Assessment of network module identification across complex diseases

Research output: Contribution to journalArticlepeer-review

Authors

  • The DREAM Module Identification Challenge Consortium

Colleges, School and Institutes

External organisations

  • University of Lausanne
  • Swiss Institute of Bioinformatics
  • Icahn School of Medicine at Mount Sinai
  • Tufts University
  • F Hoffmann La Roche Ltd
  • Verge Genomics
  • Northeastern University
  • Harvard Medical School, Wellman Center for Photomedicine, Massachusetts General Hospital
  • Broad Institute
  • Universitat Tubingen
  • Universita degli Studi di Bari
  • Sezione di Bari
  • Universitat Rovira i Virgili
  • IIT Madras
  • University of Wisconsin, Madison, WI, USA
  • Morgridge Institute for Research
  • Carnegie Institution for Science
  • Stanford University, Stanford, California 94305, USA
  • CNRS/Aix-Marseille Universite
  • Centro TIRES
  • RIKEN
  • TAGC
  • CNRS-CEA
  • Perelman School of Medicine, University of Pennsylvania
  • Österreichische Akademie der Wissenschaften
  • University of Rome Tor Vergata
  • Nestlé Product Technology Centre
  • Worcester Polytechnic Institute
  • National Institute of Health
  • Fondazione Bruno Kessler
  • University of Luxembourg
  • CIC BioGUNE
  • IKERBASQUE. Basque Foundation for Science
  • University of Cincinnati
  • Korea Institute for Advanced Study
  • Indiana University

Abstract

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Bibliographic note

Funding Information: The challenge was hosted on Sage Bionetwork’s Synapse platform (https://synapse.org/). The computations were performed at the Vital-IT (http://www.vital-it.ch) Center for high-performance computing of the SIB Swiss Institute of Bioinformatics. This work was supported by the Swiss National Science Foundation (grant no. FN 310030_152724/1 to S.B. and grant no. FN 31003A-169929 to Z.K.), SystemsX.ch (grant no. SysGenetiX to S.B. and grant no. AgingX to Z.K.), the Swiss Institute of Bioinformatics (Z.K. and S.B.), the US National Science Foundation (grant no. DMS-1812503 to L.C. and X.H.) and the National Institutes of Health (grant no. R01 HD076140 to D.K.S.). Publisher Copyright: © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

Details

Original languageEnglish
Pages (from-to)843-852
Number of pages10
JournalNature Methods
Volume16
Issue number9
Publication statusPublished - 1 Sep 2019