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A scored human protein–protein interaction network to catalyze genomic interpretation

Abstract

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.

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Figure 1: A quantitative comparison of InWeb_IM and five widely used human protein–protein interaction networks.
Figure 2: Validating the InWeb_IM score and comparing its biological signal to those of five other networks.

References

  1. Huttlin, E.L. et al. Cell 162, 425–440 (2015).

    CAS  Article  Google Scholar 

  2. Hein, M.Y. et al. Cell 163, 712–723 (2015).

    CAS  Article  Google Scholar 

  3. Lage, K. Biochim. Biophys. Acta 1842, 1971–1980 (2014).

    CAS  Article  Google Scholar 

  4. Venkatesan, K. et al. Nat. Methods 6, 83–90 (2009).

    CAS  Article  Google Scholar 

  5. Stumpf, M.P. et al. Proc. Natl. Acad. Sci. USA 105, 6959–6964 (2008).

    CAS  Article  Google Scholar 

  6. Jensen, L.J. & Bork, P. Science 322, 56–57 (2008).

    CAS  Article  Google Scholar 

  7. Neale, B.M. et al. Nature 485, 242–245 (2012).

    CAS  Article  Google Scholar 

  8. Lundby, A. et al. Nat. Methods 11, 868–874 (2014).

    CAS  Article  Google Scholar 

  9. Jostins, L. et al. Nature 491, 119–124 (2012).

    CAS  Article  Google Scholar 

  10. Okada, Y. et al. Nature 506, 376–381 (2014).

    CAS  Article  Google Scholar 

  11. Morris, A.P. et al. Nat. Genet. 44, 981–990 (2012).

    CAS  Article  Google Scholar 

  12. Zack, T.I. et al. Nat. Genet. 45, 1134–1140 (2013).

    CAS  Article  Google Scholar 

  13. Khurana, E. et al. Science 342, 1235587 (2013).

    Article  Google Scholar 

  14. Rosenbluh, J. et al. Cell Syst. 3, 302–316 (2016).

    CAS  Article  Google Scholar 

  15. Brown, K.R. & Jurisica, I. Bioinformatics 21, 2076–2082 (2005).

    CAS  Article  Google Scholar 

  16. Brown, K.R. & Jurisica, I. Genome Biol. 8, R95 (2007).

    Article  Google Scholar 

  17. Calderone, A., Castagnoli, L. & Cesareni, G. Nat. Methods 10, 690–691 (2013).

    CAS  Article  Google Scholar 

  18. Razick, S., Magklaras, G. & Donaldson, I.M. BMC Bioinformatics 9, 405 (2008).

    Article  Google Scholar 

  19. Cowley, M.J. et al. Nucleic Acids Res. 40, D862–D865 (2012).

    CAS  Article  Google Scholar 

  20. Das, J. & Yu, H. BMC Syst. Biol. 6, 92 (2012).

    Article  Google Scholar 

  21. Kotlyar, M., Pastrello, C., Sheahan, N. & Jurisica, I. Nucleic Acids Res. 44, D536–D541 (2016).

  22. Lawrence, M.S. et al. Nature 505, 495–501 (2014).

    CAS  Article  Google Scholar 

  23. Sanders, S.J. et al. Neuron 87, 1215–1233 (2015).

    CAS  Article  Google Scholar 

  24. Szklarczyk, D. et al. Nucleic Acids Res. 43, D447–D452 (2015).

    CAS  Article  Google Scholar 

  25. Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. Nucleic Acids Res. 41, D793–D800 (2013).

    CAS  Article  Google Scholar 

  26. Lee, I., Blom, U.M., Wang, P.I., Shim, J.E. & Marcotte, E.M. Genome Res. 21, 1109–1121 (2011).

    CAS  Article  Google Scholar 

  27. Lage, K. et al. Nat. Biotechnol. 25, 309–316 (2007).

    CAS  Article  Google Scholar 

  28. Bader, G.D., Betel, D. & Hogue, C.W. Nucleic Acids Res. 31, 248–250 (2003).

    CAS  Article  Google Scholar 

  29. Stark, C. et al. Nucleic Acids Res. 34, D535–D539 (2006).

    CAS  Article  Google Scholar 

  30. Xenarios, I. et al. Nucleic Acids Res. 30, 303–305 (2002).

    CAS  Article  Google Scholar 

  31. Orchard, S. et al. Nucleic Acids Res. 42, D358–D363 (2014).

    CAS  Article  Google Scholar 

  32. Launay, G., Salza, R., Multedo, D., Thierry-Mieg, N. & Ricard-Blum, S. Nucleic Acids Res. 43, D321–D327 (2015).

    CAS  Article  Google Scholar 

  33. Kandasamy, K. et al. Genome Biol. 11, R3 (2010).

    Article  Google Scholar 

  34. Croft, D. et al. Nucleic Acids Res. also available from (2014).

  35. Kutmon, M. et al. Nucleic Acids Res. 44, D488–D494 (2016).

    CAS  Article  Google Scholar 

  36. UniProt Consortium. Nucleic Acids Res. 43, D204–D212 (2015).

  37. Salwinski, L. et al. Nat. Methods 6, 860–861 (2009).

    CAS  Article  Google Scholar 

  38. Powell, S. et al. Nucleic Acids Res. 42, D231–D239 (2014).

    CAS  Article  Google Scholar 

  39. Cunningham, F. et al. Nucleic Acids Res. 43, D662–D669 (2015).

    CAS  Article  Google Scholar 

  40. NCBI Resource Coordinators. Nucleic Acids Res. 43, D6–D17 (2015).

  41. Sonnhammer, E.L. & Östlund, G. Nucleic Acids Res. 43, D234–D239 (2015).

    CAS  Article  Google Scholar 

  42. Brown, G.R. et al. Nucleic Acids Res. 43, D36–D42 (2015).

    CAS  Article  Google Scholar 

  43. Kriventseva, E.V. et al. Nucleic Acids Res. 43, D250–D256 (2015).

    CAS  Article  Google Scholar 

  44. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. Nucleic Acids Res. 44, D457–D462 (2016).

    CAS  Article  Google Scholar 

  45. Penel, S. et al. BMC Bioinformatics 10 (Suppl. 6), S3 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

Authors

Contributions

R.W., R.B.H. and T.S.J. developed the computational framework and continue to maintain InWeb_IM. T.L. led and executed the benchmarking analyses and network comparisons with input from R.W., R.B.H., H.H. and J.M. and supervision by K.L., T.L., R.W., R.B.H., H.H., J.M., G.S., C.T.W., O.R., K.R., H.H.S., S.B., T.S.J. and K.L. analyzed data. T.L., R.W., R.B.H., T.S. and K.L. wrote manuscript with input from all authors. K.L. initiated, designed, and led the study.

Corresponding authors

Correspondence to Thomas S Jensen or Kasper Lage.

Ethics declarations

Competing interests

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

Supplementary Figure 1 The computational framework leading to InWeb_IM.

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.

Supplementary Figure 2 Tissue-specific interaction counts.

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.

Supplementary Figure 3 Proteins covered by tissue-specific interactions.

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.

Supplementary Figure 5 The AUCs derived from applying NMB in autism.

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.

Supplementary Figure 6 Roadmap for updates of the InWeb_IM data.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Notes 1–13. (PDF 2066 kb)

Supplementary Table 1

Information on the raw data leading to InWeb_InBioMap (XLSX 21 kb)

Supplementary Table 2

The AUCs of NMB on 17 tumor types for each network (XLSX 36 kb)

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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

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