ArticleNucleic acids research2018
HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- A comprehensive reference catalog of human skin DNA virome reveals novel viral diversity and microenvironmental influences.Microbiology spectrum · 2025Article
- The combination of active partitioning and toxin-antitoxin systems is most advantageous for low-copy plasmid fitness.Nature communications · 2025Article
- Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping.BioData mining · 2025Article
- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- Discovery and Analysis of Repeat and Low-Complexity Architectures in Proteins and Their Conserved Evolutionary Relationships Using Self-Homology Dot Plots.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Visualizing metagenomic and metatranscriptomic data: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- Assembling bacterial puzzles: piecing together functions into microbial pathways.NAR genomics and bioinformatics · 2024Article
- Pseudogenes in plasmid genomes reveal past transitions in plasmid mobility.Nucleic acids research · 2024Article
- The evolution of antibiotic resistance islands occurs within the framework of plasmid lineages.Nature communications · 2024Article
- Insights into phage-bacteria interaction in cold seep Gigantidas platifrons through metagenomics and transcriptome analyses.Scientific reports · 2024Article
- ContScout: sensitive detection and removal of contamination from annotated genomes.Nature communications · 2024Article
- NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes.Nucleic acids research · 2024Article
- Learning from the unknown: exploring the range of bacterial functionality.Nucleic acids research · 2023Article
- Article
- HNetGO: protein function prediction via heterogeneous network transformer.Briefings in bioinformatics · 2023Article
- Origin and functional diversification of PAS domain, a ubiquitous intracellular sensor.Science advances · 2023Article
Corrections and comments
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein-protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL's scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.