BACKGROUND: Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets. DESCRIPTION: Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at http://proteome.wayne.edu/PIMdb.html CONCLUSION: The Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.
BACKGROUND: Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets. DESCRIPTION: Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at http://proteome.wayne.edu/PIMdb.html CONCLUSION: The Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.
Authors: Etienne Formstecher; Sandra Aresta; Vincent Collura; Alexandre Hamburger; Alain Meil; Alexandra Trehin; Céline Reverdy; Virginie Betin; Sophie Maire; Christine Brun; Bernard Jacq; Monique Arpin; Yohanns Bellaiche; Saverio Bellusci; Philippe Benaroch; Michel Bornens; Roland Chanet; Philippe Chavrier; Olivier Delattre; Valérie Doye; Richard Fehon; Gérard Faye; Thierry Galli; Jean-Antoine Girault; Bruno Goud; Jean de Gunzburg; Ludger Johannes; Marie-Pierre Junier; Vincent Mirouse; Ashim Mukherjee; Dora Papadopoulo; Franck Perez; Anne Plessis; Carine Rossé; Simon Saule; Dominique Stoppa-Lyonnet; Alain Vincent; Michael White; Pierre Legrain; Jérôme Wojcik; Jacques Camonis; Laurent Daviet Journal: Genome Res Date: 2005-02-14 Impact factor: 9.043
Authors: John K Kim; Harrison W Gabel; Ravi S Kamath; Muneesh Tewari; Amy Pasquinelli; Jean-François Rual; Scott Kennedy; Michael Dybbs; Nicolas Bertin; Joshua M Kaplan; Marc Vidal; Gary Ruvkun Journal: Science Date: 2005-03-24 Impact factor: 47.728
Authors: Adam A Friedman; George Tucker; Rohit Singh; Dong Yan; Arunachalam Vinayagam; Yanhui Hu; Richard Binari; Pengyu Hong; Xiaoyun Sun; Maura Porto; Svetlana Pacifico; Thilakam Murali; Russell L Finley; John M Asara; Bonnie Berger; Norbert Perrimon Journal: Sci Signal Date: 2011-10-25 Impact factor: 8.192
Authors: Daniel Marbach; Sushmita Roy; Ferhat Ay; Patrick E Meyer; Rogerio Candeias; Tamer Kahveci; Christopher A Bristow; Manolis Kellis Journal: Genome Res Date: 2012-03-28 Impact factor: 9.043
Authors: Thilakam Murali; Svetlana Pacifico; Jingkai Yu; Stephen Guest; George G Roberts; Russell L Finley Journal: Nucleic Acids Res Date: 2010-10-29 Impact factor: 16.971