Open Access Research article

Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins

Thanh-Phuong Nguyen1, Marco Scotti1*, Melissa J Morine1 and Corrado Priami12

Author Affiliations

1 The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto (Trento), Italy

2 Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 14, 38123 Povo (Trento), Italy

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BMC Systems Biology 2011, 5:195  doi:10.1186/1752-0509-5-195

Published: 2 December 2011

Additional files

Additional file 1:

This table includes details about the initial list of 200 vitamin-related proteins. These proteins were used as a reference for constructing the vitamin PPI network and are listed in alphabetical order. The first two columns indicate UniProtKB accession numbers and UniProtKB/Swiss-Prot entry names, while the third column describes vitamin associations, as extracted from UniProtKB. Number of publications related to each protein are shown in the last column (source: UniProt). The 21 most central proteins that pertain to this list are highlighted in yellow.

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Additional file 2:

Edgelist summarizing the 2,672 undirected interactions between the 1,657 proteins of the giant component. Each line of the edgelist describes an interaction between the proteins identified by the column labels protein1 and protein2.

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Additional file 3:

This table provides the centrality values computed for the 1,657 proteins of the giant component. Proteins are in alphabetical order and the 22 most central proteins are highlighted in yellow. Centrality indices included are: D = degree; EC = eigenvector score; TI1 = topological importance up to 1 step; TI4 = topological importance up to 4 steps; B = betweenness; C = closeness.

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Additional file 4:

For the 200 vitamin-related proteins we tested whether the number of interactions is determined by the number of publications associated with a given vitamin. We found that the structure of the PPI network is independent from the literature. We also analyzed patterns of vitamin associations in four organisms, observing how human differs from mouse, yeast and E. coli.

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Additional file 5:

Vitamin associations of proteins in mouse (Mus musculus).

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Additional file 6:

Vitamin associations of proteins in yeast (Saccharomyces cerevisiae).

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Additional file 7:

Vitamin associations of proteins in Escherichia coli.

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Additional file 8:

Correlation matrices describing the Goodman-Kruskal's lambda values for null models of network connectivity. These correlations between the rankings are used to construct dendrograms for each type of null model. Dendrograms illustrate the similarities between centralities (i.e., when protein orderings measured with two indices are similar, and their relationships with other centralities do not differ, these two indices are grouped into the same cluster).

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