AI Insight
Hobrac is a new automated computational workflow designed to facilitate whole-genome comparisons by addressing two key challenges: selecting appropriate reference genomes and interpreting chromosome-level synteny patterns. The tool automatically identifies suitable reference genomes based on a provided taxon identifier, combines nucleotide sequence alignments with conserved BUSCO orthologous genes for structural comparisons, and generates interactive and publication-ready visualizations including dotplots, ribbon plots, and synteny maps. This streamlines the process of validating genome assemblies and studying genome evolution across species.
Why it matters
This workflow significantly reduces the technical barriers and manual effort required for comparative genomics research, making genome-scale evolutionary studies more accessible to researchers. By automating reference selection and improving synteny visualization, Hobrac can accelerate genome assembly validation and facilitate discoveries about chromosomal rearrangements and evolutionary relationships across taxa.
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⚠️ Preprint – Noch nicht peer-reviewed
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Whole-genome comparison is fundamental for validating genome assemblies and investigating genome evolution, yet identifying suitable reference genomes and interpreting chromosome-scale synteny from often noisy nucleotide alignments remain challenging. We introduce Hobrac, an automated workflow that addresses these two major bottlenecks by combining automated reference genome selection with gene-based structural comparisons. Starting from a genome assembly and its taxon identifier, Hobrac identifies suitable reference genomes, complements nucleotide alignments with conserved BUSCO rthologues, and generates publication-quality visualizations. The workflow produces dotplots, ribbon-plots and synteny visualization that can be explored interactively or offline. Hobrac is freely available at https://github.com/Genoscope-LBGB/hobrac.
Source: Hobrac: a reference-guided workflow for genome comparison and synteny visualization