Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

BMC Genomics

Volumn 17, Issue 1, 2016, Pages

  Drouin, Alexandre ^a   Giguère, Sébastien ^b   Déraspe, Maxime ^a   Marchand, Mario ^a   Tyers, Michael ^b   Loo, Vivian G ^c,d   Bourgault, Anne Marie ^c,d   Laviolette, François ^a   Corbeil, Jacques ^a  

^a UNIVERSITÉ LAVAL   (Canada)

^b INSTITUTE FOR RESEARCH IN IMMUNOLOGY AND CANCER   (Canada)

^c MCGILL UNIVERSITY HEALTH CENTRE   (Canada)

^d MCGILL UNIVERSITY   (Canada)

Author keywords

Antibiotic resistance; Bacteria; Biomarker discovery; Genomics; Machine learning

Indexed keywords

ANTIBIOTIC AGENT; BIOLOGICAL MARKER;

ACCURACY; ANTIBIOTIC RESISTANCE; ARTICLE; GENE MUTATION; GENE SEQUENCE; GENETIC VARIABILITY; GENOME ANALYSIS; MACHINE LEARNING; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PEPTOCLOSTRIDIUM DIFFICILE; PHENOTYPE; PSEUDOMONAS AERUGINOSA; REFERENCE FREE METHOD; STATISTICAL MODEL; STREPTOCOCCUS PNEUMONIAE; VALIDATION PROCESS;

EID: 84992053162     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-016-2889-6     Document Type: Article

References (62)

