Genome-scale human protein–protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein–protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Huttlin, E.L. et al. Cell 162, 425–440 (2015).
Hein, M.Y. et al. Cell 163, 712–723 (2015).
Lage, K. Biochim. Biophys. Acta 1842, 1971–1980 (2014).
Venkatesan, K. et al. Nat. Methods 6, 83–90 (2009).
Stumpf, M.P. et al. Proc. Natl. Acad. Sci. USA 105, 6959–6964 (2008).
Jensen, L.J. & Bork, P. Science 322, 56–57 (2008).
Neale, B.M. et al. Nature 485, 242–245 (2012).
Lundby, A. et al. Nat. Methods 11, 868–874 (2014).
Jostins, L. et al. Nature 491, 119–124 (2012).
Okada, Y. et al. Nature 506, 376–381 (2014).
Morris, A.P. et al. Nat. Genet. 44, 981–990 (2012).
Zack, T.I. et al. Nat. Genet. 45, 1134–1140 (2013).
Khurana, E. et al. Science 342, 1235587 (2013).
Rosenbluh, J. et al. Cell Syst. 3, 302–316 (2016).
Brown, K.R. & Jurisica, I. Bioinformatics 21, 2076–2082 (2005).
Brown, K.R. & Jurisica, I. Genome Biol. 8, R95 (2007).
Calderone, A., Castagnoli, L. & Cesareni, G. Nat. Methods 10, 690–691 (2013).
Razick, S., Magklaras, G. & Donaldson, I.M. BMC Bioinformatics 9, 405 (2008).
Cowley, M.J. et al. Nucleic Acids Res. 40, D862–D865 (2012).
Das, J. & Yu, H. BMC Syst. Biol. 6, 92 (2012).
Kotlyar, M., Pastrello, C., Sheahan, N. & Jurisica, I. Nucleic Acids Res. 44, D536–D541 (2016).
Lawrence, M.S. et al. Nature 505, 495–501 (2014).
Sanders, S.J. et al. Neuron 87, 1215–1233 (2015).
Szklarczyk, D. et al. Nucleic Acids Res. 43, D447–D452 (2015).
Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. Nucleic Acids Res. 41, D793–D800 (2013).
Lee, I., Blom, U.M., Wang, P.I., Shim, J.E. & Marcotte, E.M. Genome Res. 21, 1109–1121 (2011).
Lage, K. et al. Nat. Biotechnol. 25, 309–316 (2007).
Bader, G.D., Betel, D. & Hogue, C.W. Nucleic Acids Res. 31, 248–250 (2003).
Stark, C. et al. Nucleic Acids Res. 34, D535–D539 (2006).
Xenarios, I. et al. Nucleic Acids Res. 30, 303–305 (2002).
Orchard, S. et al. Nucleic Acids Res. 42, D358–D363 (2014).
Launay, G., Salza, R., Multedo, D., Thierry-Mieg, N. & Ricard-Blum, S. Nucleic Acids Res. 43, D321–D327 (2015).
Kandasamy, K. et al. Genome Biol. 11, R3 (2010).
Croft, D. et al. Nucleic Acids Res. also available from (2014).
Kutmon, M. et al. Nucleic Acids Res. 44, D488–D494 (2016).
UniProt Consortium. Nucleic Acids Res. 43, D204–D212 (2015).
Salwinski, L. et al. Nat. Methods 6, 860–861 (2009).
Powell, S. et al. Nucleic Acids Res. 42, D231–D239 (2014).
Cunningham, F. et al. Nucleic Acids Res. 43, D662–D669 (2015).
NCBI Resource Coordinators. Nucleic Acids Res. 43, D6–D17 (2015).
Sonnhammer, E.L. & Östlund, G. Nucleic Acids Res. 43, D234–D239 (2015).
Brown, G.R. et al. Nucleic Acids Res. 43, D36–D42 (2015).
Kriventseva, E.V. et al. Nucleic Acids Res. 43, D250–D256 (2015).
