期刊
BMC BIOINFORMATICS
卷 22, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s12859-021-04472-2
关键词
Single-cell RNA sequencing; scRNA-seq; Web application; Gene expression
类别
资金
- US Department of Energy, Office of Science Biological and Environmental Research [DE-SC0018247]
Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis, significantly reducing the time and effort required to analyze and interpret the information in scRNA-seq datasets. It encapsulates tools for all critical steps of scRNA-seq data analysis in an user-friendly manner.
Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat's capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据