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February 8, 2026PeerJ1 citationsOpen Access

Strawberry atlas: Fragaria vesca gene expression atlas for strawberry genomics

MOMinto OdagiriCTChonprakun ThagunTKTakeshi Kurokura

Key Points

  • The study aims to create a comprehensive gene expression atlas for Fragaria vesca to enhance understanding of transcriptional regulation across various tissues.
  • Conducted quality control on RNA-seq libraries to retain high-quality samples.
  • Created nine tissue categories, including leaf, root, and fruit stages.
  • Used UMAP for dimensionality reduction to assess tissue clustering.
  • Identified tissue-specific and housekeeping genes based on gene expression metrics.
  • Evaluated structural variants and their impact on gene expression.
  • Captured expression for approximately 99% of annotated genes with clear clustering by tissue.
  • Identified tissue-enriched genes related to photosynthesis, flavonoid biosynthesis, and fruit ripening.
  • Found that structural variants were underrepresented in coding exons, resulting in decreased expression for overlapping genes.
  • Demonstrated specific expression patterns within the GA20-oxidase gene family depending on tissue type.

Abstract

Background Fragaria vesca (woodland strawberry) is a diploid model for cultivated strawberry (Fragaria × ananassa Duch. ex Rozier). While high-quality genome assemblies exist for key accessions such as Hawaii-4 (H4) and YW5AF7/Yellow Wonder (YW), a comprehensive understanding of transcriptional regulation across tissues and genotypes has been limited. Previous expression resources focused on select organs or individual studies, limiting tissue-level comparisons. Moreover, structural variants (SVs) and transposable elements (TEs), known to shape gene expression in other plants, remain understudied in F. vesca. An integrated gene expression resource spanning multiple tissues is needed to address these gaps and support functional genomics in strawberry research. Methods After quality control that removed mutant and infected samples, 233 high-quality RNA-seq libraries covering more than 50 tissues and developmental stages were retained. These libraries were consolidated into nine tissue categories: leaf, root, anther, carpel, flower bud, seed, stem, early fruit, and late fruit (mature fruit). In-house leaf libraries were used as a reference to validate data structure and batch correction. Transcript abundance was quantified against the latest F. vesca reference genome (v6), and dimensionality reduction via UMAP was used to assess tissue clustering. Tissue-specific (TS) and housekeeping (HK) genes were identified based on fold-change, adjusted p -values, and the tau tissue specificity index. To evaluate the relationship between genome structure and expression, SVs were detected between H4 and YW using assembly- and mapping-based approaches. Genes overlapping SVs were assessed for expression trends. A web interface was developed to facilitate interactive exploration. Results The resulting expression atlas captures a certain level of expression for ∼99% of 36, 173 annotated genes that include TS genes. It also reveals robust clustering by tissue, underscoring the biological coherence of the integrated dataset. Expression profiling identified tissue-enriched genes in photosynthesis, flavonoid biosynthesis, and fruit ripening, and a core set of 719 stably expressed HK genes. SVs were significantly underrepresented in coding exons, and genes overlapping SVs had reduced expression. One example, FvesChr6G00002800 (FvH4₆g02210), exhibited exonization of a TE-derived region in H4, supported by multiple datasets. The expression of GA20-oxidase family members revealed functional partitioning: GA20ox3 was seed-specific, GA20ox2 was active in early fruit, and GA20ox1 showed broad, low-level expression across tissues, consistent with a subfunctionalization model. To support knowledge integration, we provide a v4-to-v6 gene ID conversion tool. All data, including SV and TE annotations, are available via a public web portal: https: //strawberryatlas. org/easygdb, enabling rapid cross-tissue expression analysis.

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Cite This Study

Odagiri et al. (2026) studied this question.

synapsesocial.com/papers/698828330fc35cd7a88476b1https://doi.org/10.7717/peerj.20740
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