Deep Learning Barcoding for Improved DNA Sequence Classification

The introduction of DNA barcoding has significantly transformed species identification, utilizing short DNA sequences from a uniform region of the genome. However, the conventional methods of DNA barcoding often depend on manual analysis and struggle with precise classification of species that are closely related. In our research, we introduce an innovative approach to DNA barcoding classification using deep learning techniques and sophisticated neural networks. This method leverages the capabilities of deep learning algorithms to improve the accuracy and efficiency of identifying species, especially in taxonomically complex groups. This approach has demonstrated more than 90 percent accuracy in classifying both simulated and real datasets. Despite the exceptional results deep learning brings to species classification using DNA sequences, implementing it remains challenging. The deep barcoding model we developed holds promise as a tool for species classification and can significantly contribute to understanding DNA barcode-based species identification processes.