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
this https URL

利用 DNA 条形码参考库，提出了一种名为 BarcodeBERT 的自监督方法，对物种和属级别进行高准确性的 DNA 条形码识别，该方法兼顾数据集细节和覆盖范围对模型选择的影响，并强调了自监督预训练在高准确性 DNA 条形码分析中的作用。