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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: Mar 2, 2026

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Computational approaches for interpreting scRNA-seq data.

Raghd Rostom¹, Valentine Svensson², Sarah A Teichmann¹

¹Wellcome Trust Sanger Institute, Cambridge, UK.

|May 20, 2017

Summary

This summary is machine-generated.

High-throughput single-cell RNA sequencing (scRNA-seq) generates extensive cellular data. This review covers computational analysis tools for cell and gene-level insights, including clustering and pseudotime inference.

Keywords:

single-cell analysis methods and tools single-cell genomics

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Last Updated: Mar 2, 2026

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

High-throughput single-cell RNA sequencing (scRNA-seq) provides unprecedented cellular resolution transcriptomic data.
Advances in scRNA-seq necessitate sophisticated computational analysis techniques for high-dimensional data mining.

Purpose of the Study:

To review biological questions addressable by scRNA-seq data.
To describe available computational tools for analyzing scRNA-seq data at both cell and gene levels.

Main Methods:

Exploration of data mining techniques applicable to high-dimensional transcriptomic datasets.
Identification and categorization of computational tools for specific scRNA-seq analyses.

Main Results:

scRNA-seq data analysis involves addressing biological questions at cellular and gene expression levels.
Key computational analysis areas include clustering, pseudotime inference, branching inference, and gene-level analyses.

Conclusions:

scRNA-seq is a rapidly evolving field with significant potential for biological discovery.
Computational tools are crucial for extracting meaningful insights from complex scRNA-seq datasets.