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Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes.

Chae H, Park J, Lee SW, Nephew KP, Kim S - Nucleic Acids Res. (2013)

Bottom Line: First, by calculating genome distance based on rank correlation tests, we show that k-mer and k-flank patterns around CpG sites can be used to correctly reconstruct the phylogeny of 10 mammalian genomes.Further, we used various machine learning algorithms to demonstrate that CpG islands sequences can be characterized using k-mers.In addition, by testing a human model on the nine different mammalian genomes, we provide the first evidence that k-mer signatures are consistent with evolutionary history.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.

ABSTRACT
CpG islands are GC-rich regions often located in the 5' end of genes and normally protected from cytosine methylation in mammals. The important role of CpG islands in gene transcription strongly suggests evolutionary conservation in the mammalian genome. However, as CpG dinucleotides are over-represented in CpG islands, comparative CpG island analysis using conventional sequence analysis techniques remains a major challenge in the epigenetics field. In this study, we conducted a comparative analysis of all CpG island sequences in 10 mammalian genomes. As sequence similarity methods and character composition techniques such as information theory are particularly difficult to conduct, we used exact patterns in CpG island sequences and single character discrepancies to identify differences in CpG island sequences. First, by calculating genome distance based on rank correlation tests, we show that k-mer and k-flank patterns around CpG sites can be used to correctly reconstruct the phylogeny of 10 mammalian genomes. Further, we used various machine learning algorithms to demonstrate that CpG islands sequences can be characterized using k-mers. In addition, by testing a human model on the nine different mammalian genomes, we provide the first evidence that k-mer signatures are consistent with evolutionary history.

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Machine learning analysis human as positive data set and others as negative data set.
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gkt144-F15: Machine learning analysis human as positive data set and others as negative data set.

Mentions: To further compare CpG island sequences among species, we used the human CpG island sequences as the positive data set and other species sequences as the negative data set and performed 10-fold cross validation experiments. Figure 15 illustrates the experimental scheme. The result in Figure 16 is consistent with evolutionary history: prediction accuracy was low for close species (e.g., human versus chimp), and high prediction accuracy was observed for distant species, e.g., human versus opossum.Figure 15.


Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes.

Chae H, Park J, Lee SW, Nephew KP, Kim S - Nucleic Acids Res. (2013)

Machine learning analysis human as positive data set and others as negative data set.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643570&req=5

gkt144-F15: Machine learning analysis human as positive data set and others as negative data set.
Mentions: To further compare CpG island sequences among species, we used the human CpG island sequences as the positive data set and other species sequences as the negative data set and performed 10-fold cross validation experiments. Figure 15 illustrates the experimental scheme. The result in Figure 16 is consistent with evolutionary history: prediction accuracy was low for close species (e.g., human versus chimp), and high prediction accuracy was observed for distant species, e.g., human versus opossum.Figure 15.

Bottom Line: First, by calculating genome distance based on rank correlation tests, we show that k-mer and k-flank patterns around CpG sites can be used to correctly reconstruct the phylogeny of 10 mammalian genomes.Further, we used various machine learning algorithms to demonstrate that CpG islands sequences can be characterized using k-mers.In addition, by testing a human model on the nine different mammalian genomes, we provide the first evidence that k-mer signatures are consistent with evolutionary history.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, School of Informatics and Computing, Indiana University, Bloomington, IN, USA.

ABSTRACT
CpG islands are GC-rich regions often located in the 5' end of genes and normally protected from cytosine methylation in mammals. The important role of CpG islands in gene transcription strongly suggests evolutionary conservation in the mammalian genome. However, as CpG dinucleotides are over-represented in CpG islands, comparative CpG island analysis using conventional sequence analysis techniques remains a major challenge in the epigenetics field. In this study, we conducted a comparative analysis of all CpG island sequences in 10 mammalian genomes. As sequence similarity methods and character composition techniques such as information theory are particularly difficult to conduct, we used exact patterns in CpG island sequences and single character discrepancies to identify differences in CpG island sequences. First, by calculating genome distance based on rank correlation tests, we show that k-mer and k-flank patterns around CpG sites can be used to correctly reconstruct the phylogeny of 10 mammalian genomes. Further, we used various machine learning algorithms to demonstrate that CpG islands sequences can be characterized using k-mers. In addition, by testing a human model on the nine different mammalian genomes, we provide the first evidence that k-mer signatures are consistent with evolutionary history.

Show MeSH