MOTIVATION: Genome-wide association studies (GWAS) are widely used to investigate the role of genetics in disease traits, but the resulting file sizes from these studies are large, posing barriers to efficient storage, sharing, and querying. This issue is especially important for biobanks like the UK Biobank that publish GWAS for thousands of traits, increasing the volume of data that must be effectively managed. Current compression and query methods reduce file sizes and allow for quick genomic position-based queries but do not provide utility for quickly finding loci based on their summary statistics. For example, finding all SNVs in a particular p-value range would require decompressing and scanning the whole file. We propose a new tool, STABIX, which introduces summary-statistic-based queries and improves upon the standard bgzip compression and Tabix query tool in both compression ratio and decompression speed. RESULTS: When applied to 10 GWAS files from PanUKBB, STABIX created smaller compressed data and indices than Tabix for all files, where bgzip and tbi files were an average of 1.2 times the size of STABIX compressed files and indexes. In the same 10 files, STABIX per gene decompression was, on average 7× faster than Tabix per gene decompression, and achieved faster per gene decompression times for over 99% of nearly 20,000 genes. AVAILABILITY AND IMPLEMENTATION: Software freely available for download at GitHub: https://github.com/kristen-schneider/stabix/.

Transposable elements (TEs) replicate within genomes and are an active source of genetic variability in many species. Their role in immunity and domestication underscores their biological significance. However, analyzing TEs, especially within lesser-studied and wild populations, poses considerable challenges. To address this, we introduce TEPEAK, a simple and efficient approach to identify and characterize TEs in populations without any prior sequence or loci information. In addition to processing user-submitted genomes, TEPEAK integrates with the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) to increase cohort sizes or incorporate proximate species. Our application of TEPEAK to 257 horse genomes spanning 11 groups reaffirmed established genetic histories and highlighted disruptions in crucial genes. Some identified TEs were also detectable in species closely related to horses. TEPEAK paves the way for comprehensive genetic variation analysis in traditionally understudied populations by simplifying TE studies. TEPEAK is open-source and freely available at https://github.com/ryanlayerlab/TEPEAK.

We report our experiences implementing standards-based grading at scale in an Algorithms course, which serves as the terminal required CS Theory course in our department's undergraduate curriculum. The course had 200-400 students, taught by two instructors, eight graduate teaching assistants, and supported by two additional graders and several undergraduate course assistants. We highlight the role of standards-based grading (SBG) in supporting our students during the COVID-19 pandemic. We conclude by detailing the successes and adjustments we would make to the course structure.

SUMMARY: Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps-including deletions, duplications, mobile element insertions, inversions and other rearrangements-in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g. LUMPY), while providing fast and affordable joint analysis at the scale of ≥100 000 genomes. These tools will help enable the next generation of human genetics studies. AVAILABILITY AND IMPLEMENTATION: svtools is implemented in Python and freely available (MIT) from https://github.com/hall-lab/svtools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

A great CS assignment is a delight to all, but the path to one can be most roundabout. Many CS students have had their characters built up on assignments that worked better as an idea than as an actual assignment. Assignments are hard to come up with, yet they are the key to student learning. The Nifty Assignments special session is all about promoting and sharing the ideas and ready-to-use materials of successful assignments.

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.

During the summer of 2011, twenty-four high school students participated in an intense, three-week computer science course at the University of Virginia. The course met for twenty-one three-hour sessions, thus encompassing more contact time than a standard college-level course. The course was structured in an “Inform, Experience, Implement” active-learning format: students were exposed to the history of a particular problem in context and participated in an active learning lesson regarding the topic before learning how to address the examined problem through programming. This structure helped integrate into the course best practices from experiential learning, kinesthetic outreach activities, and active learning pedagogy. Utilizing this three-part rotation curriculum achieved some important goals, including holding the interest of students during the summer for six hours a day and successfully motivating students who had no programming background.