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1. Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking

Author

Kouchaki, S, Yang, Y, Lachapelle, A, Walker, T, Walker, SA, Hoosdally, S, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Fowler, P, Iqbal, Z, Hunt, M, Knaggs, J, Smith, GE, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaler, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Merker, M, Hoffmann, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaiprasert, A, Comas, I, Roig, CR, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Supply, P, van Soolingen, D, Peto, TEA, Crook, D, Clifton, D, Kouchaki, S, Yang, Y, Lachapelle, A, Walker, T, Walker, SA, Hoosdally, S, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Fowler, P, Iqbal, Z, Hunt, M, Knaggs, J, Smith, GE, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaler, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Merker, M, Hoffmann, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaiprasert, A, Comas, I, Roig, CR, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Supply, P, van Soolingen, D, Peto, TEA, Crook, D, and Clifton, D

Published
2020

2. GenomegaMap: within-species genome-wide d_N/d_S estimation from over 10,000 genomes

Author

Wilson, D, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Lachapelle, A, Yang, Y, Clifton, DA, Fowler, PW, Iqbal, Z, Hunt, M, Knaggs, J, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, NIemann, S, Kohl, TA, Merker, M, Hoffman, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thoung, TNT, Ngoc, NH, Srinivasan, V, Walker, TM, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaipresert, A, Comas, I, Roig, CJ, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Wilson, D, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Lachapelle, A, Yang, Y, Clifton, DA, Fowler, PW, Iqbal, Z, Hunt, M, Knaggs, J, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, NIemann, S, Kohl, TA, Merker, M, Hoffman, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thoung, TNT, Ngoc, NH, Srinivasan, V, Walker, TM, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaipresert, A, Comas, I, Roig, CJ, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, and Sintchenko, V

Abstract

The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters such as dN/dS, but analysing very large datasets poses a major statistical challenge. Here I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: (i) it is fast no matter how large the sample size and (ii) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well-estimated even when genomegaMap’s simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in M. tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.

Published
2020

3. DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Author

Yang, Y, Walker, TM, Walker, AS, Wilson, DJ, Peto, TEA, Crook, DW, Shamout, F, Zhu, T, Clifton, DA, Arandjelovic, I, Comas, I, Farhat, MR, Gao, Q, Sintchenko, V, Van Soolingen, D, Hoosdally, S, Cruz, ALG, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Fowler, PW, Iqbal, Z, Hunt, M, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, T, Merker, M, Hoffmann, H, Molodtsov, N, Plesnik, S, Ismail, N, Omar, SV, Thwaites, G, Thuong, NTT, Nhung, HN, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Ma, A, Liu, C, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, M-H, Lin, W-H, Ferrazoli, L, De Oliveira, RS, and Wellcome Trust

Subjects
DRUG-RESISTANCE, Technology, Biochemistry & Molecular Biology, Science & Technology, INFORMATION, MUTATIONS, CRyPTIC Consortium, Bioinformatics, Statistics & Probability, SUSCEPTIBILITY, 06 Biological Sciences, GENE, Biochemical Research Methods, CLASSIFICATION, DIMENSIONALITY REDUCTION, Biotechnology & Applied Microbiology, Physical Sciences, Computer Science, Computer Science, Interdisciplinary Applications, Mathematical & Computational Biology, 08 Information and Computing Sciences, Life Sciences & Biomedicine, Mathematics, 01 Mathematical Sciences
Abstract

Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php.

