Comparative Genomics Final Results
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1 Comparative Genomics Final Results April 20, 2016 Juan Castro, Aroon Chande, Cheng Chen, Evan Clayton, Hector Espitia, Alli Gombolay, Walker Gussler, Ken Lee, Tyrone Lee, Hari Prasanna, Carlos Ruiz, Niveda Sundararaman, and Peijue Zhang
2 Outline Background Classic Classification Exploratory Methods Results ANI MASH Phenotypic Method Results Typing Scheme SVM-RFE Virulence Factors - Other classification targets
3 Background Our data: Haemophilus influenzae Two major categories: unencapsulated strains (untypable) and the encapsulated strains (a, b, c, d, e, f). Genetic diversity among unencapsulated strains is greater than within the encapsulated group.
4 Produce an inflammatory response in humans. H. influenzae type b (Hib) causes bacteremia, pneumonia, epiglottitis and acute bacterial meningitis in infants and young children The pathogenesis of H. influenzae infections is not completely understood
5 Getting to Know your Data (64 isolates) Sources Blood Sinus Drainage Ankle fluid Years Average assembly (bp) Average GC Content
6 What is the goal Are the NTHi evolutionarily different from the typable Hi Yes Develop a typing scheme for various strains of Nontypeable Haemophilus influenzae (NTHi)
7 Classic Classification does not work
8
9
10 Grouping H. haemolyticus (ANI Group 1) Other Haemophilus (ANI Group 1) Hi serotype F-like (ANI Group 2) NTHi group 1 (ANI Group 3) NTHi group 2 (ANI Group 4) Hi serotype b Strains without clear grouping M25364 (Unknown species)
11 Grouping H. haemolyticus (ANI Group 1) Other Haemophilus (ANI Group 1) Hi serotype F-like (ANI Group 2) NTHi group 1 (ANI Group 3) NTHi group 2 (ANI Group 4) Hi serotype b } } } Serotype C Serotype A Serotype D Serotype B Strains without clear grouping M25364 (Unknown species) Serotype F } } Serotype E Serotype A
12 All of the methods we used were insufficient for the differentiation of NTHi from typeable Hi.
13 Serotyping
14 Serotyping Res
15 Serotyping Res
16 Serotyping Typeable! Res Non Typeable!
17 Our Typing Scheme Davis, G. et al; Journal of Clinical Microbiology, 2011
18 Our Typing Scheme Davis, G. et al; Journal of Clinical Microbiology, 2011
19 Typing Scheme Region I Region III Region II bexa bexb bexc bexd ccsa bexa bexb bexc bexd dcsa bexa bexb bexc bexd bcsb ccsb dcsb ccsc dcsc bcsd ccsd dcsd dcse hcsa hcsb hcsa hcsb hcsa hcsb
20 Typing Scheme Region I Region III Region II bexa bexb bexc bexd ccsa bexa bexb bexc bexd dcsa bexa bexb bexc bexd ccsb dcsb bcsb 1 ccsc dcsc ccsd dcsd bcsd 1 dcse hcsa hcsb hcsa hcsb hcsa hcsb 1 1 8
21 Typing Scheme
22 Typing Scheme
23 SVM-RFE Approach SVM Support Vector Machine + RFE Recursive Feature Elimination Machine learning technique intended to extract informative features from a data set Conceived to obtain the most informative genes associated to cancer, using gene expression data
24 SVMs (Support Vector Machines) A data set in which we have features (genes) and samples (genomes) 2 features => 2 dimensions (x, y)
25 SVMs (Support Vector Machines) A data set in which we have features (genes) and samples (genomes) 2 features = 2 dimensions (x, y) 2 classes (red, blue)
26 SVMs (Support Vector Machines) The SVM can separate the data in two classes: Using training data Finding the best separation of the data (optimal hyperplane)
27 SVMs (Support Vector Machines) How to classify new data (with no prior knowledge)
28 SVMs (Support Vector Machines) How to classify new data (with no prior knowledge) Using what the SVM learned from training: Optimal hyperplane
