Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis. (2020)
- Record Type:
- Journal Article
- Title:
- Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis. (2020)
- Main Title:
- Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis
- Authors:
- Nascimento, Fernanda S.
Barratt, Joel
Houghton, Katelyn
Plucinski, Mateusz
Kelley, Julia
Casillas, Shannon
Bennett, Carolyne (Cody)
Snider, Cathy
Tuladhar, Rashmi
Zhang, Jenny
Clemons, Brooke
Madison-Antenucci, Susan
Russell, Alexis
Cebelinski, Elizabeth
Haan, Jisun
Robinson, Trisha
Arrowood, Michael J.
Talundzic, Eldin
Bradbury, Richard S.
Qvarnstrom, Yvonne - Abstract:
- 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.
- Is Part Of:
- Epidemiology and infection. Volume 148(2020)
- Journal:
- Epidemiology and infection
- Issue:
- Volume 148(2020)
- Issue Display:
- Volume 148, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 148
- Issue:
- 2020
- Issue Sort Value:
- 2020-0148-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020
- Subjects:
- Cyclospora cayetanensis, -- clustering, -- cyclosporiasis, -- deep sequencing, -- distance-statistic, -- genotype, -- genotyping, -- machine learning, -- MLST
Communicable diseases -- Periodicals
Epidemiology -- Periodicals
614.4 - Journal URLs:
- http://journals.cambridge.org/action/displayJournal?jid=HYG ↗
http://journals.cambridge.org/action/displayJournal?jid=HYG ↗ - DOI:
- 10.1017/S0950268820001697 ↗
- Languages:
- English
- ISSNs:
- 0950-2688
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library STI - ELD Digital Store
- Ingest File:
- 14642.xml