MotifClick: prediction of <it>cis</it>-regulatory binding sites via merging cliques

MotifClick: prediction of <it>cis</it>-regulatory binding sites via merging cliques

Su Zhengchang, Pham Phuc T, NIU Meng, Zhang Shaoqiang, Li SHAN

Abstract Background Although dozens of algorithms and tools have been developed to find a set of cis-regulatory binding sites called a motif in a set of intergenic sequences using various approaches, most of these tools focus on identifying binding sites that are significantly different from their background sequences. However, some motifs may have a similar nucleotide distribution to that of their background sequences. Therefore, such binding sites can be missed by these tools. Results Here, we present a graph-based polynomial-time algorithm, MotifClick, for the prediction of cis-regulatory binding sites, in particular, those that have a similar nucleotide distribution to that of their background sequences. To find binding sites with length k, we construct a graph using some 2(k-1)-mers in the input sequences as the vertices, and connect two vertices by an edge if the maximum number of matches of the local gapless alignments between the two 2(k-1)-mers is greater than a cutoff value. We identify a motif as a set of similar k-mers from a merged group of maximum cliques associated with some vertices. Conclusions When evaluated on both synthetic and real datasets of prokaryotes and eukaryotes, MotifClick outperforms existing leading motif-finding tools for prediction accuracy and balancing the prediction sensitivity and specificity in general. In particular, when the distribution of nucleotides of binding sites is similar to that of their background sequences, MotifClick is more likely to identify the binding sites than the other tools.

2011

DOI: 10.1186/1471-2105-12-238

BMC Bioinformatics

BMC

Biology (General), Computer applications to medicine. Medical informatics

EN