Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

The Ashkenazi Jews (AJ) are a population isolate sharing ancestry with both European and Middle Eastern populations that has likely resided in Central Europe since at least the tenth century. Between the 11th and 16th centuries, the AJ population expanded eastward leading to two culturally distinct communities in Western/Central and Eastern Europe. Our aim was to determine whether the western and eastern groups are genetically distinct, and if so, what demographic processes contributed to population differentiation. We used Approximate Bayesian Computation to choose among models of AJ history and to infer demographic parameter values, including divergence times, effective population sizes, and levels of gene flow. For the ABC analysis, we used allele frequency spectrum and identical by descent-based statistics to capture information on a wide timescale. We also mitigated the effects of ascertainment bias when performing ABC on SNP array data by jointly modeling and inferring SNP discovery. We found that the most likely model was population differentiation between Eastern and Western AJ ∼400 years ago. The differentiation between the Eastern and Western AJ could be attributed to more extreme population growth in the Eastern AJ (0.250 per generation) than the Western AJ (0.069 per generation).

The Sequence Read Archive (SRA) is a large public repository that stores raw next-generation sequencing data from thousands of diverse scientific investigations. Despite its promise, reuse and re-analysis of SRA data has been challenged by the heterogeneity and poor quality of the metadata that describe its biological samples. Recently, the MetaSRA project standardized these metadata by annotating each sample with terms from biomedical ontologies. In this work, we present a pair of Jupyter notebook-based tools that utilize the MetaSRA for building structured datasets from the SRA in order to facilitate secondary analyses of the SRA’s human RNA-seq data. The first tool, called the Case-Control Finder, finds suitable case and control samples for a given disease or condition where the cases and controls are matched by tissue or cell type. The second tool, called the Series Finder, finds ordered sets of samples for the purpose of addressing biological questions pertaining to changes over a numerical property such as time. These tools were the result of a three-day-long NCBI Codeathon in March 2019 held at the University of North Carolina at Chapel Hill.

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource,stdpopsim, that attempts to rectify this situation.Stdpopsimis a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating howstdpopsimcan be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.