Proteomes 2013, 1(1), 3-24; doi:10.3390/proteomes1010003
Article

Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration

1,* email, 2email, 3email and 1,4email
Received: 18 April 2013; in revised form: 16 May 2013 / Accepted: 21 May 2013 / Published: 31 May 2013
(This article belongs to the Special Issue Insights and Trends into Proteome Science)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions.
Keywords: bioinformatics; calsenilin; choline kinase; data integration; DREAM; gene acronym; gene redundancy; HGNC; HUGO; human interactome; KChIP3; protein accession; protein interactions; protein-protein prediction; uromodulin
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MDPI and ACS Style

Casado-Vela, J.; Matthiesen, R.; Sellés, S.; Naranjo, J.R. Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration. Proteomes 2013, 1, 3-24.

AMA Style

Casado-Vela J, Matthiesen R, Sellés S, Naranjo JR. Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration. Proteomes. 2013; 1(1):3-24.

Chicago/Turabian Style

Casado-Vela, Juan; Matthiesen, Rune; Sellés, Susana; Naranjo, José R. 2013. "Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration." Proteomes 1, no. 1: 3-24.

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