SUMMARY: Missense mutations that change protein stability are strongly associated with human genetic disease. With the recent availability of predicted structures for all human proteins generated using the AlphaFold2 prediction model, genome-wide assessment of the stability effects of genetic variation can, for the first time, be easily performed. This facilitates the interrogation of personal genetic variation for potentially pathogenic effects through the application of stability metrics. Here, we present a novel tool to prioritize variants predicted to cause strong instability in essential proteins. We show that by filtering by ΔΔG values and then prioritizing by StabilitySort Z-scores, we are able to more accurately discriminate pathogenic, protein-destabilizing mutations from population variation, compared with other mutation effect predictors. AVAILABILITY AND IMPLEMENTATION: StabilitySort is available as a web service (https://www.stabilitysort.org), as a data download for integration with other tools (https://www.stabilitysort.org/download) or can be deployed as a standalone system from source code (https://gitlab.com/baaron/StabilitySort). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Increasingly, cost-effective high-throughput DNA sequencing technologies are being utilized to sequence human pedigrees to elucidate the genetic cause of a wide variety of human diseases. While numerous tools exist for variant prioritization within a single genome, the ability to concurrently analyze variants within pedigrees remains a challenge, especially should there be no prior indication of the underlying genetic cause of the disease. Here, we present a tool, variant analysis of sequenced pedigrees (VASP), a flexible data integration environment capable of producing a summary of pedigree variation, providing relevant information such as compound heterozygosity, genome phasing and disease inheritance patterns. Designed to aggregate data across a sequenced pedigree, VASP allows both powerful filtering and custom prioritization of both single nucleotide variants (SNVs) and small indels. Hence, clinical and research users with prior knowledge of a disease are able to dramatically reduce the variant search space based on a wide variety of custom prioritization criteria. AVAILABILITY AND IMPLEMENTATION: Source code available for academic non-commercial research purposes at https://github.com/mattmattmattmatt/VASP.