Monitoring the ecological status of rivers with diatom eDNA metabarcoding: a comparison of taxonomic markers and analytical approaches for the inference of a molecular diatom index

Recently, several studies demonstrated the usefulness of diatom eDNA metabarcoding as an alternative to assess the ecological quality of rivers and streams. However, the choice of the taxonomic marker as well as the methodology for data analysis differ between these studies, hampering the comparison of their results and effectiveness.

Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring

Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement of the burdensome morphological identification, to screen known morphologically distinguishable bioindicator taxa.

SLIM: a flexible web application for the reproducible processing of environmental DNA metabarcoding data

High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) has become a routine tool for biodiversity survey and ecological studies. By including sample-specific tags in the primers prior PCR amplification, it is possible to multiplex hundreds of samples in a single sequencing run. The analysis of millions of sequences spread into hundreds to thousands of samples prompts for efficient, automated yet flexible analysis pipelines.

BBI: an R package for the computation of Benthic Biotic Indices from composition data

The monitoring of impacts of anthropic activities in marine environments, such as aquaculture, oil-drilling platforms or deep-sea mining, relies on Benthic Biotic Indices (BBI). Several indices have been formalised to reduce the multivariate composition data into a single continuous value that is ascribed to a discrete ecological quality status. Such composition data is traditionally obtained from macrofaunal inventories, which is time-consuming and expertise-demanding.

Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring

Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations of the traditional morphotaxonomy-based bioassessment. Recently, we demonstrated that supervised machine learning (SML) can be used to predict accurate biotic indices values from eDNA metabarcoding data, regardless of the taxonomic affiliation of the sequences.

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring.

Accurate multiplexing and filtering for high-throughput amplicon-sequencing

Tagging amplicons with tag sequences appended to PCR primers allow the multiplexing of numerous samples for high-throughput sequencing (HTS). This approach is routinely used in HTS-based diversity analyses, especially in microbial ecology and biomedical diagnostics. However, amplicon library preparation is subject to pervasive sample sequence cross-contaminations as a result of tag switching events referred to as mistagging.

Next-generation environmental diversity surveys of foraminifera: preparing the future

Foraminifera are commonly defined as marine testate protists, and their diversity is mainly assessed on the basis of the morphology of their agglutinated or mineralized tests. Diversity surveys based on environmental DNA (eDNA) have dramatically changed this view by revealing an unexpected diversity of naked and organic-walled lineages as well as detecting foraminiferal lineages in soil and freshwater environments.