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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: Dec 12, 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

Comparison of visualization tools for single-cell RNAseq data.

Batuhan Cakir¹, Martin Prete¹, Ni Huang¹

¹Wellcome Sanger Institute, Hinxton, CB10 1SA, UK.

NAR Genomics and Bioinformatics

|August 9, 2020

Summary

This summary is machine-generated.

This study benchmarks web-based single-cell RNA sequencing (scRNAseq) visualization tools, assessing their performance with increasing dataset sizes. An R package, sceasy, was developed to improve data format compatibility for sharing scRNAseq data.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNAseq) datasets have rapidly expanded in scale.
High dimensionality of scRNAseq data poses challenges for traditional visualization and sharing in publications.
Interactive tools are emerging to facilitate knowledge transfer in single-cell genomics.

Purpose of the Study:

To review and benchmark web-based tools for visualizing and sharing scRNAseq data.
To evaluate the performance of these tools concerning dataset size, memory, and time requirements.
To assess user experience and features of available web interfaces.

Main Methods:

Benchmarking of selected scRNAseq web visualization tools.
Evaluation of data preparation time and memory usage with increasing cell numbers.
Assessment of user interface features and overall user experience.
Development of the R package 'sceasy' for data format conversion.

Main Results:

Identified and benchmarked a subset of scRNAseq visualization tools suitable for web-based sharing.
Quantified the impact of dataset size on data preparation time and memory consumption.
Assessed the usability and feature set of various web visualization interfaces.
Demonstrated the utility of 'sceasy' for overcoming data format compatibility issues.

Conclusions:

Web-based visualization tools offer a viable solution for sharing large scRNAseq datasets.
'sceasy' simplifies data preparation for visualization, enhancing accessibility.
Performance and user experience vary among tools, guiding selection for specific research needs.