Abstract
Algorithmic comparison of DNA sequence motifs is a problem in bioinformatics that has received increased attention during the last years. Its main applications concern characterization of potentially novel motifs and clustering of a motif collection in order to remove redundancy. Despite growing interest in motif clustering, the question which motif clusters to aim at has so far not been systematically addressed. Here we analyzed motif similarities in a comprehensive set of vertebrate transcription factor classes. For this we developed enhanced similarity scores by inclusion of the information coverage (IC) criterion, which evaluates the fraction of information an alignment covers in aligned motifs. A network-based method enabled us to identify motif clusters with high correspondence to DNA-binding domain phylogenies and prior experimental findings. Based on this analysis we derived a set of motif families representing distinct binding specificities. These motif families were used to train a classifier which was further integrated into a novel algorithm for unsupervised motif clustering. Application of the new algorithm demonstrated its superiority to previously published methods and its ability to reproduce entrained motif families. As a result, our work proposes a probabilistic approach to decide whether two motifs represent common or distinct binding specificities.
Highlights
An important goal of biological research is to understand the mechanisms that control gene expression
Databases store a growing number of known DNA sequence patterns, denoted as DNA sequence motifs that are recognized by transcription factors
On the basis of these results, we were able to predict whether two motifs belong to the same subgroup and constructed a novel, fully-automated method for motif clustering, which enables users to assess the similarity of a newly found motif with all known motifs in the collection
Summary
An important goal of biological research is to understand the mechanisms that control gene expression. One is to search a library of known motifs with a newly discovered pattern to check its novelty or to derive hypotheses about TF families that could be assigned to the search pattern. This database search application is of increasing importance for the widely adopted ChIP-seq and ChIP-chip assays that enable computational extraction of DNA sequence motifs from large sets of genomic regions bound by a transcription factor of interest [3,4]. The growing body of known binding motifs for different transcription factors has stimulated interest to assign patterns to groups representing distinct specificities. While DNA sequence motifs in databases are typically defined for a narrow selection of proteins such as a group of isoforms, a subfamily or a complex, motif families may widen the scope to represent the DNA-binding properties, e.g., of a whole class of transcription factors
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