pLAST - a tool for rapid comparison and classification of bacterial plasmid sequences

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ID: 323031
2026
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Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: 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.
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openalex_W4416935370 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kamil Krakowski, Małgorzata Orłowska, Kamil Kamiński, Dariusz Bartosik, Stanisław Dunin-Horkawicz
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag574
URL
https://doi.org/10.1093/bioinformatics/btag574
Keywords Keywords not found

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