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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Sep 16, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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SeuratExtend: streamlining single-cell RNA-seq analysis through an integrated and intuitive framework.

Yichao Hua1,2, Linqian Weng3, Fang Zhao2,4

  • 1Department of Applied Computational Cancer Research, Institute for AI in Medicine (IKIM), University Hospital Essen, Essen 45131, Germany.

Gigascience
|July 8, 2025
PubMed
Summary
This summary is machine-generated.

SeuratExtend simplifies single-cell RNA sequencing (scRNA-seq) analysis by integrating diverse tools and databases within the Seurat framework. This R package enhances data visualization and accessibility for complex genomic studies.

Keywords:
R packageSeurat frameworkbioinformaticseducationmultitool integrationpathway analysissingle-cell RNA-seqvisualization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) generates complex cellular heterogeneity data.
  • The proliferation of analytical tools presents challenges for researchers.
  • A unified and accessible platform is needed for scRNA-seq data analysis.

Purpose of the Study:

  • To introduce SeuratExtend, an R package designed to streamline scRNA-seq data analysis.
  • To integrate essential analytical tools and databases into a user-friendly interface.
  • To enhance data visualization and accessibility for complex genomic analyses.

Main Methods:

  • Development of SeuratExtend, an R package built on the Seurat framework.
  • Integration of functional enrichment, trajectory inference, and gene regulatory network reconstruction tools.
  • Incorporation of databases like Gene Ontology and Reactome, and Python tools (scVelo, Palantir, SCENIC) via an R interface.

Main Results:

  • SeuratExtend provides a unified R interface for diverse scRNA-seq analyses.
  • Demonstrated utility in case studies of tumor-associated high-endothelial venules and autoinflammatory diseases.
  • Enhanced data visualization with optimized plotting functions and curated color schemes.

Conclusions:

  • SeuratExtend empowers researchers to conduct complex scRNA-seq analyses more efficiently.
  • The package makes advanced bioinformatics tools accessible to a broader audience.
  • Freely available on GitHub, SeuratExtend serves as a valuable resource for the single-cell genomics community.