Authors
Kamil Krakowski, Malgorzata Orlowska, Kamil Kaminski, Dariusz Bartosik, Stanislaw Dunin-Horkawicz
Published in
Bioinformatics (Oxford, England). Jul 29, 2026. Epub Jul 29, 2026.
Abstract
The increasing number of fully sequenced bacterial plasmids being annotated and catalogued has prompted the development of computational tools for comparing and classifying them. Existing approaches typically compare full-length DNA sequences (e.g., Mash, BLASTn, ANI-based methods) or translated open reading frames (ORFs) (e.g., DIAMOND), with plasmid-level scores obtained by aggregating ORF-to-ORF similarities; however, they are either restricted to closely related plasmids or become computationally demanding in large-scale analyses.
We describe pLAST (plasmid Language Analysis and Search Tool), a plasmid-search tool built using word2vec representations of protein-family content informed by local genomic context. Benchmarks indicate that pLAST outperforms nucleotide-based methods and performs comparably to DIAMOND in identifying functionally similar plasmids and, compared with the widely used Mash, it achieves 26% and 24% improvements in detecting shared mating-pair formation (MPF) system type and relaxase type, respectively. This performance scales to database searches across hundreds of thousands of sequences, as demonstrated using the precomputed PlasmidScope collection of ∼750,000 plasmids. Beyond global similarity, pLAST also returns per-ORF plasmid-plasmid alignments, enabling detection of shared functional modules.
pLAST is freely accessible as a web server at https://plast.lbs.cent.uw.edu.pl/ and available as a Python module along with a precomputed database at https://github.com/labstructbioinf/pLAST for customized analysis.
PMID:
42525290
Bibliographic data and abstract were imported from PubMed on 29 Jul 2026.
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