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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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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

scHLens: a web server for hierarchically and interactively exploring single cell RNA-seq data.

Jiazhi Xia¹, Zhiwei Deng¹, Chen He¹

¹School of Computer Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083, China.

Briefings in Bioinformatics

|December 17, 2025

Summary

This summary is machine-generated.

scHLens is a new web server that helps researchers analyze single-cell RNA sequencing (scRNA-seq) data to identify cellular heterogeneity. It offers a flexible, hierarchical approach, overcoming limitations of existing computational tools for biological discovery.

Keywords:

cell type identification custom pipeline hierarchical exploration scRNA-seq web app

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Last Updated: Jan 8, 2026

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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Published on: July 16, 2017

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Published on: June 28, 2018

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Existing analysis methods present challenges including technical barriers, lack of workflow flexibility, and difficulty in detecting hierarchical subtypes.
Addressing these limitations is vital for advancing biomedical research.

Purpose of the Study:

To develop a user-friendly, interactive web server for scRNA-seq data analysis.
To provide a flexible and hierarchical approach for identifying cellular heterogeneity.
To overcome the limitations of existing computational tools in scRNA-seq analysis.

Main Methods:

Development of a hierarchical and interactive web server named scHLens.
Implementation of user-defined analysis pipelines and hierarchical exploration modes.
Integration of various visualization views and interaction operations for data exploration.

Main Results:

scHLens successfully demonstrates its capability in identifying cellular heterogeneity through three case studies.
The web server offers a flexible and hierarchical approach, overcoming limitations of conventional clustering methods.
Provides accessible analysis for researchers lacking computational expertise.

Conclusions:

scHLens provides an effective and accessible solution for scRNA-seq data analysis.
The tool facilitates the identification of cellular heterogeneity and hierarchical subtypes.
Enhances the usability and flexibility of bioinformatics tools for biomedical researchers.