Format

Send to

Choose Destination
Nat Methods. 2019 Sep;16(9):843-852. doi: 10.1038/s41592-019-0509-5. Epub 2019 Aug 30.

Assessment of network module identification across complex diseases.

Collaborators (155)

Aicheler F, Amoroso N, Arenas A, Azhagesan K, Baker A, Banf M, Batzoglou S, Baudot A, Bellotti R, Bergmann S, Boroevich KA, Brun C, Cai S, Caldera M, Calderone A, Cesareni G, Chen W, Chichester C, Choobdar S, Cowen L, Crawford J, Cui H, Dao P, De Domenico M, Dhroso A, Didier G, Divine M, Del Sol A, Fang T, Feng X, Flores-Canales JC, Fortunato S, Gitter A, Gorska A, Guan Y, Guénoche A, Gómez S, Hamza H, Hartmann A, He S, Heijs A, Heinrich J, Hescott B, Hu X, Hu Y, Huang X, Hughitt VK, Jeon M, Jeub L, Johnson NT, Joo K, Joung I, Jung S, Kalko SG, Kamola PJ, Kang J, Kaveelerdpotjana B, Kim M, Kim YA, Kohlbacher O, Korkin D, Krzysztof K, Kunji K, Kutalik Z, Lage K, Lamparter D, Lang-Brown S, Le TD, Lee J, Lee S, Lee J, Li D, Li J, Lin J, Liu L, Loizou A, Luo Z, Lysenko A, Ma T, Mall R, Marbach D, Mattia T, Medvedovic M, Menche J, Mercer J, Micarelli E, Monaco A, Müller F, Narayan R, Narykov O, Natoli T, Norman T, Park S, Perfetto L, Perrin D, Pirrò S, Przytycka TM, Qian X, Raman K, Ramazzotti D, Ramsahai E, Ravindran B, Rennert P, Saez-Rodriguez J, Schärfe C, Sharan R, Shi N, Shin W, Shu H, Sinha H, Slonim DK, Spinelli L, Srinivasan S, Subramanian A, Suver C, Szklarczyk D, Tangaro S, Thiagarajan S, Tichit L, Tiede T, Tripathi B, Tsherniak A, Tsunoda T, Türei D, Ullah E, Vahedi G, Valdeolivas A, Vivek J, von Mering C, Waagmeester A, Wang B, Wang Y, Weir BA, White S, Winkler S, Xu K, Xu T, Yan C, Yang L, Yu K, Yu X, Zaffaroni G, Zaslavskiy M, Zeng T, Zhang JD, Zhang L, Zhang W, Zhang L, Zhang X, Zhang J, Zhou X, Zhou J, Zhu H, Zhu J, Zuccon G.

Author information

1
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
2
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
3
Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
4
Department of Computer Science, Tufts University, Medford, MA, USA.
5
Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.
6
Verge Genomics, San Francisco, CA, USA.
7
Department of Mathematics, Tufts University, Medford, MA, USA.
8
College of Computer and Information Science, Northeastern University, Boston, MA, USA.
9
Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
10
Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA, USA.
11
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
12
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
13
University Institute of Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
14
Institute for Biological Psychiatry, Mental Health Center Sct. Hans, University of Copenhagen, Roskilde, Denmark.
15
Department of Immunology, Tufts University School of Medicine, Boston, MA, USA.
16
Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, Bioquant, Heidelberg, Germany.
17
RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine, Aachen, Germany.
18
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. sven.bergmann@unil.ch.
19
Swiss Institute of Bioinformatics, Lausanne, Switzerland. sven.bergmann@unil.ch.
20
Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa. sven.bergmann@unil.ch.
21
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. daniel.marbach.dm1@roche.com.
22
Swiss Institute of Bioinformatics, Lausanne, Switzerland. daniel.marbach.dm1@roche.com.
23
Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland. daniel.marbach.dm1@roche.com.

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.

PMID:
31471613
PMCID:
PMC6719725
DOI:
10.1038/s41592-019-0509-5
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center