Polly: An R package for genotyping microsatellites and detecting highly polymorphic DNA markers from short-read data.

Highly polymorphic markers, such as microsatellites, are invaluable for the study of natural populations. However, contemporary methods for genotyping highly polymorphic variants have serious drawbacks that impede their efficiency. We created Polly, an R package with C++ source code that uses Illumina short-read data to genotype microsatellites, detect highly polymorphic variants and identify clusters of highly polymorphic SNPs, indels and microsatellites. We tested Polly on short-read data from Xiphophorus birchmanni (Teleostei: Poeciliidae) and Arabidopsis thaliana, finding it to be efficient and accurate both for microsatellite genotyping and polymorphic marker detection. This program can be applied to any diploid population for which there exists short-read data and at least one scaffolded reference genome.

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

BACKGROUND: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research. OBJECTIVES: a) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and b) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards. METHODS: We created an open-source data set with data at the county level on exposure to four tropical cyclone hazards: peak sustained wind, rainfall, flooding, and tornadoes. The data cover all eastern U.S. counties for all land-falling or near-land Atlantic basin storms, covering 1996-2011 for all metrics and up to 1988-2018 for specific metrics. We validated measurements against other data sources and investigated patterns and agreement among binary exposure classifications based on these metrics, as well as compared them to use of distance from the storm's track, which has been used as a proxy for exposure in some epidemiological studies. RESULTS: Our open-source data set was typically consistent with data from other sources, and we present and discuss areas of disagreement and other caveats. Over the study period and area, tropical cyclones typically brought different hazards to different counties. Therefore, when comparing exposure assessment between different hazard-specific metrics, agreement was usually low, as it also was when comparing exposure assessment based on a distance-based proxy measurement and any of the hazard-specific metrics. DISCUSSION: Our results provide a multihazard data set that can be leveraged for epidemiological research on tropical cyclones, as well as insights that can inform the design and analysis for tropical cyclone epidemiological research. https://doi.org/10.1289/EHP6976.

Hosting Data Packages via drat: A Case Study with Hurricane Exposure Data.

Data-only packages offer a way to provide extended functionality for other R users. However, such packages can be large enough to exceed the package size limit (5 megabytes) for the Comprehensive R Archive Network (CRAN). As an alternative, large data packages can be posted to additional repostiories beyond CRAN itself in a way that allows smaller code packages on CRAN to access and use the data. The drat package facilitates creation and use of such alternative repositories and makes it particularly simple to host them via GitHub. CRAN packages can draw on packages posted to drat repositories through the use of the 'Additonal_repositories' field in the DESCRIPTION file. This paper describes how R users can create a suite of coordinated packages, in which larger data packages are hosted in an alternative repository created with drat, while a smaller code package that interacts with this data is created that can be submitted to CRAN.