Dysdera silvatica genome

Dysdera silvatica genome provides a draft genome assembly and functional annotations to support evolutionary and functional genomics of the spider Dysdera silvatica.

Key Features:

• Genome Assembly: A draft assembly spanning 1.36 gigabases was generated using short-read (Illumina) and long-read (PacBio and Nanopore) sequencing and covers approximately 80% of the estimated 1.7 gigabase genome size as determined by flow cytometry.
• Repetitive Elements: The assembly contains a high fraction of interspersed repetitive elements, constituting 53.8% of the genome.
• Assembly Completeness: Completeness was evaluated with BUSCO and core eukaryotic genes, yielding estimated completeness between 90% and 96%.
• Functional Annotations: Functional annotation combined ab initio predictions and evidence-based methods incorporating RNA sequencing data, identifying 48,619 protein-coding sequences of which 74.9% (36,398 sequences) have known protein domains or sequence similarity to Swiss-Prot entries.
• Evolutionary Significance: As the first representative genome of the superfamily Dysderoidea and one of two genomes from the Synspermiata lineage (Araneomorphae), the resource enables comparative and evolutionary analyses of traits such as adaptation, venom, and silk.

Scientific Applications:

• Subterranean adaptation: Comparative analyses of genomic regions and gene families implicated in adaptation to underground environments.
• Trophic specialization: Investigation of genetic bases underlying dietary and trophic specialization among spider lineages.
• Reproductive genetics: Study of genetic components potentially involved in cryptic female choice and reproductive traits.
• Trait evolution (venom and silk): Analysis of molecular evolution and diversification of venom- and silk-related genes.

Methodology:

Assembly was produced using Illumina short reads and PacBio and Nanopore long reads; completeness was assessed with BUSCO and core eukaryotic genes; functional annotation combined ab initio predictions and evidence-based methods incorporating RNA sequencing data.