CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions

doi:10.1186/1471-2105-9-64

BMC Bioinformatics

Volume 9

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Research article

CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions

Woochang Hwang¹ , Young-Rae Cho¹ , Aidong Zhang¹ and Murali Ramanathan²

¹Department of Computer Science and Engineering, State University of New York, Buffalo, NY 14260, USA

²Department of Pharmaceutical Sciences, State University of New York, Buffalo, NY 14260, USA

author email corresponding author email

BMC Bioinformatics 2008, 9:64doi:10.1186/1471-2105-9-64


Published:	29 January 2008

Additional files

Additional file 1:

Clusters obtained using CASCADE for the yeast PPI network. The first column is a cluster identifier; the Size column indicates the number of proteins in each cluster; the Density indicates the percentage of possible protein interactions that are present; the H column indicates the percentage of proteins concordant with the major function indicated in the last column; the D column indicates the percentage of proteins discordant with the major function and U column indicates the percentage of proteins not assigned to any function. The -log p values for biological function are shown.

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Additional file 2:

Functional term distribution. Functional term distribution in MIPS functional category for the top 10 largest clusters in Additional File 1. (a) cluster 1, size 411. (b) cluster 2, size 303. (c) cluster 3, size 240. (d) cluster 4, size 176. (e) cluster 5, size 170. (f) cluster 6, size 104. (g) cluster 7, size 96. (h) cluster 8, size 79. (i) cluster 9, size 78. (j) cluster 10, size 73. Each figure presents the percentile of proteins that are accordant with the top ten best accordant functional terms for each cluster.

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Additional file 3:

Normalized number of functional terms for each cluster detected by CACASDE. The first column is a cluster identifier; the Size column indicates the number of proteins in each cluster. The normalized numbers of functional terms in the MIPS functional hierarchy for each identified cluster are presented in the third, the fourth, and the fifth column. The number of functional terms per each cluster is normalized by its cluster size. The third column represents the normalized number of functional terms that are more specific than 2nd level functional hierarchy. The fourth column represents the normalized number of functional terms that are more specific than 3rd level functional hierarchy. The fifth column represents the normalized number of functional terms that are more specific than 4th level functional hierarchy.

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Additional file 4:

Topological shape of a cluster and its functional annotations. Cluster 20 in Additional File 1. (a) sub graph of Cluster 20 extracted from DIP PPI network. Each protein is annotated by MIPS functional category. (b) MIPS functional IDs and their corresponding literal names. The best accordant functional term is boldfaced.

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Additional file 5:

Topological shape of a cluster and its functional annotations. Cluster 21 in Additional File 1. (a) sub graph of Cluster 21 extracted from DIP PPI network. Each protein is annotated by MIPS functional category. (b) MIPS functional IDs and their corresponding literal names. The best accordant functional term is boldfaced.

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Additional file 6:

Topological shape of a cluster and its functional annotations. Cluster 22 in Additional File 1. (a) sub graph of Cluster 22 extracted from DIP PPI network. Each protein is annotated by MIPS functional category. (b) MIPS functional IDs and their corresponding literal names. The best accordant functional term is boldfaced.

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Additional file 7:

Topological shape of a cluster and its functional annotations. Cluster 25 in Additional File 1. (a) sub graph of Cluster 25 extracted from DIP PPI network. Each protein is annotated by MIPS functional category. (b) MIPS functional IDs and their corresponding literal names. The best accordant functional term is boldfaced.

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