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Updated: Sep 27, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
Published on: August 9, 2019
Identification of regulatory binding sites using minimum spanning trees
Victor Olman1, Dong Xu, Ying Xu
1Protein Informatics Group, Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6480, USA.
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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