L Perfetto1,2, M L Acencio3, G Bradley4, G Cesareni1,5, N Del Toro2, D Fazekas6,7, H Hermjakob2,8, T Korcsmaros7,9, M Kuiper10, A Lægreid3, P Lo Surdo1, R C Lovering11, S Orchard2, P Porras2, P D Thomas12, V Touré10, J Zobolas10, L Licata1. 1. Department of Biology, University of Rome Tor Vergata, Rome, Italy. 2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK. 3. Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. 4. Computational Biology and Statistics, Target Sciences, GSK, UK. 5. IRCCS, Fondazione Santa Lucia, Rome, Italy. 6. Department of Genetics, Eötvös Loránd University, Budapest, Hungary. 7. Earlham Institute, Norwich, UK. 8. State Key Laboratory of Proteomics, Beijing Institute of Life Omics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, China. 9. Quadram Institute, Norwich, UK. 10. Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. 11. Department of Preclinical and Fundamental Science, Institute of Cardiovascular Science, University College London, UK. 12. Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
Abstract
MOTIVATION: Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources. RESULTS: Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term 'causal interaction', and related child terms, which are children of the PSI-MI 'molecular interaction' term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before. AVAILABILITY AND IMPLEMENTATION: PSI-MITAB 2.8 format and the new reference implementation of PSICQUIC are available online (https://psicquic.github.io/ and https://psicquic.github.io/MITAB28Format.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources. RESULTS: Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term 'causal interaction', and related child terms, which are children of the PSI-MI 'molecular interaction' term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before. AVAILABILITY AND IMPLEMENTATION: PSI-MITAB 2.8 format and the new reference implementation of PSICQUIC are available online (https://psicquic.github.io/ and https://psicquic.github.io/MITAB28Format.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Bruno Aranda; Hagen Blankenburg; Samuel Kerrien; Fiona S L Brinkman; Arnaud Ceol; Emilie Chautard; Jose M Dana; Javier De Las Rivas; Marine Dumousseau; Eugenia Galeota; Anna Gaulton; Johannes Goll; Robert E W Hancock; Ruth Isserlin; Rafael C Jimenez; Jules Kerssemakers; Jyoti Khadake; David J Lynn; Magali Michaut; Gavin O'Kelly; Keiichiro Ono; Sandra Orchard; Carlos Prieto; Sabry Razick; Olga Rigina; Lukasz Salwinski; Milan Simonovic; Sameer Velankar; Andrew Winter; Guanming Wu; Gary D Bader; Gianni Cesareni; Ian M Donaldson; David Eisenberg; Gerard J Kleywegt; John Overington; Sylvie Ricard-Blum; Mike Tyers; Mario Albrecht; Henning Hermjakob Journal: Nat Methods Date: 2011-06-29 Impact factor: 28.547
Authors: Samuel Kerrien; Sandra Orchard; Luisa Montecchi-Palazzi; Bruno Aranda; Antony F Quinn; Nisha Vinod; Gary D Bader; Ioannis Xenarios; Jérôme Wojcik; David Sherman; Mike Tyers; John J Salama; Susan Moore; Arnaud Ceol; Andrew Chatr-Aryamontri; Matthias Oesterheld; Volker Stümpflen; Lukasz Salwinski; Jason Nerothin; Ethan Cerami; Michael E Cusick; Marc Vidal; Michael Gilson; John Armstrong; Peter Woollard; Christopher Hogue; David Eisenberg; Gianni Cesareni; Rolf Apweiler; Henning Hermjakob Journal: BMC Biol Date: 2007-10-09 Impact factor: 7.431
Authors: Sandra Orchard; Mais Ammari; Bruno Aranda; Lionel Breuza; Leonardo Briganti; Fiona Broackes-Carter; Nancy H Campbell; Gayatri Chavali; Carol Chen; Noemi del-Toro; Margaret Duesbury; Marine Dumousseau; Eugenia Galeota; Ursula Hinz; Marta Iannuccelli; Sruthi Jagannathan; Rafael Jimenez; Jyoti Khadake; Astrid Lagreid; Luana Licata; Ruth C Lovering; Birgit Meldal; Anna N Melidoni; Mila Milagros; Daniele Peluso; Livia Perfetto; Pablo Porras; Arathi Raghunath; Sylvie Ricard-Blum; Bernd Roechert; Andre Stutz; Michael Tognolli; Kim van Roey; Gianni Cesareni; Henning Hermjakob Journal: Nucleic Acids Res Date: 2013-11-13 Impact factor: 16.971
Authors: M Sivade Dumousseau; D Alonso-López; M Ammari; G Bradley; N H Campbell; A Ceol; G Cesareni; C Combe; J De Las Rivas; N Del-Toro; J Heimbach; H Hermjakob; I Jurisica; M Koch; L Licata; R C Lovering; D J Lynn; B H M Meldal; G Micklem; S Panni; P Porras; S Ricard-Blum; B Roechert; L Salwinski; A Shrivastava; J Sullivan; N Thierry-Mieg; Y Yehudi; K Van Roey; S Orchard Journal: BMC Bioinformatics Date: 2018-04-11 Impact factor: 3.169