DAPPER: a data-mining resource for protein-protein interactions

Abstract

Background. The identification of interaction networks between proteins and complexes holds the promise of offering novel insights into the molecular mechanisms that regulate many biological processes. With increasing volumes of such datasets, especially in model organisms such as Drosophila melanogaster, there exists a pressing need for specialised tools, which can seamlessly collect, integrate and analyse these data. Here we describe a database coupled with a mining tool for protein-protein interactions (DAPPER), developed as a rich resource for studying multi-protein complexes in Drosophila melanogaster. Results. This proteomics database is compiled through mass spectrometric analyses of many protein complexes affinity purified from Drosophila tissues and cultured cells. The web access to DAPPER is provided via an accelerated version of BioMart software enabling data-mining through customised querying and output formats. The protein-protein interaction dataset is annotated with FlyBase identifiers, and further linked to the Ensembl database using BioMart's data-federation model, thereby enabling complex multi-dataset queries. DAPPER is open source, with all its contents and source code are freely available. Conclusions. DAPPER offers an easy-to-navigate and extensible platform for real-time integration of diverse resources containing new and existing protein-protein interaction datasets of Drosophila melanogaster.

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© 2015 Haider et al. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Received: 25 April 2015 Accepted: 16 September 2015. Published: 24 September 2015. This work was supported financially by grants from the Cancer Research UK (CRUK), the Biotechnology and Biological Sciences Research Council and the Medical Research Council to DMG (C3/A11431, BB/I013938/1, G1001696), by a Cancer Research UK Career Development Fellowship to YK (C40697/A12874), and by Cancer Research UK grants to PPD (C12296/A8039 and C12296/A12541). ZL is on leave from the Biological Research Centre of the Hungarian Academy of Sciences (Institute of Biochemistry, Szeged, Hungary) and was supported by a Long-Term Fellowship of the Federation of European Biochemical Societies (FEBS). Authors' contributions: SH, ZL, MRP, PL and DMG initiated the project. SH performed the analytical work and implementation of DAPPER with contributions from YL. ZL, MRP, PPD and YK performed the experimental work and contributed to software testing. All authors contributed to the writing of the paper. PL and DMG supervised the bioinformatics and experimental work, respectively. All authors read and approved the final manuscript. The authors declare that they have no competing interests.

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