DNAi

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.