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RNA-seq

RNA-seq

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: Jun 28, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data.

Weideng Wei¹, Xiaoqiang Xia¹, Taiwen Li¹

¹State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China.

|April 24, 2024

Summary

This summary is machine-generated.

Shaoxia is a new open-source platform that simplifies single-cell RNA sequencing (scRNA-seq) analysis. It provides a user-friendly interface and high-performance computing, making complex scRNA-seq data accessible to all researchers.

Keywords:

Analysis framework Analysis platform Pipeline Single cell RNA sequencing

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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is becoming more accessible.
Interpreting scRNA-seq data requires advanced programming and bioinformatics skills.
A need exists for user-friendly software to analyze scRNA-seq data.

Purpose of the Study:

To develop an accessible software for scRNA-seq data analysis.
To democratize scRNA-seq analysis for researchers without programming expertise.
To provide a comprehensive understanding of the scRNA-seq analysis procedure.

Main Methods:

Developed a clear analysis framework for scRNA-seq data.
Emphasized cell identity annotation within upstream, cell annotation, and downstream stages.
Engineered Shaoxia, a platform leveraging high-performance computing and a user-friendly interface.

Main Results:

Shaoxia offers a streamlined approach to scRNA-seq data interpretation.
The platform accelerates processing through high-performance computing.
Provides an accessible interface for wet-lab researchers without programming expertise.

Conclusions:

Shaoxia is a powerful, user-friendly, open-source software for automated scRNA-seq analysis.
Offers comprehensive functionality for streamlined functional genomics studies.
Freely accessible online with publicly available source code.