Mattia Tomasoni1,2, Sergio Gómez3, Jake Crawford4,5, Weijia Zhang6, Sarvenaz Choobdar1,2, Daniel Marbach1,2,7, Sven Bergmann1,2,8. 1. Department of Computational Biology, University of Lausanne. 2. Swiss Institute of Bioinformatics, Lausanne, Switzerland. 3. Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain. 4. Department of Computer Science, Tufts University, MA. 5. Graduate Group in Genomics and Computational Biology Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 6. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia. 7. Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, 4070 Basel, Switzerland. 8. Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa.
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.
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.
Authors: Sarvenaz Choobdar; Mehmet E Ahsen; Jake Crawford; Mattia Tomasoni; Tao Fang; David Lamparter; Junyuan Lin; Benjamin Hescott; Xiaozhe Hu; Johnathan Mercer; Ted Natoli; Rajiv Narayan; Aravind Subramanian; Jitao D Zhang; Gustavo Stolovitzky; Zoltán Kutalik; Kasper Lage; Donna K Slonim; Julio Saez-Rodriguez; Lenore J Cowen; Sven Bergmann; Daniel Marbach Journal: Nat Methods Date: 2019-08-30 Impact factor: 28.547
Authors: Erik C B Johnson; E Kathleen Carter; Eric B Dammer; Duc M Duong; Ekaterina S Gerasimov; Yue Liu; Jiaqi Liu; Ranjita Betarbet; Lingyan Ping; Luming Yin; Geidy E Serrano; Thomas G Beach; Junmin Peng; Philip L De Jager; Vahram Haroutunian; Bin Zhang; Chris Gaiteri; David A Bennett; Marla Gearing; Thomas S Wingo; Aliza P Wingo; James J Lah; Allan I Levey; Nicholas T Seyfried Journal: Nat Neurosci Date: 2022-02-03 Impact factor: 28.771