PlasBin-flow

PlasBin-flow identifies plasmid contigs in short-read bacterial genome assemblies by formulating contig binning as a flow-based mixed integer linear programming problem to separate plasmid from chromosomal DNA and support studies of antimicrobial resistance.

Key Features:

• Network flow-based MILP model: Uses a mixed integer linear programming framework incorporating network flow principles to optimize binning while accounting for sequencing coverage.
• Assembly graph subgraph definition: Defines contig bins as subgraphs within the assembly graph to capture structural relationships between contigs.
• Contig feature integration: Considers contig length, circularity, read coverage, and GC content as inputs to inform binning decisions.
• Plasmid gene and GC-content markers: Incorporates presence of plasmid-specific genes and distinctive GC content patterns to distinguish plasmid from chromosomal contigs.
• Hybrid de novo and reference-based approach: Combines de novo signal from contig features and assembly graph structure with reference-based comparisons to known plasmids or markers from completed bacterial genomes.
• Evaluation on real bacterial short-read datasets: Has been applied to real bacterial sample datasets to demonstrate practical performance on short-read assemblies.

Scientific Applications:

• Plasmid contig binning: Identification and grouping of plasmid-derived contigs in fragmented short-read assemblies of bacterial isolates.
• Differentiation of plasmid versus chromosomal DNA: Separation of plasmid and chromosomal sequences to enable analyses that depend on accurate sequence origin assignment.
• Antimicrobial resistance studies: Support for investigating plasmid-mediated dissemination of antimicrobial resistance genes in bacterial populations.

Methodology:

Contig bins are defined as subgraphs of the assembly graph and identified using a mixed integer linear programming (MILP) model that leverages network flow principles and sequencing coverage.