SoMaCX: a complex generative genome modeling framework

The soMaCX framework provides superior generative modeling-based performance when compared to other simulation frameworks with respect to real data. Our open-source method introduces an important conceptual element to simulation by utilizing biological relevant regions (genes and regulatory elements) as the distribution controls along with the biological modulation of known pathways (end-joining) leading to more detailed and realistic simulated genomes. By designing a generative method to explore the most difficult genomic conditions, we provide a means to measure germline variation calling performance and to calibrate the results for rare variants needed in the clinical setting. We provide a python 3 implementation at: https://github.com/timothyjamesbecker/somacx .