DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

O’Neill J. Tackling drug-resistant infections globally: final report and recommendations. Rev Antimicrob Resist. 2016;1:1-84.

Brogan DM, Mossialos E. A critical analysis of the review on antimicrobial resistance report and the infectious disease financing facility. Glob Health. 2016;12:8.

O’Neill J. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. Review on antimicrobial resistance. Rev Antimicrob Resist. 2014;

Vuong C, Yeh AJ, Cheung GY, Otto M. Investigational drugs to treat methicillin-resistant Staphylococcus Aureus. Expert Opin Investig Drugs. 2016;25:73–93.

Gandhi NR, Nunn P, Dheda K, Schaaf HS, Zignol M, Van Soolingen D, Jensen P, Bayona J. Multidrug-resistant and extensively drug-resistant tuberculosis: a threat to global control of tuberculosis. Lancet. 2010;375:1830–43.

Mediavilla JR, Patrawalla A, Chen L, Chavda KD, Mathema B, Vinnard C, Dever LL, Kreiswirth BN. Colistin-and carbapenem-resistant Escherichia Coli harboring mcr-1 and blaNDM-5, causing a complicated urinary tract infection in a patient from the United States. MBio. 2016;7:e01191–16.

Hu Y, Liu F, Lin IY, Gao GF, Zhu B. Dissemination of the mcr-1 colistin resistance gene. Lancet Infect Dis. 2016;16:146–7.

Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DG. The structure and diversity of human, animal and environmental resistomes. Microbiome. 2016;4:54.

Forsberg KJ, Patel S, Gibson MK, Lauber CL, Knight R, Fierer N, Dantas G. Bacterial phylogeny structures soil resistomes across habitats. Nature. 2014;509:612–6.

Berendonk TU, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Walsh F, Bürgmann H, Sørum H, Norström M, Pons M-N. Tackling antibiotic resistance: the environmental framework. Nat Rev Microbiol. 2015;13:310–7.

Pruden A, Larsson DJ, Amézquita A, Collignon P, Brandt KK, Graham DW, Lazorchak JM, Suzuki S, Silley P, Snape JR. Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environ Health Perspect. 2013;121:878.

Fahrenfeld N, Knowlton K, Krometis LA, Hession WC, Xia K, Lipscomb E, Libuit K, Green BL, Pruden A. Effect of manure application on abundance of antibiotic resistance genes and their attenuation rates in soil: field-scale mass balance approach. Environ Sci Technol. 2014;48:2643–50.

Mao D, Yu S, Rysz M, Luo Y, Yang F, Li F, Hou J, Mu Q, Alvarez P. Prevalence and proliferation of antibiotic resistance genes in two municipal wastewater treatment plants. Water Res. 2015;85:458–66.

Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DG, Johansson A. The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrob Agents Chemother. 2015;59:6551–60.

Pehrsson EC, Tsukayama P, Patel S, Mejia-Bautista M, Sosa-Soto G, Navarrete KM, Calderon M, Cabrera L, Hoyos-Arango W, Bertoli MT, et al. Interconnected microbiomes and resistomes in low-income human habitats. Nature. 2016;533:212–6.

States DJ, Agarwal P. Compact encoding strategies for DNA sequence similarity search. Proc Int Conf Intell Syst Mol Biol. 1996;4:211–7.

Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.

Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.

Yang Y, Jiang X, Chai B, Ma L, Li B, Zhang A, Cole JR, Tiedje JM, Zhang T. ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. Bioinformatics. 2016;32:2346–51.

Kleinheinz KA, Joensen KG, Larsen MV. Applying the ResFinder and VirulenceFinder web-services for easy identification of acquired antibiotic resistance and E. coli virulence genes in bacteriophage and prophage nucleotide sequences. Bacteriophage. 2014;4(2):e27943.

Davis JJ, Boisvert S, Brettin T, Kenyon RW, Mao C, Olson R, Overbeek R, Santerre J, Shukla M, Wattam AR, et al. Antimicrobial resistance prediction in PATRIC and RAST. Sci Rep. 2016;6:27930.

McArthur AG, Tsang KK. Antimicrobial resistance surveillance in the genomic age. Ann N Y Acad Sci. 2017;1388:78-91.

Rowe W, Baker KS, Verner-Jeffreys D, Baker-Austin C, Ryan JJ, Maskell D, Pearce G. Search engine for antimicrobial resistance: a cloud compatible pipeline and web Interface for rapidly detecting antimicrobial resistance genes directly from sequence data. PLoS One. 2015;10:e0133492.

Bradley P, Gordon NC, Walker TM, Dunn L, Heys S, Huang B, Earle S, Pankhurst LJ, Anson L, de Cesare M, et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus Aureus and mycobacterium tuberculosis. Nat Commun. 2015;6:10063.

Li B, Yang Y, Ma L, Ju F, Guo F, Tiedje JM, Zhang T. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. The ISME journal. 2015;9:2490–502.

Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, et al. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2004;32:D115–9.

Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, Lago BA, Dave BM, Pereira S, Sharma AN, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 2017;45:D566–73.

Liu B, Pop M. ARDB--antibiotic resistance genes database. Nucleic Acids Res. 2009;37:D443–7.

Pearson WR. An introduction to sequence similarity (“homology”) searching. Curr Protoc Bioinformatics. 2013;Chapter 3:Unit3 1.