Published
2019

4. GenomegaMap: within-species genome-widedN/dSestimation from over 10,000 genomes

Author

Wilson, DJ, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Lachapelle, A, Yang, Y, Clifton, DA, Fowler, PW, Iqbal, Z, Hunt, M, Knaggs, J, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, TA, Merker, M, Hoffmann, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Walker, TM, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, M-H, Lin, W-H, Ferrazoli, L, De Oliveira, RS, Arandjelovic, I, Chaiprasert, A, Comas, I, Roig, CJ, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Supply, P, Van Soolingen, D, University of Oxford, Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 (CIIL), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), D.J.W. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant no. 101237/Z/13/B) and is a Big Data Institute Robertson Fellow. The CRyPTIC Consortium was supported by grants from the Bill and Melinda Gates Foundation (OPP1133541) and a Wellcome Trust/Newton Fund-MRC Collaborative Award (200205/Z/15/Z). F.A.D. was supported by the Imperial Biomedical Research Centre., Members of the CRyPTIC Consortium : Derrick W. Crook, Timothy E.A. Peto, A. Sarah Walker, Sarah J. Hoosdally, Ana L. Gibertoni Cruz, Joshua Carter, Clara Grazian, Sarah G. Earle, Samaneh Kouchaki, Alexander Lachapelle, Yang Yang, David A. Clifton, and Philip W. Fowler, University of Oxford, Zamin Iqbal, Martin Hunt, and Jeffrey Knaggs, European Bioinformatics Institute, E. Grace Smith, Priti Rathod, Lisa Jarrett, and Daniela Matias, Public Health England, Birmingham, Daniela M. Cirillo, Emanuele Borroni, Simone Battaglia, Arash Ghodousi, Andrea Spitaleri, and Andrea Cabibbe, Emerging Bacterial Pathogens Unit, IRCCS San Raffaele Scientific Institute, Milan, Sabira Tahseen, National Tuberculosis Control Program Pakistan, Islamabad, Kayzad Nilgiriwala and Sanchi Shah, The Foundation for Medical Research, Mumbai, Camilla Rodrigues, Priti Kambli, Utkarsha Surve, and Rukhsar Khot, P.D. Hinduja National Hospital and Medical Research Centre, Mumbai, Stefan Niemann, Thomas A. Kohl, and Matthias Merker, Research Center Borstel, Harald Hoffmann, Katharina Todt, and Sara Plesnik, Institute of Microbiology & Laboratory Medicine, IML Red, Gauting, Nazir Ismail, Shaheed Vally Omar, and Lavania Joseph, National Institute for Communicable Diseases, Johannesburg, Guy Thwaites, Thuong Nguyen Thuy Thuong, Nhung Hoang Ngoc, Vijay Srinivasan, and Timothy M. Walker, Oxford University Clinical Research Unit, Ho Chi Minh City, David Moore, Jorge Coronel and Walter Solano, London School of Hygiene and Tropical Medicine and Universidad Peruana Cayetano Heredá, Lima, George F. Gao, Guangxue He, Yanlin Zhao, and Chunfa Liu, China CDC, Beijing, Aijing Ma, Shenzhen Third People’s Hospital, Shenzhen, Baoli Zhu, Institute of Microbiology, CAS, Beijing, Ian Laurenson and Pauline Claxton, Scottish Mycobacteria Reference Laboratory, Edinburgh, Anastasia Koch, Robert Wilkinson, University of Cape Town, Ajit Lalvani, Imperial College London, James Posey, CDC Atlanta, Jennifer Gardy, University of British Columbia, Jim Werngren, Public Health Agency of Sweden, Nicholas Paton, National University of Singapore, Ruwen Jou, Mei-Hua Wu, Wan-Hsuan Lin, CDC Taiwan, Lucilaine Ferrazoli, Rosangela Siqueira de Oliveira, Institute Adolfo Lutz, São Paulo. Authors contributing to the CRyPTIC Consortium are (in alphabetical order): Irena Arandjelovic (Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia), Angkana Chaiprasert (Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand), Iñaki Comas (Instituto de Biomedicina de Valencia [IBV-CSIC], Calle Jaime Roig, Valencia, Spain, FISABIO Public Health, Valencia, Spain, CIBER in Epidemiology and Public Health, Madrid, Spain), Francis A. Drobniewski (Imperial College, London, UK), Maha R. Farhat (Harvard Medical School, Boston, USA), Qian Gao (Shanghai Medical College, Fudan University, Shanghai, China), Rick Ong Twee Hee (Saw Swee Hock School of Public Health, National University of Singapore, Singapore), Vitali Sintchenko (Centre for Infectious Diseases and Microbiology—Public Health, University of Sydney, Sydney, Australia), Philip Supply (Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019—UMR 8204—CIIL—Centre d’Infection et d’Immunité de Lille, F-59000 Lille, France), and Dick van Soolingen (National Institute for Public Health and the Environment [RIVM], Bilthoven, The Netherlands)., Supply, Philip, Consortium, CRyPTIC, University of Oxford [Oxford], Centre National de la Recherche Scientifique (CNRS)-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Royal Society (UK), Bill & Melinda Gates Foundation, Newton Fund, Comas, Iñaki [0000-0001-5504-9408], and Comas, Iñaki

Subjects
Natural selection, [SDV]Life Sciences [q-bio], Population genetics, adaptation, Computational biology, Biology, AcademicSubjects/SCI01180, 0601 Biochemistry and Cell Biology, Genome, Coalescent theory, DEAD-box RNA Helicases, Big data, 03 medical and health sciences, 0603 Evolutionary Biology, big data, Parent-dependent mutation, Genetics, dN/dS, Adaptation, Selection, Genetic, Molecular Biology, Allele frequency, Ecology, Evolution, Behavior and Systematics, Silent Mutation, Selection (genetic algorithm), 030304 developmental biology, Evolutionary Biology, 0604 Genetics, 0303 health sciences, Models, Genetic, Phylogenetic tree, 030306 microbiology, AcademicSubjects/SCI01130, natural selection, Mycobacterium tuberculosis, Recombination, Resources, recombination, 3. Good health, [SDV] Life Sciences [q-bio], Genetic Techniques, Mutation (genetic algorithm), parent-dependent mutation, Genome, Bacterial
Abstract

