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- Title
iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.
- Authors
Arias, Pablo Millan; Hill, Kathleen A; Kari, Lila
- Abstract
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 unlabeled DNA sequences. Availability and implementation i DeLUCS is available at https://github.com/Kari-Genomics-Lab/iDeLUCS under the terms of the MIT licence.
- Subjects
MASSACHUSETTS Institute of Technology; DEEP learning; DNA sequencing; INTERACTIVE learning; GRAPHICAL user interfaces; ARTIFICIAL chromosomes; MICROBIAL genomes
- Publication
Bioinformatics, 2023, Vol 39, Issue 9, p1
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btad508