Evidence map›Paper›PMID 40866974›Full record

ArticleParasites & vectors2025

PGIP: a web server for the rapid taxonomic identification of parasite genomes.

Jiayao Zhang, Feng Tang, Bixian Ni, Qiang Zhang, Xinyi Gong, Fanzhen Mao, Jun Cao, Yaobao Liu

Abstract read
In one paragraph

Article in Parasites & vectors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jiayao ZhangNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China.
Feng TangNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China.
Bixian NiNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China.
Qiang ZhangNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China.
Xinyi GongNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China.
Fanzhen MaoNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China.
Jun CaoNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China. caojuncn@hotmail.com.
Yaobao LiuNational Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Provincial Medical Key Laboratory, Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, Jiangsu, China. yaobao0721@163.com.

Funding

Jiangsu Province Capability Improvement Project through Science, Technology, and Education ZDXYS202207National Key R&D Program of China 2020YFC1200100National Natural Science Foundation of China 82372275
6 · The paper itself

Abstract

backgroundParasitic diseases remain a global health challenge, and traditional methods in their diagnosis face limitations in sensitivity and scalability. Genome-based sequencing technologies have improved and are increasingly employed for the identification of parasites; however, their clinical adoption remains hindered by the complexity of bioinformatics analysis, reliance on incomplete reference databases, and accessibility barriers for nonspecialists. Overcoming these challenges necessitates the development of standardized analytical workflows and high-quality genomic resources specifically tailored for parasite identification.

methodsWe developed a user-friendly web server named the Parasite Genome Identification Platform (PGIP). The reference database was sourced from the National Center for Biotechnology Information (NCBI), WormBase, European Nucleotide Archive (ENA), and VEuPathDB, rigorously filtered for quality, and deduplicated using Cluster Database at High Identity with Tolerance (CD-HIT) to ensure accuracy and nonredundancy. To streamline analysis, we integrated a standardized identification pipeline built on Nextflow, which encompasses host DNA depletion, quality control, parasite species identification via both reads mapping and assembly-based approaches, and automated report generation for comprehensive diagnostic insights.

resultsPGIP integrates a curated database of 280 parasite genomes; which is rigorously filtered for quality and taxonomic accuracy. Validation across diverse datasets demonstrated the precise species-level resolution of PGIP, and its compatibility with clinical samples. The platform features an intuitive graphic interface; and one-click analysis significantly reduces reliance on bioinformatics expertise, thus enabling rapid diagnosis.

conclusionsPGIP offers an accurate, efficient, and a user-friendly web server designed to simplify and accelerate the taxonomic identification of parasite genomes using data from metagenomic next-generation sequencing. Its automated framework reduces the need for specialized expertise, enabling rapid application in clinical and public health settings.

Indexed as

Computational BiologyGenomicsInternetParasitesSoftwareAnimalsDatabases, GeneticParasitic DiseasesBioinformatics platformMetagenomics next-generation sequencingParasite genomicsParasite identificationWeb server

Identifiers

PMID40866974
PMCPMC12392538

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.