BarcodeBERT: Transformers for Biodiversity Analysis

Understanding biodiversity is a global challenge, in which DNA barcodes - short snippets of DNA that cluster by species - play a pivotal role. In particular, invertebrates, a highly diverse and under-explored group, pose unique taxonomic complexities. We explore machine learning approaches, comparing supervised CNNs, fine-tuned foundation models, and a DNA barcode-specific masking strategy across datasets of varying complexity. While simpler datasets and tasks favor supervised CNNs or fine-tuned transformers, challenging species-level identification demands a paradigm shift towards self-supervised pretraining. We propose BarcodeBERT, the first self-supervised method for general biodiversity analysis, leveraging a 1.5 M invertebrate DNA barcode reference library. This work highlights how dataset specifics and coverage impact model selection, and underscores the role of self-supervised pretraining in achieving high-accuracy DNA barcode-based identification at the species and genus level. Indeed, without the fine-tuning step, BarcodeBERT pretrained on a large DNA barcode dataset outperforms DNABERT and DNABERT-2 on multiple downstream classification tasks. The code repository is available at https://github.com/Kari-Genomics-Lab/BarcodeBERT

Map of Life: Measuring and Visualizing Species' Relatedness with "Molecular Distance Maps"

We propose a novel combination of methods that (i) portrays quantitative characteristics of a DNA sequence as an image, (ii) computes distances between these images, and (iii) uses these distances to output a map wherein each sequence is a point in a common Euclidean space. In the resulting "Molecular Distance Map" each point signifies a DNA sequence, and the geometric distance between any two points reflects the degree of relatedness between the corresponding sequences and species. Molecular Distance Maps present compelling visual representations of relationships between species and could be used for taxonomic clarifications, for species identification, and for studies of evolutionary history. One of the advantages of this method is its general applicability since, as sequence alignment is not required, the DNA sequences chosen for comparison can be completely different regions in different genomes. In fact, this method can be used to compare any two DNA sequences. For example, in our dataset of 3,176 mitochondrial DNA sequences, it correctly finds the mtDNA sequences most closely related to that of the anatomically modern human (the Neanderthal, the Denisovan, and the chimp), and it finds that the sequence most different from it belongs to a cucumber. Furthermore, our method can be used to compare real sequences to artificial, computer-generated, DNA sequences. For example, it is used to determine that the distances between a Homo sapiens sapiens mtDNA and artificial sequences of the same length and same trinucleotide frequencies can be larger than the distance between the same human mtDNA and the mtDNA of a fruit-fly. We demonstrate this method's promising potential for taxonomical clarifications by applying it to a diverse variety of cases that have been historically controversial, such as the genus Polypterus, the family Tarsiidae, and the vast (super)kingdom Protista.