IAGS: Inferring Ancestor Genome Structure under a wide range of evolutionary scenarios
conda install -c gurobi -c conda-forge -c xjtuomics iags
The number of novel species with high quality genomes are rapidly accumulating, signaling the start of a golden age for the study of genome structure evolution. Here, we develop IAGS, a generalized novel computational framework to infer ancestral genome structure for a variety of evolutionary scenarios. IAGS provides four basic models to solve simple single-copy (GMP and GGHP) and complex multi-copy ancestor problems (Multi-copy GMP and GGHP) with blocks / endpoints matching optimization (self-BMO and EMO) strategies and their combinations to decode complex evolutionary history in a bottom-up manner.
