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

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Published on: October 19, 2021

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks.

Ariful Azad¹, Georgios A Pavlopoulos², Christos A Ouzounis³

¹Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720-8150, USA.

Nucleic Acids Research

|January 10, 2018

Summary

High-performance Markov Clustering (HipMCL) accelerates biological network analysis by enabling parallel processing on distributed-memory systems. This approach significantly enhances scalability for clustering large biological networks, overcoming previous computational bottlenecks.

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Area of Science:

Bioinformatics
Computational Biology
Network Science

Background:

Biological networks represent functional and structural relationships between entities like genes and proteins.
Existing clustering algorithms, including Markov Clustering (MCL), struggle with the scalability demands of large biological datasets.
The increasing volume of biological data necessitates more efficient network analysis tools.

Purpose of the Study:

To develop a high-performance, scalable implementation of the Markov Clustering algorithm.
To address the computational bottlenecks (running time and memory) of the original MCL algorithm for large-scale biological networks.
To enable the analysis of significantly larger biological networks than previously feasible.

Main Methods:

Implementation of a parallel version of the Markov Clustering algorithm named High-performance MCL (HipMCL).
Utilized distributed-memory computing environments leveraging MPI and OpenMP.
Tested HipMCL on large-scale biological networks to evaluate performance and scalability.

Main Results:

HipMCL demonstrated efficient utilization of up to 2000 compute nodes.
Successfully clustered a network of approximately 70 million nodes and 68 billion edges in about 2.4 hours.
Achieved clustering speeds several orders of magnitude faster than the original MCL algorithm.

Conclusions:

HipMCL significantly enhances the scalability of Markov Clustering for large biological networks.
The parallel implementation enables the analysis of massive datasets, previously intractable.
HipMCL provides a powerful, freely available tool for biological network clustering.