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Mäklin T, Kallonen T, Alanko J, Samuelsen Ø, Hegstad K, Mäkinen V, Corander J, Heinz E, Honkela A. Bacterial genomic epidemiology with mixed samples. Microb Genom 2021; 7:000691. [PMID: 34779765 PMCID: PMC8743562 DOI: 10.1099/mgen.0.000691] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/13/2021] [Indexed: 11/18/2022] Open
Abstract
Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.
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Affiliation(s)
- Tommi Mäklin
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Teemu Kallonen
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Jarno Alanko
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Ørjan Samuelsen
- Norwegian National Advisory Unit on Detection of Antimicrobial Resistance, Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway
- Department of Pharmacy, UT The Arctic University of Norway, Tromsø, Norway
| | - Kristin Hegstad
- Norwegian National Advisory Unit on Detection of Antimicrobial Resistance, Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway
- Research group for Host-Microbe Interactions, Department of Medical Biology, Faculty of Health Sciences, UT The Arctic University of Norway, Tromsø, Norway
| | - Veli Mäkinen
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Jukka Corander
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Eva Heinz
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Antti Honkela
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
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Mäklin T, Kallonen T, David S, Boinett CJ, Pascoe B, Méric G, Aanensen DM, Feil EJ, Baker S, Parkhill J, Sheppard SK, Corander J, Honkela A. High-resolution sweep metagenomics using fast probabilistic inference. Wellcome Open Res 2021; 5:14. [PMID: 34746439 PMCID: PMC8543175 DOI: 10.12688/wellcomeopenres.15639.2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/30/2021] [Indexed: 01/13/2023] Open
Abstract
Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP pipeline for identifying and estimating the relative sequence abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our pipeline facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.
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Affiliation(s)
- Tommi Mäklin
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Teemu Kallonen
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Sophia David
- Centre for Genomic Pathogen Surveillance, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Christine J. Boinett
- Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Ben Pascoe
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Guillaume Méric
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - David M. Aanensen
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Edward J. Feil
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Stephen Baker
- Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Julian Parkhill
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Samuel K. Sheppard
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Jukka Corander
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Antti Honkela
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
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Mäklin T, Kallonen T, David S, Boinett CJ, Pascoe B, Méric G, Aanensen DM, Feil EJ, Baker S, Parkhill J, Sheppard SK, Corander J, Honkela A. High-resolution sweep metagenomics using fast probabilistic inference. Wellcome Open Res 2020; 5:14. [PMID: 34746439 PMCID: PMC8543175 DOI: 10.12688/wellcomeopenres.15639.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/02/2020] [Indexed: 12/29/2022] Open
Abstract
Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP pipeline for identifying and estimating the relative sequence abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our pipeline facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.
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Affiliation(s)
- Tommi Mäklin
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Teemu Kallonen
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Sophia David
- Centre for Genomic Pathogen Surveillance, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Christine J. Boinett
- Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Ben Pascoe
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Guillaume Méric
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - David M. Aanensen
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Edward J. Feil
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Stephen Baker
- Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Julian Parkhill
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Samuel K. Sheppard
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Jukka Corander
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Biostatistics, University of Oslo, Oslo, Norway
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Antti Honkela
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
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