Antimicrobial Resistance Prediction in PATRIC and RAST

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Davis, James J ^a,b   Boisvert, Sébastien ^c   Brettin, Thomas ^a,b   Kenyon, Ronald W ^d   Mao, Chunhong ^d   Olson, Robert ^a,b   Overbeek, Ross ^b,e   Santerre, John ^f   Shukla, Maulik ^a,b   Wattam, Alice R ^d   Will, Rebecca ^d   Xia, Fangfang ^a,b   Stevens, Rick ^a,b,f  

^a UNIVERSITY OF CHICAGO   (United States)

^b ARGONNE NATIONAL LABORATORY   (United States)

^c Gydle Inc   (Canada)

^d VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

^e Fellowship for Interpretation of Genomes   (United States)

^f THE UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIINFECTIVE AGENT;

ANTIBIOTIC RESISTANCE; BACTERIAL GENOME; BACTERIAL INFECTION; BIOLOGY; CLINICAL DECISION MAKING; GENETIC DATABASE; GENETICS; HUMAN; INFORMATION PROCESSING; MACHINE LEARNING; MICROBIAL SENSITIVITY TEST; MOLECULAR GENETICS; NATIONAL HEALTH ORGANIZATION; PROGNOSIS; UNITED STATES;

ANTI-BACTERIAL AGENTS; BACTERIAL INFECTIONS; CLINICAL DECISION-MAKING; COMPUTATIONAL BIOLOGY; DATA CURATION; DATABASES, GENETIC; DRUG RESISTANCE, MICROBIAL; GENOME, BACTERIAL; HUMANS; MACHINE LEARNING; MICROBIAL SENSITIVITY TESTS; MOLECULAR SEQUENCE ANNOTATION; NATIONAL INSTITUTES OF HEALTH (U.S.); PROGNOSIS; UNITED STATES;

EID: 84974799196     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep27930     Document Type: Article

References (67)

