Validating a web application’s use of genetic distance to determine helminth species boundaries and aid in identification
Issued Date
2025-12-01
Resource Type
eISSN
14712105
Scopus ID
2-s2.0-105000826799
Journal Title
BMC Bioinformatics
Volume
26
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Bioinformatics Vol.26 No.1 (2025)
Suggested Citation
Chan A.H.E., Thaenkham U., Wichaita T., Saralamba S. Validating a web application’s use of genetic distance to determine helminth species boundaries and aid in identification. BMC Bioinformatics Vol.26 No.1 (2025). doi:10.1186/s12859-025-06098-0 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/108595
Title
Validating a web application’s use of genetic distance to determine helminth species boundaries and aid in identification
Author(s)
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Parasitic helminths exhibit significant diversity, complicating both morphological and molecular species identification. Moreover, no helminth-specific tool is currently available to aid in species identification of helminths using molecular data. To address this, we developed and validated a straightforward, user-friendly application named Applying Taxonomic Boundaries for Species Identification of Helminths (ABIapp) using R and the Shiny framework. Serving as a preliminary step in species identification, ABIapp is designed to assist in visualizing taxonomic boundaries for nematodes, trematodes, and cestodes. ABIapp employs a database of genetic distance cut-offs determined by the K-means algorithm to establish taxonomic boundaries for ten genetic markers. Validation of ABIapp was performed both in silico and with actual specimens to determine its classification accuracy. The in silico validation involved 591 genetic distances sourced from 117 publications, while the validation with actual specimens utilized ten specimens. ABIapp’s accuracy was also compared with other online platforms to ensure its robustness to assist in helminth identification. Results: ABIapp achieved an overall classification accuracy of 76% for in silico validation and 75% for actual specimens. Additionally, compared to other platforms, the classification accuracy of ABIapp was superior, proving its effectiveness to determine helminth taxonomic boundaries. With its user-friendly interface, minimal data input requirements, and precise classification capabilities, ABIapp offers multiple benefits for helminth researchers and can aid in identification. Conclusions: Built on a helminth-specific database, ABIapp serves as a pioneering tool for helminth researchers, offering an invaluable resource for determining species boundaries and aiding in species identification of helminths. The availability of ABIapp to the community of helminth researchers may further enhance research in the field of helminthology. To enhance ABIapp’s accuracy and utility, the database will be updated annually.