Josephine R Paris, Roberta Bisconti, Andrea Chiocchio, Linelle Abueg, Dominic E Absolon, Tatiana Tilley, Nivesh Jain, Jennifer Balacco, Brian O'Toole, Erich D Jarvis, Giulio Formenti, Daniele Salvi, Daniele Canestrelli
{"title":"Chromosome-level genome assembly of the Tyrrhenian tree frog (Hyla sarda).","authors":"Josephine R Paris, Roberta Bisconti, Andrea Chiocchio, Linelle Abueg, Dominic E Absolon, Tatiana Tilley, Nivesh Jain, Jennifer Balacco, Brian O'Toole, Erich D Jarvis, Giulio Formenti, Daniele Salvi, Daniele Canestrelli","doi":"10.1038/s41597-025-05760-9","DOIUrl":null,"url":null,"abstract":"<p><p>The Tyrrhenian tree frog (Hyla sarda) is a small cryptically coloured amphibian found in Corsica, Sardinia, and the Tuscan Archipelago. Investigation into the species' evolutionary history has revealed phenotypic changes triggered by glaciation-induced range expansion, but understanding the genetic basis of this trait variation has been hampered by the lack of a reference genome. To address this, we assembled a chromosome-level genome of Hyla sarda using PacBio HiFi long reads, Bionano optical maps, and Hi-C data. The assembly comprises 13 assembled chromosomes, spanning a total length of 4.15 Gb with a scaffold N50 of 385 Mb, a BUSCO completeness of 94.60%, and a k-mer completeness of 98.30%. Approximately 75% of the genome consists of repetitive elements. We annotated 22,847 protein-coding genes with a BUSCO completeness of 94.60% and an OMArk completeness of 93.74%. This high-quality assembly provides a valuable resource for studying phenotypic evolution and its genomic basis during range expansion, and will assist future investigations into the population and conservation genomics of Hyla sarda.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1539"},"PeriodicalIF":6.9000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12405506/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05760-9","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The Tyrrhenian tree frog (Hyla sarda) is a small cryptically coloured amphibian found in Corsica, Sardinia, and the Tuscan Archipelago. Investigation into the species' evolutionary history has revealed phenotypic changes triggered by glaciation-induced range expansion, but understanding the genetic basis of this trait variation has been hampered by the lack of a reference genome. To address this, we assembled a chromosome-level genome of Hyla sarda using PacBio HiFi long reads, Bionano optical maps, and Hi-C data. The assembly comprises 13 assembled chromosomes, spanning a total length of 4.15 Gb with a scaffold N50 of 385 Mb, a BUSCO completeness of 94.60%, and a k-mer completeness of 98.30%. Approximately 75% of the genome consists of repetitive elements. We annotated 22,847 protein-coding genes with a BUSCO completeness of 94.60% and an OMArk completeness of 93.74%. This high-quality assembly provides a valuable resource for studying phenotypic evolution and its genomic basis during range expansion, and will assist future investigations into the population and conservation genomics of Hyla sarda.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.