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Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study.

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons134787

Villaveces,  J. M.
Habermann, Bianca / Computational Biology, Max Planck Institute of Biochemistry, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons101406

Habermann,  B. H.
Habermann, Bianca / Computational Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Database-2015-Villaveces-database_bau131.pdf
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Zitation

Villaveces, J. M., Jimenez, R. C., Porras, P., Del-Toro, N., Duesbury, M., Dumousseau, M., et al. (2015). Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study. Database: the journal of biological databases and curation, 2015: bau 131. doi:10.1093/database/bau131.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0025-776F-C
Zusammenfassung
The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative-molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.