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. Nucleic Acids Res. 44, D457–D462 (2016).
Penel, S. et al. BMC Bioinformatics 10 (Suppl. 6), S3 (2009).
The authors would like to thank L. Wich for developing the graphical user interface for InWeb_IM at https://www.intomics.com/inbio/map. K.L. and H.H. are supported by grant 1P01HD068250, and H.H. is supported by a Fund for Medical Discovery Award from the Executive Committee on Research of Massachusetts General Hospital (project period 02/01/2015-01/31/2016). T.L., H.H., J.M. and K.L. are supported by a grant from the Stanley Center at the Broad Institute of MIT and Harvard (PI: K.L.), a Broadnext10 Grant from the Broad Institute of MIT and Harvard (PI: K.L.), grant 1R01MH109903 from the NIMH (PI: K.L.) and a grant from the Lundbeck Foundation (PI: K.L.). S.B. acknowledges funding from the Novo Nordisk Foundation (grant agreement NNF14CC0001).
R.W., R.B.H. and T.S.J. are employees of Intomics A/S. K.L., S.B., T.S.J. and R.W. are on the scientific advisory board and founders of Intomics A/S with equity in the company. InWeb_IM is a product of Intomics A/S that is freely available to academic users from http://lagelab.org/resources and http://www.intomics.com/inbio/map.
Integrated supplementary information
Details can be seen using the Adobe Zoom Tool and in Text, Figures, and Supplementary Notes as indicated. InWeb_IM is available from https://www.intomics.com/inbiomap and http://www.lagelab.org/resources/ Moreover, the data is accessible from a graphical user interface https://www.intomics.com/inbiomap and http://apps.broadinstitute.org/genets#InWeb_InBiomap so that it can be interactively explored by any individual researcher who wishes to study the interactions of proteins of interest.
Networks are indicated on the x axis and interactions between proteins in which the corresponding genes are involved in a tissue-specific expression quantitative trait locus on the y axis. The analysis is made for 27 tissues as indicated on the top of each panel.
Networks are indicated on the x axis and proteins that have at least one interaction to another protein with a similar tissue-specific expression quantitative trait locus on the y axis. The analysis is made for 27 tissues as indicated on the top of each panel.
Supplementary Figure 4 Tissue-specific interactions across networks in InWeb_IM compared to other networks.
The x-axis represents the 27 different tissue types from GTEx and the y-axis shows the amount of data in InWeb_IM compared to other networks. Light blue bars denote the values for the amount interactions in the next-largest network, dark blue bars denote the values for the amount of proteins covered by data in the network with the next highest count, light green denotes the values for the median amount of interactions across all five comparable networks, and dark green denotes the values for the median amount of proteins covered by interactions across all five comparable networks.
The AUC observed in the network denoted on the x axis is indicated with the blue diamonds (InWeb_IM = 0.65, I2D = 0.58, Mentha = 0.61, iRefIndex = 0.63, PINA = 0.59, HINT = 0.66). The null distribution of AUCs of 120 matched random sets is shown with box whiskers plots. Of these AUCs only the one of InWeb_IM is significant at the Adj. P < 0.05 level.
About this article
Cite this article
Li, T., Wernersson, R., Hansen, R. et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat Methods 14, 61–64 (2017). https://doi.org/10.1038/nmeth.4083
BMC Bioinformatics (2022)
Small RNA sequencing and bioinformatics analysis of RAW264.7-derived exosomes after Mycobacterium Bovis Bacillus Calmette-Guérin infection
BMC Genomics (2022)
BMC Bioinformatics (2022)
Comprehensive bioinformatic analysis reveals a cancer-associated fibroblast gene signature as a poor prognostic factor and potential therapeutic target in gastric cancer
BMC Cancer (2022)
BRCA1 mutations in high-grade serous ovarian cancer are associated with proteomic changes in DNA repair, splicing, transcription regulation and signaling
Scientific Reports (2022)