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Beyond Fixed-Resolution Alignment-Free Measures for Mammalian Enhancers Sequence Comparison
July-Aug. 2014 (vol. 11 no. 4)
pp. 628-637
Matteo Comin, Department of Information Engineering, University of Padova, via Gradenigo 6/A, 35131 Padova,
Davide Verzotto, Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore,
The cell-type diversity is to a large degree driven by transcription regulation, i.e., enhancers. It has been recently shown that in high-level eukaryotes enhancers rarely work alone, instead they collaborate by forming clusters of cis-regulatory modules (CRMs). Even if the binding of transcription factors is sequence-specific, the identification of functionally similar enhancers is very difficult. A similarity measure to detect related regulatory sequences is crucial to understand functional correlation between two enhancers. This will allow large-scale analyses, clustering and genome-wide classifications. In this paper we present $Under_2$, a parameter-free alignment-free statistic based on variable-length words. As opposed to traditional alignment-free methods, which are based on fixed-length patterns or, in other words, tied to a fixed resolution, our statistic is built upon variable-length words, and thus multiple resolutions are allowed. This will capture the great variability of lengths of CRMs. We evaluate several alignment-free statistics on simulated data and real ChIP-seq sequences. The new statistic is highly successful in discriminating functionally related enhancers and, in almost all experiments, it outperforms fixed-resolution methods. Finally, experiments on mouse enhancers show that $Under_2$ can separate enhancers active in different tissues. Availability:
Index Terms:
Bioinformatics,Genomics,Customer relationship management,Computational modeling,Computational biology,regulatory sequences comparison,Alignment-free statistics,pattern discovery
Matteo Comin, Davide Verzotto, "Beyond Fixed-Resolution Alignment-Free Measures for Mammalian Enhancers Sequence Comparison," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. 4, pp. 628-637, July-Aug. 2014, doi:10.1109/TCBB.2014.2306830
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