MOTIVATION: Variant call format (VCF) files are the standard output format for various software tools that identify genetic variation from DNA sequencing experiments. Downstream analyses require the ability to query, filter, and modify them simply and efficiently. Several tools are available to perform these operations from the command line, including BCFTools, vembrane, slivar, and others. RESULTS: Here, we introduce vcfexpress, a new, high-performance toolset for the analysis of VCF files, written in the Rust programming language. It is nearly as fast as BCFTools, but adds functionality to execute user expressions in the lua programming language for precise filtering and reporting of variants from a VCF or BCF file. We demonstrate performance and flexibility by comparing vcfexpress to other tools using the vembrane benchmark. AVAILABILITY AND IMPLEMENTATION: vcfexpress is available under the MIT license at https://github.com/brentp/vcfexpress with code used for the manuscript deposited in https://doi.org/10.5281/zenodo.14756838.

SUMMARY: We present trfermikit, a software tool designed to detect deletions larger than 50 bp occurring in Variable Number Tandem Repeats using Illumina DNA sequencing reads. In such regions, it achieves a better tradeoff between sensitivity and false discovery than a state-of-the-art structural variation caller, Manta and complements it by recovering a significant number of deletions that Manta missed. trfermikit is based upon the fermikit pipeline, which performs read assembly, maps the assembly to the reference genome and calls variants from the alignment. AVAILABILITY AND IMPLEMENTATION: https://github.com/petermchale/trfermikit. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Despite numerous molecular and computational advances, roughly half of patients with a rare disease remain undiagnosed after exome or genome sequencing. A particularly challenging barrier to diagnosis is identifying variants that cause deleterious alternative splicing at intronic or exonic loci outside of canonical donor or acceptor splice sites. RESULTS: Several existing tools predict the likelihood that a genetic variant causes alternative splicing. We sought to extend such methods by developing a new metric that aids in discerning whether a genetic variant leads to deleterious alternative splicing. Our metric combines genetic variation in the Genome Aggregate Database with alternative splicing predictions from SpliceAI to compare observed and expected levels of splice-altering genetic variation. We infer genic regions with significantly less splice-altering variation than expected to be constrained. The resulting model of regional splicing constraint captures differential splicing constraint across gene and exon categories, and the most constrained genic regions are enriched for pathogenic splice-altering variants. Building from this model, we developed ConSpliceML. This ensemble machine learning approach combines regional splicing constraint with multiple per-nucleotide alternative splicing scores to guide the prediction of deleterious splicing variants in protein-coding genes. ConSpliceML more accurately distinguishes deleterious and benign splicing variants than state-of-the-art splicing prediction methods, especially in "cryptic" splicing regions beyond canonical donor or acceptor splice sites. CONCLUSION: Integrating a model of genetic constraint with annotations from existing alternative splicing tools allows ConSpliceML to prioritize potentially deleterious splice-altering variants in studies of rare human diseases.

BACKGROUND: Identification of deleterious genetic variants using DNA sequencing data relies on increasingly detailed filtering strategies to isolate the small subset of variants that are more likely to underlie a disease phenotype. Datasets reflecting population allele frequencies of different types of variants serve as powerful filtering tools, especially in the context of rare disease analysis. While such population-scale allele frequency datasets now exist for structural variants (SVs), it remains a challenge to match SV calls between multiple datasets, thereby complicating estimates of a putative SV's population allele frequency. RESULTS: We introduce SVAFotate, a software tool that enables the annotation of SVs with variant allele frequency and related information from existing SV datasets. As a result, VCF files annotated by SVAFotate offer a variety of metrics to aid in the stratification of SVs as common or rare in the broader human population. CONCLUSIONS: Here we demonstrate the use of SVAFotate in the classification of SVs with regards to their population frequency and illustrate how SVAFotate's annotations can be used to filter and prioritize SVs. Lastly, we detail how best to utilize these SV annotations in the analysis of genetic variation in studies of rare disease.

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.

Summary: Mosdepth is a new command-line tool for rapidly calculating genome-wide sequencing coverage. It measures depth from BAM or CRAM files at either each nucleotide position in a genome or for sets of genomic regions. Genomic regions may be specified as either a BED file to evaluate coverage across capture regions, or as a fixed-size window as required for copy-number calling. Mosdepth uses a simple algorithm that is computationally efficient and enables it to quickly produce coverage summaries. We demonstrate that mosdepth is faster than existing tools and provides flexibility in the types of coverage profiles produced. Availability and implementation: mosdepth is available from https://github.com/brentp/mosdepth under the MIT license. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Extracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets. Results: We present hts-nim, a high-performance library written in the Nim programming language that provides a simple, scripting-like syntax without sacrificing performance. Availability and implementation: hts-nim is available at https://github.com/brentp/hts-nim and the example tools are at https://github.com/brentp/hts-nim-tools both under the MIT license.

MOTIVATION: Variant call format (VCF) files document the genetic variation observed after DNA sequencing, alignment and variant calling of a sample cohort. Given the complexity of the VCF format as well as the diverse variant annotations and genotype metadata, there is a need for fast, flexible methods enabling intuitive analysis of the variant data within VCF and BCF files. RESULTS: We introduce cyvcf2 , a Python library and software package for fast parsing and querying of VCF and BCF files and illustrate its speed, simplicity and utility. CONTACT: [email protected] or [email protected]. AVAILABILITY AND IMPLEMENTATION: cyvcf2 is available from https://github.com/brentp/cyvcf2 under the MIT license and from common python package managers. Detailed documentation is available at http://brentp.github.io/cyvcf2/.

