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Beyond fixed-resolution alignment-free measures for mammalian enhancers sequence comparison
PrePrint
ISSN: 1545-5963
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 Under2, a parameter-free alignment-free statistic based on variable-length words. As opposed to traditional alignment-free methods, which are based on fixedlength 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 Under2 can separate enhancers active in different tissues. Availability: http://www.dei.unipd.it/ciompin/main/UnderIICRMS.html
Index Terms:
regulatory sequences comparsion,alignment-free statistics,pattern discovery
Citation:
Davide Verzotto, "Beyond fixed-resolution alignment-free measures for mammalian enhancers sequence comparison," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 28 Feb. 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TCBB.2014.2306830>
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