Applying taxonomic boundaries for species identification (ABIapp): A convenient and accurate application for species delimitation of parasitic helminths
Authors: En Chan Abigail Hui , Thaenkham Urusa , Wichaita Tanaphum , Saralamba Sompob 
Published: June 2022

Background

Parasitic helminths are highly diverse and ubiquitously distributed in various environments and hosts. Their vast species diversity renders morphological and molecular species delimitation challenging, due to phenotypic and genotypic variations. Currently used approaches to species delimitation are generally computationally intensive. Here, using genetic distances, we developed and validated a simple and easy-to-use application, Applying taxonomic Boundaries for species Identification (ABIapp), to aid in helminth species delimitation.

Methodology/Principal Findings

ABIapp uses a database of cut-off genetic distances obtained using the K-means algorithm to determine helminth taxonomic boundaries for ten genetic markers: The nuclear 18S and 28S rRNA genes, ITS1 and 2 regions, and the mitochondrial 12S and 16S rRNA, COI, COII, cytB, and ND1 genes. ABIapp was written in R, and the Shiny framework was used to produce an interactive and user-friendly interface. ABIapp requires just three types of input (genetic distance, genetic marker, helminth group) that are easily generated through basic morphological and molecular analysis. To validate ABIapp’s accuracy and robustness for use, validation was performed both in silico and with actual specimens. Prior to validation, ABIapp’s database of genetic distances and species used was increased to broaden the app’s applicability. In silico validation was conducted by obtaining 534 genetic distances from 91 publications and inputting these into ABIapp. Using confusion matrices, an overall classification accuracy of 79% was achieved, demonstrating the robustness and accuracy of ABIapp. Using sequences of the 12S, 16S, COI, and 18S rRNA genes obtained from ten representative helminth specimens, an overall classification accuracy of 75% was achieved.

Conclusions/Significance

Our results demonstrate the applicability and robustness of ABIapp for helminth species delimitation using ten common genetic markers. With a user-friendly interface, coupled with minimal and simple data input and robust classification accuracy, ABIapp provides helminth researchers with a convenient tool for helminth species delimitation.

Author summary

Species delimitation of organisms is often an issue of debate, with varying criteria used to determine species boundaries. Helminths are no exception, and their vast species diversity renders species delimitation challenging due to both physical and genetic variations. Moreover, as climate change progresses, helminths are also adapting to the changing environment through morphological and molecular changes. These variations render it challenging for helminthologists to determine whether a particular helminth belongs to the same or a different species. We have developed an application, ABIapp, a simple tool to aid helminth species delimitation using genetic distances; this app is readily available for a wide audience. Encompassing ten genetic markers for the three parasitic helminth groups (nematodes, trematodes, and cestodes), ABIapp uses cut-off genetic distances generated via machine learning to define species boundaries at each taxonomic level. To use ABIapp, just three types of information are needed, requiring only basic morphological and molecular expertise. We validated ABIapp using both mathematically modeled genetic distances and actual specimens and demonstrated a classification accuracy of 79% and 75%, respectively. This new, convenient, and validated application for helminth species delimitation will aid species identification applicable in the fields of helminth taxonomy, disease diagnosis, and biodiversity.