Accelerating Metagenomic Identification of DNA Sequences Using Artificial Neural Networks

Background: The growing volume of DNA sequence data demands efficient metagenomic identification. It provides the possibility of constructing tools with sustainability performance to monitor environmental conditions, including risks related to organisms and pathogens. Methods: A convolutional neural network (CNN) leveraging contrastive learning is used to select representative sequences, which improve computational efficiency. Results: We present Exquisitor, which is a CNN-based tool. Benchmarking against classical methods shows higher classification quality and competitive execution time within this setting. Conclusion: This paper highlights the potential of CNNs for improving the performance of metagenomic identification including taxonomic classification.