Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches

The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method base...

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Autores principales: Zha, Yuguo, Chong, Hui, Qiu, Hao, Kang, Kai, Dun, Yuzheng, Chen, Zhixue, Cui, Xuefeng, Ning, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040266/
https://www.ncbi.nlm.nih.gov/pubmed/35473941
http://dx.doi.org/10.1186/s13073-022-01047-5
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author Zha, Yuguo
Chong, Hui
Qiu, Hao
Kang, Kai
Dun, Yuzheng
Chen, Zhixue
Cui, Xuefeng
Ning, Kang
author_facet Zha, Yuguo
Chong, Hui
Qiu, Hao
Kang, Kai
Dun, Yuzheng
Chen, Zhixue
Cui, Xuefeng
Ning, Kang
author_sort Zha, Yuguo
collection PubMed
description The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking. ONN4MST outperformed other methods with near-optimal accuracy when source tracking among 125,823 samples from 114 niches. ONN4MST also has a broad spectrum of applications. Overall, this study represents the first model-based method for source tracking among sub-million microbial community samples from hundreds of niches, with superior speed, accuracy, and interpretability. ONN4MST is available at https://github.com/HUST-NingKang-Lab/ONN4MST. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01047-5.
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spelling pubmed-90402662022-04-27 Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches Zha, Yuguo Chong, Hui Qiu, Hao Kang, Kai Dun, Yuzheng Chen, Zhixue Cui, Xuefeng Ning, Kang Genome Med Method The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking. ONN4MST outperformed other methods with near-optimal accuracy when source tracking among 125,823 samples from 114 niches. ONN4MST also has a broad spectrum of applications. Overall, this study represents the first model-based method for source tracking among sub-million microbial community samples from hundreds of niches, with superior speed, accuracy, and interpretability. ONN4MST is available at https://github.com/HUST-NingKang-Lab/ONN4MST. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01047-5. BioMed Central 2022-04-26 /pmc/articles/PMC9040266/ /pubmed/35473941 http://dx.doi.org/10.1186/s13073-022-01047-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Zha, Yuguo
Chong, Hui
Qiu, Hao
Kang, Kai
Dun, Yuzheng
Chen, Zhixue
Cui, Xuefeng
Ning, Kang
Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
title Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
title_full Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
title_fullStr Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
title_full_unstemmed Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
title_short Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
title_sort ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040266/
https://www.ncbi.nlm.nih.gov/pubmed/35473941
http://dx.doi.org/10.1186/s13073-022-01047-5
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