About InnateDB

[Array logo] InnateDB has been developed to facilitate systems level investigations of the innate immune response in human and mice. Its goal is to provide a manually-curated knowledgebase of the genes, proteins, and particularly, the interactions and signaling responses involved in the mammalian innate immune response. InnateDB incorporates information of the whole human and mouse interactomes by integrating interaction and pathway information from several of the major publicly available databases but aims to capture an improved coverage of the innate immunity interactome through manual curation. Our curation team consists of 3 full-time individuals with backgrounds in molecular biology who systematically review and curate experimentally-validated human and mouse interactions from the biomedical literature. These interactions are curated with rich contextual annotations including information on the participant molecules, the reference publication, the interaction detection method, the cell and tissue types in which the interaction was described and a variety of other information in compliance with the recently proposed minimum information for molecular interactions (MIMIx) standard (Nat. Biotech., 2007). We have developed the InnateDB submission system to allow curators to submit data using a structured controlled vocabulary for the annotation of protein-protein interaction experiments (Developed by the HUPO Proteomics Standards Initiative).

Searching and Mining InnateDB

InnateDB is freely available to the public as a tool for innate immunity research and can be mined as a knowledgebase where users can search for particular genes or proteins of interest and their associated interactions and pathways. Alternatively, InnateDB can be mined in a more high-throughput fashion, where users can upload a list of genes and carry out batch searches of InnateDB. Batch-searching allows users to return all interactions or pathways associated with a list genes and to integrate their own gene-expression data to investigate differential gene expression in an interaction network or pathway context. All interactions may be downloaded in several formats including text-based formats (tab, csv, Excel), the simple interaction format (sif), and most importantly, the PSI-MI extensible markup language (XML) interchange format. Interaction networks may also be visualized in our Cerebral program, a java plugin for the Cytoscape network visualization software, which uses subcellular localization information to orientate interaction networks in more biologically intuitive pathway-like layouts. Our latest version of Cerebral allows the overlay of up-loaded gene expression data from up to ten different experimental conditions. InnateDB also has integrated human, mouse and bovine orthology predictions generated using our Ortholuge software. Ortholuge uses a phylogenetic distance-based method to identify possible paralogs in high-throughput orthology predictions. Integrated human and mouse conserved gene order and synteny information has also been determined to provide further support for orthology predictions. Orthology information can be used with our associated tools for cross-species analysis of innate immunity gene expression data. The InnateDB data structure is based on the BioPAX data model, which will allow us to provide curated pathway information in a BioPAX compliant manner in the future. Currently, pathway cross-references can be downloaded in other text-based formats.

Project Team and Funding

InnateDB is a collaboration between the Brinkman Bioinformatics group at Simon Fraser University, the Hancock Laboratory at the University of British Columbia and the Lynn Systems Biology Group at the Teagasc Animal Bioscience Department, Ireland.

Team Leader: Dr. David Lynn.

Website Development: Geoff Winsor, Calvin Chan, Amir Foroushani, Nicolas Richard.

Database Development: Geoff Winsor, Karin Breuer, Matthew Laird, Dr. Fiona Roche, Tim Chan, Nicolas Richard.

Submission System and Curator Tool: Calvin Chan, Naisha Shah.

Curation Team: Misbah Naseer, Melissa Yau, Raymond Lo, Anastasia Sribnaia, Jaimmie Que, Kathleen Wee.

Cerebral: Aaron Barsky, Dr. Jennifer Gardy, Dr. Tamara Muzner.

Orthology Predictions: Matthew Whiteside.

Gene Order & Synteny: Dr. Dan Tulpan, Mark Sun.

Systems Administration: Matthew Laird.