Molecular approaches to trematode systematics: ‘best practice’ and implications for future study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-03

AUTHORS

Isabel Blasco-Costa, Scott C. Cutmore, Terrence L. Miller, Matthew J. Nolan

ABSTRACT

To date, morphological analysis has been the cornerstone to trematode systematics. However, since the late-1980s we have seen an increased integration of genetic data to overcome problems encountered when morphological data are considered in isolation. Here, we provide advice regarding the 'best molecular practice' for trematode taxonomy and systematic studies, in an attempt to help unify the field and provide a solid foundation to underpin future work. Emphasis is placed on defining the study goals and recommendations are made regarding sample preservation, extraction methods, and the submission of molecular vouchers. We advocate generating sequence data from all parasite species/host species/geographic location combinations and stress the importance of selecting two independently evolving loci (one ribosomal and one mitochondrial marker). We recommend that loci should be chosen to provide genetic variation suitable to address the question at hand and for which sufficient 'useful' comparative sequence data already exist. Quality control of the molecular data via using proof-reading Taq polymerase, sequencing PCR amplicons using both forward and reverse primers, ensuring that a minimum of 85% overlap exists when constructing consensus sequences, and checking electropherograms by eye is stressed. We advise that all genetic results are best interpreted using a holistic biological approach, which considers morphology, host identity, collection locality, and ecology. Finally, we consider what advances next-generation sequencing holds for trematode taxonomy and systematics. More... »

PAGES

295-306

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11230-016-9631-2

DOI

http://dx.doi.org/10.1007/s11230-016-9631-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046615101

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26898592


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