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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 microarray-based...

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Updated: Jun 16, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Interpretable scRNA-seq Analysis with Intelligent Gene Selection.

Xinyu Zhang¹, Jiadai Xu², Kaixiu Jin³

¹Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.

Applied Biochemistry and Biotechnology

|June 13, 2026

Summary

This summary is machine-generated.

AIGS is a new framework for single-cell RNA sequencing analysis. It improves gene selection and cell similarity measurement for more accurate clustering and visualization of complex gene expression patterns.

Keywords:

Clustering Gene selection Single-cell analysis Visualization

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Last Updated: Jun 16, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Published on: June 28, 2018

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In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

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Published on: March 29, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) data analysis presents challenges like high dimensionality, noise, and data loss.
Existing methods struggle to accurately cluster cells and visualize complex gene expression patterns.

Purpose of the Study:

To introduce AIGS, a robust and transparent framework for scRNA-seq data analysis.
To enhance clustering accuracy and multi-resolution visualization quality in scRNA-seq data.
To uncover complex, stage-specific gene expression patterns.

Main Methods:

AIGS employs an intelligent gene selection method using normalized mutual information.
It utilizes a scale-invariant distance metric for assessing cell-to-cell similarity.
The framework was compared against state-of-the-art techniques.

Main Results:

AIGS demonstrated superior performance in clustering accuracy compared to existing methods.
The framework achieved higher quality in multi-resolution visualization.
AIGS successfully uncovered complex, stage-specific gene expression patterns.

Conclusions:

AIGS offers a robust and transparent solution for scRNA-seq data analysis challenges.
The framework enhances the ability to identify and visualize cellular heterogeneity.
AIGS facilitates deeper insights into developmental cell biology and gene expression dynamics.