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Poster De Conférence Année : 2023

Towards a machine learning approach for automated detection of well-to-well contamination in metagenomic data

Lindsay Goulet
  • Fonction : Auteur
  • PersonId : 1274425
Florian Plaza Oñate
Emmanuelle Le Chatelier
Guillaume Gautreau

Résumé

Samples subjected to metagenomic sequencing can be accidentally contaminated during wet lab steps (DNA extraction, library preparation) by DNA from an external source (e.g.: lab reagents) or from other samples processed on the same plate (well-to-well contamination). These can lead to biased results and eventually to false conclusions if not detected. Although a critical issue, well-to-well contamination remains understudied. A few tools have been developed but suffer from several limitations, such as a lack of sensitivity. By inspecting species abundance profiles of published cohort samples, we identified specific patterns associated with well-to-well contamination. Here, we propose an original method based on the recognition of such patterns that accurately detects contamination events even at low rates (up to 1%). Our approach does not require negative controls, works with related samples that may naturally share strains (e.g.: mother/child), discriminates contamination sources from contaminated samples and estimates contamination rates. However, this method is time-consuming and requires human expertise to manually inspect suspect cases. We are developing a fully automated tool, based on deep learning, trained with semi-simulated sequencing data to classify contaminated samples. As preliminary results are promising, we believe this method will significantly impact the field, making metagenomic experiments more robust.
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Dates et versions

hal-04177345 , version 1 (04-08-2023)

Identifiants

  • HAL Id : hal-04177345 , version 1

Citer

Lindsay Goulet, Florian Plaza Oñate, Edi Prifti, Eugeni Belda, Emmanuelle Le Chatelier, et al.. Towards a machine learning approach for automated detection of well-to-well contamination in metagenomic data. 31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational Biology ( ECCB/ISMB 2023 ), Jul 2023, Lyon, France. ⟨hal-04177345⟩
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