Bacterial relatedness test data

Name: Bacterial relatedness test data
Published: 2017-01-01T00:00:00Z
License: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/2/

Wyllie, D

Documentation:: These are mapped data from short read sequencing of microorganisms and are provided as a test data set.

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APA Style

Wyllie, D. (2017). Bacterial relatedness test data. University of Oxford.

MLA Style

Wyllie, D. Bacterial Relatedness Test Data. University of Oxford, 2017.

Chicago Style

Wyllie, D. 2017. Bacterial Relatedness Test Data. University of Oxford.
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Files:: NGON_FASTA.tar

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SE_FASTA.tar

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TB_FASTA_0.tar

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TB_FASTA_1.tar

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TB_FASTA_f.tar

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Authors/Creators

+ Wyllie, D More by this author/creator

Institution:: University of Oxford
Division:: MSD
Department:: NDM
Sub department:: Jenner Institute
Role:: Principal Investigator (PI), Creator

+ National Institute for Health Research More from this funder

Grant:: HPRU-2012-10041

Publisher:: University of Oxford
Publication date:: 2017

UUID:: uuid:82ce6500-fa71-496a-8ba5-ba822b6cbb50
Deposit date:: 2017-08-09
ARK identifier:: ark:/29072/ora_82ce6500fa71496a8ba5ba822b6cbb50

Related item:: BugMat and FindNeighbour: command line and server applications for investigating bacterial relatedness
Description:: Background: Large scale bacterial sequencing has made the determination of genetic relationships within large collections of bacterial genomes an increasingly common task. Solutions to the problem have application to public health (for example, in the detection of possible disease transmission), and as part of divide-and-conquer strategies selecting groups of similar isolates for computationally intensive methods of phylogenetic inference using (for example) maximal likelihood methods. However, the generation and maintenance of distance matrices is computationally intensive, and rapid methods of doing so are needed to allow translation of microbial genomics into public health actions. Results: We developed, tested and deployed three solutions. BugMat is a fast C++ application which generates one-off in-memory distance matrices. FindNeighbour and FindNeighbour2 are server-side applications which build, maintain, and persist either complete (for FindNeighbour) or sparse (for FindNeighbour2) distance matrices given a set of sequences. FindNeighbour and BugMat use a variation model to accelerate computation, while FindNeighbour2 uses reference-based compression. Performance metrics show scalability into tens of thousands of sequences, with options for scaling further. Conclusion: Three applications, each with distinct strengths and weaknesses, are available for distance-matrix based analysis of large bacterial collections. Deployed as part of the Public Health England solution for M. tuberculosis genomic processing, they will have wide applicability. Key words: Bacterial genomes, Phylogenetic analysis, Distance matrix

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Licence:: National Archives Open Government Licence (OGL)

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