1. Azuaje F. Bioinformatics and Biomarker Discovery. "Omic" Data Analysis for Personalized Medicine. Chichester, United Kingdom: John Wiley & Sons; 2011.
2. Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER. The next-generation sequencing revolution and its impact on genomics. Cell. 2013; 155(1):27-38.
3. Mbianda C, El-Meanawy A, Sorokin A. Mechanisms of BK virus infection of renal cells and therapeutic implications. J Clin Virol. 2015; 71:59-62.
4. Simon R. Genomic biomarkers in predictive medicine: an interim analysis. EMBO Mol Med. 2011; 3(8):429-35.
5. van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet. 2014; 30(9):418-26.
6. Brookes AJ. The essence of snps. Gene. 1999; 234(2):177-86.
7. Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet. 2011; 12(6):443-51.
8. Bonham-Carter O, Steele J, Bastola D. Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis. Brief Bioinform. 2014; 15(6):890-905.
9. Leimeister CA, Boden M, Horwege S, Lindner S, Morgenstern B. Fast alignment-free sequence comparison using spaced-word frequencies. Bioinformatics. 2014; 177:1991-1999.
10. Song K, Ren J, Reinert G, Deng M, Waterman MS, Sun F. New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing. Brief Bioinform. 2014; 15(3):343-53.
11. Vinga S, Almeida J. Alignment-free sequence comparison-a review. Bioinformatics. 2003; 19(4):513-23.
12. Vinga S. Biological squence analysis by vector-valued functions: revisiting alignment-free methodologies for dna and protein classification. Adv Comput Methods for Biocomputing Bioimaging. 2007:71-107.
13. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Berlin, Germany: Springer; 2013.
14. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007; 23(19):2507-17.
15. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003; 3:1157-82.
16. Marchand M, Shawe-Taylor J. The set covering machine. J Mach Learn Res. 2002; 3:723-46.
17. World Health Organization. Antimicrobial Resistance: Global Report on Surveillance. Geneva, Switzerland: World Health Organization; 2014.
18. Davies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev. 2010; 74(3):417-33.
19. Earle SG, Wu CH, Charlesworth J, Stoesser N. Identifying lineage effects when controlling for population structure improves power in bacterial association studies. Nature Microbiology. 2016; 1:16041.
20. Bradley P, Gordon NC, Walker TM, Dunn L, Heys S, Huang B, Earle S, Pankhurst LJ, Anson L, de Cesare M, Piazza P, Votintseva AA, Golubchik T, Wilson DJ, Wyllie DH, Diel R, Niemann S, Feuerriegel S, Kohl TA, Ismail N, Omar SV, Smith EG, Buck D, McVean G, Walker AS, Peto TEA, Crook DW, Iqbal Z. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun. 2015; 6:10063.
21. Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees. New York: CRC press; 1984.
22. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995; 20(3):273-97.
23. Cheng B, Titterington DM. Neural networks: a review from a statistical perspective. Stat Sci. 1994; 9:2-30.
24. Merker M, Blin C, Mona S, Duforet-Frebourg N, Lecher S, Willery E, Blum MGB, Rüsch-Gerdes S, Mokrousov I, Aleksic E, Allix-Béguec C, Antierens A, Augustynowicz-Kopeć E, Ballif M, Barletta F, Beck HP, Barry CE, Bonnet M, Borroni E, Campos-Herrero I, Cirillo D, Cox H, Crowe S, Crudu V, Diel R, Drobniewski F, Fauville-Dufaux M, Gagneux S, Ghebremichael S, Hanekom M, Hoffner S, Jiao WW, Kalon S, Kohl TA, Kontsevaya I, Lillebæk T, Maeda S, Nikolayevskyy V, Rasmussen M, Rastogi N, Samper S, Sanchez-Padilla E, Savic B, Shamputa IC, Shen A, Sng LH, Stakenas P, Toit K, Varaine F, Vukovic D, Wahl C, Warren R, Supply P, Niemann S, Wirth T. Evolutionary history and global spread of the Mycobacterium tuberculosis Beijing lineage. Nat Genet. 2015; 47(3):242-9.
25. Kos VN, Deraspe M, McLaughlin RE, Whiteaker JD, Roy PH, Alm RA, Corbeil J, Gardner H. The resistome of Pseudomonas aeruginosa in relationship to phenotypic susceptibility. Antimicrob Agents Chemother. 2015; 59(1):427-36.
26. Croucher NJ, Finkelstein JA, Pelton SI, Mitchell PK, Lee GM, Parkhill J, Bentley SD, Hanage WP, Lipsitch M. Population genomics of post-vaccine changes in pneumococcal epidemiology. Nat Genet. 2013; 45(6):656-63.
27. Schölkopf B, Tsuda K, Vert JP. Kernel Methods in Computational Biology. Cambridge, Massachusetts: MIT press; 2004.
28. Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis: Cambridge university press; 2004.
29. Kingsford C, Salzberg SL. What are decision trees?Nat Biotechnol. 2008; 26(9):1011-3.
30. Noble WS. What is a support vector machine?Nat Biotechnol. 2006; 24(12):1565-1567.
31. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É. Scikit-learn: machine learning in python. J Mach Learn Res. 2011; 12:2825-30.
32. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ. LIBLINEAR: a library for large linear classification. J Mach Learn Res. 2008; 9:1871-4.
33. Chang CC, Lin CJ. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST). 2011; 2(3):27.
