Abstract
BackgroundTranscription factor binding site (TFBS) motifs can be accurately represented by position frequency matrices (PFM) or other equivalent forms. We often need to compare TFBS motifs using their PFMs in order to search for similar motifs in a motif database, or cluster motifs according to their binding preference. The majority of current methods for motif comparison involve a similarity metric for column-to-column comparison and a method to find the optimal position alignment between the two compared motifs. In some applications, alignment-free methods might be preferred; however, few such methods with high accuracy have been described.Methodology/Principal FindingsHere we describe a novel alignment-free method for quantifying the similarity of motifs using their PFMs by converting PFMs into k-mer vectors. The motifs could then be compared by measuring the similarity among their corresponding k-mer vectors.Conclusions/SignificanceWe demonstrate that our method in general achieves similar performance or outperforms the existing methods for clustering motifs according to their binding preference and identifying similar motifs of transcription factors of the same family.
Highlights
Transcription factors (TFs) play important roles in the regulation of gene transcription through binding to specific DNA sequences called TF binding sites (TFBSs), which are usually 5–25 bp in length [1,2]
A TFBS motif is often represented by a position frequency matrix (PFM), which consists of nucleotide frequencies at each position of the motif [3]
We evaluated our algorithm for identifying the TFBS motifs of structural and/or evolutionarily related TFs using all three datasets by the ‘‘best-hit’’ approach used in Mahony et al [8]
Summary
Transcription factors (TFs) play important roles in the regulation of gene transcription through binding to specific DNA sequences called TF binding sites (TFBSs), which are usually 5–25 bp in length [1,2]. A PFM is derived from the alignment of known TFBSs of the TF, and it largely reflects the TF’s DNA binding preference at each position. In genome-scale TFBS prediction applications, redundant and sub motifs of the same TFs are often returned by motif finders, and they need to be clustered to form unique motifs [9,10]. In all these applications, the similarity between two motifs needs to be accurately calculated for the desired purposes. Transcription factor binding site (TFBS) motifs can be accurately represented by position frequency matrices (PFM) or other equivalent forms. Alignment-free methods might be preferred; few such methods with high accuracy have been described
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