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CitationSnead, A.A., Meng, F., Largotta, N. et al. Diploid chromosome-level genome assembly and annotation for Lycorma delicatula. Sci Data 12, 579 (2025). https://doi.org/10.1038/s41597-025-04854-8AbstractThe spotted lanternfly (Lycorma delicatula) is a planthopper species (Hemiptera: Fulgoridae) native to China but invasive in South Korea, Japan, and the United States where it is a significant threat to agriculture. Hence, genomic resources are critical to both management and understand the genomic characteristics of successful invaders. Here, we report a haplotype-phased genome assembly and annotation using PacBio long-read sequencing, Hi-C technology, and RNA-seq data. The 2.2 Gbp genome comprises 13 chromosomes, and our whole genome sequencing of eighty-two adults indicated chromosome four as the sex chromosome and anXO sex-determination system.We identified over 12,000 protein coding genes and performed functional annotation, facilitating identification of several candidate genes which may hold importance for spotted lanternfly control. Both the assemblies and annotations were highly complete with over 96% of BUSCO genes complete regardless of the database employed (i.e., Eukaryota, Arthropoda, Insecta). This reference-quality genome will serve as an important resource for both development and optimization of management practices for the spotted lanternfly and invasive genomics as a whole.Description of the data and file structureThis dataset contains the haplotype-phased chromosome-level genome assembly of the spotted lanternfly (Lycorma delicatula) described and published in Snead & Meng et al. (in review). The genome combined long-read data and HiC data (SRA31402152-SRA31402153) to assembly and scaffold each haplotype. The annotation uses RNAseq data from 12 adults (SRA31411873-SRA31411894) to structurally annotate both haplotypes. Finally, whole-genome sequencing of 82 adult spotted lanternfly (bioproject PRJNA1136004) described in the metadata csv provided was used to identify punitive sex chromosomes. The dataset also include GO results for each chromosome not explicitly described in the results of the manuscript.Files and variablesFile: SLF_Hap1.fastaDescription: A fasta file of the assembled genome for the cleaned 13 chromosome haplotype 1 assembly.File: SLF_Hap2.fastaDescription: A fasta file of the assembled genome for the cleaned 13 chromosome haplotype 2 assembly.File: SLF_Hap1_Repeats.gffDescription: A gff file of the repeats annotated in the cleaned 13 chromosome haplotype 1 assembly.File: SLF_Hap2_Repeats.gffDescription: A gff file of the repeats annotated in the cleaned 13 chromosome haplotype 2 assembly.File: SLF_Hap1.gffDescription: A structural annotation of the 13 chromosome haplotype 1 assembly with functional annotations.File: SLF_Hap2.gffDescription: A structural annotation of the 13 chromosome haplotype 2 assembly with functional annotations.File: GO_plot_chr_1.pngDescription: An image of the top 20 GO term results for chromosome 1.File: GO_plot_chr_2.pngDescription: An image of the top 20 GO term results for chromosome 2.File: GO_plot_chr_3.pngDescription: An image of the top 20 GO term results for chromosome 3.File: GO_plot_chr_8.pngDescription: An image of the top 20 GO term results for chromosome 8.File: GO_plot_chr_5.pngDescription: An image of the top 20 GO term results for chromosome 5.File: GO_plot_chr_4.pngDescription: An image of the top 20 GO term results for chromosome 4.File: GO_plot_chr_6.pngDescription: An image of the top 20 GO term results for chromosome 6.File: GO_plot_chr_7.pngDescription: An image of the top 20 GO term results for chromosome 7.File: GO_plot_chr_11.pngDescription: An image of the top 20 GO term results for chromosome 11.File: GO_plot_chr_9.pngDescription: An image of the top 20 GO term results for chromosome 9.File: GO_plot_chr_10.pngDescription: An image of the top 20 GO term results for chromosome 10.File: GO_plot_chr_12.pngDescription: An image of the top 20 GO term results for chromosome 12.File: GO_plot_chr_13.pngDescription: An image of the top 20 GO term results for chromosome 13.File: SLF_Samples_SRA.csvDescription: A csv with the sequencing information, SRA numbers, and sexes of the adults used in to identify the putative sex chromosome.File: SLF_RNAseq_Metadata.csvDescription: A csv with the sequencing information, SRA numbers, and other metadata for the RNAseq used to annotation the genomes.Variablesaccession: The SRA accession numberstudy: The studyobject_status: If the NCBI submission was new or not.bioproject_accession: The bioproject accession numberbiosample_accession: The Biosample accession numberlibrary_ID: The ID used to identify that genomic library.title: The title of the study (the bioproject)library_strategy: Specific sequencing technique used to prepare the library.library_source: The biological material used to create the sequencing library.library_selection: The library preparation method.library_layout: The arrangement of reads within the sequencing library.platform: The sequencing platform.instrument_model: The model of the sequences.design_description: Description of the study design.filetype: Type of filefilename: First filefilename2: Second filesex: The biological sex of the adult.Code/softwareThe initial haplotype-phased scaffolded genome was assembled by Dovetail Genomics (Cantata Bio) with standard software outlined in the methods with default settings. Scripts for the remaining work including annotation, gene ontology enrichment, and other analyses are located in the Github repository (https://github.com/anthonysnead/SLF-Genome-Assembly(opens in new window)).Access informationOther publicly accessible locations of the data:The raw sequencing data and the annotated haplotype-phased genome assembly of Lycorma delicatula have been deposited at the National Center for Biotechnology Information (NCBI). The Hi-C and HiFi data can be found under SRA31402152 and SRA31402153. The RNA-seq data can be found under SRA31411873-SRA31411894, while the DNA-seq data can be found under bioproject PRJNA1136004. 

Drift and gene flow affect genetic diversity. Given that the strength of genetic drift increases as population size decreases, management activities have focused on increasing population size through preserving habitats to preserve genetic diversity. Few studies have empirically evaluated the impacts of drift and gene flow on genetic diversity.Kryptolebias marmoratus, henceforth ‘rivulus’, is a small killifish restricted to fragmented New World mangrove forests with gene flow primarily associated with ocean currents. Rivulus form distinct populations across patches, making them a well-suited system to test the extent to which habitat area, fragmentation and connectivity are associated with genetic diversity. Using over 1000 individuals genotyped at 32 microsatellite loci, high-resolution landcover data and oceanographic simulations with graph theory, we demonstrate that centrality (connectivity) to the metapopulation is more strongly associated with genetic diversity than habitat area or fragmentation. By comparing models with and without centrality standardized by the source population’s genetic diversity, our results suggest that metapopulation centrality is critical to genetic diversity regardless of the diversity of adjacent populations. While we find evidence that habitat area and fragmentation are related to genetic diversity, centrality is always a significant predictor with a larger effect than any measure of habitat configuration. 

Fewer than half of individuals with a suspected Mendelian or monogenic condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control data sets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project (1KGP) Oxford Nanopore Technologies Sequencing Consortium aims to generate LRS data from at least 800 of the 1KGP samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37× and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.