Abstract
The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.
Highlights
The success of functional genomics is associated with the massive production of quantitative information related to genes, proteins or other macromolecules
We demonstrate the performance of MOGAMUN over state-of-the-art methods, and illustrate its usefulness in unveiling perturbed biological processes in Facio-ScapuloHumeral muscular Dystrophy
JActiveModules can filter such set of nodes, in order to keep the top-scoring single connected component(s). Methods such as COSINE [11], the algorithm proposed by Muraro et al [12], the one proposed by Ozisik et al [13] or the one proposed by Chen et al [5], are all based on genetic algorithms
Summary
The success of functional genomics is associated with the massive production of quantitative information related to genes, proteins or other macromolecules These data include, for instance, -omics molecular profiles measuring the expression or activity of thousands of genes/proteins, sensitivity scores resulting from RNA interference or CRISPR screenings, and GWAS scores providing significance of association between genes and phenotypic traits. These scores and measurements, often presented as p-values, intend to inform on the cellular responses associated to different cellular contexts. Many tools exist that can take as input a list of genes, selected after defining a threshold for significance or ranked according to their p-values [1] Such enrichment approaches will consider only the genes/proteins annotated in databases. These active modules facilitate the investigation of the perturbed cellular responses, as functional modules are the building blocks of cellular processes and pathways [2]
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