Andrea Martín-Díaz, Clara de Vega, Sara Martín-Hernanz, Abelardo Aparicio, Rafael G Albaladejo
{"title":"De novo transcriptome assembly of the plant Helianthemum marifolium for the study of adaptive mechanisms.","authors":"Andrea Martín-Díaz, Clara de Vega, Sara Martín-Hernanz, Abelardo Aparicio, Rafael G Albaladejo","doi":"10.1038/s41597-025-04888-y","DOIUrl":null,"url":null,"abstract":"<p><p>The genus Helianthemum, commonly known as rockroses, encompasses 140 species primarily distributed in the Palearctic region, with notable diversification driven by climatic and geological changes. These plants are valuable for studying speciation processes and ecological divergence. The chemical properties of the leaves have also been investigated for containing valuable bioactive compounds with several therapeutic properties. However, the availability of genomic resources for species in this genus are almost entirely lacking. Here, we assembled and annotated the first reference transcriptome of Helianthemum marifolium, a species with wide morphological variability and infraspecific diversity. Illumina paired-end RNA sequences were generated using leaves from 16 individuals, representing the four recognized subspecies, all cultivated in a greenhouse. RNA reads were assembled with Trinity and Oases, and EvidentialGene produced a transcriptome with 122,002 transcripts. The transcriptome showed 59524 hits on the UniProtBK database through BLASTx. This transcriptome will be an invaluable resource for transcriptome-level population studies, conservation genetics of the many endangered species within the genus, and for deepen into the metabolic pathways of leaf-derived compounds.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"515"},"PeriodicalIF":5.8000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950303/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04888-y","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 genus Helianthemum, commonly known as rockroses, encompasses 140 species primarily distributed in the Palearctic region, with notable diversification driven by climatic and geological changes. These plants are valuable for studying speciation processes and ecological divergence. The chemical properties of the leaves have also been investigated for containing valuable bioactive compounds with several therapeutic properties. However, the availability of genomic resources for species in this genus are almost entirely lacking. Here, we assembled and annotated the first reference transcriptome of Helianthemum marifolium, a species with wide morphological variability and infraspecific diversity. Illumina paired-end RNA sequences were generated using leaves from 16 individuals, representing the four recognized subspecies, all cultivated in a greenhouse. RNA reads were assembled with Trinity and Oases, and EvidentialGene produced a transcriptome with 122,002 transcripts. The transcriptome showed 59524 hits on the UniProtBK database through BLASTx. This transcriptome will be an invaluable resource for transcriptome-level population studies, conservation genetics of the many endangered species within the genus, and for deepen into the metabolic pathways of leaf-derived compounds.
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.