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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network

Anindya Bhattacharya^1,2,3, Yan Cui^4,5

¹Department of Microbiology, Immunology and Biochemistry, Memphis, TN, 38163, USA. anindyamail123@gmail.com.

Scientific Reports

|June 25, 2017

Summary

This summary is machine-generated.

We developed a GPU-accelerated biclustering algorithm called Condition-dependent Correlation Subgroups (CCS) to find gene groups with similar expression patterns. CCS outperforms existing methods on gene expression data analysis.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Analyzing large-scale gene expression data requires identifying genes with similar expression patterns.
Existing biclustering algorithms struggle with comprehensive discovery of functionally coherent biclusters in large datasets.

Purpose of the Study:

To propose a novel GPU-accelerated biclustering algorithm, Condition-dependent Correlation Subgroups (CCS).
To enhance the discovery of functionally coherent biclusters from large gene expression datasets.

Main Methods:

Developed a GPU-accelerated biclustering algorithm based on Condition-dependent Correlation Subgroups (CCS).
Implemented the algorithm using C and CUDA C for GPU computing.
Compared CCS performance against thirteen established biclustering algorithms.

Main Results:

CCS demonstrated superior performance compared to thirteen widely used biclustering algorithms.
The algorithm achieved consistent outperformance on both synthetic and real gene expression datasets.
CCS effectively identifies condition-dependent coexpression network modules.

Conclusions:

The proposed GPU-accelerated CCS algorithm offers an effective solution for biclustering large gene expression datasets.
CCS provides a robust method for discovering functionally coherent gene groups and coexpression modules.
The algorithm's performance surpasses existing biclustering techniques.