ParaRef: a decontaminated reference database for parasite detection in ancient and modern metagenomic datasets

Shotgun metagenomics holds great potential for identifying parasite DNA in biological samples, but its effectiveness is limited by widespread contamination in publicly available reference genomes, which hinders accurate detection. In this study, we systematically quantify and remove contamination from 831 published endoparasite genomes to create ParaRef, a curated reference database for species-level parasite detection. We show that decontamination significantly reduces false detection rates and improves overall detection accuracy. Our study highlights the pervasive issue of contamination in public databases and offers a resource that will enhance the reliability of parasite detection using metagenomics.

Authors

Institutions

Publication Details

Journal
Genome biology
Published
2025-10-23
DOI
https://doi.org/10.1186/s13059-025-03818-w
Primary Topic
Parasite Biology and Host Interactions
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ParaRef: a decontaminated reference database for parasite detection in ancient and modern metagenomic datasets

Martin Sikora, Yue-Jiao Huang, Robert R. Dunn, Liam Thomas Lanigan et al.
Genome biology
Parasite Biology and Host Interactions
article

ParaRef: a decontaminated reference database for parasite detection in ancient and modern metagenomic datasets

Martin Sikora, Yue-Jiao Huang, Robert R. Dunn, Liam Thomas Lanigan, Hannes Schroeder, Jonas Niemann, Arve L. Willingham Grijalba
article en

Abstract

Shotgun metagenomics holds great potential for identifying parasite DNA in biological samples, but its effectiveness is limited by widespread contamination in publicly available reference genomes, which hinders accurate detection. In this study, we systematically quantify and remove contamination from 831 published endoparasite genomes to create ParaRef, a curated reference database for species-level parasite detection. We show that decontamination significantly reduces false detection rates and improves overall detection accuracy. Our study highlights the pervasive issue of contamination in public databases and offers a resource that will enhance the reliability of parasite detection using metagenomics.

Genome biologyVol. 26(1)
University of Copenhagen (DK), Centre National de la Recherche Scientifique (FR), North Carolina State University (US), Institut Pasteur (FR), Université Paris Cité (FR), Department of Archaeology (LK)
European Commission, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 71%
Parasite Biology and Host Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.