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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: Nov 26, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Published on: March 7, 2018

Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data.

Tallulah S Andrews¹, Vladimir Yu Kiselev¹, Davis McCarthy^2,3

¹Wellcome Sanger Institute, Hinxton, UK.

Nature Protocols

|December 8, 2020

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) generates vast datasets requiring specialized computational analysis. This guide overviews scRNA-seq data processing workflows and tools for biological insights.

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Last Updated: Nov 26, 2025

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables whole transcriptome profiling of individual cells.
Analyzing large scRNA-seq datasets necessitates advanced statistical and computational methodologies.

Purpose of the Study:

To provide an overview of the computational workflow for processing scRNA-seq data.
To discuss common tasks and available tools for addressing biological questions using scRNA-seq.
To offer guidelines on best practices for computational analysis of scRNA-seq data.

Main Methods:

Review of computational workflows for scRNA-seq data processing.
Identification and discussion of common analytical tasks and tools.
Development of best practice guidelines for computational analysis.

Main Results:

An overview of the computational pipeline for scRNA-seq data analysis.
A curated list of tools and methods for common scRNA-seq tasks.
Guidelines for best practices in computational analysis.

Conclusions:

Effective computational analysis is crucial for extracting biological insights from scRNA-seq data.
This resource serves as a guide for experimentalists and bioinformaticians working with scRNA-seq data.