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      iRefIndex: A consolidated protein interaction database with provenance

      1 , 2 , 1 , , 1 , 3

      BMC Bioinformatics

      BioMed Central

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          Abstract

          Background

          Interaction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping.

          Results

          We present a method to generate a key for a protein interaction record and a key for each participant protein. These keys may be generated by anyone using only the primary sequence of the proteins, their taxonomy identifiers and the Secure Hash Algorithm. Two interaction records will have identical keys if they refer to the same set of identical protein sequences and taxonomy identifiers. We define records with identical keys as a redundant group. Our method required that we map protein database references found in interaction records to current protein sequence records. Operations performed during this mapping are described by a mapping score that may provide valuable feedback to source interaction databases on problematic references that are malformed, deprecated, ambiguous or unfound. Keys for protein participants allow for retrieval of interaction information independent of the protein references used in the original records.

          Conclusion

          We have applied our method to protein interaction records from BIND, BioGrid, DIP, HPRD, IntAct, MINT, MPact, MPPI and OPHID. The resulting interaction reference index is provided in PSI-MITAB 2.5 format at http://irefindex.uio.no. This index may form the basis of alternative redundant groupings based on gene identifiers or near sequence identity groupings.

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          Most cited references 36

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          BIND: the Biomolecular Interaction Network Database.

          The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.
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            Development of human protein reference database as an initial platform for approaching systems biology in humans.

            Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.
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              A human phenome-interactome network of protein complexes implicated in genetic disorders.

              We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2008
                30 September 2008
                : 9
                : 405
                Affiliations
                [1 ]The Biotechnology Centre of Oslo, University of Oslo, P.O. Box 1125 Blindern, 0317 Oslo, Norway
                [2 ]Biomedical Research Group, Department of Informatics, University of Oslo, P.O. Box 1080 Blindern, 0316 Oslo, Norway
                [3 ]Department for Molecular Biosciences, University of Oslo, P.O. Box 1041 Blindern, 0316 Oslo, Norway
                Article
                1471-2105-9-405
                10.1186/1471-2105-9-405
                2573892
                18823568
                Copyright © 2008 Razick et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Research Article

                Bioinformatics & Computational biology

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