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.

SUMMARY: Unfazed is a command-line tool to determine the parental gamete of origin for de novo mutations from paired-end Illumina DNA sequencing reads. Unfazed uses variant information for a sequenced trio to identify the parental gamete of origin by linking phase-informative inherited variants to de novo mutations using read-based phasing. It achieves a high success rate by chaining reads into haplotype groups, thus increasing the search space for informative sites. Unfazed provides a simple command-line interface and scales well to large inputs, determining parent-of-origin for nearly 30 000 de novo variants in under 60 h. AVAILABILITY AND IMPLEMENTATION: Unfazed is available at https://github.com/jbelyeu/unfazed. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit's performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourself software is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues.