Automatic detection of topical trends at scale is both challenging and essential for maintaining a dynamic content ecosystem on social media platforms. In this work, we present a large-scale system for identifying emerging topical trends on Snapchat, one of the world's largest short-video social platforms. Our system integrates multimodal topic extraction, time-series burst detection, and LLM-based consolidation and enrichment to enable accurate and timely trend discovery. To the best of our knowledge, this is the first published end-to-end system for topical trend detection on short-video platforms at production scale. Continuous offline human evaluation over six months demonstrates high precision in identifying meaningful trends. The system has been deployed in production at global scale and applied to downstream surfaces including content ranking and search, driving measurable improvements in content freshness and user experience.

MOTIVATION: Recombination is an essential driver of virus evolution and adaption, giving rise to new chimeric viruses, structural variants, sub-genomic RNAs and defective RNAs. Next-generation sequencing (NGS) of virus samples, either from experimental or clinical settings, has revealed a complex distribution of recombination events that contributes to intrahost diversity. We and others have previously developed alignment tools to discover and map these diverse recombination events in NGS data. However, there is no standard for data visualization to contextualize events of interest, and downstream analysis often requires bespoke coding. RESULTS: We present ViReMaShiny, a web-based application built using the R Shiny framework to allow interactive exploration and point-and-click visualization of viral recombination data provided in BED format generated by computational pipelines such as ViReMa (Viral-Recombination-Mapper). AVAILABILITY AND IMPLEMENTATION: The application is hosted at https://routhlab.shinyapps.io/ViReMaShiny/ with associated documentation at https://jayeung12.github.io/. Code is available at https://github.com/routhlab/ViReMaShiny. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.