iDeLUCS: A deep learning interactive tool for alignment-free clustering of DNA sequences

Summary We present an interactive Deep Learning-based software tool for Unsupervised Clustering of DNA Sequences ( i DeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic identifiers. i DeLUCS is scalable and user-friendly: Its graphical user interface, with support for hardware acceleration, allows the practitioner to fine-tune the different hyper-parameters involved in the training process without requiring extensive knowledge of deep learning. The performance of i DeLUCS was evaluated on a diverse set of datasets: several real genomic datasets from organisms in kingdoms Animalia, Protista, Fungi, Bacteria, and Archaea, three datasets of viral genomes, a dataset of simulated metagenomic reads from microbial genomes, and multiple datasets of synthetic DNA sequences. The performance of i DeLUCS was compared to that of two classical clustering algorithms ( k -means++ and GMM) and two clustering algorithms specialized in DNA sequences (MeShClust v3.0 and DeLUCS), using both intrinsic cluster evaluation metrics and external evaluation metrics. In terms of unsupervised clustering accuracy, i DeLUCS outperforms the two classical algorithms by an average of ∼ 20%, and the two specialized algorithms by an average of ∼ 12%, on the datasets of real DNA sequences analyzed. Overall, our results indicate that i DeLUCS is a robust clustering method suitable for the clustering of large and diverse datasets of unlabelled DNA sequences. Availability and implementation i DeLUCS is available at our github repository under the terms of the MIT licence. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.