Dataset Information

Chromosome-level genome assembly of the northern Pacific seastar Asterias amurensis.

ABSTRACT: Asterias amurensis has attracted widespread concern because of its population outbreaks, which has impacted fisheries and aquaculture, as well as disrupting local ecosystems. A high-quality reference genome is necessary to better investigate mechanisms of outbreak and adaptive changes. Combining PacBio HiFi and Hi-C sequencing data, we generated a chromosome-level A. amurensis genome with a size of 491.53 Mb. The contig N50 and scaffold N50 were 8.05 and 23.75 Mb, respectively. The result of BUSCO analysis revealed a completeness score of 98.85%. A total of 16,531 protein-coding genes were predicted in the genome, of which 94.63% were functionally annotated. The high-quality genome assembly resulting from this study will provide a valuable genetic resource for future research on the mechanism of population outbreaks and invasion ecology.

SUBMITTER: Wang Y

PROVIDER: S-EPMC10625585 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Chromosome-level genome assembly of the northern Pacific seastar Asterias amurensis.

Wang Yanlin Y Wang Yixin Y Yang Yujia Y Ni Gang G Li Yulong Y Chen Muyan M

Scientific data 20231104 1

Asterias amurensis has attracted widespread concern because of its population outbreaks, which has impacted fisheries and aquaculture, as well as disrupting local ecosystems. A high-quality reference genome is necessary to better investigate mechanisms of outbreak and adaptive changes. Combining PacBio HiFi and Hi-C sequencing data, we generated a chromosome-level A. amurensis genome with a size of 491.53 Mb. The contig N50 and scaffold N50 were 8.05 and 23.75 Mb, respectively. The result of BUS ...[more]

PMID: 37925473

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Project description:Invasive species are a major threat to global biodiversity but can also serve as valuable model systems to examine important evolutionary processes. While the ecological aspects of invasions have been well documented, the genetic basis of adaptive change during the invasion process has been hampered by a lack of genomic resources for the majority of invasive species. Here we report the first larval transcriptomic resource for the Northern Pacific Seastar, Asterias amurensis, an invasive marine predator in Australia. Approximately 117.5 million 100 base-pair (bp) paired-end reads were sequenced from a single RNA-Seq library from a pooled set of full-sibling A. amurensis bipinnaria larvae. We evaluated the efficacy of a pre-assembly error correction pipeline on subsequent de novo assembly. Error correction resulted in small but important improvements to the final assembly in terms of mapping statistics and core eukaryotic genes representation. The error-corrected de novo assembly resulted in 115,654 contigs after redundancy clustering. 41,667 assembled contigs were homologous to sequences from NCBI's non-redundant protein and UniProt databases. We assigned Gene Ontology, KEGG Orthology, Pfam protein domain terms and predicted protein-coding sequences to > 36,000 contigs. The final transcriptome dataset generated here provides functional information for 18,319 unique proteins, comprising at least 11,355 expressed genes. Furthermore, we identified 9,739 orthologs to P. miniata proteins, evaluated our annotation pipeline and generated a list of 150 candidate genes for responses to several environmental stressors that may be important for adaptation of A. amurensis in the invasive range. Our study has produced a large set of A. amurensis RNA contigs with functional annotations that can serve as a resource for future comparisons to other echinoderm transcriptomes and gene expression studies. Our data can be used to study the genetic basis of adaptive change and other important evolutionary processes during a successful invasion.

Dataset Information

Chromosome-level genome assembly of the northern Pacific seastar Asterias amurensis.

Publications

Chromosome-level genome assembly of the northern Pacific seastar Asterias amurensis.

Similar Datasets

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