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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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Related Experiment Video

Updated: Aug 23, 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

SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud.

Carlos Prieto¹, David Barrios¹, Angela Villaverde¹

¹Bioinformatics Service, Nucleus, University of Salamanca, Salamanca, Spain.

Frontiers in Bioinformatics

|October 28, 2022

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-Seq) analysis is complex, requiring bioinformatics expertise. SingleCAnalyzer is a cloud platform simplifying full scRNA-Seq data analysis through an intuitive web interface.

Keywords:

ScRNA-seq data analysis data visualization single cell web server

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-Seq) offers high-resolution transcriptomic data for individual cells.
Analyzing scRNA-Seq data presents significant computational challenges.
Existing methods often demand extensive bioinformatics expertise, hindering accessibility.

Purpose of the Study:

To develop an accessible cloud platform for comprehensive scRNA-Seq analysis.
To simplify the process from raw FASTQ files to insightful biological interpretation.
To provide researchers with an easy-to-use, self-exploratory web interface for scRNA-Seq data.

Main Methods:

Developed SingleCAnalyzer, a cloud-based platform with a user-friendly web interface.
Integrated a full analysis pipeline: demultiplexing, alignment, quality control, feature selection, empty droplet detection, dimensionality reduction, cell clustering, and type prediction.
Included advanced analyses: pseudotime/trajectory analysis, differential expression, and gene set enrichment analysis.

Main Results:

SingleCAnalyzer enables complete scRNA-Seq analysis from FASTQ files.
The platform provides interactive visualizations for data exploration and analysis.
Handles complex tasks like cell type prediction and trajectory inference.

Conclusions:

SingleCAnalyzer democratizes scRNA-Seq data analysis by lowering bioinformatics barriers.
The platform facilitates reliable and reproducible results through an integrated, user-friendly system.
Freely available at https://singleCAnalyzer.eu, empowering broader research applications.