Abstract
BackgroundCellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data.ResultsWe propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data.ConclusionsOur proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID Bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.
Highlights
Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions
We have carried out several computational experiments on nine protein interaction networks (PINs) data from eight different species using our proposed FAC-PIN algorithm
In this paper, we have proposed a new agglomerative clustering approach, FAC-PIN algorithm, for detecting the communities of a given PIN networks, and compared our method with two fast hierarchical techniques discussed in literature
Summary
Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. Single proteins may participate in more than one complex or functional module. Clique techniques are not quite scalable to large PINs and the identified modules are too strict in the biological sense of modules since proteins participating in a complex may not all interact with each other. Many biologically meaningful modules are ignored due to their low topological connectivity [15]
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