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References
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Acknowledgements
We thank members of the Saez laboratory, especially L. Tobalina for useful feedback on the manuscript and for testing pypath and A. Zinovyev, G. Cesareni, H. Hermjakob and L. Perfetto for useful discussions. D.T. was supported by the EMBL Interdisciplinary Postdoc Programme (EIPOD) under Marie Skłodowska-Curie COFUND Actions (grant number 291772). T.K. was funded by a fellowship in computational biology at the Earlham Institute (Norwich, UK) in partnership with the Institute of Food Research (Norwich, UK), and strategically supported by Biotechnological and Biosciences Research Council, UK (BB/J004529/1). We thank the developers of all the very useful resources we have used in this study and their funding agencies as described in their respective publications and webpages.
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Competing interests
Although we did our best to avoid bias, we should mention that we contributed to the development of ARN, NRF2ome and SignaLink3—three of the resources analyzed here. Apart from this, we declare no conflict of interest.
Integrated supplementary information
Supplementary Figure 1 Journals ranked by number of references.
The top 100 most cited journals in all resources.
Supplementary Figure 2 Journals ranked by number of references for all resources.
The top 10 most cited journals for each individual resource.
Supplementary Figure 3 Total number of articles published in journals.
Total number of articles published in the top 10 most often cited, molecular biology specific journals over the last two decades. Journals with broader spectrum have been omitted. Figure generated with the Scopus webservice.
Supplementary Figure 4 Coverage of resources on various groups of proteins.
Categories and shaded areas as on Supplementary Fig. 5. (a) Percentage of human receptors (as listed in HPMR) covered by pathway resources; (b) Percentage of transcription factors (TFs; from TF Census) covered by pathway resources; (c) Number of complexes (as defined in CORUM) and (d) number of post-translational modifications (PTMs) from PTM resources that can be mapped onto the network (i.e. corresponding group of proteins or interacting protein pair can be found). (e-f) Percentage of cancer driver genes (as listed in Cancer Gene Census and IntOGen) covered by pathway resources covered by pathway resources. (g) Percentage of disease related genes, as listed in DisGeNet, covered by pathway resources; (h) Number of all enzyme-substrate pairs by PTM resources, and number of the enzyme-substrate interactions for a corresponding edge can be found in any of the resources.
Supplementary Figure 5 Network features of resources
The left column subplots show the cumulative numbers for each category. In that plot, shaded areas on the bars represent quantities unique for each category; in the other plots, quantities unique for each resource within its category. (a) proteins per database; (b) interacting pairs of proteins per database (directed networks converted to undirected for comparability); (c) network density; (d) transitivity. Databases are sorted by decreasing value of their protein count.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–5, Supplementary Tables 1–4, Supplementary Results 1–5, Supplementary Methods and Supplementary Notes 1 and 2. (PDF 1068 kb)
Supplementary Software
pypath software and instructions. (ZIP 4162 kb)
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Türei, D., Korcsmáros, T. & Saez-Rodriguez, J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat Methods 13, 966–967 (2016). https://doi.org/10.1038/nmeth.4077
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DOI: https://doi.org/10.1038/nmeth.4077
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