A Simulator for Somatic Evolution Study Design

MotivationSomatic evolution plays a key role in development, cell differentiation, and normal aging, but also diseases such as cancer, which is now mainly thought of as a disease of genetic and epigenetic modification. Understanding mechanisms of somatic mutability -- variant types and frequencies, phylogenetic structure, mutational signatures, and clonal heterogeneity -- and how they can vary between cell lineages will likely play a crucial role in biological discovery and medical applications. This need has led to a proliferation of new technologies for profiling single-cell variation, each with distinctive capabilities and limitations that can be leveraged alone or in combination with other technologies. The enormous space of options for assaying somatic variation, however, presents unsolved informatics problems with regards to selecting optimal combinations of technologies for designing appropriate studies for any particular scientific questions. Versatile simulation tools are needed to make it possible to explore and optimize potential study designs if researchers are to deploy multiomic technologies effectively. ResultsIn this paper, we present a simulator allowing for the generation of synthetic data from a wide range of clonal lineages, variant classes, and sequencing technology choices, intended to provide a platform for effective study design in somatic lineage analysis. Our simulation framework allows for the assessment of study design setups and their statistical validity in determining different ground-truth cancer mechanisms. The user is able to input various properties of the somatic evolutionary system, mutation classes (e.g., single nucleotide polymorphisms, copy number changes, and classes of structural variation), and biotechnology options (e.g., coverage, bulk vs single cell, whole genome vs exome, error rate, number of samples) and can then generate samples of synthetic sequence reads and their corresponding ground-truth parameters for a given study design. We demonstrate the utility of the simulator for testing and optimizing study designs for various experimental queries. Contactrussells@andrew.cmu.edu Availabilityhttps://github.com/CMUSchwartzLab/MosaicSim