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RNA-seq

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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: Sep 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

CytoAnalyst web platform facilitates comprehensive single cell RNA sequencing analysis.

Phi Bya¹, Duy Tran¹, Khoi Nguyen¹

¹Department of Computer Science and Software Engineering, Auburn University, Auburn, 36849, AL, USA.

Scientific Reports

|August 6, 2025

Summary

This summary is machine-generated.

CytoAnalyst is a web platform simplifying single-cell data analysis. It offers custom pipelines, parallel analysis, and advanced visualization for robust, collaborative research.

Keywords:

AI-guided cell annotation Cell type annotation Clustering analysis Embedding analysis Interactive visualization Single-cell analysis

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Last Updated: Sep 12, 2025

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Area of Science:

Computational Biology
Bioinformatics
Data Science

Background:

Single-cell technologies offer high-resolution insights into cellular heterogeneity and dynamics.
Analyzing large single-cell datasets is complex, requiring extensive collaboration and custom pipelines.
Existing tools often lack the flexibility and integration needed for comprehensive analysis.

Purpose of the Study:

To introduce CytoAnalyst, a web-based platform designed to streamline single-cell data analysis.
To provide a flexible, user-friendly solution for custom pipeline configuration, parallel analysis, and data visualization.
To facilitate collaboration and improve the efficiency of analyzing large-scale single-cell datasets.

Main Methods:

Development of a web-based platform with a study management system and diverse analysis modules.
Implementation of parallel analysis instances for method and parameter comparison.
Integration of an advanced sharing system for real-time synchronization and cross-device analysis continuation.
Design of a grid-layout visualization system supporting simultaneous data aspect displays and customizable plot blending.

Main Results:

CytoAnalyst enables custom pipeline configuration and supports parallel analysis for method comparison.
The platform facilitates real-time collaboration and seamless analysis continuation across devices.
Advanced visualization tools allow for side-by-side comparison of multiple data aspects and plot blending.
The platform offers high analytical rigor with a user-friendly interface and comprehensive documentation.

Conclusions:

CytoAnalyst addresses the challenges of single-cell data analysis by providing a flexible and collaborative platform.
The integrated tools enhance analytical rigor and efficiency for researchers working with large single-cell datasets.
CytoAnalyst is freely available and supports all major web browsers, promoting wider accessibility.