Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies

Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding data sets, and therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences into molecular operational taxonomic units (MOTUs), each of which ideally represents a homogeneous taxonomic entity (e.g., a species or a genus). However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g., Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96-0.99) are generally appropriate, while more specific markers require lower values (0.85-0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.

Automated summary

Clustering approaches are crucial for handling DNA metabarcoding data, but the choice of clustering threshold is often not well justified. This study evaluated clustering threshold values for different metabarcoding markers and proposed a method to select appropriate thresholds based on different criteria.