11 págs, 4 figuras y fórmulas matemáticas. Material suplementario en: http://dx.doi.org/10.1093/molbev/msaa069, The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap's simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species., D.J.W. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant no. 101237/Z/13/B) and is a Big Data Institute Robertson Fellow. The CRyPTIC Consortium was supported by grants from the Bill and Melinda Gates Foundation (OPP1133541) and a Wellcome Trust/Newton Fund-MRC Collaborative Award (200205/Z/15/Z). F.A.D. was supported by the Imperial Biomedical Research Centre

Published
2019
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5. Application of machine learning techniques to tuberculosis drug resistance analysis

Author

Kouchaki, S, Yang, YY, Walker, TM, Walker, AS, Wilson, DJ, Peto, TEA, Crook, DW, Clifton, DA, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Fowler, PW, Iqbal, Z, Hunt, M, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, T, Merker, M, Hoffmann, H, Molodtsov, N, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Marubini, E, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Ma, A, Liu, C, Zhu, B, Laurenson, I, Claxton, P, Wilkinson, RJ, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, De Oliveira, RS, Arandjelovic, I, Comas, I, Drobniewski, F, Gao, Q, Sintchenko, V, Supply, P, Van Soolingen, D, Kouchaki, S, Yang, YY, Walker, TM, Walker, AS, Wilson, DJ, Peto, TEA, Crook, DW, Clifton, DA, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Fowler, PW, Iqbal, Z, Hunt, M, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, T, Merker, M, Hoffmann, H, Molodtsov, N, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Marubini, E, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Ma, A, Liu, C, Zhu, B, Laurenson, I, Claxton, P, Wilkinson, RJ, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, De Oliveira, RS, Arandjelovic, I, Comas, I, Drobniewski, F, Gao, Q, Sintchenko, V, Supply, P, and Van Soolingen, D

Abstract

Motivation: Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. Results: Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P < 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers.

Published
2019

6. Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe.

Author

Hunt, M, Bradley, P, Lapierre, SG, Heys, S, Thomsit, M, Hall, MB, Malone, KM, Wintringer, P, Walker, TM, Cirillo, DM, Comas, I, Farhat, MR, Fowler, P, Gardy, J, Ismail, N, Kohl, TA, Mathys, V, Merker, M, Niemann, S, Omar, SV, Sintchenko, V, Smith, G, van Soolingen, D, Supply, P, Tahseen, S, Wilcox, M, Arandjelovic, I, Peto, TEA, Crook, DW, Iqbal, Z, Hunt, M, Bradley, P, Lapierre, SG, Heys, S, Thomsit, M, Hall, MB, Malone, KM, Wintringer, P, Walker, TM, Cirillo, DM, Comas, I, Farhat, MR, Fowler, P, Gardy, J, Ismail, N, Kohl, TA, Mathys, V, Merker, M, Niemann, S, Omar, SV, Sintchenko, V, Smith, G, van Soolingen, D, Supply, P, Tahseen, S, Wilcox, M, Arandjelovic, I, Peto, TEA, Crook, DW, and Iqbal, Z

Abstract

Two billion people are infected with Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor, which provided offline species identification and drug resistance predictions for M. tuberculosis from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations. Here we present a new tool, Mykrobe, which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. Mykrobe is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 M. tuberculosis Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 M. tuberculosis isolates. Using culture based DST as the reference, we estimate Mykrobe to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that Mykrobe gives concordant results with nanopore data. We measure the ability of Mykrobe-based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showi

