DIPSBC - data integration platform for systems biology collaborations

BMC Bioinformatics

Felix Dreher¹  Thomas Kreitler¹  Christopher Hardt¹  Atanas Kamburov¹  Reha Yildirimman¹  Karl Schellander²  Hans Lehrach¹  Bodo MH Lange¹  Ralf Herwig¹ 

[1] Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, 14195, Berlin, Germany

[2] Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115, Bonn, Germany

关键词: Data visualization;    XML;    Data integration;   

Others  :  1088285
DOI  :  10.1186/1471-2105-13-85

 received in 2011-12-20, accepted in 2012-05-01,  发布年份 2012

【 摘 要 】

Background

Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis.

Results

We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de webcite.

Conclusions

DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.

【 授权许可】

   
2012 Dreher et al.; licensee BioMed Central Ltd

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

【 参考文献 】

• [1]Smith , et al.: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007, 25:1251-1255.
• [2]Goble C, Stevens R: State of the nation in data integration for bioinformatics. J Biomed Inform. 2008, 41:687-693.
• [3]Lee , et al.: Incorporating collaboratory concepts into informatics in support of translational interdisciplinary biomedical research. Int J Med Inform. 2009, 78:10-21.
• [4]Cochrane , et al.: Petabyte-scale innovations at the European Nucleotide Archive. Nucleic Acids Res. 2009, 37:D19-25.
• [5]Brazma , et al.: Standards for systems biology. Nat Rev Genet. 2006, 7:593-605.
• [6]Taylor , et al.: Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechnol. 2008, 26:889-896.
• [7]Brazma A: Minimum Information About a Microarray Experiment (MIAME)–sucesses, failures, challenges. ScientificWorldJournal 2009, 9:420-423.
• [8]Taylor , et al.: The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 2007, 25:887-893.
• [9]Orchard , et al.: The minimum information required for reporting a molecular interaction experiment (MIMIx). Nat Biotechnol. 2007, 25:894-898.
• [10]Deutsch , et al.: Minimum information specification for in situ hybridization and Immunohistochemistry experiments (MISFISHIE). Nat. Biotechnol. 2008, 26:305-312.
• [11]Lefever , et al.: RDML: structured language and reporting guidelines for real-time quantitative PCR data. Nucleic Acids Res. 2009, 37:2065-2069.
• [12]Field , et al.: The minimum information about a genome sequence (MIGS) specification. Nat. Biotechnol. 2008, 26:541-547.
• [13]Le Novère , et al.: Minimum information requested in the annotation of biochemical models (MIRIAM). Nat. Biotechnol. 2005, 23:1509-1515.
• [14]Le Novère , et al.: The Systems Biology Graphical Notation. Nat Biotechnol. 2009, 27(8):735-741.
• [15]Whirl-Carrillo , et al.: An XML-based interchange format for genotype-phenotype data. Hum Mutat. 2008, 29:212-219.
• [16]Ball CA, Brazma A: MGED standards: work in progress. OMICS 2006, 10:138-144.
• [17]Jones , et al.: The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics. Nat Biotechnol. 2007, 25:1127-1133.
• [18]Sansone , et al.: The first RSBI (ISA-TAB) workshop: "can a simple format work for complex studies?". OMICS 2008, 12:143-149.
• [19]Orchard , et al.: Proteomic data exchange and storage: the need for common standards and public repositories. Methods Mol Biol. 2007, 367:261-270.
• [20]Spellman , et al.: Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol 2002, 3:1-0046.9. research0046
• [21]Kerrien , et al.: Broadening the horizon--level 2.5 of the HUPO-PSI format for molecular interactions. BMC Biol. 2007, 5:44. BioMed Central Full Text
• [22]Engels R, Yu T, Burge C, Mesirov JP, DeCaprio D, Galagan JE: Combo: a whole genome comparative browser. Bioinformatics 2006, 22(14):1782-1783.
• [23]Gry J, et al.: Correlations between RNA and protein expression profiles in 23 human cell lines. BMC Genomics 2009, 10:365. BioMed Central Full Text
• [24]Seliger , et al.: Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma. Proteomics 2009, 9(6):1567-1581.
• [25]Rogers , et al.: Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinformatics 2008, 24(24):2894-2900.
• [26]Yu , et al.: Stable isotope dilution multidimensional liquid chromatography-tandem mass spectrometry for pancreatic cancer serum biomarker discovery. J Proteome Res. 2009, 8(3):1565-1576.
• [27]Varambally , et al.: Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. Cancer Cell 2005, 8(5):393-406.
• [28]Richiardi , et al.: Promoter methylation in APC, RUNX3, and GSTP1 and mortality in prostate cancer patients. J Clin Oncol. 2009, 27(19):3161-3168.
• [29]Sawhney , et al.: A novel role of ERK5 in integrin-mediated cell adhesion and motility in cancer cells via Fak signaling. J Cell Physiol. 2009, 219(1):152-161.
• [30]Drake , et al.: ZEB1 enhances transendothelial migration and represses the epithelial phenotype of prostate cancer cells. Mol Biol Cell. 2009, 20(8):2207-2217.
• [31]Perkins , et al.: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20:3551-3567.
• [32]Dreher F, Kamburov A, Herwig R: Construction of a pig physical interactome using sequence homology and a comprehensive reference human interactome. Evol Bioinform Online , 8:119-126.
• [33]Liu G, et al.: A genome scan reveals QTL for growth, fatness, leanness and meat quality in a Duroc-Pietrain resource population. Anim Genet 2007, 38:241-252.
• [34][http://www.animalgenome.org/cgi-bin/QTLdb/SS/link_rh2hs?chromos=5] webciteComparative map at PigQTLdb.
• [35]Tsai S, et al.: Annotation of the Affymetrix porcine genome microarray. Anim Genet. 2006, 37(4):423-424.
• [36]Kamburov A, Wierling C, Lehrach H, Herwig R: ConsensusPathDB–a database for integrating human functional interaction networks. Nucleic Acids Res. 2009, 37:D623-628.
• [37]Rual , et al.: Towards a proteome-scale map of the human protein-protein interaction network. Nature 2005, 437(7062):1173-1178.
• [38]Stelzl , et al.: A human protein-protein interaction network: a resource for annotating the proteome. Cell 2005, 122(6):957-968.
• [39]Kerrien , et al.: IntAct - Open Source Resource for Molecular Interaction Data. Nucleic Acids Res 2006, 35(Database issue):D561-565.
• [40]Ryo , et al.: Stable suppression of tumorigenicity by Pin1-targeted RNA interference in prostate cancer. Clin Cancer Res. 2005, 11(20):7523-7531.
• [41]Li , et al.: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Systems Biology 2010, 4:92. BioMed Central Full Text
• [42]Kim , et al.: A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways. Oncogene 2007, 26(31):4571-4579.
• [43]Rocca-Serra P, et al.: ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 2010, 26:2354-2356.
• [44]Zhang J, et al.: BioMart: a data federation framework for large collaborative projects. Database (Oxford) 2011, 2011:bar038.
• [45]Zhang J, et al.: International Cancer Genome Consortium Data Portal - a one-stop shop for cancer genomics data. Database (Oxford) 2011, 2011:bar026.