MSpangepop: Simulating complex structural variants under advanced demographic scenarios using the coalescent
Résumé
Background Structural variants (SVs) — large genomic changes such as insertions, deletions and inversions — are known to significantly contribute to genetic variation. Despite their impact on phenotypic diver- sity and adaptation, they remain relatively understudied [1]. The recent emergence of variation graphs provides an efficient way to integrate these large variants in population and pangenome studies, capturing the full genomic landscape of genes within clades. Simulating genomes is essential for evaluating analytical methods and improving genetic studies by providing controlled datasets for testing hypotheses. Although several programs exist to simulate SVs genome-wide (e.g. [2]), none are able to account for complex evolutionary scenarios such as speciation events, resulting in unrealistic star-like locus genealogies. On the other hand, coalescent- based simulators such as MSprime [3], which can simulate a wide variety of ancestry and demo- graphic models, do not currently support the simulation of large and complex SVs. Results To fill this gap and allow biologists to directly produce simulated variation graphs, we develop MSpangepop, a Python-based simulation workflow inspired by VISOR [2] and managed by Snake- make [4]. Starting with a reference sequence and a demographic scenario, MSprime [3] simulates the coalescent of each locus (i.e. recombination block) of the genome. Each coalescent tree is then traversed lineage by lineage in order to chronologically map SVs simulated from a large panel of classes, allowing for complex nested variants. A coalescent-aware variation graph is thus progres- sively constructed and written to a Graphical Fragment Assembly file (GFA). For greater flexibility, genome sequences and alignments can also be generated directly as FASTA files. Conclusion MSpangepop allows biologists to simulate variation graphs under realistic demographic models for a variety of purposes, including benchmarking and simulation-based inferences. Future developments include the simulation of transposable elements and the modulation of the mutational landscape to account for genomic features like ORFs.
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |