Identification of regulatory binding sites using minimum spanning trees.

Victor Olman; Dong Xu; Ying Xu

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PubMed · 12603039

Identification of regulatory binding sites using minimum spanning trees.

Victor Olman·Dong Xu·Ying Xu

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Abstract

Recognition of protein-binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we present a new approach for studying this challenging issue. We formulate the binding-site recognition problem as a cluster identification problem, i.e., to identify clusters in a data set that exhibit significantly different features (e.g., density) than the overall background of the data set. We have developed a general framework for solving such a cluster identification problem. The foundation of the framework is a rigorous relationship between data clusters and subtrees of a minimum spanning tree (MST) representation of a data set. We have proposed a formal and general definition of clusters, and have demonstrated that a cluster is always represented as a connected component of the MST, and further it corresponds to a substring of a linear representation of the MST. Hence a cluster identification problem is reduced to a problem of finding substrings with certain features, for which algorithms have been developed. We have applied this MST-based cluster identification algorithm to a number of binding site identification problems. The results are highly encouraging.

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Algorithms Base Sequence Binding Sites Cluster Analysis Computational Biology DNA, Bacterial DNA, Fungal Databases, Protein Escherichia coli Proteins Humans Protein Binding Proteins Saccharomyces cerevisiae Proteins Systems Analysis Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Victor Olman, Dong Xu, Ying Xu. 2003. Identification of regulatory binding sites using minimum spanning trees.. https://pubmed.ncbi.nlm.nih.gov/12603039/

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