FIVEx: an interactive eQTL browser across public datasets.

SUMMARY: Expression quantitative trait loci (eQTLs) characterize the associations between genetic variation and gene expression to provide insights into tissue-specific gene regulation. Interactive visualization of tissue-specific eQTLs or splice QTLs (sQTLs) can facilitate our understanding of functional variants relevant to disease-related traits. However, combining the multi-dimensional nature of eQTLs/sQTLs into a concise and informative visualization is challenging. Existing QTL visualization tools provide useful ways to summarize the unprecedented scale of transcriptomic data but are not necessarily tailored to answer questions about the functional interpretations of trait-associated variants or other variants of interest. We developed FIVEx, an interactive eQTL/sQTL browser with an intuitive interface tailored to the functional interpretation of associated variants. It features the ability to navigate seamlessly between different data views while providing relevant tissue- and locus-specific information to offer users a better understanding of population-scale multi-tissue transcriptomic profiles. Our implementation of the FIVEx browser on the EBI eQTL catalogue, encompassing 16 publicly available RNA-seq studies, provides important insights for understanding potential tissue-specific regulatory mechanisms underlying trait-associated signals. AVAILABILITY AND IMPLEMENTATION: A FIVEx instance visualizing EBI eQTL catalogue data can be found at https://fivex.sph.umich.edu. Its source code is open source under an MIT license at https://github.com/statgen/fivex. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Describing the patient experience from Yelp reviews of community pharmacies.

OBJECTIVES: To examine the characteristics of patient experience in community pharmacies through pattern exploration techniques of the unstructured free-text data from an online review website. DESIGN: Retrospective observational study design using structural topic model (STM) and term frequency-inverse document frequency (tf-idf) to categorize free-text data. Tf-idf scores words in terms of importance, and STM extracts latent themes from free-text data based on the co-occurrence of words in a review. Human labels were assigned to STM output, with each topic's prevalence mapped to each level of the 1- to 5-star review ratings. SETTING AND PARTICIPANTS: Data were obtained from the Yelp Academic data set from April 2006 through December 2017. These data were available for analysis from certain cities in the United States, Canada, and Europe. Included reviews were filtered based on the presence of pharmacy-specific character strings (e.g., "prescri"). MAIN OUTCOME MEASURES: Descriptive statistics of Yelp review characteristics, tf-idf scores, and topics produced from STM were used to characterize the content of Yelp reviews at each star-rating level. RESULTS: The filtered data set contained 4463 reviews from 964 pharmacies in 8 U.S. states. The mean (±SD) review rating was 2.97 ± 0.91. The mean number of words in a review was 135 ± 116. STM revealed 9 topics that influenced patient experiences at community pharmacies, including waiting time, service attitude, and physical store characteristics. Friendly and helpful staff accounted for 28.3% of content in 5-star ratings, whereas waiting time accounted for 19.4% of 1-star ratings. CONCLUSION: Yelp reviews provide a public look into patient experience at community pharmacies, and the reviews likely influence other patients' decisions to use the pharmacy. Pharmacies should focus their efforts on enabling pharmacy staff to provide high-quality care and minimizing unnecessary waiting times for patients.