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Pipeline for the identification and classification of ion channels in parasitic flatworms

Overview of attention for article published in Parasites & Vectors, March 2016
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2 tweeters

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3 Dimensions

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24 Mendeley
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Title
Pipeline for the identification and classification of ion channels in parasitic flatworms
Published in
Parasites & Vectors, March 2016
DOI 10.1186/s13071-016-1428-2
Pubmed ID
Authors

Bahiyah Nor, Neil D. Young, Pasi K. Korhonen, Ross S. Hall, Patrick Tan, Andrew Lonie, Robin B. Gasser

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Uruguay 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 21%
Professor > Associate Professor 4 17%
Researcher 4 17%
Student > Master 3 13%
Student > Ph. D. Student 2 8%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 21%
Engineering 2 8%
Computer Science 2 8%
Biochemistry, Genetics and Molecular Biology 2 8%
Veterinary Science and Veterinary Medicine 1 4%
Other 6 25%
Unknown 6 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 March 2016.
All research outputs
#3,795,093
of 7,430,207 outputs
Outputs from Parasites & Vectors
#1,100
of 2,016 outputs
Outputs of similar age
#149,019
of 275,880 outputs
Outputs of similar age from Parasites & Vectors
#102
of 167 outputs
Altmetric has tracked 7,430,207 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,016 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,880 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 167 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.