1. Blair, J. M., Webber, M. A., Baylay, A. J., Ogbolu, D. O., Piddock, L. J. Molecular mechanisms of antibiotic resistance. Nature Reviews Microbiology 13, 42-51 (2015).
2. Alanis, A. J. Resistance to antibiotics: are we in the post-antibiotic era? Archives of medical research 36, 697-705 (2005).
3. World Heath Organization, Antimicrobial resistance: global report on surveillance. http://www.who.int/drugresistance/documents/surveillancereport/en/(2014) (Date of access: 11/04/2016).
4. United States Department of Health and Human Services. Antibiotic resistance threats in the United States, 2013. Centers for Disease Control and Prevention, Atlanta, GA: http://www.cdc.gov/drugresistance/threat-report-2013/(2013) (access:04/01/2016).
5. Ling, L. L. et al. A new antibiotic kills pathogens without detectable resistance. Nature 517, 455-459 (2015).
6. Hicks, L. A., Blaser, M. J. Variability in Antibiotic Prescribing: An Inconvenient Truth. Journal of the Pediatric Infectious Diseases Society, piu106 (2014). doi: 10.1093/jpids/piu106.
7. Gerber, J. S. et al. Variation in antibiotic prescribing across a pediatric primary care network. Journal of the Pediatric Infectious Diseases Society, piu086, doi: 10.1093/jpids/piu086 (2014).
8. Tang, Y.-W., Procop, G. W., Persing, D. H. Molecular diagnostics of infectious diseases. Clinical Chemistry 43, 2021-2038 (1997).
9. Wade, W. Unculturable bacteria-the uncharacterized organisms that cause oral infections. Journal of the Royal Society of Medicine 95, 81-83 (2002).
10. Taitt, C. R. et al. Antimicrobial resistance determinants in Acinetobacter baumannii isolates taken from military treatment facilities. Antimicrobial agents and chemotherapy, AAC. 01897-01813 (2013).
11. Leski, T. A. et al. Molecular characterization of multidrug resistant hospital isolates using the Antimicrobial Resistance Determinant Microarray, doi: 10.1371/journal.pone.0069507 (2013).
12. Inouye, M. et al. SRST2: Rapid genomic surveillance for public health and hospital microbiology labs. Genome Med 6, 90 (2014).
13. Kumarasamy, K. K. et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, the UK: a molecular, biological, epidemiological study. The Lancet infectious diseases 10, 597-602 (2010).
14. Dijkshoorn, L., Nemec, A., Seifert, H. An increasing threat in hospitals: multidrug-resistant Acinetobacter baumannii. Nature Reviews Microbiology 5, 939-951 (2007).
15. Liu, Y.-Y. et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. The Lancet Infectious Diseases, doi: 10.1016/S1473-3099(15)00424-7 (2015).
16. Chewapreecha, C. et al. Dense genomic sampling identifies highways of pneumococcal recombination. Nature genetics 46, 305-309 (2014).
17. Chewapreecha, C. et al. Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes, doi: 10.1371/journal.pgen.1004547 (2014).
18. Croucher, N. J. et al. Population genomics of post-vaccine changes in pneumococcal epidemiology. Nature genetics 45, 656-663 (2013).
19. Read, T. D., Massey, R. C. Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology. Genome Med 6, 109 (2014).
20. Stoesser, N. et al. Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae isolates using whole genomic sequence data. Journal of Antimicrobial Chemotherapy 68, 2234-2244 (2013).
21. Gordon, N. et al. Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing. Journal of clinical microbiology 52, 1182-1191 (2014).
22. Bradley, P. et al. Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis. bioRxiv, 018564, doi: 10.1038/ncomms10063 (2015).
23. Walker, T. M. et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: A retrospective cohort study. The Lancet Infectious Diseases 15, 1193-1202 (2015).
24. Bradley, P. et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nature communications 6, doi: 10.1038/ncomms10063 (2015).
25. Hiramatsu, K., Cui, L., Kuroda, M., Ito, T. The emergence and evolution of methicillin-resistant Staphylococcus aureus. Trends in microbiology 9, 486-493 (2001).
26. Libbrecht, M. W., Noble, W. S. Machine learning applications in genetics and genomics. Nature Reviews Genetics (2015), doi: 10.1038/nrg3920.
27. Whitney, D. H. et al. Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC medical genomics 8, 18 (2015).
28. Griffith, O. L. et al. A robust prognostic signature for hormone-positive node-negative breast cancer. Genome Med 5, 92 (2013).
29. Shipp, M. A. et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine 8, 68-74 (2002).
30. Drouin, A. et al. Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine. arXiv preprint arXiv:1412.1074 (2014).
31. Santerre, J., Boisvert, S., Davis, J., Xia, F., Stevens, R. Gene identification and strain classification using Random Forests. Great Lakes Bioinformatics Conference 2015. Purdue University, West Lafayette, Indiana, USA (2015).
32. Wattam, A. R. et al. PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic acids research 42, D581-D591 (2014).
33. Gillespie, J. J. et al. PATRIC: the comprehensive bacterial bioinformatics resource with a focus on human pathogenic species. Infection and immunity 79, 4286-4298 (2011).
