Antimicrobial resistance has become an imminent concern for public health. As methods for detection and characterization of antimicrobial resistance move from targeted culture and polymerase chain reaction to high throughput metagenomics, appropriate resources for the analysis of large-scale data are required. Currently, antimicrobial resistance databases are tailored to smaller-scale, functional profiling of genes using highly descriptive annotations. Such characteristics do not facilitate the analysis of large-scale, ecological sequence datasets such as those produced with the use of metagenomics for surveillance. In order to overcome these limitations, we present MEGARes (https://megares.meglab.org), a hand-curated antimicrobial resistance database and annotation structure that provides a foundation for the development of high throughput acyclical classifiers and hierarchical statistical analysis of big data. MEGARes can be browsed as a stand-alone resource through the website or can be easily integrated into sequence analysis pipelines through download. Also via the website, we provide documentation for AmrPlusPlus, a user-friendly Galaxy pipeline for the analysis of high throughput sequencing data that is pre-packaged for use with the MEGARes database.

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of their abilities or use of assistive technologies. However, prior work has shown there still exist important accessibility barriers within apps [20,21,37,40,42,43]. Awareness of the need to create more accessible apps is increasing. Google and Apple are the primary organizations that facilitate mobile technology and the app marketplace, through the Android and iOS platforms. Both have released developer and design guidelines for accessibility [16,30], provide accessibility services as part of their platforms [2,15], have app development libraries that include built-in compatibility with assistive technologies, and have released accessibility testing scanners [17,28] and suites [10]. Some companies creating popular apps have also made statements and taken actions to create more accessible apps [19,45]. Such approaches have included creating internal guidelines [45], having specialized accessibility teams [19], actively prioritizing accessibility [19,45], and working with people with disabilities during app development and testing [45]. Despite these accessibility-focused efforts, studies of relatively small groups of apps have found they still include significant accessibility barriers [20,21,37,42,43]. This suggests a continuing need for accessibility improvements, however, the field lacks a detailed understanding of the state of mobile app accessibility at a large-scale, "population" level. Many design patterns in mobile apps are image and icon focused and use image-based buttons for main functionalities. One key component of accessibility for screen reader users is labeling image-based buttons. This need is parallel to the need for alt-text for images on the web. However, there is no large-scale understanding of the prevalence of unlabeled image-based buttons, how effective tools are at promoting labeling, nor the potential causes of failure to label. Ross et al. [42] present an epidemiology-based framework that suggests large-scale analyses can help answer some of these questions. The framework emphasizes that apps do not exist in isolation. It suggests that, in addition to testing individual apps, additional benefits and insights can be gained by exploring app accessibility at the population-level, situated within the richer ecosystem of influential factors. Such analyses can give unique insights into the state of app accessibility and opportunities for improvements. An epidemiologically-inspired analysis can also establish a baseline against which to measure the evolution of app accessibility over time.