong BLAST preference? Group 2 Group 3 Group 1a Group 1b Yes! No!
  32. 32. Performance on different sequence lengths 32
  33. 33. Running time 33 0.01 0.1 1 10 100 Runningtime(h) Running times on 116,244 sequences
  34. 34. Application to human microbiome data sets 34 Homology+CompositionComposition Without HMP genomes: Clostridium, Bacteroides and Eubacterium, but lots of low-confidence calls too With HMP reference genomes: Add Ruminococcus, Faecalibacterium, Lachnospiraceae Good Less Good Data from Turnbaugh et al., 2010
  35. 35. Application to bioremediation metagenome 35Hug et al., 2012 Three sets of microbes, all can clean up PCEs. Are there differences in the composition of these sets?
  36. 36. Summary 36 - Naïve Bayes is FAST and performs as well as alternative, more complicated approaches - The combination of composition and similarity is superior to either approach in isolation - The accuracy on short reads is good, but a substantial minority of reads are misclassified so the question of “who is doing what” remains somewhat open
  37. 37. What’s next? 37 - Apply to emerging metagenomic data sets: - Bioremediation - Aging and frailty in mice and humans - Refine the approach to include both unsupervised and supervised components
  38. 38. Coda #1: mammalian fertility 38 Random mating CONTROL (105) Selective breeding SELECTED (344) Starting colony 30 years of…. Examine genetic variation at >8000 positions within the genome. Are there any genetic differences at one or more sites that distinguish the populations and individuals within the populations? Alex Keddy Katherine Rutherford
  39. 39. Machine-learning results 39 Different ML approaches with feature selection Observed vs Predicted reproductive rate for RF regression model
  40. 40. What’s next? 40Jeremy Koenig - Expand the project: more data, and more types of data! - Integrating lines of evidence from multiple sources will be a significant challenge – each yields overlapping / different predictions - Map interesting results into the cow genome and test effectiveness Developer to be named later
  41. 41. Coda #2: data retrieval and GIS 41 20,304 samples 1.7 billion sequences
  42. 42. 42 Conor Meehan
  43. 43. Objectives 43 - Automated classification of data from sources such as the EMP - Retrieval of data from EMP via Web services under development (some plugins already completed – come in October for the story)
  44. 44. What’s next? 44 My Dal Homecoming lecture on October 4
  45. 45. Classifying DNA: Adventures in Multidisciplinarity 45 Genetics Evolution Statistics Machine Learning Throw in the challenges of massive data sets, data retrieval challenges, emerging technologies, and uncertain reliability of some data sets, And there is a lot of work still to be done!! Chris Whidden Donovan Parks Morgan Langille
  46. 46. Open Science 46 @rob_beiko Github Preprint servers This presentation: http://www.slideshare.net/beiko/beiko-dcsi2013
  47. 47. Fin 47 Fin
  48. 48. Image credits Please follow links for copyright information Slide 1: http://commons.wikimedia.org/wiki/File:DNA Overview2.png Slide 2: s3.documentcloud.org/documents/706661/francis-crick-letter.pdf Slide 3: http://www.nature.com/nature/journal/v393/n6685/full/393537a0.html http://www.ncbi.nlm.nih.gov/sutils/static/GP IMAGE/Mycobacterium.jpg http://commons.wikimedia.org/wiki/File:Shakespeare.jpg http://commons.wikimedia.org/wiki/File:Wolllaus.jpg http://phenomena.nationalgeographic.com/files/2013/06/Tremblaya Moranella.jpg (Ryuichi Koga, National Institute of Advanced Industrial Science and Technology, Japan) http://commons.wikimedia.org/wiki/File:Daphnia pulex.png http://commons.wikimedia.org/wiki/File:Paris japonica Kinugasasou in Hakusan 2003 7 27.jpg Slide 4: http://upload.wikimedia.org/wikipedia/commons/2/21/DNA human male chromosomes.gif http://commons.wikimedia.org/wiki/File:LKB1 complex structure 2WTK.png Slide 6: http://commons.wikimedia.org/wiki/File:Yogurt of the Bulgarija Pavilion of Expo 2005 Aichi Japan.jpg http://en.wikipedia.org/wiki/File:Grand prismatic spring.jpg http://www.nsf.gov/od/lpa/news/03/images/scsmoker2th.jpg http://www.nsf.gov/od/lpa/news/03/images/strain121 thin th.jpg Slide 21: http://commons.wikimedia.org/wiki/File:EPA_TECHNICIAN_COLLECTS_WATER_SAMPLE_FROM_PAHRANAGAT_LAKE_ABOUT_10_MILES_SOUTH _OF_ALAMO_-_NARA_-_549007.jpg http://commons.wikimedia.org/wiki/File:DNA_orbit_animated_small.gif http://commons.wikimedia.org/wiki/File:DNA_sequence.svg Slide 24:Stein, L. Genome Biology 2010 11:207 Slide 36: http://commons.wikimedia.org/wiki/File:Mouse-19-Dec-2004.jpg 48

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