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. 2016 Jan 4;44(D1):D536-41.
doi: 10.1093/nar/gkv1115. Epub 2015 Oct 29.

Integrated interactions database: tissue-specific view of the human and model organism interactomes

Max Kotlyar  1 , Chiara Pastrello  1 , Nicholas Sheahan  2 , Igor Jurisica  3
Affiliations

Integrated interactions database: tissue-specific view of the human and model organism interactomes

Max Kotlyar et al. Nucleic Acids Res. .

Abstract

IID (Integrated Interactions Database) is the first database providing tissue-specific protein-protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), C. elegans (worm), D. melonogaster (fly), R. norvegicus (rat), M. musculus (mouse) and H. sapiens (human)) and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables searches across species and tissues automatically by using their orthologs; for example, retrieving interactions in a given tissue, conserved in human and mouse. Interaction data in IID comprises three types of PPI networks: experimentally detected PPIs from major databases, orthologous PPIs and high-confidence computationally predicted PPIs. Interactions are assigned to tissues where their proteins pairs or encoding genes are expressed. IID is a major replacement of the I2D interaction database, with larger PPI networks (a total of 1,566,043 PPIs among 68,831 proteins), tissue annotations for interactions, and new query, analysis and data visualization capabilities. IID is available at http://ophid.utoronto.ca/iid.

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Figures

Figure 1.
Figure 1.
Percentages of PPIs annotated with tissues.
Figure 2.
Figure 2.
Tissue specificity of PPIs (i.e. are most PPIs annotated to few or many tissues). The figure considers only PPIs associated with at least 1 tissue, and shows the percentage of these PPIs (y-axis) associated with up to a given percentage (i.e. ≤ k percent) of tissues (x-axis)
Figure 3.
Figure 3.
Tissue distribution of PPIs. Shown are percentages of each species’ PPIs in given tissues.
Figure 4.
Figure 4.
Conservation of human tissue-specific PPI networks in model organisms. IID annotates human PPIs with up to 29 tissues. For each of these tissues, the figure shows the number of experimentally detected human PPIs annotated with the tissue (black), the numbers of orthologous protein pairs in model organisms (grey) and the numbers of orthologous pairs annotated with the same tissue in the model organisms (red).
Figure 5.
Figure 5.
Clustering human tissues by graphlet degree distributions.
See this image and copyright information in PMC

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References

    1. Warde-Farley D., Donaldson S.L., Comes O., Zuberi K., Badrawi R., Chao P., Franz M., Grouios C., Kazi F., Lopes C.T., et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38:W214–W220. - PMC - PubMed
    1. Mostafavi S., Morris Q. Combining many interaction networks to predict gene function and analyze gene lists. Proteomics. 2012;12:1687–1696. - PubMed
    1. Navlakha S., Kingsford C. The power of protein interaction networks for associating genes with diseases. Bioinformatics. 2010;26:1057–1063. - PMC - PubMed
    1. Wang X., Gulbahce N., Yu H. Network-based methods for human disease gene prediction. Br. Funct. Genomics. 2011;10:280–293. - PubMed
    1. Barabasi A.L., Gulbahce N., Loscalzo J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 2011;12:56–68. - PMC - PubMed

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