The comparison of sets of genome intervals (e.g., genes, repeats, ChIP-seq peaks) is essential to genome research, especially as modern sequencing technologies enable ever larger and more complex experiments. Relationships between genomic features are commonly identified by their intersection: that is, if feature sets contain overlapping intervals then it is inferred that they share a common biological function or origin. Using this technique, researchers identify genomic regions that are common among multiple (or unique to individuals) data sets. While there have been recent advances in algorithms for pairwise intersections between two sets of genomic intervals, few advances have been made to the intersection of many sets of genomic intervals. Identifying intersections among many interval sets is particularly important when attempting to distill biological insights from the massive, multidimensional data sets that are common to modern genome research. For such analyses, speed and efficiency are crucial, given the size and sheer number of data sets involved. To solve this problem, we present a novel “slice-then-sweep” algorithm that, given N interval sets, efficiently reveals the subset of intervals that are common to all N sets. We demonstrate that our algorithm is more efficient in the sequential case and has a vastly higher capacity for parallelization with a 19x speedup over the existing algorithm.

UNLABELLED: Current strategies for SNP and INDEL discovery incorporate sequence alignments from multiple individuals to maximize sensitivity and specificity. It is widely accepted that this approach also improves structural variant (SV) detection. However, multisample SV analysis has been stymied by the fundamental difficulties of SV calling, e.g. library insert size variability, SV alignment signal integration and detecting long-range genomic rearrangements involving disjoint loci. Extant tools suffer from poor scalability, which limits the number of genomes that can be co-analyzed and complicates analysis workflows. We have developed an approach that enables multisample SV analysis in hundreds to thousands of human genomes using commodity hardware. Here, we describe Hydra-Multi and measure its accuracy, speed and scalability using publicly available datasets provided by The 1000 Genomes Project and by The Cancer Genome Atlas (TCGA). AVAILABILITY AND IMPLEMENTATION: Hydra-Multi is written in C++ and is freely available at https://github.com/arq5x/Hydra. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Nanopore sequencing may be the next disruptive technology in genomics, owing to its ability to detect single DNA molecules without prior amplification, lack of reliance on expensive optical components, and the ability to sequence long fragments. The MinION™ from Oxford Nanopore Technologies (ONT) is the first nanopore sequencer to be commercialized and is now available to early-access users. The MinION™ is a USB-connected, portable nanopore sequencer that permits real-time analysis of streaming event data. Currently, the research community lacks a standardized toolkit for the analysis of nanopore datasets. RESULTS: We introduce poretools, a flexible toolkit for exploring datasets generated by nanopore sequencing devices from MinION™ for the purposes of quality control and downstream analysis. Poretools operates directly on the native FAST5 (an application of the HDF5 standard) file format produced by ONT and provides a wealth of format conversion utilities and data exploration and visualization tools. AVAILABILITY AND IMPLEMENTATION: Poretools is an open-source software and is written in Python as both a suite of command line utilities and a Python application programming interface. Source code is freely available in Github at https://www.github.com/arq5x/poretools.

MOTIVATION: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery. RESULTS: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals. AVAILABILITY: https://github.com/arq5x/bits.

Modern DNA sequencing technologies enable geneticists to rapidly identify genetic variation among many human genomes. However, isolating the minority of variants underlying disease remains an important, yet formidable challenge for medical genetics. We have developed GEMINI (GEnome MINIng), a flexible software package for exploring all forms of human genetic variation. Unlike existing tools, GEMINI integrates genetic variation with a diverse and adaptable set of genome annotations (e.g., dbSNP, ENCODE, UCSC, ClinVar, KEGG) into a unified database to facilitate interpretation and data exploration. Whereas other methods provide an inflexible set of variant filters or prioritization methods, GEMINI allows researchers to compose complex queries based on sample genotypes, inheritance patterns, and both pre-installed and custom genome annotations. GEMINI also provides methods for ad hoc queries and data exploration, a simple programming interface for custom analyses that leverage the underlying database, and both command line and graphical tools for common analyses. We demonstrate GEMINI's utility for exploring variation in personal genomes and family based genetic studies, and illustrate its ability to scale to studies involving thousands of human samples. GEMINI is designed for reproducibility and flexibility and our goal is to provide researchers with a standard framework for medical genomics.

MOTIVATION: Analysis of genomic sequencing data requires efficient, easy-to-use access to alignment results and flexible data management tools (e.g. filtering, merging, sorting, etc.). However, the enormous amount of data produced by current sequencing technologies is typically stored in compressed, binary formats that are not easily handled by the text-based parsers commonly used in bioinformatics research. RESULTS: We introduce a software suite for programmers and end users that facilitates research analysis and data management using BAM files. BamTools provides both the first C++ API publicly available for BAM file support as well as a command-line toolkit. AVAILABILITY: BamTools was written in C++, and is supported on Linux, Mac OSX and MS Windows. Source code and documentation are freely available at http://github.org/pezmaster31/bamtools.

SUMMARY: pybedtools is a flexible Python software library for manipulating and exploring genomic datasets in many common formats. It provides an intuitive Python interface that extends upon the popular BEDTools genome arithmetic tools. The library is well documented and efficient, and allows researchers to quickly develop simple, yet powerful scripts that enable complex genomic analyses. AVAILABILITY: pybedtools is maintained under the GPL license. Stable versions of pybedtools as well as documentation are available on the Python Package Index at http://pypi.python.org/pypi/pybedtools. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. RESULTS: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. AVAILABILITY AND IMPLEMENTATION: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.