DNAi
High throughput eDNA processing using artificial intelligence for ecosystem monitoring
Abstract
The current biodiversity crisis demands novel approaches to monitor how human activities influence the biosphere. The rapid development of ‘omics’ tools, in particular the metabarcoding of environmental DNA (eDNA), has opened a new area of comprehensive biodiversity data generation across many regions of the world. Yet, the development of efficient data processing pipelines has not matched the exponential increase in size and quality of ‘omics’ data, which limits the application of eDNA for ecological monitoring. Currently, processing eDNA requires multiple expensive and error-prone bioinformatic steps, with each step relying on poorly automatized disparate software, and many paths to choose and results sensitive to subjective decisions.
Moving toward large-scale biodiversity monitoring requires a fast, objective, and automated processing pipeline that transforms eDNA data into meaningful information about ecosystems, including (i) standardized taxonomic lists for each sampled location that guide species management, (ii) standardized classification of samples from their DNA composition which can guide ecosystem management. In this project, we developed several machine learning approaches to directly process raw eDNA metabarcoding data to improve ecosystem monitoring and decision-making.
People
Collaborators
Steven Stalder joined the SDSC in 2022 as a Data Scientist in the academia team. He received both his BSc and MSc in computer science from ETH Zürich, with a main focus on machine learning and high-performance computing. His first contact with the SDSC was during his master’s thesis, where he worked on explainable neural network models for image classification. Outside of work, Steven loves playing football, reading an interesting book, or watching a good movie.
Michele Volpi holds a Ph.D. in Environmental Sciences from the University of Lausanne (2013). He was a postdoctoral researcher in the CALVIN group at the University of Edinburgh’s School of Informatics (2014–2016), and then a member of the Multimodal Remote Sensing and Geocomputation groups at the University of Zurich’s Department of Geography (2016–2017). He joined SDSC in 2018 as Senior Data Scientist in the Research Team and became Lead Data Scientist in 2022. Since 2025, Michele serves as Climate and Environment Domain Lead, overseeing SDSC’s activities in this area. He leads projects in geospatial machine learning, working with Earth observation and geolocated spatio-temporal data, with applications in climate and weather, ecology, natural hazards, and agriculture, among others. Michele is also a Lecturer in the Department of Environmental Systems Sciences at ETH Zurich.
description
Motivation
Human-related disturbances are affecting all ecosystems of the world from terrestrial to marine habitats, threatening biodiversity and disrupting ecosystem services. Thus, monitoring ecosystems and how they respond to human influences, is crucial. eDNA has revolutionized biodiversity monitoring, offering non-invasive means to assess ecosystem health. However, the complexity of eDNA data poses significant challenges for conventional bioinformatics. This project tackled some of these challenges by relying on novel machine learning methods and combining various sources of biological data.
Proposed Approach / Solution
The project first tackled the problem of identifying taxonomic compositions in eDNA samples, given the lack of complete reference databases. We have successfully utilized information from phylogenetic trees as well as species co-occurrence data in order to guide the correct classification of (collections of) DNA sequences to marine fish species (WP1 in Figure 1).
We have further developed a method for the ordination of raw (uncurated) eDNA samples, where the aim is to find low-dimensional representations of the data highlighting the main ecological gradients. Lacking ground truth data for this task, a contrastive self-supervised learning approach was paired with an attention-based neural network in order to extract the main distinguishing factors in eDNA samples consisting of large amounts of uncurated DNA strings (WP2 in Figure 1).
The learned latent representations of eDNA samples have also been directly used as inputs for downstream estimations of ecosystem properties of interest, such as the conservation status of various regions in the Mediterranean Sea (WP3 in Figure 1).
Impact
The methods and models developed in this project have highlighted how different sources of biological data can be effectively combined with machine learning to solve important problems in ecology. They have the potential to transform how eDNA data is parsed, processed and analyzed by ecologists and practitioners. This, in turn, affects the monitoring of environmental health - a crucial task considering the global biodiversity crisis.
Presentation
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Additional resources
Bibliography
- Flück, B., Mathon, L., Manel, S., Valentini, A., Dejean, T., Albouy, C., ... & Pellissier, L. (2022). Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem. Scientific Reports, 12(1), 10247. https://doi.org/10.1038/s41598-022-13412-w
- Cordier, T., Lanzén, A., Apothéloz-Perret-Gentil, L., Stoeck, T., & Pawlowski, J. (2019). Embracing environmental genomics and machine learning for routine biomonitoring. Trends in microbiology, 27(5), 387-397. https://doi.org/10.1016/j.tim.2018.10.012
- Gauch Jr, H. G. (1982). Noise reduction by eigenvector ordinations. Ecology, 63(6), 1643-1649. https://doi.org/10.2307/1940105
- Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823). https://doi.org/10.1109/CVPR.2015.7298682
Publications
Related Pages
- Public source code for work package 1 (including guide for gradio app on renkulab.io): https://gitlab.renkulab.io/dnai/TAXDNA
- Public source code for work package 2: https://gitlab.renkulab.io/dnai/ordna
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