Using MCL to extract clusters from networks.

By Stijn van Dongen, Cei Abreu-Goodger. Methods in molecular biology, 2012

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Gateways to the FANTOM5 promoter level mammalian expression atlas (2015, methodology): “For further in-depth examination, users can activate the clustering option based on the Markov Cluster Algorithm (MCL) [54] and adjust the parameters in order to obtain co-expression classes, or clusters, of samples sharing similar patterns in expression.”
Linking pangenomes and metagenomes: the Prochlorococcus metapangenome (2018, methodology): “…described in ITEP ( Benedict et al., 2014 ), to filter weak hits based on the aligned fraction between the two reads, (3) uses the MCL algorithm ( Van Dongen & Abreu-Goodger, 2012 ) to identify gene clusters in the remaining blastp search results, (4) computes the occurrence of gene clusters…”
Orthology Inference in Nonmodel Organisms Using Transcriptomes and Low-Coverage Genomes: Improving Accuracy and Matrix Occupancy for Phylogenomics (2014, methodology): “MCL (MCL v12-068; van Dongen 2000; Enright et al. 2002; van Dongen and Abreu-Goodger 2012) was performed on filtered all-by-all BLASTP hits, with the E value cutoff set to 10 5 and an inflation value of 1.4.”
AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential (2019, methodology): “All distances at various thresholds were used as input for Markov clustering using the MCL application (44) to assign unique clan IDs.”