• Energy Research

Wildlife diseases, such as the sea star wasting (SSW) epizootic that outbroke in the mid-2010s, appear to be associated with acute and/or chronic abiotic environmental change; dissociating the effects of different drivers can be difficult. The sunflower sea star, Pycnopodia helianthoides, was the species most severely impacted during the SSW outbreak, which overlapped with periods of anomalous atmospheric and oceanographic conditions, and there is not yet a consensus on the cause(s). Genomic data may reveal underlying molecular signatures that implicate a subset of factors and, thus, clarify past events while also setting the scene for effective restoration efforts. To advance this goal, we used Pacific Biosciences HiFi long sequencing reads and Dovetail Omni-C proximity reads to generate a highly contiguous genome assembly that was then annotated using RNA-seq-informed gene prediction. The genome assembly is 484 Mb long, with contig N50 of 1.9 Mb, scaffold N50 of 21.8 Mb, BUSCO completeness score 96.1%, and 22 major scaffolds consistent with prior evidence that sea star genomes comprise 22 autosomes. These statistics generally fall between those of other recently assembled chromosome-scale assemblies for two species in the distantly related asteroid genus Pisaster. These novel genomic resources for Pycnopodia helianthoides will underwrite population genomic, comparative genomic, and phylogenomic analyses — as well as their integration across scales — of SSW and environmental stressors. This data resource contains the files associated with gene prediction. # Gene prediction for: A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides [https://doi.org/10.5061/dryad.51c59zwfd](https://doi.org/10.5061/dryad.51c59zwfd) The files contained here represent the results of gene prediction for the sunflower star, *Pycnopodia helianthoides*. ## Description of the data files * interproscan.tsv.gz : table with function assignment to transcript IDs * interproscan.gff3 : functional annotation in gff3 format, annotations for all the predicted coding genes in the genome based on the protein translation * augustus.hints.codingseq : nucleotide sequences for all the predicted genes, gene set delivered by bioinformatics@greifswald as coding sequences in fasta format; this file can be used for transcriptome quantification * augustus.hints.aa : protein sequences for all the predicted genes, gene set delivered by bioinformatics@greifswald as protein sequences in fasta format * augustus.hints.gtf : coordinates for all genomic features, gene set delivered by bioinformatics@greifswald in gtf format ## Authors Lars Gabriel, Katharina J. Hoff

Abstract Wildlife diseases, such as the sea star wasting (SSW) epizootic that outbroke in the mid-2010s, appear to be associated with acute and/or chronic abiotic environmental change; dissociating the effects of different drivers can be difficult. The sunflower sea star, Pycnopodia helianthoides, was the species most severely impacted during the SSW outbreak, which overlapped with periods of anomalous atmospheric and oceanographic conditions, and there is not yet a consensus on the cause(s). Genomic data may reveal underlying molecular signatures that implicate a subset of factors and, thus, clarify past events while also setting the scene for effective restoration efforts. To advance this goal, we used Pacific Biosciences HiFi long sequencing reads and Dovetail Omni-C proximity reads to generate a highly contiguous genome assembly that was then annotated using RNA-seq-informed gene prediction. The genome assembly is 484 Mb long, with contig N50 of 1.9 Mb, scaffold N50 of 21.8 Mb, BUSCO completeness score of 96.1%, and 22 major scaffolds consistent with prior evidence that sea star genomes comprise 22 autosomes. These statistics generally fall between those of other recently assembled chromosome-scale assemblies for two species in the distantly related asteroid genus Pisaster. These novel genomic resources for P. helianthoides will underwrite population genomic, comparative genomic, and phylogenomic analyses—as well as their integration across scales—of SSW and environmental stressors.