Published
2019

7. Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing

Author

CRyPTIC Consortium, 100000 Genomes Project, Allix-Beguec, C, Arandjelovic, I, Bi, L, Beckert, P, Bonnet, M, Bradley, P, Cabibbe, AM, Cancino-Munoz, I, Caulfield, MJ, Chaiprasert, A, Cirillo, DM, Clifton, D, Comas, I, Crook, DW, De Filippo, MR, de Neeling, H, Diel, R, Drobniewski, FA, Faksri, K, Farhat, MR, Fleming, J, Fowler, P, Fowler, TA, Gao, Q, Gardy, J, Gascoyne-Binzi, D, Gibertoni-Cruz, A-L, Gil-Brusola, A, Golubchik, T, Gonzalo, X, Grandjean, L, He, G, Guthrie, JL, Hoosdally, S, Hunt, M, Iqbal, Z, Ismail, N, Johnston, J, Khanzada, FM, Khor, CC, Kohl, TA, Kong, C, Lipworth, S, Liu, Q, Maphalala, G, Martinez, E, Mathys, V, Merker, M, Miotto, P, Mistry, N, Moore, DAJ, Murray, M, Niemann, S, Ong, RT-H, Peto, TEA, Posey, JE, Prammananan, T, Pym, A, Rodrigues, C, Rodrigues, M, Rodwell, T, Rossolini, GM, Padilla, ES, Schito, M, Shen, X, Shendure, J, Sintchenko, V, Sloutsky, A, Smith, EG, Snyder, M, Soetaert, K, Starks, AM, Supply, P, Suriyapol, P, Tahseen, S, Tang, P, Teo, Y-Y, Thuong, TNT, Thwaites, G, Tortoli, E, Omar, SV, van Soolingen, D, Walker, AS, Walker, TM, Wilcox, M, Wilson, DJ, Wyllie, D, Yang, Y, Zhang, H, Zhao, Y, and Zhu, B

Abstract

Background: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear.\ud \ud \ud \ud Methods: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance.\ud \ud \ud \ud Results: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted.\ud \ud \ud \ud Conclusions: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.)

Published
2018

9. Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe.

Author

Hunt M, Bradley P, Lapierre SG, Heys S, Thomsit M, Hall MB, Malone KM, Wintringer P, Walker TM, Cirillo DM, Comas I, Farhat MR, Fowler P, Gardy J, Ismail N, Kohl TA, Mathys V, Merker M, Niemann S, Omar SV, Sintchenko V, Smith G, van Soolingen D, Supply P, Tahseen S, Wilcox M, Arandjelovic I, Peto TEA, Crook DW, and Iqbal Z

Abstract

Two billion people are infected with Mycobacterium tuberculosis , leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor , which provided offline species identification and drug resistance predictions for M. tuberculosis from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations.  Here we present a new tool, Mykrobe , which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. Mykrobe is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 M. tuberculosis Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 M. tuberculosis isolates . Using culture based DST as the reference, we estimate Mykrobe to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that Mykrobe gives concordant results with nanopore data.  We measure the ability of Mykrobe -based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools., Competing Interests: No competing interests were disclosed., (Copyright: © 2019 Hunt M et al.)

Published
2019
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10. Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing.

Author

Allix-Béguec C, Arandjelovic I, Bi L, Beckert P, Bonnet M, Bradley P, Cabibbe AM, Cancino-Muñoz I, Caulfield MJ, Chaiprasert A, Cirillo DM, Clifton DA, Comas I, Crook DW, De Filippo MR, de Neeling H, Diel R, Drobniewski FA, Faksri K, Farhat MR, Fleming J, Fowler P, Fowler TA, Gao Q, Gardy J, Gascoyne-Binzi D, Gibertoni-Cruz AL, Gil-Brusola A, Golubchik T, Gonzalo X, Grandjean L, He G, Guthrie JL, Hoosdally S, Hunt M, Iqbal Z, Ismail N, Johnston J, Khanzada FM, Khor CC, Kohl TA, Kong C, Lipworth S, Liu Q, Maphalala G, Martinez E, Mathys V, Merker M, Miotto P, Mistry N, Moore DAJ, Murray M, Niemann S, Omar SV, Ong RT, Peto TEA, Posey JE, Prammananan T, Pym A, Rodrigues C, Rodrigues M, Rodwell T, Rossolini GM, Sánchez Padilla E, Schito M, Shen X, Shendure J, Sintchenko V, Sloutsky A, Smith EG, Snyder M, Soetaert K, Starks AM, Supply P, Suriyapol P, Tahseen S, Tang P, Teo YY, Thuong TNT, Thwaites G, Tortoli E, van Soolingen D, Walker AS, Walker TM, Wilcox M, Wilson DJ, Wyllie D, Yang Y, Zhang H, Zhao Y, and Zhu B

Subjects
Antitubercular Agents therapeutic use, Ethambutol pharmacology, Genotype, Humans, Isoniazid pharmacology, Microbial Sensitivity Tests, Mutation, Mycobacterium tuberculosis drug effects, Mycobacterium tuberculosis isolation & purification, Phenotype, Pyrazinamide pharmacology, Rifampin pharmacology, Tuberculosis microbiology, Antitubercular Agents pharmacology, Drug Resistance, Bacterial genetics, Genome, Bacterial, Mycobacterium tuberculosis genetics, Tuberculosis drug therapy, Whole Genome Sequencing
Abstract

Background: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear., Methods: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance., Results: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted., Conclusions: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.).

Published
2018
Full Text
View/download PDF

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