Computational Biology
@ Comenius University in Bratislava

Mattia Sgro, Brona Bejova, Yuri Pirola, Tomas Vinar, Paola Bonizzoni, Cedric Chauve. Pangebin: improving plasmid binning in bacterial isolates using pangenome-assembly graphs. Technical Report 2025.04.10.648291, bioRxiv, 2025.

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Download from publisher: https://doi.org/10.1101/2025.04.10.648291

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Abstract:

Short-read genome assemblies typically consist of many contigs of variable 
lengths and their putative connections represented as an assembly graph. 
Assembly graphs produced by different tools from the same data may differ 
significantly, posing a challenge to tools for downstream processing tasks. 
One such task is plasmid binning, that is identifying plasmids in sequenced 
bacterial isolates, which is crucial for monitoring the spread of 
antimicrobial resistance. When plasmid binning tools are applied to assembly 
graphs produced by different tools, they may exhibit different performance, 
and choosing the best results a priori can be difficult. To address the 
above issue, we propose the use of a pangenome graph, built from assembly 
graphs produced by assembling short reads of the same sample with different 
assemblers. The resulting pangenome-assembly graph highlights similarities 
between contigs from different assemblies while retaining information on 
contigs that appear only in one of the input assemblies. We then used the 
PlasBin-flow plasmid binning tool customized to take into account pangenome 
information to identify plasmid bins. The results for pangenome-assemblies 
built by Unicycler and Skesa show an increase in accuracy measures compared 
to the mean results obtained on single assemblies, leading to an overall 
more accurate prediction than a blind choice of assemblers. The source code 
of the pipeline is available at https://github.com/AlgoLab/pangebin along 
with the dataset used in this study.