DOMINO: a network‐based active module identification algorithm with reduced rate of false calls

Levi, Hagai; Elkon, Ran; Shamir, Ron

doi:10.15252/msb.20209593

Cited by 49 publications

(62 citation statements)

References 51 publications

(64 reference statements)

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“…This makes the workflow for lipid-metabolic networks fundamentally different to working with PPI or GR networks. Dedicated algorithms such as KeyPathwayMiner [ 3 , 4 ], DOMINO [ 5 ] or HotNet2 [ 6 ] perform an enrichment of deregulated genes on the whole network of possible interactions. However, with lipid species networks, the networks themselves carry information about the composition of the lipidome and its associations.…”

Section: Discussionmentioning

confidence: 99%

“…In the case of lipid metabolic networks, these characterize transformations of lipids catalyzed by enzymes. Dedicated bioinformatics tools such as KeyPathwayMiner [ 3 , 4 ], DOMINO [ 5 ] or HotNet2 [ 6 ] have been developed, which extract functionally associated network modules enriched with deregulated genes/proteins from PPI networks in a case/control setting. Such network modules can hint towards biochemical mechanisms, which connect a phenotype to its underlying molecular machinery.…”

Section: Introductionmentioning

confidence: 99%

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Investigating Global Lipidome Alterations with the Lipid Network Explorer

et al. 2021

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Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway information remains challenging, leaving lipidomics behind compared to other omics disciplines. An especially uncharted territory is the integration of statistical and network-based approaches for studying global lipidome changes. Here we developed the Lipid Network Explorer (LINEX), a web-tool addressing this gap by providing a way to visualize and analyze functional lipid metabolic networks. It utilizes metabolic rules to match biochemically connected lipids on a species level and combine it with a statistical correlation and testing analysis. Researchers can customize the biochemical rules considered, to their tissue or organism specific analysis and easily share them. We demonstrate the benefits of combining network-based analyses with statistics using publicly available lipidomics data sets. LINEX facilitates a biochemical knowledge-based data analysis for lipidomics. It is availableas a web-application and as a publicly available docker container.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating Global Lipidome Alterations with the Lipid Network Explorer

et al. 2021

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“…In addition, since the aggregated network is denser than any of the individual network layers, the chances of connecting high-scoring nodes is also higher. In the future, a benchmark based on biological proxies, using for instance Gene Ontology annotation enrichments [ 48 ], as recently proposed in the DOMINO approach [ 49 ], could be used to compare the active modules obtained from aggregated versus multiplex networks.…”

Section: Discussionmentioning

confidence: 99%

A multi-objective genetic algorithm to find active modules in multiplex biological networks

Novoa-Del-Toro

Mezura-Montes²,

Vignes³

et al. 2021

PLoS Comput Biol

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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

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“…Defining a network-only baseline is a more subtle issue, and was discussed in two recent papers [68, 67]. Levi et al [68] benchmarks altered subnetwork algorithms on randomly permuted vertex scores while keeping the network G fixed.…”

Section: Methodsmentioning

confidence: 99%

NetMix2: Unifying network propagation and altered subnetworks

Chitra

Park

Raphael

2022

Preprint

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A standard paradigm in computational biology is to use interaction networks to analyze high-throughput biological data. Two common approaches for leveraging interaction networks are: (1) network ranking, where one ranks vertices in the network according to both vertex scores and network topology; (2) altered subnetwork identification, where one identifies one or more subnetworks in an interaction network using both vertex scores and network topology. The dominant approach in network ranking is network propagation which smooths vertex scores over the network using a random walk or diffusion process, thus utilizing the global structure of the network. For altered subnetwork identification, existing algorithms either restrict solutions to subnetworks in subnetwork families with simple topological constraints, such as connected subnetworks, or utilize ad hoc heuristics that lack a rigorous statistical foundation. In this work, we unify the network propagation and altered subnetwork approaches. We derive a subnetwork family which we call the propagation family that approximates the subnetworks ranked highly by network propagation. We introduce NetMix2, a principled algorithm for identifying altered subnetworks from a wide range of subnetwork families, including the propagation family, thus combining the advantages of the network propagation and altered subnetwork approaches. We show that NetMix2 outperforms network propagation on data simulated using the propagation family. Furthermore, NetMix2 outperforms other methods at recovering known disease genes in pan-cancer somatic mutation data and in genome-wide association data from multiple human diseases. NetMix2 is publicly available at https://github.com/raphael-group/netmix2.

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DOMINO: a network‐based active module identification algorithm with reduced rate of false calls

Cited by 49 publications

References 51 publications

Investigating Global Lipidome Alterations with the Lipid Network Explorer

Investigating Global Lipidome Alterations with the Lipid Network Explorer

A multi-objective genetic algorithm to find active modules in multiplex biological networks

NetMix2: Unifying network propagation and altered subnetworks

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