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Abstract Subtelomeres are dynamic genomic regions shaped by elevated rates of recombination, mutation, and gene birth/death. These processes contribute to formation of lineage-specific gene family expansions that commonly occupy subtelomeres across eukaryotes. Investigating the evolution of subtelomeric gene families is complicated by the presence of repetitive DNA and high sequence similarity among gene family members that prevents accurate assembly from whole genome sequences. Here, we investigated the evolution of the telomere-associated (TLO) gene family in Candida albicans using 189 complete coding sequences retrieved from 23 genetically diverse strains across the species. Tlo genes conformed to the 3 major architectural groups (α/β/γ) previously defined in the genome reference strain but significantly differed in the degree of within-group diversity. One group, Tloβ, was always found at the same chromosome arm with strong sequence similarity among all strains. In contrast, diverse Tloα sequences have proliferated among chromosome arms. Tloγ genes formed 7 primary clades that included each of the previously identified Tloγ genes from the genome reference strain with 3 Tloγ genes always found on the same chromosome arm among strains. Architectural groups displayed regions of high conservation that resolved newly identified functional motifs, providing insight into potential regulatory mechanisms that distinguish groups. Thus, by resolving intraspecies subtelomeric gene variation, it is possible to identify previously unknown gene family complexity that may underpin adaptive functional variation. 

The term "threat intelligence" has swiftly become a staple buzzword in the computer security industry. The entirely reasonable premise is that, by compiling up-to-date information about known threats (i.e., IP addresses, domain names, file hashes, etc.), recipients of such information may be able to better defend their systems from future attacks. Thus, today a wide array of public and commercial sources distribute threat intelligence data feeds to support this purpose. However, our understanding of this data, its characterization and the extent to which it can meaningfully support its intended uses, is still quite limited. In this paper, we address these gaps by formally defining a set of metrics for characterizing threat intelligence data feeds and using these measures to systematically characterize a broad range of public and commercial sources. Further, we ground our quantitative assessments using external measurements to qualitatively investigate issues of coverage and accuracy. Unfortunately, our measurement results suggest that there are significant limitations and challenges in using existing threat intelligence data for its purported goals.