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dc.contributor.authorPage, AJ
dc.contributor.authorWailan, A
dc.contributor.authorShao, Y
dc.contributor.authorJudge, K
dc.contributor.authorDougan, G
dc.contributor.authorKlemm, EJ
dc.contributor.authorThomson, NR
dc.contributor.authorKeane, JA
dc.date.accessioned2020-12-10T00:02:28Z
dc.date.available2020-12-10T00:02:28Z
dc.date.issued2018-03-01
dc.identifier.citationPage, A. J., Wailan, A., Shao, Y., Judge, K., Dougan, G., Klemm, E. J., Thomson, N. R. & Keane, J. A. (2018). PlasmidTron: assembling the cause of phenotypes and genotypes from NGS data. MICROBIAL GENOMICS, 4 (3), https://doi.org/10.1099/mgen.0.000164.
dc.identifier.issn2057-5858
dc.identifier.urihttp://hdl.handle.net/11343/253397
dc.description.abstractIncreasingly rich metadata are now being linked to samples that have been whole-genome sequenced. However, much of this information is ignored. This is because linking this metadata to genes, or regions of the genome, usually relies on knowing the gene sequence(s) responsible for the particular trait being measured and looking for its presence or absence in that genome. Examples of this would be the spread of antimicrobial resistance genes carried on mobile genetic elements (MGEs). However, although it is possible to routinely identify the resistance gene, identifying the unknown MGE upon which it is carried can be much more difficult if the starting point is short-read whole-genome sequence data. The reason for this is that MGEs are often full of repeats and so assemble poorly, leading to fragmented consensus sequences. Since mobile DNA, which can carry many clinically and ecologically important genes, has a different evolutionary history from the host, its distribution across the host population will, by definition, be independent of the host phylogeny. It is possible to use this phenomenon in a genome-wide association study to identify both the genes associated with the specific trait and also the DNA linked to that gene, for example the flanking sequence of the plasmid vector on which it is encoded, which follows the same patterns of distribution as the marker gene/sequence itself. We present PlasmidTron, which utilizes the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographical information, to identify traits that are likely to be present on DNA that can randomly reassort across defined bacterial populations. It is also possible to use this methodology to associate unknown genes/sequences (e.g. plasmid backbones) with a specific molecular signature or marker (e.g. resistance gene presence or absence) using PlasmidTron. PlasmidTron uses a k-mer-based approach to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs in a fast and scalable-to-large manner. PlasmidTron is written in Python 3 and is available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron.
dc.languageEnglish
dc.publisherMICROBIOLOGY SOC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titlePlasmidTron: assembling the cause of phenotypes and genotypes from NGS data
dc.typeJournal Article
dc.identifier.doi10.1099/mgen.0.000164
melbourne.affiliation.departmentMicrobiology and Immunology
melbourne.source.titleMicrobial Genomics
melbourne.source.volume4
melbourne.source.issue3
dc.rights.licenseCC BY
melbourne.elementsid1313670
melbourne.contributor.authorDougan, Gordon
dc.identifier.eissn2057-5858
melbourne.accessrightsOpen Access


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