34. Wilcoxon F. Individual Comparisons by Ranking Methods. Biometrics Bulletin. 1945; 1(6):80.
35. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990; 215(3):403-10.
36. Cade CE, Dlouhy AC, Medzihradszky KF, Salas-Castillo SP, Ghiladi RA. Isoniazid-resistance conferring mutations in mycobacterium tuberculosis katg: Catalase, peroxidase, and inh-nadh adduct formation activities. Protein Sci. 2010; 19(3):458-74.
37. Da Silva PEA, Palomino JC. Molecular basis and mechanisms of drug resistance in mycobacterium tuberculosis: classical and new drugs. J Antimicrob Chemother. 2011; 66(7):1417-30.
38. Daly MM, Doktor S, Flamm R, Shortridge D. Characterization and prevalence of MefA, MefE, and the associated msr(D) gene in Streptococcus pneumoniae clinical isolates. J Clin Microbiol. 2004; 42(8):3570-4.
39. Ambrose KD, Nisbet R, Stephens DS. Macrolide efflux in streptococcus pneumoniae is mediated by a dual efflux pump (mel and mef) and is erythromycin inducible. Antimicrob Agents Chemother. 2005; 49(10):4203-9.
40. Basavanna S, Chimalapati S, Maqbool A, Rubbo B, Yuste J, Wilson RJ, Hosie A, Ogunniyi AD, Paton JC, Thomas G, Brown JS. The effects of methionine acquisition and synthesis on streptococcus pneumoniae growth and virulence. PLoS ONE. 2013; 8(1):49638.
41. Tenson T, Lovmar M, Ehrenberg M. The mechanism of action of macrolides, lincosamides and streptogramin B reveals the nascent peptide exit path in the ribosome. J Mol Biol. 2003; 330(5):1005-14.
42. Farrow KA, Lyras D, Rood JI. The macrolide-lincosamide-streptogramin B resistance determinant from Clostridium difficile 630 contains two erm(B) genes. Antimicrob Agents Chemother. 2000; 44(2):411-3.
43. Vester B, Douthwaite S. Macrolide resistance conferred by base substitutions in 23S rRNA. Antimicrob Agents Chemother. 2001; 45(1):1-12.
44. Brouwer MSM, Warburton PJ, Roberts AP, Mullany P, Allan E. Genetic organisation, mobility and predicted functions of genes on integrated, mobile genetic elements in sequenced strains of Clostridium difficile. PLoS ONE. 2011; 6(8):23014.
45. Kaminska KH, Purta E, Hansen LH, Bujnicki JM, Vester B, Long KS. Insights into the structure, function and evolution of the radical-SAM 23S rRNA methyltransferase Cfr that confers antibiotic resistance in bacteria. Nucleic Acids Res. 2010; 38(5):1652-63.
46. Waxman DJ, Strominger JL. Penicillin-binding proteins and the mechanism of action of beta-lactam antibiotics. Ann Rev Biochem. 1983; 52:825-69.
47. Wasels F, Spigaglia P, Barbanti F, Mastrantonio P. Clostridium difficile erm(B)-containing elements and the burden on the in vitro fitness. J Med Microbiol. 2013; 62(Pt 9):1461-7.
48. Hsu PD, Lander ES, Zhang F. Development and applications of crispr-cas9 for genome engineering. Cell. 2014; 157(6):1262-78.
49. Nair J, Rouse DA, Bai GH, Morris SL. The rpsL gene and streptomycin resistance in single and multiple drug-resistant strains of Mycobacterium tuberculosis. Mol Microbiol. 1993; 10(3):521-7.
50. Fani F, Leprohon P, Zhanel GG, Bergeron MG, Ouellette M. Genomic analyses of DNA transformation and penicillin resistance in Streptococcus pneumoniae clinical isolates. Antimicrob Agents Chemother. 2014; 58(3):1397-1403.
51. FastQC A Quality Control Tool for High Throughput Sequence Data (version 0.11.5). http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 30 June 2016.
52. Loman NJ, Constantinidou C, Chan J. High-throughput bacterial genome sequencing: an embarrassment of choice, a world of opportunity. Nat Rev. 2012; 10:599-606.
53. Adam M, Murali B, Glenn NO, Potter SS. Epigenetic inheritance based evolution of antibiotic resistance in bacteria. BMC Evol Biol. 2008; 8(1):1-12. doi: 10.1186/1471-2148-8-52.
54. Casadesús J, Low D. Epigenetic gene regulation in the bacterial world. Microbiol Mol Biol Rev. 2006; 70(3):830-56.
55. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham SK, Prjibelski AD, Pyshkin A, Sirotkin A, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012; 19(5):455-77.
56. Boisvert S, Laviolette F, Corbeil J. Ray: simultaneous assembly of reads from a mix of high-throughput sequencing technologies. J Comput Biol. 2010; 17(11):1519-33.
57. Boisvert S, Raymond F, Godzaridis É, Laviolette F, Corbeil J. Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol. 2012; 13(12):122.
58. Zhou K, Aertsen A, Michiels CW. The role of variable DNA tandem repeats in bacterial adaptation. FEMS Microbiol Rev. 2014; 38(1):119-41.
59. Floyd S, Warmuth M. Sample compression, learnability, and the Vapnik-Chervonenkis dimension. Mach Learn. 1995; 21(3):269-304.
60. Littlestone N, Warmuth M. Relating data compression and learnability. Technical report. 1986.
61. Marchand M, Sokolova M. Learning with decision lists of data-dependent features. J Mach Learn Res. 2005; 6:427-51.
62. The HDF Group. Hierarchical Data Format, Version 5. http://www.hdfgroup.org/HDF5/. Accessed 30 June 2016.