Discovering regulatory and signalling circuits in molecular interaction networks.
Trey Ideker,Owen Ozier,Benno Schwikowski,Andrew F. Siegel +3 more
- Vol. 18, pp 233-240
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TLDR
This paper introduces an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions.Abstract:
Motivation: In model organisms such as yeast, large databases of protein–protein and protein-DNA interactions have become an extremely important resource for the study of protein function, evolution, and gene regulatory dynamics. In this paper we demonstrate that by integrating these interactions with widely-available mRNA expression data, it is possible to generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression. To perform this integration systematically and at large scale, we introduce an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions. The method we present here combines a rigorous statistical measure for scoring subnetworks with a search algorithm for identifying subnetworks with high score. Results: We evaluated our procedure on a small network of 332 genes and 362 interactions and a large network of 4160 genes containing all 7462 protein–protein and protein-DNA interactions in the yeast public databases. In the case of the small network, we identified five significant subnetworks that covered 41 out of 77 (53%) of all significant changes in expression. Both network analyses returned several top-scoring subnetworks with good correspondence to known regulatory mechanisms in the literature. These results demonstrate how large-scale genomic approaches may be used to uncover signalling and regulatory pathways in a systematic, integrative fashion. Availability: The methods presented in this paper are implemented in the Cytoscape software package which is available to the academic community at http://www.read more
Citations
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Journal ArticleDOI
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Paul Shannon,Andrew Markiel,Owen Ozier,Nitin S. Baliga,Jonathan T. Wang,Daniel Ramage,Nada Amin,Benno Schwikowski,Trey Ideker +8 more
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Journal ArticleDOI
Integration of biological networks and gene expression data using Cytoscape
Melissa S. Cline,Michael E. Smoot,Ethan Cerami,Allan Kuchinsky,Nerius Landys,Christopher T. Workman,Rowan H. Christmas,Iliana Avila-Campilo,Iliana Avila-Campilo,Michael L. Creech,Benjamin Gross,Kristina Hanspers,Ruth Isserlin,Ryan Kelley,Sarah Killcoyne,Samad Lotia,Steven Maere,John H. Morris,Keiichiro Ono,Vuk Pavlovic,Alexander R. Pico,Aditya Vailaya,Peng-Liang Wang,Annette M. Adler,Bruce R. Conklin,Leroy Hood,Martin Kuiper,Chris Sander,Ilya Schmulevich,Benno Schwikowski,Guy J. Warner,Trey Ideker,Gary D. Bader +32 more
TL;DR: This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest.
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Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation
TL;DR: This work developed “Enrichment Map”, a network-based visualization method for gene-set enrichment results that is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software and is a significant advance in the interpretation of enrichment analysis.
Journal ArticleDOI
Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis
Benjamin F. Voight,Benjamin F. Voight,Laura J. Scott,Valgerdur Steinthorsdottir,Andrew P. Morris,Christian Dina,Christian Dina,Ryan P. Welch,Eleftheria Zeggini,Eleftheria Zeggini,Cornelia Huth,Yurii S. Aulchenko,Gudmar Thorleifsson,Laura J. McCulloch,Teresa Ferreira,Harald Grallert,Najaf Amin,Guanming Wu,Cristen J. Willer,Soumya Raychaudhuri,Soumya Raychaudhuri,Soumya Raychaudhuri,Steve McCarroll,Steve McCarroll,Claudia Langenberg,Oliver Hofmann,Josée Dupuis,Lu Qi,Lu Qi,Ayellet V. Segrè,Ayellet V. Segrè,Mandy van Hoek,Pau Navarro,Kristin Ardlie,Beverley Balkau,Rafn Benediktsson,Amanda J. Bennett,Roza Blagieva,Eric Boerwinkle,Lori L. Bonnycastle,Kristina Bengtsson Boström,Bert Bravenboer,Suzannah Bumpstead,N P Burtt,Guillaume Charpentier,Peter S. Chines,Marilyn C. Cornelis,David Couper,Gabe Crawford,Alex S. F. Doney,Katherine S. Elliott,Amanda F. Elliott,Amanda F. Elliott,Michael R. Erdos,Caroline S. Fox,Christopher S. Franklin,Martha Ganser,Christian Gieger,Niels Grarup,Todd Green,Todd Green,Simon J. Griffin,Christopher J. Groves,Candace Guiducci,Samy Hadjadj,Neelam Hassanali,Christian Herder,Bo Isomaa,Anne U. Jackson,Paul Johnson,Torben Jørgensen,Torben Jørgensen,Wen H. L. Kao,Norman Klopp,Augustine Kong,Peter Kraft,Johanna Kuusisto,Torsten Lauritzen,Man Li,Aloysius G Lieverse,Cecilia M. Lindgren,Valeriya Lyssenko,Michel Marre,Michel Marre,Thomas Meitinger,Kristian Midthjell,Mario A. Morken,Narisu Narisu,Peter M. Nilsson,Katharine R. Owen,Felicity Payne,John R. B. Perry,Ann-Kristin Petersen,Carl G. P. Platou,Christine Proença,Inga Prokopenko,Inga Prokopenko,Wolfgang Rathmann,N. William Rayner,N. William Rayner,Neil R. Robertson,Neil R. Robertson,Ghislain Rocheleau,Michael Roden,Mike Sampson,Richa Saxena,Richa Saxena,Beverley M. Shields,Peter Shrader,Gunnar Sigurdsson,Thomas Sparsø,Klaus Strassburger,Heather M. Stringham,Qi Sun,Amy J. Swift,Barbara Thorand,Jean Tichet,Tiinamaija Tuomi,Rob M. van Dam,Timon W. van Haeften,Thijs T. W. van Herpt,Jana V. van Vliet-Ostaptchouk,G. Bragi Walters,Michael N. Weedon,Cisca Wijmenga,Jacqueline C. M. Witteman,Richard N. Bergman,Stéphane Cauchi,Francis S. Collins,Anna L. Gloyn,Ulf Gyllensten,Torben Hansen,Winston Hide,Graham A. Hitman,Albert Hofman,David J. Hunter,Kristian Hveem,Markku Laakso,Karen L. Mohlke,Andrew D. Morris,Colin N. A. Palmer,Peter P. Pramstaller,Igor Rudan,Igor Rudan,Eric J.G. Sijbrands,Lincoln Stein,Jaakko Tuomilehto,Jaakko Tuomilehto,André G. Uitterlinden,Mark Walker,Nicholas J. Wareham,Richard M. Watanabe,Gonçalo R. Abecasis,Bernhard O. Boehm,Harry Campbell,Mark J. Daly,Mark J. Daly,Andrew T. Hattersley,Frank B. Hu,Frank B. Hu,James B. Meigs,James S. Pankow,Oluf Pedersen,H-Erich Wichmann,Inês Barroso,Jose C. Florez,Timothy M. Frayling,Leif Groop,Leif Groop,Robert Sladek,Unnur Thorsteinsdottir,Unnur Thorsteinsdottir,James F. Wilson,Thomas Illig,Philippe Froguel,Philippe Froguel,Cornelia M. van Duijn,Kari Stefansson,Kari Stefansson,David Altshuler,Michael Boehnke,Mark I. McCarthy,Mark I. McCarthy,Mark I. McCarthy +183 more
TL;DR: By combining genome-wide association data from 8,130 individuals with type 2 diabetes and 38,987 controls of European descent and following up previously unidentified meta-analysis signals, 12 new T2D association signals are identified with combined P < 5 × 10−8.
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