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High-resolution sweep metagenomics using ultrafast read mapping and inference

View ORCID ProfileTommi Mäklin, View ORCID ProfileTeemu Kallonen, Sophia David, View ORCID ProfileBen Pascoe, View ORCID ProfileGuillaume Méric, David M. Aanensen, Edward J. Feil, View ORCID ProfileSamuel K. Sheppard, View ORCID ProfileJukka Corander, View ORCID ProfileAntti Honkela
doi: https://doi.org/10.1101/332544
Tommi Mäklin
1Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Finland
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Teemu Kallonen
2Department of Biostatistics, University of Oslo, Norway
3Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
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Sophia David
4Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
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Ben Pascoe
5The Milner Center for Evolution, Department of Biology and Biochemistry, Bath University, Bath, United Kingdom
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Guillaume Méric
5The Milner Center for Evolution, Department of Biology and Biochemistry, Bath University, Bath, United Kingdom
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David M. Aanensen
3Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
6Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
7Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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Edward J. Feil
5The Milner Center for Evolution, Department of Biology and Biochemistry, Bath University, Bath, United Kingdom
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Samuel K. Sheppard
5The Milner Center for Evolution, Department of Biology and Biochemistry, Bath University, Bath, United Kingdom
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Jukka Corander
1Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Finland
2Department of Biostatistics, University of Oslo, Norway
3Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
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Antti Honkela
1Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Finland
8Department of Public Health, University of Helsinki, Finland
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Abstract

Traditional 16S ribosomal RNA sequencing and whole-genome shotgun metagenomics can determine the composition of bacterial communities on genus level and species level but high-resolution inference on the strain level is challenging due to close relatedness between strain genomes. We present the mSWEEP pipeline for identifying and estimating relative abundances of bacterial strains from plate sweeps of enrichment cultures. mSWEEP uses a database of biologically grouped sequence assemblies as a reference and achieves ultra-fast mapping and accurate inference using pseudoalignment, Bayesian probabilistic modeling, and a control for false positive results. We use sequencing data from the major human pathogens Campylobacter jejuni, Campylobacter coli, Klebsiella pneumoniae and Staphylococcus epidermidis to demonstrate that mSWEEP significantly outperforms previous state-of-the-art in strain quantification and detection accuracy. The introduction of mSWEEP opens up a new field of plate sweep metagenomics and facilitates investigation of bacterial cultures composed of mixtures of organisms at differing levels of variation.

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Posted June 05, 2018.
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High-resolution sweep metagenomics using ultrafast read mapping and inference
Tommi Mäklin, Teemu Kallonen, Sophia David, Ben Pascoe, Guillaume Méric, David M. Aanensen, Edward J. Feil, Samuel K. Sheppard, Jukka Corander, Antti Honkela
bioRxiv 332544; doi: https://doi.org/10.1101/332544
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High-resolution sweep metagenomics using ultrafast read mapping and inference
Tommi Mäklin, Teemu Kallonen, Sophia David, Ben Pascoe, Guillaume Méric, David M. Aanensen, Edward J. Feil, Samuel K. Sheppard, Jukka Corander, Antti Honkela
bioRxiv 332544; doi: https://doi.org/10.1101/332544

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