MOTIVATION: Scientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes. RESULTS: We describe PRAWNS, an efficient and scalable tool for multiple-genome analysis. PRAWNS defines a concise set of genomic features (metablocks), as well as pairwise relationships between them, which can be used as a basis for large-scale genotype-phenotype association studies. We demonstrate the effectiveness of PRAWNS by identifying genomic regions associated with antibiotic resistance in Acinetobacter baumannii. AVAILABILITY AND IMPLEMENTATION: PRAWNS is implemented in C++ and Python3, licensed under the GPLv3 license, and freely downloadable from GitHub (https://github.com/KiranJavkar/PRAWNS.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advances in biotechnology now allow users to obtain their genetic information, including ancestry and predisposition to various diseases and health issues, with relative ease. With these new commercial services come a host of privacy concerns with respect to data sharing and access. User data is being sold to third parties, including pharmaceutical and biotechnology companies, and may be accessed by law enforcement in accordance with proper legal procedures. Moreover, many users of these services go on to deposit the data they obtain into online, public repositories that are fully accessible to anyone with an internet connection. The full extent of the risks they face may not be apparent to users. This paper reports on a semistructured interview study ($n=24$) examining user concerns regarding these tests, what information they believe they are revealing, and what they think companies are doing with their data. We find that users are concerned with privacy, and understand at a basic level the nature of the data they are revealing. However, their privacy concerns are often insufficient to deter them from taking such a test, and many have difficulty grasping some of the implications of sharing their genetic information with commercial entities.