Crossgenome

Crossgenome is a term used in genomics to describe analyses and methods that compare the genomes of different species or strains to identify shared and divergent features. It encompasses cross-genome alignment, comparative genomics, orthology inference, and pan-genome analysis. The objective is to understand evolutionary relationships, functional conservation, and lineage-specific innovations.

Key concepts in crossgenome work include cross-genome alignment and synteny analysis, which align sequences across genomes while accounting for rearrangements and duplications. Orthology inference groups genes across species into orthologs and paralogs to illuminate functional conservation and evolutionary history. A pan-genome framework distinguishes core genes present in all genomes from accessory genes present in a subset, reflecting diversity within a species or clade. Researchers often integrate these components to build a cohesive view of genome evolution and function.

Typical workflows involve collecting high-quality genome assemblies and annotations, predicting genes and assigning functions, constructing multi-genome alignments or utilizing graph-based pangenomes, identifying conserved elements and structural rearrangements, and transferring annotations across genomes via orthology relationships. These steps support broader analyses such as identifying lineage-specific innovations or conserved regulatory networks.

Applications of crossgenome analysis include informing evolutionary studies and the identification of conserved regulatory elements, aiding annotation transfer to poorly characterized genomes, supporting crop and livestock improvement through comparative genomics, and examining pathogen diversity to locate potential drug targets or vaccine candidates. Crossgenome approaches are widely used in both basic research and applied contexts.

Challenges include uneven assembly quality and annotation consistency, rapid genome rearrangements, gene duplication, and horizontal gene transfer, which complicate comparisons. Emerging directions emphasize graph-based pangenomes and genome graphs, improved orthology and alignment algorithms, and the integration of genomic data with transcriptomic and epigenomic information to provide richer cross-genome insights.