Comparative analysis of human and mouse CpG islands using dbCGI

Yalcin, Dicle; Otu, Hasan H.

doi:10.1109/eit.2017.8053357

2017 IEEE International Conference on Electro Information Technology (EIT) 2017

DOI: 10.1109/eit.2017.8053357

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Comparative analysis of human and mouse CpG islands using dbCGI

Dicle Yalcin

Hasan H. Otu

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2021

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Cited by 1 publication

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An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

Yalcin

Otu

2021

CBIO

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Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

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An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

Yalcin

Otu

2021

CBIO

View full text Add to dashboard Cite

show abstract

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Comparative analysis of human and mouse CpG islands using dbCGI

Cited by 1 publication

References 31 publications

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

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