SUMMARY: Structural variants (SVs) are key drivers of genetic variation and disease in the genome. Their discovery remains challenging, however, in large part due to the scarcity of validated SV callsets and comprehensive benchmarks, which are essential for method development and evaluation. The growing number of data-driven learning-based approaches for SV discovery, in particular, requires large, diverse, and well-balanced training datasets to achieve reliable performance. To address this need, SV simulation has served as a key tool for assessing method performance and training SV models. However, existing SV simulators only support a fixed and limited set of SV classes and do not provide fine-grained control over the placement of SVs within specific contexts of the genome. Here we present insilicoSV, a versatile framework for SV simulation, which models SVs using a simple and flexible grammar, allowing users to easily define standard and custom arbitrary genome rearrangements, as well as encode genome placement constraints. This design allows insilicoSV to naturally support new and bespoke SV types, such as the complex rearrangements of cancer genomes. In addition to grammar-based modeling, insilicoSV provides built-in support for 26 predefined SV types, placement of user-provided SVs, small variant simulation, streamlined workflows for the simulation of genome evolution and genome mixtures, read simulation, alignment, and visualization. These features enable the creation of comprehensive genomic datasets for a variety of downstream applications, such as in-depth benchmarking of alignment and variant calling methods, as well as training of data-driven learning-based approaches for SV detection. AVAILABILITY AND IMPLEMENTATION: insilicoSV is available under the MIT license at https://github.com/PopicLab/insilicoSV and https://doi.org/10.5281/zenodo.17402009.

MOTIVATION: Efficient simulation of population genetic samples under a given demographic model is a prerequisite for many analyses. Coalescent theory provides an efficient framework for such simulations, but simulating longer regions and higher recombination rates remains challenging. Simulators based on a Markovian approximation to the coalescent scale well, but do not support simulation of selection. Gene conversion is not supported by any published coalescent simulators that support selection. RESULTS: We describe cosi2, an efficient simulator that supports both exact and approximate coalescent simulation with positive selection. cosi2 improves on the speed of existing exact simulators, and permits further speedup in approximate mode while retaining support for selection. cosi2 supports a wide range of demographic scenarios, including recombination hot spots, gene conversion, population size changes, population structure and migration. cosi2 implements coalescent machinery efficiently by tracking only a small subset of the Ancestral Recombination Graph, sampling only relevant recombination events, and using augmented skip lists to represent tracked genetic segments. To preserve support for selection in approximate mode, the Markov approximation is implemented not by moving along the chromosome but by performing a standard backwards-in-time coalescent simulation while restricting coalescence to node pairs with overlapping or near-overlapping genetic material. We describe the algorithms used by cosi2 and present comparisons with existing selection simulators. AVAILABILITY AND IMPLEMENTATION: A free C++ implementation of cosi2 is available at http://broadinstitute.org/mpg/cosi2.

This paper presents a lightweight interval analysis technique for determining the lower and upper bounds for program variables and its application in improving software model checking techniques. The experiments demonstrate that it is an effective approach to alleviate the state explosion problem in software model checking.

With the success of formal verification techniques like equivalence checking and model checking for hardware designs, there has been growing interest in applying such techniques for formal analysis and automatic verification of software programs. This paper provides a brief tutorial on model checking of C programs. The essential approach is to model the semantics of C programs in the form of finite state systems by using suitable abstractions. The use of abstractions is key, both for modeling programs as finite state systems and for reducing the model sizes in order to manage verification complexity. We provide illustrative details of a verification platform called F-Soft, which provides a range of abstractions for modeling software, and uses customized SAT-based and BDD-based model checking techniques targeted for software.

Declarative models, in which conjunction and negation are freely used, are susceptible to unintentional overconstraint. Core extraction is a new analysis that mitigates this problem in the context of a checker based on reduction to SAT (systems analysis tools). It exploits a recently developed facility of SAT solvers that provides an "unsatisfiable core" of an unsatisfiable set of clauses, often much smaller than the clause set as a whole. The unsatisfiable core is mapped back into the syntax of the original model, showing the user fragments of the model found to be irrelevant. This information can be a great help in discovering and localizing overconstraint, and in some cases pinpoints it immediately. The construction of the mapping is given for a generalized modeling language, along with a justification of the soundness of the claim that the marked portions of the model are irrelevant. Experiences in applying core extraction to a variety of existing models are discussed.

A simple mechanism for structuring specifications is described. By modelling structures as atoms, it remains entirely first-order and thus amenable to automatic analysis. And by interpreting fields of structures as relations, it allows the same relational operators used in the formula language to be used for dereferencing. An extension feature allows structures to be developed incrementally, but requires no textual inclusion nor any notion of subtyping. The paper demonstrates the flexibility of the mechanism by application in a variety of common idioms.

Alcoa is a tool for analyzing object models. It has a range of uses. At one end, it can act as a support tool for object model diagrams, checking for consistency of multiplicities and generating sample snapshots. At the other end, it embodies a lightweight formal method in which subtle properties of behaviour can be investigated.