Xavier BB, Das AJ, Cochrane G, De Ganck S, Kumar-Singh S, Aarestrup FM, Goossens H, Malhotra-Kumar S. Consolidating and exploring antibiotic resistance gene data resources. J Clin Microbiol. 2016;54:851–9.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

Tabar YR, Halici U. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng. 2017;14:016003.

Hinton G, Deng L, Yu D, Dahl GE, Mohamed A-r, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29:82–97.

Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33:831–8.

Pan X, Shen HB. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinformatics. 2017;18:136.

Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26:680–2.

Eyre TA, Ducluzeau F, Sneddon TP, Povey S, Bruford EA, Lush MJ. The HUGO gene nomenclature database, 2006 updates. Nucleic Acids Res. 2006;34:D319–21.

Yujian L, Bo L. A normalized Levenshtein distance metric. IEEE Trans Pattern Anal Mach Intell. 2007;29:1091–5.

Healy, M.D. (2007) Using BLAST for performing sequence alignment. Curr Protoc Hum Genet, chapter 6, unit 6 8.

Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DJ. Co-occurrence of resistance genes to antibiotics, biocides and metals reveals novel insights into their co-selection potential. BMC Genomics. 2015;16:964.

Li L-G, Xia Y, Zhang T. Co-occurrence of antibiotic and metal resistance genes revealed in complete genome collection. ISME J. 2017;11:651–62.

Sorensen L, Loog M, Lo P, Ashraf H, Dirksen A, Duin RP, de Bruijne M. Image dissimilarity-based quantification of lung disease from CT. Med Image Comput Comput Assist Interv. 2010;13:37–44.

Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017;18:851-69.

Coates A, Ng A, Lee H. Proceedings of the fourteenth international conference on artificial intelligence and statistics; 2011. p. 215–23.

Sun Y, Wang X, Tang X. Proceedings of the IEEE conference on computer vision and pattern recognition; 2014. p. 1891–8.

Buggenthin F, Buettner F, Hoppe PS, Endele M, Kroiss M, Strasser M, Schwarzfischer M, Loeffler D, Kokkaliaris KD, Hilsenbeck O, et al. Prospective identification of hematopoietic lineage choice by deep learning. Nat Methods. 2017;14:403–6.

Qin Q, Feng J. Imputation for transcription factor binding predictions based on deep learning. PLoS Comput Biol. 2017;13:e1005403.

Dong X, Qian L, Guan Y, Huang L, Yu Q, Yang J. Scientific data summit (NYSDS), 2016 New York, IEEE; 2016. p. 1–10.

Bhatkoti P, Paul M. Image and vision computing New Zealand (IVCNZ), 2016 international conference on, IEEE; 2016. p. 1–5.

Baldi P, Sadowski P. The dropout learning algorithm. Artif Intell. 2014;210:78–122.

Dunne, R.A. and Campbell, N.A. (1997), Proc. 8th Aust. Conf. On the neural networks, Melbourne, 181, Vol 185.

Sundermeyer M, Schlüter R, Ney H. Interspeech; 2012. p. 194–7.

Van Merriënboer, B., Bahdanau, D., Dumoulin, V., Serdyuk, D., Warde-Farley, D., Chorowski, J. and Bengio, Y. (2015) Blocks and fuel: frameworks for deep learning. arXiv preprint arXiv:1506.00619.

Bergstra J, Bastien F, Breuleux O, Lamblin P, Pascanu R, Delalleau O, Desjardins G, Warde-Farley D, Goodfellow I, Bergeron A. Theano: Deep learning on gpus with python. NIPS 2011. InBigLearning Workshop, Granada, Spain: Citeseer; 2011. p. Vol. 3.

Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.

Chen C-M, Ke S-C, Li C-R, Wu Y-C, Chen T-H, Lai C-H, Wu X-X, Wu L-T. High diversity of antimicrobial resistance genes, class 1 Integrons, and genotypes of multidrug-resistant Escherichia Coli in beef carcasses. Microb Drug Resist. 2017;23:915-924.

Linhares I, Raposo T, Rodrigues A, Almeida A. Incidence and diversity of antimicrobial multidrug resistance profiles of uropathogenic bacteria. Biomed Res Int. 2015;2015:11.

Lakin SM, Dean C, Noyes NR, Dettenwanger A, Ross AS, Doster E, Rovira P, Abdo Z, Jones KL, Ruiz J. MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res. 2017;45:D574–80.

Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, Rolain JM. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 2014;58:212–20.

Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012;67:2640–4.

Berglund F, Marathe NP, Österlund T, Bengtsson-Palme J, Kotsakis S, Flach C-F, Larsson DJ, Kristiansson E. Identification of 76 novel B1 metallo-β-lactamases through large-scale screening of genomic and metagenomic data. Microbiome. 2017;5:134.

Bengtsson-Palme J, Larsson DJ, Kristiansson E. Using metagenomics to investigate human and environmental resistomes. J Antimicrob Chemother. 2017;72:2690–703.

Wattam AR, Abraham D, Dalay O, Disz TL, Driscoll T, Gabbard JL, Gillespie JJ, Gough R, Hix D, Kenyon R, et al. PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res. 2014;42:D581–91.

Arango-Argoty G, Singh G, Heath LS, Pruden A, Xiao W, Zhang L. MetaStorm: a public resource for customizable Metagenomics annotation. PLoS One. 2016;11:e0162442.