Please use this identifier to cite or link to this item: https://doi.org/10.1186/s13071-016-1428-2
Title: Pipeline for the identification and classification of ion channels in parasitic flatworms
Authors: Nor, B
Young, N.D
Korhonen, P.K
Hall, R.S
Tan, P 
Lonie, A
Gasser, R.B
Keywords: ion channel
membrane protein
ion channel
accuracy
amino acid sequence
Article
bioinformatics
cancer risk
carcinogenicity
classification
microbial genome
multi screening ion channel classifier
nonhuman
Opisthorchis viverrini
pipeline
pipeline embolization device
platyhelminth
protein analysis
protein domain
reliability
sensitivity analysis
support vector machine
animal
biology
enzymology
genetics
parasitology
platyhelminth
procedures
Animals
Computational Biology
Ion Channels
Parasitology
Platyhelminths
Issue Date: 2016
Citation: Nor, B, Young, N.D, Korhonen, P.K, Hall, R.S, Tan, P, Lonie, A, Gasser, R.B (2016). Pipeline for the identification and classification of ion channels in parasitic flatworms. Parasites and Vectors 9 (1) : 155. ScholarBank@NUS Repository. https://doi.org/10.1186/s13071-016-1428-2
Rights: Attribution 4.0 International
Abstract: Background: Ion channels are well characterised in model organisms, principally because of the availability of functional genomic tools and datasets for these species. This contrasts the situation, for example, for parasites of humans and animals, whose genomic and biological uniqueness means that many genes and their products cannot be annotated. As ion channels are recognised as important drug targets in mammals, the accurate identification and classification of parasite channels could provide major prospects for defining unique targets for designing novel and specific anti-parasite therapies. Here, we established a reliable bioinformatic pipeline for the identification and classification of ion channels encoded in the genome of the cancer-causing liver fluke Opisthorchis viverrini, and extended its application to related flatworms affecting humans. Methods: We built an ion channel identification + classification pipeline (called MuSICC), employing an optimised support vector machine (SVM) model and using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) classification system. Ion channel proteins were first identified and grouped according to amino acid sequence similarity to classified ion channels and the presence and number of ion channel-like conserved and transmembrane domains. Predicted ion channels were then classified to sub-family using a SVM model, trained using ion channel features. Results: Following an evaluation of this pipeline (MuSICC), which demonstrated a classification sensitivity of 95.2 % and accuracy of 70.5 % for known ion channels, we applied it to effectively identify and classify ion channels in selected parasitic flatworms. Conclusions: MuSICC provides a practical and effective tool for the identification and classification of ion channels of parasitic flatworms, and should be applicable to a broad range of organisms that are evolutionarily distant from taxa whose ion channels are functionally characterised. © 2016 Nor et al.
Source Title: Parasites and Vectors
URI: https://scholarbank.nus.edu.sg/handle/10635/179950
ISSN: 17563305
DOI: 10.1186/s13071-016-1428-2
Rights: Attribution 4.0 International
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