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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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Updated: Jun 15, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

CDCA: Community detection in RNA-seq data using centrality-based approach.

Tonmoya Sarmah¹, Dhruba K Bhattacharyya

¹Department of Computer Science and Engineering, Tezpur University, Napaam, Tezpur 784 028, India.

Journal of Biosciences

|August 28, 2024

Summary

This summary is machine-generated.

This study introduces a new community detection method (CDCA) for biological networks. CDCA effectively identifies gene groups in brain disorders, offering superior performance for disease insights.

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Area of Science:

Bioinformatics
Network Analysis
Computational Biology

Background:

Community detection is crucial for network analysis, especially in biological networks like gene co-expression networks.
Identifying gene communities aids in downstream analysis for understanding diseases.

Purpose of the Study:

To present an effective community detection method called community detection using centrality-based approach (CDCA).
To evaluate CDCA's performance on benchmark datasets for schizophrenia and bipolar disorder.

Main Methods:

Developed CDCA based on graph centrality.
Tested CDCA using four benchmark bulk RNA-seq datasets.
Assessed community quality using modularity and homogeneity.
Determined biological significance via pathway enrichment analysis.

Main Results:

CDCA demonstrated superior performance compared to existing methods.
The method successfully identified biologically relevant gene communities.
Pathway enrichment analysis confirmed the significance of the detected communities.

Conclusions:

CDCA is an effective method for community detection in biological networks.
The approach provides valuable insights into the genetic underpinnings of schizophrenia and bipolar disorder.