SUMMARY: Whole-exome (WES) and whole-genome (WGS) sequencing are rapidly becoming preferred methods for population-scale analysis of the human genetic landscape. However, there are currently no standardized quality control (QC) pipelines for human WES and WGS datasets. In this paper, we present WxS-QC, a powerful, scalable, and convenient pipeline for the QC of human germline short-variant WGS and WES cohorts for population-scale analyses. Our pipeline is suitable for both rare-variant discovery and common-variant association studies. It is based on deeply refactored gnomAD v3 and v4 quality control pipelines, contains several methods we have developed de novo, and is aligned with current best practices in WGS/WES germline cohort QC. We provide all methods in a single codebase, aligned to work together and controlled via a single YAML config, with automatic export of resulting graphs and summary tables, excellent performance and scalability, and comprehensive documentation. The pipeline can run in any UNIX-like environment and can efficiently process cohorts of up to 200 000 whole-exome samples, with the potential to handle bigger datasets. AVAILABILITY AND IMPLEMENTATION: The pipeline code is written in Python using the Hail library and is freely available under the BSD-3 license here: https://github.com/wtsi-hgi/wxs-qc. The detailed description of the pipeline is available in the pipeline documentation: https://github.com/wtsi-hgi/wxs-qc/blob/main/README.md. We also provide an open dataset with all required metadata, which is available at https://wxs-qc-data.cog.sanger.ac.uk/wxs-qc_public_dataset_v3.tar. An example of test dataset analysis is available in the supplementary materials.

Simran Khanuja, Vivek Iyer, Xiaoyu He, Graham Neubig. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc. State-of-the-art (SOTA) Ontology Alignment systems typically use naive domain-dependent approaches with handcrafted rules or domainspecific architectures, making them unscalable and inefficient.In this work, we propose VeeAlign, a Deep Learning based model that uses a novel dual-attention mechanism to compute the contextualized representation of a concept which, in turn, is used to discover alignments.By doing this, not only is our approach able to exploit both syntactic and semantic information encoded in ontologies, it is also, by design, flexible and scalable to different domains with minimal effort.We evaluate our model on four different datasets from different domains and languages, and establish its superiority through these results as well as detailed ablation studies.The code and datasets used are available at https://github.com/Remorax/VeeAlign.

UNLABELLED: The rapid development of CRISPR-Cas9 mediated genome editing techniques has given rise to a number of online and stand-alone tools to find and score CRISPR sites for whole genomes. Here we describe the Wellcome Trust Sanger Institute Genome Editing database (WGE), which uses novel methods to compute, visualize and select optimal CRISPR sites in a genome browser environment. The WGE database currently stores single and paired CRISPR sites and pre-calculated off-target information for CRISPRs located in the mouse and human exomes. Scoring and display of off-target sites is simple, and intuitive, and filters can be applied to identify high-quality CRISPR sites rapidly. WGE also provides a tool for the design and display of gene targeting vectors in the same genome browser, along with gene models, protein translation and variation tracks. WGE is open, extensible and can be set up to compute and present CRISPR sites for any genome. AVAILABILITY AND IMPLEMENTATION: The WGE database is freely available at www.sanger.ac.uk/htgt/wge CONTACT: : [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Buffer overflow is known to be a common memory vulnerability affecting software. It is exploited to gain various kinds of privilege escalation. C and C++ are very commonly used to develop applications; due to the efficient “unmanaged” executions these languages are not safe. These attacks are highly successful as every executing copy of a shipped binary is the same. This work presents two approaches to randomizing the memory layout which does not require modifications at the developer end. Both techniques are implemented at the user-end machines and have no requirement for source code. The feasibility of the two techniques is shown by randomizing complex applications and demonstrating that the run-time penalty for the randomization schemes is very less.

There are approximately 10 million blind and visually-impaired persons in the United States. As the information economy accounts for a larger percent of jobs, people who are blind face barriers when competing for jobs that require vision to access, absorb, and use information. While much research effort has been expended toward developing assistive devices for the blind, many of the technological solutions proposed are unworkable. Many of these assistive devices fall short of meeting the needs of users, and sometimes even create more problems than they solve. In order to avoid repeating these mistakes, a user-centric approach is vital to the design of assistive devices. This paper describes our own efforts to develop and employ such a design methodology in the iCare project. In this project, the research efforts are being focused on those problems that will have the greatest impact on the day-to-day life of the user, and that are technologically feasible.