Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis.

Nascimento, FS; Barratt, J; Houghton, K; Plucinski, M; Kelley, J; Casillas, S; Bennett, CC; Snider, C; Tuladhar, R; Zhang, J; Clemons, B; Madison-Antenucci, S; Russell, A; Cebelinski, E; Haan, J; Robinson, T; Arrowood, MJ; Talundzic, E; Bradbury, RS; Qvarnstrom, Y

Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis.

Nascimento, FS Barratt, J

Houghton, K Plucinski, M Kelley, J Casillas, S Bennett, CC Snider, C Tuladhar, R Zhang, J Clemons, B Madison-Antenucci, S Russell, A Cebelinski, E Haan, J Robinson, T Arrowood, MJ Talundzic, E Bradbury, RS Qvarnstrom, Y

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Publisher:: Cambridge University Press (CUP)
Publication Type:: Journal Article
Citation:: Epidemiology and infection, 2020, 148, pp. 1-10
Issue Date:: 2020-08-03

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Field	Value	Language
dc.contributor.author	Nascimento, FS
dc.contributor.author	Barratt, J https://orcid.org/0000-0001-8711-2408
dc.contributor.author	Houghton, K
dc.contributor.author	Plucinski, M
dc.contributor.author	Kelley, J
dc.contributor.author	Casillas, S
dc.contributor.author	Bennett, CC
dc.contributor.author	Snider, C
dc.contributor.author	Tuladhar, R
dc.contributor.author	Zhang, J
dc.contributor.author	Clemons, B
dc.contributor.author	Madison-Antenucci, S
dc.contributor.author	Russell, A
dc.contributor.author	Cebelinski, E
dc.contributor.author	Haan, J
dc.contributor.author	Robinson, T
dc.contributor.author	Arrowood, MJ
dc.contributor.author	Talundzic, E
dc.contributor.author	Bradbury, RS
dc.contributor.author	Qvarnstrom, Y
dc.date.accessioned	2021-04-23T05:10:10Z
dc.date.available	2021-04-23T05:10:10Z
dc.date.issued	2020-08-03
dc.identifier.citation	Epidemiology and infection, 2020, 148, pp. 1-10
dc.identifier.issn	0950-2688
dc.identifier.issn	1469-4409
dc.identifier.uri	http://hdl.handle.net/10453/148312
dc.description.abstract	Outbreaks of cyclosporiasis, a food-borne illness caused by the coccidian parasite Cyclospora cayetanensis have increased in the USA in recent years, with approximately 2300 laboratory-confirmed cases reported in 2018. Genotyping tools are needed to inform epidemiological investigations, yet genotyping Cyclospora has proven challenging due to its sexual reproductive cycle which produces complex infections characterized by high genetic heterogeneity. We used targeted amplicon deep sequencing and a recently described ensemble-based distance statistic that accommodates heterogeneous (mixed) genotypes and specimens with partial genotyping data, to genotype and cluster 648 C. cayetanensis samples submitted to CDC in 2018. The performance of the ensemble was assessed by comparing ensemble-identified genetic clusters to analogous clusters identified independently based on common food exposures. Using these epidemiologic clusters as a gold standard, the ensemble facilitated genetic clustering with 93.8% sensitivity and 99.7% specificity. Hence, we anticipate that this procedure will greatly complement epidemiologic investigations of cyclosporiasis.
dc.format	Electronic
dc.language	eng
dc.publisher	Cambridge University Press (CUP)
dc.relation.ispartof	Epidemiology and infection
dc.relation.isbasedon	10.1017/s0950268820001697
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1117 Public Health and Health Services
dc.subject.classification	Epidemiology
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Cyclospora
dc.subject.mesh	Cyclosporiasis
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Databases, Factual
dc.subject.mesh	Feces
dc.subject.mesh	Genetic Markers
dc.subject.mesh	Haplotypes
dc.subject.mesh	Humans
dc.subject.mesh	Multilocus Sequence Typing
dc.subject.mesh	Feces
dc.subject.mesh	Humans
dc.subject.mesh	Cyclospora
dc.subject.mesh	Cyclosporiasis
dc.subject.mesh	Genetic Markers
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Haplotypes
dc.subject.mesh	Databases, Factual
dc.subject.mesh	Multilocus Sequence Typing
dc.subject.mesh	Cluster Analysis
dc.subject.mesh	Cyclospora
dc.subject.mesh	Cyclosporiasis
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Databases, Factual
dc.subject.mesh	Feces
dc.subject.mesh	Genetic Markers
dc.subject.mesh	Haplotypes
dc.subject.mesh	Humans
dc.subject.mesh	Multilocus Sequence Typing
dc.title	Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis.
dc.type	Journal Article
utslib.citation.volume	148
utslib.location.activity	England
utslib.for	1117 Public Health and Health Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-23T05:10:07Z
pubs.publication-status	Published
pubs.volume	148

Abstract:

Outbreaks of cyclosporiasis, a food-borne illness caused by the coccidian parasite Cyclospora cayetanensis have increased in the USA in recent years, with approximately 2300 laboratory-confirmed cases reported in 2018. Genotyping tools are needed to inform epidemiological investigations, yet genotyping Cyclospora has proven challenging due to its sexual reproductive cycle which produces complex infections characterized by high genetic heterogeneity. We used targeted amplicon deep sequencing and a recently described ensemble-based distance statistic that accommodates heterogeneous (mixed) genotypes and specimens with partial genotyping data, to genotype and cluster 648 C. cayetanensis samples submitted to CDC in 2018. The performance of the ensemble was assessed by comparing ensemble-identified genetic clusters to analogous clusters identified independently based on common food exposures. Using these epidemiologic clusters as a gold standard, the ensemble facilitated genetic clustering with 93.8% sensitivity and 99.7% specificity. Hence, we anticipate that this procedure will greatly complement epidemiologic investigations of cyclosporiasis.

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http://hdl.handle.net/10453/148312