MONET: a toolbox integrating top-performing methods for network modularization

Abstract

Summary We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the ‘Disease Module Identification (DMI) DREAM Challenge’, a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community. Availability and implementation MONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git. Supplementary information Supplementary data are available at Bioinformatics online.

Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level

“…Because different network clustering algorithms can produce disparate networks, we tested the robustness of the TMT consensus network generated by the WGCNA algorithm by also generating a co-expression network using an independent algorithm-the MONET M1 algorithm. MONET M1 was identified as one of the top performers in the Disease Module Identification DREAM Challenge and is based on a modularity optimization algorithm rather than the hierarchical clustering approach used in WGCNA 18,19 . We found that all 44 WGCNA modules were highly preserved in the MONET M1 network (Extended Data Fig.…”

“…The three top-performing methods from the Disease Module Identification DREAM Challenge were compiled in the MONET toolbox and released to the public for use (https://github.com/BergmannLab/MONET. git) 19 . We selected the M1 method from this toolbox as a complementary network analysis method to explore the AD TMT network.…”

Large-Scale Deep Multi-Layer Analysis of Alzheimer’s Disease Brain Reveals Strong Proteomic Disease-Related Changes Not Observed at the RNA Level

“…Because different network clustering algorithms can produce disparate networks, we tested the robustness of the TMT consensus network generated by the WGCNA algorithm by also generating a co-expression network using an independent algorithm—the MONET M1 algorithm. MONET M1 was identified as one of the top performers in the Disease Module Identification DREAM challenge, and is based on a modularity optimization algorithm rather than the hierarchical clustering approach used in WGCNA 22, 23 . We found that all 44 WGCNA modules were highly preserved in the MONET M1 network ( Supplementary Figure 2B ), demonstrating the robustness of the TMT consensus network to clustering algorithm.…”

“…The three top-performing methods from the DMI DREAM Challenge were compiled in the MONET toolbox and released to the public for use (https://github.com/BergmannLab/MONET.git) 23 . We selected the M1 method from this toolbox as a complimentary network analysis method to explore the AD TMT network.…”

A review of COVID-19 biomarkers and drug targets: resources and tools

“…This approach has been employed by [ 93 ] to score a set of drugs based on a the similarity of diffusion states between each drug target node-set and COVID-19 target nodes. This methods can be easily implemented using the diffusion state distance (DSD) tool available in the MONET toolbox [ 96 ]…”