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Abstract Nucella lapillus is an important player in rocky shore food chains and has been a focal organism of ecological and evolutionary studies for decades. Despite poor dispersal, they have a broad geographic range, which makes them an ideal species to examine isolation by distance and selection across environmental gradients. Here we present the fully annotated genome of N. lapillus generated with Oxford Nanopore Techonology sequencing at ∼37× coverage. The genome assembly is 2.32 Gbp and consists of 2,525 contigs, with an N50 length of 2 Mbp. Repeat annotation identified 2,491 families that cover 67.56% of the genome, which is similar to other gastropods. Despite its large size and high proportion of repeats, the genome is of high quality. Benchmarking Universal Single-Copy Ortholog (BUSCO) analysis revealed a score of 96.8%. Functional annotation of the genome produced 45,848 protein-coding genes with a 96.6% BUSCO score. Genomic resources for mollusks lag behind that of other phyla, perhaps because many of their innate characteristics complicate DNA extraction, sequencing, and assembly. This new N. lapillus genome will increase our genomic understanding of the second largest phylum (and the most diverse class within said phylum) and serve as a key resource to advance studies on the organismal biology and population genetics of this iconic species as well as the connection between genomic variation and community-level processes. 

Abstract Coral diseases contribute to the rapid decline in coral reefs worldwide, and yet coral bacterial pathogens have proved difficult to identify because 16S rRNA gene surveys typically identify tens to hundreds of disease‐associate bacteria as putative pathogens. An example is white band disease (WBD), which has killed up to 95% of the now‐endangered CaribbeanAcroporacorals since 1979, yet the pathogen is still unknown. The 16S rRNA gene surveys have identified hundreds of WBD‐associated bacterial amplicon sequencing variants (ASVs) from at least nine bacterial families with little consensus across studies. We conducted a multi‐year, multi‐site 16S rRNA gene sequencing comparison of 269 healthy and 143 WBD‐infectedAcropora cervicornisand used machine learning modelling to accurately predict disease outcomes and identify the top ASVs contributing to disease. Our ensemble ML models accurately predicted disease with greater than 97% accuracy and identified 19 disease‐associated ASVs and five healthy‐associated ASVs that were consistently differentially abundant across sampling periods. Using a tank‐based transmission experiment, we tested whether the 19 disease‐associated ASVs met the assumption of a pathogen and identified two pathogenic candidate ASVs—ASV25Cysteiniphilum litoraleand ASV8Vibriosp. to target for future isolation, cultivation, and confirmation of Henle‐Koch's postulate via transmission assays.