34. Benson, D. A. et al. GenBank. Nucleic acids research 43, D30 (2015).
35. Kodama, Y., Shumway, M., Leinonen, R. The Sequence Read Archive: explosive growth of sequencing data. Nucleic acids research 40, D54-D56 (2012).
36. Brettin, T. et al. RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Scientific reports 5, doi: 10.1038/srep08365 (2015).
37. Overbeek, R. et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic acids research 42, D206-D214 (2014).
38. Deorowicz, S., Kokot, M., Grabowski, S., Debudaj-Grabysz, A. KMC 2: Fast and resource-frugal k-mer counting. Bioinformatics 31, 1569-1576 (2015).
39. Freund, Y., Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences 55, 119-139 (1997).
40. Zhu, J., Zou, H., Rosset, S., Hastie, T. Multi-class adaboost. Statistics and its Interface 2, 349-360 (2009).
41. Camacho, C. et al. BLAST+ : architecture and applications. BMC bioinformatics 10, 421 (2009).
42. TB-ARC MRC SA initiative, Broad Institute. https://olive.broadinstitute.org/projects/tb-mrc-sa. (2016) (Date of access:11/04/2016).
43. Merker, M. et al. Evolutionary history and global spread of the Mycobacterium tuberculosis Beijing lineage. Nature genetics, doi: 10.1038/ng.3195 (2015).
44. Kanehisa, M., Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research 28, 27-30 (2000).
45. Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic acids research 42, D199-D205 (2014).
46. Liu, B., Pop, M. ARDB-antibiotic resistance genes database. Nucleic acids research 37, D443-D447 (2009).
47. McArthur, A. G. et al. The comprehensive antibiotic resistance database. Antimicrobial agents and chemotherapy 57, 3348-3357 (2013).
48. Barrett, T. et al. BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata. Nucleic acids research 40, D57-D63 (2012).
49. Cockerill, Franklin R., Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing: twenty-second informational supplement; [... provides updated tables for... M02-A11 and M07-A9]. National Committee for Clinical Laboratory Standards. (2012).
50. Edwards, R. A. et al. Real Time Metagenomics: Using k-mers to annotate metagenomes. Bioinformatics 28, 3316-3317 (2012).
51. Wood, D. E., Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome biology 15, R46 (2014).
52. Rojas, R. AdaBoost and the super bowl of classifiers a tutorial introduction to adaptive boosting. Computer Science Department, Freie Universitat, Berlin. http://www.inf.fu-berlin.de/inst/ag-ki/adaboost4.pdf (2009) (Date of access: 11/04/2016).
53. Schapire, R. E. Explaining AdaBoost. In Empirical inference. pp. 37-52. Springer, doi: 10.1007/978-3-642-41136-6-5 (2013).
54. Breiman, L. Random forests. Machine learning 45, 5-32 (2001).
55. Cortes, C., Vapnik, V. Support-vector networks. Machine learning 20, 273-297 (1995).
56. Marchand, M., Taylor, J. S. The set covering machine. The Journal of Machine Learning Research 3, 723-746 (2003).
57. Poirel, L., Nordmann, P. Carbapenem resistance in Acinetobacter baumannii: mechanisms and epidemiology. Clinical Microbiology and Infection 12, 826-836 (2006).
58. Wielders, C., Fluit, A., Brisse, S., Verhoef, J., Schmitz, F. mecA gene is widely disseminated in Staphylococcus aureus population. Journal of clinical microbiology 40, 3970-3975 (2002).
59. Huovinen, P., Sundstr?, L., Swedberg, G., Sk?d, O. Trimethoprim and sulfonamide resistance. Antimicrobial Agents and Chemotherapy 39, 279 (1995).
60. Heym, B., Alzari, P. M., Honore, N., Cole, S. T. Missense mutations in the catalase?peroxidase gene, katG, are associated with isoniazid resistance in Mycobacterium tuberculosis. Molecular microbiology 15, 235-245 (1995).
61. Suzuki, Y. et al. Detection of Kanamycin-ResistantMycobacterium tuberculosis by Identifying Mutations in the 16S rRNA Gene. Journal of clinical microbiology 36, 1220-1225 (1998).
62. Shi, R., Zhang, J., Li, C., Kazumi, Y., Sugawara, I. Emergence of ofloxacin resistance in Mycobacterium tuberculosis clinical isolates from China as determined by gyrA mutation analysis using denaturing high-pressure liquid chromatography and DNA sequencing. Journal of clinical microbiology 44, 4566-4568 (2006).
63. Telenti, A. et al. Detection of rifampicin-resistance mutations in Mycobacterium tuberculosis. The Lancet 341, 647-651 (1993).
64. Honore, N., Cole, S. T. Streptomycin resistance in mycobacteria. Antimicrobial agents and chemotherapy 38, 238-242 (1994).
65. Sreevatsan, S. et al. Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations. Antimicrobial Agents and Chemotherapy 41, 1677-1681 (1997).
66. Morlock, G. P., Metchock, B., Sikes, D., Crawford, J. T., Cooksey, R. C. ethA, inhA, katG loci of ethionamide-resistant clinical Mycobacterium tuberculosis isolates. Antimicrobial agents and chemotherapy 47, 3799-3805 (2003).
67. National Institute of Allergy and Infectious Diseases, Genomic Centers for Infectious Diseases, https://www.niaid.nih.gov/labsandresources/resources/dmid/gsc/pages/default.aspx (2016) (Date of access:01/04/2016)