29 SVM-RFE...
30 SVM-RFE...
31 SVM-RFE...
32 SVM-RFE...
33 SVM-RFE...
34 SVM-RFE
35 SVM-RFE
36 SVM-RFE
37 SVM-RFE Input matrix (BSR values + class) BLAST Score Ratio (BSR) Gene1 Gene2 Gene3... GeneN Genome1 T bsr1,1 bsr1,2 bsr1,3... bsr1,n Genome2 NTHi bsr2,1 bsr2,2 bsr2,3... bsr2,n Genome3 NTHi bsr3,1 bsr4,2 bsr3,3... bsr3,n GenomeM T bsrm,1 bsrm,2 bsr1m,3... bsrm,n... Class... Relatedness of pangenome genes across all genomes
38 SVM-RFE Testing the SVM-RFE method 4 subsets (24 genomes each one) 12 Typeable 12 NTHi
39 SVM-RFE Top-20 genes intersection
![sputorum] BexC [Hi] 4 M09394_0_294 HcsB [Hi] 5 M09394_0_299 Capsule biosynthesis protein CapC [H.](/docs-images/78/76776452/images/40-1.jpg "sputorum] Ccs1 [Hi] 6 M04744_0_747 Capsule biosynthesis protein [Hi] BexC [Hi] 7 M04744_0_748 Sugar ABC")
40 SVM-RFE Top-20 genes intersection # Gene Id Description (NCBI nr microbial protein database) 1 M04828_0_262 Bcs3 [Hi] 2 M03959_0_297 HcsB' [Hi] 3 M03959_0_305 Capsule polysaccharide transporter [H. sputorum] BexC [Hi] 4 M09394_0_294 HcsB [Hi] 5 M09394_0_299 Capsule biosynthesis protein CapC [H. sputorum] Ccs1 [Hi] 6 M04744_0_747 Capsule biosynthesis protein [Hi] BexC [Hi] 7 M04744_0_748 Sugar ABC transporter permease [Hi] BexB [Hi]
41 SVM-RFE All shared genes from the top-20 intersection correspond to capsule-related genes (present in the typing scheme) SVM-RFE has potential to be a useful technique to find genes with discriminative power
42 The time of reckoning
43 Now what Class III Spahich, N,, et al; Journal of Bacteriology, 2012
44 Now what Typeable strains Class III Spahich, N,, et al; Journal of Bacteriology, 2012
45 Now what Class II Typeable strains Class III Spahich, N,, et al; Journal of Bacteriology, 2012
46 Now what Class I Class II Typeable strains Class III Spahich, N,, et al; Journal of Bacteriology, 2012
47 Experimental setup Many Genes Few genes
48 Experimental setup PCR primers Many Genes Few genes
49 Experimental setup Gene Amplicon size (bp) Tm (FP/RP) (Cº) Forward primer / Reverse primer KfiC /60.0 5'-AGTCAGAGGGCGAAACCGACCA-3' / 5'AACTCGCCCTTGGCAACGGATG-3' WbaP/RfbP /60.5 5'-TCCTGAAGCAAGAGCTGAATGGGA-3' / 5'ACGTCCGCTGACTTGCCAAAGC-3' HscA /59.9 5'-AGCGGAAAAGGCTCGGTCACAA-3' / 5'CACACCCCAGCCAGCAAACCAT-3' HxuA /59.4 5'-TCAACGCAGACGCCGTTGGAAA-3' / 5'ATCAATTGAGCCAGGCGCACCA-3' BexC /59.9 5'-AGCAGCGACACAAACTGCGGAT-3' / 5'GCCCAGTCTGGCTTGCTTGGTT-3'
50 Multiplexed PCR* Marker Typeable Typeable Strain Strain A, B, or F C, D, or E NTHi Class I NTHi Class II NTHi Class III Strain M25364 Neg Control 1200bp 1000bp 900bp 800bp 700bp KfiC 600bp 500bp HxuA HscA 400bp 300bp 200bp WbaP-RfbP BexC 100bp *Dramatization
51 What could be improved Train SVM-RFE with the classes found with HMM-SOM and Virulence patterns. Reduce the number of features separating typeable from NTHi
52 Why not
53 THANKS! QUESTIONS
54 References Davis, G. S., Sandstedt, S. A., Patel, M., Marrs, C. F., & Gilsdorf, J. R. (2011). Use of bexb to detect the capsule locus in Haemophilus influenzae. Journal of Clinical Microbiology, 49(7), doi: /JCM Spahich, N. A., Hood, D. W., Moxon, E. R., & St Geme, J. W. (2012). Inactivation of Haemophilus influenzae lipopolysaccharide biosynthesis genes interferes with outer membrane localization of the hap autotransporter. Journal of Bacteriology, 194(7), doi: /jb Sahl, J. W., Caporaso, J. G., Rasko, D. A., & Keim, P. (2014). The large-scale blast score ratio (LS-BSR) pipeline: a method to rapidly compare genetic content between bacterial genomes. PeerJ, 2, e332. doi: /peerj.332 Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning, 46(1-3), doi: /a:
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