Abstract
A set of conserved binding sites recognized by a transcription factor is called a motif, which can be found by many applications of comparative genomics for identifying over-represented segments. Moreover, when numerous putative motifs are predicted from a collection of genome-wide data, their similarity data can be represented as a large graph, where these motifs are connected to one another. However, an efficient clustering algorithm is desired for clustering the motifs that belong to the same groups and separating the motifs that belong to different groups, or even deleting an amount of spurious ones. In this work, a new motif clustering algorithm, CLIMP, is proposed by using maximal cliques and sped up by parallelizing its program. When a synthetic motif dataset from the database JASPAR, a set of putative motifs from a phylogenetic foot-printing dataset, and a set of putative motifs from a ChIP dataset are used to compare the performances of CLIMP and two other high-performance algorithms, the results demonstrate that CLIMP mostly outperforms the two algorithms on the three datasets for motif clustering, so that it can be a useful complement of the clustering procedures in some genome-wide motif prediction pipelines. CLIMP is available at http://sqzhang.cn/climp.html.
Highlights
The rapid development of new technologies has led to the declining cost of genome sequencing, and as a result, thousands of genomes are being sequenced [1, 2]
When a synthetic motif dataset from the database JASPAR, a set of putative motifs from a phylogenetic foot-printing dataset, and a set of putative motifs from a ChIP dataset are used to compare the performances of CLIMP and two other high-performance algorithms, the results demonstrate that CLIMP mostly outperforms the two algorithms on the three datasets for motif clustering, so that it can be a useful complement of the clustering procedures in some genome-wide motif prediction pipelines
If the binding sites of a transcription factors (TFs) are shuffled to generate a series of sub-sets, a clustering algorithm is necessarily proposed to test whether these sub-motifs can be clustered together again
Summary
The rapid development of new technologies has led to the declining cost of genome sequencing, and as a result, thousands of genomes are being sequenced [1, 2]. Numerous comparative genomics-based algorithms have been developed in order to decipher the biological functions of various sequenced genomes; this can be computed because these biological functions are encoded and relatively conserved in a group of closely related genomes. Transcription regulation is usually triggered by the binding of proteins called transcription factors (TFs) to specific DNA segments known as TF binding sites (TFBSs). These TFBSs are for the most part predicted by comparing multiple non-coding sequences that potentially contain the TFBSs. A set of TFBSs recognized by the same TF is called a motif, which summarizes the commonalities among the binding sites of a TF [3]. PLOS ONE | DOI:10.1371/journal.pone.0160435 August 3, 2016
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