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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: Jul 6, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Clustering gene expression data using graph separators.

Bangaly Kaba¹, Nicolas Pinet, Gaëlle Lelandais

¹LIMOS, UMR CNRS 6158, Ensemble des Cézeaux, 63173 Aubière cedex, France. kaba@isima.fr.

In Silico Biology

|April 9, 2008

Summary

This summary is machine-generated.

This study introduces a novel graph-based clustering method for gene expression data, improving gene partitioning and revealing biological insights from yeast data.

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Last Updated: Jul 6, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Gene expression data from microarray experiments are often clustered to identify groups of genes with similar functions.
Classical clustering methods may impose artificial constraints, such as non-overlapping clusters, which may not reflect biological reality.

Purpose of the Study:

To improve gene expression data clustering by allowing overlapping clusters and using graph structure to determine the number of clusters.
To apply a novel clique separator decomposition method to gene expression data analysis.

Main Methods:

Utilized a graph-based approach to model gene expression data.
Introduced decomposition by clique separators for cluster analysis.
Applied the method to the Saccharomyces cerevisiae (yeast) database.
Organized identified clusters into a secondary graph for further analysis.

Main Results:

The proposed method successfully partitioned genes into coherent clusters based on expression profiles.
The number of clusters was effectively determined by the graph's structure.
The identified clusters could be organized and ordered, reflecting the chronological order of the yeast sporulation process.

Conclusions:

The clique separator decomposition method offers a biologically sound approach to gene expression clustering, allowing for overlapping clusters.
This method enhances the interpretation of gene expression data by leveraging graph structures and revealing temporal relationships, as demonstrated in yeast sporulation.