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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


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Online
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1544-6115
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Volume 10, Issue 1

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Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains

Meromit Singer / Alexander Engström / Alexander Schönhuth / Lior Pachter
Published Online: 2011-09-23 | DOI: https://doi.org/10.2202/1544-6115.1677

Recent experimental and computational work confirms that CpGs can be unmethylated inside coding exons, thereby showing that codons may be subjected to both genomic and epigenomic constraint. It is therefore of interest to identify coding CpG islands (CCGIs) that are regions inside exons enriched for CpGs. The difficulty in identifying such islands is that coding exons exhibit sequence biases determined by codon usage and constraints that must be taken into account.

We present a method for finding CCGIs that showcases a novel approach we have developed for identifying regions of interest that are significant (with respect to a Markov chain) for the counts of any pattern. Our method begins with the exact computation of tail probabilities for the number of CpGs in all regions contained in coding exons, and then applies a greedy algorithm for selecting islands from among the regions. We show that the greedy algorithm provably optimizes a biologically motivated criterion for selecting islands while controlling the false discovery rate.

We applied this approach to the human genome (hg18) and annotated CpG islands in coding exons. The statistical criterion we apply to evaluating islands reduces the number of false positives in existing annotations, while our approach to defining islands reveals significant numbers of undiscovered CCGIs in coding exons. Many of these appear to be examples of functional epigenetic specialization in coding exons.

Keywords: Markov chains; pattern statistics; CpG islands; genomics

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Published Online: 2011-09-23


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 10, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1677.

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