{"title":"ScASplicer:用于单细胞测序的选择性剪接分析的交互式闪亮/R应用程序。","authors":"Pengwei Hu, Jixiang Xing, Wuritu Yang, Hongxia Chi, Yongqiang Xing, Yongchun Zuo","doi":"10.1016/j.ygeno.2025.111116","DOIUrl":null,"url":null,"abstract":"<p><p>Alternative splicing (AS) in single cell is crucial for cell heterogeneity, gene expression and transcriptome diversity. However, given the complexity of AS analysis in single-cell RNA sequencing (scRNA-seq), employing a continuous and iterative process to refine data and uncover relevant latent information is crucial. While several tools have been developed to address various aspects of scRNA-seq AS analysis, a versatile and user-friendly web application that can perform all essential steps of AS analysis on scRNA-seq data is still lacking. Here, we made significant advancements in improving the usability and functionality of MARVEL. Firstly, we developed a Python package that can easily and efficiently generate input files, reducing the technical barrier. Secondly, we developed a Shiny-based R package that extends MARVEL's analysis capabilities to multiple cell populations, enabling interactive, code-free ex-ploration of AS and gene expression dynamics at single-cell level.The package, named ScASplicer (Single-Cell Alternative Splicing Shiny Explorer), provides a user-friendly platform for more efficient and comprehensive single-cell AS analysis.</p>","PeriodicalId":12521,"journal":{"name":"Genomics","volume":" ","pages":"111116"},"PeriodicalIF":3.0000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ScASplicer: An interactive shiny/R application for alternative splicing analysis of single-cell sequencing.\",\"authors\":\"Pengwei Hu, Jixiang Xing, Wuritu Yang, Hongxia Chi, Yongqiang Xing, Yongchun Zuo\",\"doi\":\"10.1016/j.ygeno.2025.111116\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Alternative splicing (AS) in single cell is crucial for cell heterogeneity, gene expression and transcriptome diversity. However, given the complexity of AS analysis in single-cell RNA sequencing (scRNA-seq), employing a continuous and iterative process to refine data and uncover relevant latent information is crucial. While several tools have been developed to address various aspects of scRNA-seq AS analysis, a versatile and user-friendly web application that can perform all essential steps of AS analysis on scRNA-seq data is still lacking. Here, we made significant advancements in improving the usability and functionality of MARVEL. Firstly, we developed a Python package that can easily and efficiently generate input files, reducing the technical barrier. Secondly, we developed a Shiny-based R package that extends MARVEL's analysis capabilities to multiple cell populations, enabling interactive, code-free ex-ploration of AS and gene expression dynamics at single-cell level.The package, named ScASplicer (Single-Cell Alternative Splicing Shiny Explorer), provides a user-friendly platform for more efficient and comprehensive single-cell AS analysis.</p>\",\"PeriodicalId\":12521,\"journal\":{\"name\":\"Genomics\",\"volume\":\" \",\"pages\":\"111116\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genomics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ygeno.2025.111116\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.ygeno.2025.111116","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
单细胞的选择性剪接(AS)对细胞异质性、基因表达和转录组多样性至关重要。然而,考虑到单细胞RNA测序(scRNA-seq)中AS分析的复杂性,采用连续迭代的过程来完善数据并发现相关的潜在信息至关重要。虽然已经开发了一些工具来解决scRNA-seq AS分析的各个方面,但仍然缺乏一个通用的、用户友好的web应用程序,可以在scRNA-seq数据上执行AS分析的所有基本步骤。在这里,我们在提高MARVEL的可用性和功能方面取得了重大进展。首先,我们开发了一个Python包,它可以轻松有效地生成输入文件,减少了技术障碍。其次,我们开发了一个基于shine的R包,将MARVEL的分析能力扩展到多个细胞群体,从而实现了在单细胞水平上对AS和基因表达动态的交互式、无代码探索。该软件包名为ScASplicer (Single-Cell Alternative Splicing Shiny Explorer),为更高效和全面的单细胞AS分析提供了一个用户友好的平台。
ScASplicer: An interactive shiny/R application for alternative splicing analysis of single-cell sequencing.
Alternative splicing (AS) in single cell is crucial for cell heterogeneity, gene expression and transcriptome diversity. However, given the complexity of AS analysis in single-cell RNA sequencing (scRNA-seq), employing a continuous and iterative process to refine data and uncover relevant latent information is crucial. While several tools have been developed to address various aspects of scRNA-seq AS analysis, a versatile and user-friendly web application that can perform all essential steps of AS analysis on scRNA-seq data is still lacking. Here, we made significant advancements in improving the usability and functionality of MARVEL. Firstly, we developed a Python package that can easily and efficiently generate input files, reducing the technical barrier. Secondly, we developed a Shiny-based R package that extends MARVEL's analysis capabilities to multiple cell populations, enabling interactive, code-free ex-ploration of AS and gene expression dynamics at single-cell level.The package, named ScASplicer (Single-Cell Alternative Splicing Shiny Explorer), provides a user-friendly platform for more efficient and comprehensive single-cell AS analysis.
期刊介绍:
Genomics is a forum for describing the development of genome-scale technologies and their application to all areas of biological investigation.
As a journal that has evolved with the field that carries its name, Genomics focuses on the development and application of cutting-edge methods, addressing fundamental questions with potential interest to a wide audience. Our aim is to publish the highest quality research and to provide authors with rapid, fair and accurate review and publication of manuscripts falling within our scope.