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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: Sep 22, 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

webSCST: an interactive web application for single-cell RNA-sequencing data and spatial transcriptomic data

Zilong Zhang^1,2,3, Feifei Cui^1,2,3, Wei Su⁴

¹School of Computer Science and Technology, Hainan University, Haikou 570228, China.

Bioinformatics (Oxford, England)

|May 23, 2022

Summary

This summary is machine-generated.

A new tool, webSCST, integrates single-cell RNA sequencing (scRNA-seq) with spatial data to predict cell locations. This facilitates biological study by overcoming data rarity and format challenges.

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Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Integrating single-cell RNA sequencing (scRNA-seq) with spatial data is crucial for predicting cell locations and advancing biological research.
Limited availability and varied formats of public spatial sequencing datasets hinder integrative analyses.
Existing challenges necessitate novel computational tools for seamless data integration.

Purpose of the Study:

To introduce webSCST, a novel web-based tool for integrative scRNA-seq and spatial data analysis.
To provide a user-friendly platform for processing raw scRNA-seq data and predicting spatial locations for cell types.
To consolidate well-organized spatial transcriptome sequencing datasets by species and organs.

Main Methods:

Development of a web-based application using Shiny, supporting all major browsers.
Integration of popular scRNA-seq data processing and integration methods.
Implementation of a system to categorize and present spatial transcriptome datasets by species and organs.

Main Results:

webSCST enables users to submit raw scRNA-seq data for analysis.
The tool predicts spatial locations for individual cells and cell types.
It offers a unified interface for accessing and analyzing diverse spatial transcriptomic datasets.

Conclusions:

webSCST addresses the scarcity and heterogeneity of spatial sequencing data.
The tool facilitates biological discovery by enabling spatial mapping of single cells.
webSCST is accessible via a web portal and as an R package for broader research community use.