MOTIVATION: Structural variants (SVs) play an important role in evolutionary and functional genomics but are challenging to characterize. High-accuracy, long-read sequencing can substantially improve SV characterization when coupled with effective calling methods. While state-of-the-art long-read SV callers are highly accurate, further improvements are achievable by systematically modeling local haplotypes during SV discovery and genotyping. RESULTS: We describe sawfish, an SV caller for mapped high-quality long reads incorporating systematic SV haplotype modeling to improve accuracy and resolution. Assessment against the draft Genome in a Bottle (GIAB) SV benchmark from the T2T-HG002-Q100 diploid assembly shows that sawfish has the highest accuracy among state-of-the-art long-read SV callers across every tested SV size group. Additionally, sawfish maintains the highest accuracy at every tested depth level from 10- to 32-fold coverage, such that other callers required at least 30-fold coverage to match sawfish accuracy at 15-fold coverage. Sawfish also shows the highest accuracy in the GIAB challenging medically relevant genes benchmark, demonstrating improvements in both comprehensive and medically relevant contexts.When joint-genotyping seven samples from CEPH-1463, sawfish has over 9000 more pedigree-concordant calls than other state-of-the-art SV callers, with the highest proportion of concordant SVs (81%). Sawfish's quality model enables selection for an even higher proportion of concordant SVs (88%), while still calling nearly 5000 more pedigree-concordant SVs than other callers. These results demonstrate that sawfish improves on the state-of-the-art for long-read SV calling accuracy across both individual and joint-sample analyses. AVAILABILITY AND IMPLEMENTATION: Sawfish source code, pre-compiled Linux binaries, and documentation are released on GitHub: https://github.com/PacificBiosciences/sawfish.

MOTIVATION: In diploid organisms, phasing is the problem of assigning the alleles at heterozygous variants to one of two haplotypes. Reads from PacBio HiFi sequencing provide long, accurate observations that can be used as the basis for both calling and phasing variants. HiFi reads also excel at calling larger classes of variation, such as structural or tandem repeat variants. However, current phasing tools typically only phase small variants, leaving larger variants unphased. RESULTS: We developed HiPhase, a tool that jointly phases SNVs, indels, structural, and tandem repeat variants. The main benefits of HiPhase are (i) dual mode allele assignment for detecting large variants, (ii) a novel application of the A*-algorithm to phasing, and (iii) logic allowing phase blocks to span breaks caused by alignment issues around reference gaps and homozygous deletions. In our assessment, HiPhase produced an average phase block NG50 of 480 kb with 929 switchflip errors and fully phased 93.8% of genes, improving over the current state of the art. Additionally, HiPhase jointly phases SNVs, indels, structural, and tandem repeat variants and includes innate multi-threading, statistics gathering, and concurrent phased alignment output generation. AVAILABILITY AND IMPLEMENTATION: HiPhase is available as source code and a pre-compiled Linux binary with a user guide at https://github.com/PacificBiosciences/HiPhase.

SUMMARY: We describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci. AVAILABILITY AND IMPLEMENTATION: ExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.