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Title: Transcriptome sequences spanning key developmental states as a resource for the study of the cestode Schistocephalus solidus, a threespine stickleback parasite
Authors: Hébert, François Olivier
Grambauer, Stephan
Barber, Iain
Landry, Christian R.
Aubin-Horth, Nadia
First Published: 2-Jun-2016
Publisher: BioMed Central
Citation: Gigascience, 2016, 5 (24), DOI: 10.1186/s13742-016-0128-3
Abstract: BACKGROUND: Schistocephalus solidus is a well-established model organism for studying the complex life cycle of cestodes and the mechanisms underlying host-parasite interactions. However, very few large-scale genetic resources for this species are available. We have sequenced and de novo-assembled the transcriptome of S. solidus using tissues from whole worms at three key developmental states - non-infective plerocercoid, infective plerocercoid and adult plerocercoid - to provide a resource for studying the evolution of complex life cycles and, more specifically, how parasites modulate their interactions with their hosts during development. FINDINGS: The de novo transcriptome assembly reconstructed the coding sequence of 10,285 high-confidence unigenes from which 24,765 non-redundant transcripts were derived. 7,920 (77 %) of these unigenes were annotated with a protein name and 7,323 (71 %) were assigned at least one Gene Ontology term. Our raw transcriptome assembly (unfiltered transcripts) covers 92 % of the predicted transcriptome derived from the S. solidus draft genome assembly currently available on WormBase. It also provides new ecological information and orthology relationships to further annotate the current WormBase transcriptome and genome. CONCLUSION: This large-scale transcriptomic dataset provides a foundation for studies on how parasitic species with complex life cycles modulate their response to changes in biotic and abiotic conditions experienced inside their various hosts, which is a fundamental objective of parasitology. Furthermore, this resource will help in the validation of the S solidus gene features that have been predicted based on genomic sequence.
DOI Link: 10.1186/s13742-016-0128-3
ISSN: 2047-217X
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © The Authors, 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Appears in Collections:Published Articles, Dept. of Neuroscience, Psychology and Behaviour

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