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Peer-reviewedCC BYNature Communications

CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data

  • Lindsay Goulet1,2
  • Florian Plaza Oñate1,2
  • Alexandre Famechon1,2
  • Benoît Quinquis1,2
  • Eugeni Belda3,4,5
  • Edi Prifti3,4,5
  • Emmanuelle Le Chatelier6,2
  • Guillaume Gautreau7,8,9
9 affiliations
  1. 1Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France
  2. 2Université Paris-Saclay, INRAE, MGP, F-78350, Jouy-en-Josas, France
  3. 3Nutrition and Obesities: Systemic Approaches, NutriOmics, Research Unit, Sorbonne Université, INSERM, Paris, France
  4. 4Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, Sorbonne Université, IRD, Bondy, France
  5. 5Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, Sorbonne Université, IRD, F-914, Bondy, France
  6. 6Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France. [email protected]
  7. 7Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France. [email protected]
  8. 8Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France. [email protected]
  9. 9Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France
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Abstract

Metagenomic sequencing provides insights into microbial communities, but it can be compromised by technical biases, including cross-sample contamination. This phenomenon arises when microbial content is inadvertently exchanged among concurrently processed samples, distorting microbial profiles and compromising the reliability of metagenomic data and downstream analyses. Existing detection methods rely on negative controls, which are insufficiently used and do not detect cross-contamination within non-control samples. Meanwhile, strain-level bioinformatics approaches do not distinguish contamination from natural strain sharing and lack sensitivity. To fill this gap, we introduce CroCoDeEL, a decision-support tool for detecting and quantifying cross-sample contamination. Leveraging linear modeling and a pre-trained supervised model, CroCoDeEL identifies specific contamination patterns in species abundance profiles. It requires no negative controls or prior knowledge of sample processing positions, offering improved accuracy and versatility. Benchmarks across three public datasets demonstrate that CroCoDeEL can detect contaminated samples and identify their contamination sources, even at low rates (<0.1%), provided sufficient sequencing depth. Application of CroCoDeEL to several existing studies reveals previously undetected contamination.

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Cite

BibTeXRIS
BibTeX
@article{goulet2026crocodeel,
  title = {{CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data}},
  author = {Lindsay Goulet and Florian Plaza Oñate and Alexandre Famechon and Benoît Quinquis and Eugeni Belda and Edi Prifti and Emmanuelle Le Chatelier and Guillaume Gautreau},
  journal = {Nature Communications},
  year = {2026},
  month = may,
  doi = {10.1038/s41467-026-72637-9},
  url = {https://doi.org/10.1038/s41467-026-72637-9},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data
AU  - Lindsay Goulet
AU  - Florian Plaza Oñate
AU  - Alexandre Famechon
AU  - Benoît Quinquis
AU  - Eugeni Belda
AU  - Edi Prifti
AU  - Emmanuelle Le Chatelier
AU  - Guillaume Gautreau
PY  - 2026
DA  - 2026/05/09
JO  - Nature Communications
DO  - 10.1038/s41467-026-72637-9
UR  - https://doi.org/10.1038/s41467-026-72637-9
AB  - Metagenomic sequencing provides insights into microbial communities, but it can be compromised by technical biases, including cross-sample contamination. This phenomenon arises when microbial content is inadvertently exchanged among concurrently processed samples, distorting microbial profiles and compromising the reliability of metagenomic data and downstream analyses. Existing detection methods rely on negative controls, which are insufficiently used and do not detect cross-contamination within non-control samples. Meanwhile, strain-level bioinformatics approaches do not distinguish contamination from natural strain sharing and lack sensitivity. To fill this gap, we introduce CroCoDeEL, a decision-support tool for detecting and quantifying cross-sample contamination. Leveraging linear modeling and a pre-trained supervised model, CroCoDeEL identifies specific contamination patterns in species abundance profiles. It requires no negative controls or prior knowledge of sample processing positions, offering improved accuracy and versatility. Benchmarks across three public datasets demonstrate that CroCoDeEL can detect contaminated samples and identify their contamination sources, even at low rates (<0.1%), provided sufficient sequencing depth. Application of CroCoDeEL to several existing studies reveals previously undetected contamination.
SN  - 2041-1723
ER  -