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

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),

SUBMITTER: Azad A 

PROVIDER: S-EPMC5888241 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature