Activities of DNA are to a great extent controlled epigenetically through the internal struc- ture of chromatin. This structure is dynamic and is influenced by different modifications of histone proteins. Various combinations of epigenetic modification of histones pinpoint to different functional regions of the DNA determining the so-called chromatin states. How- ever, the characterization of chromatin states by the DNA sequence properties remains largely unknown. In this study we aim to explore whether DNA sequence patterns in the human genome can characterize different chromatin states.
Using DNA sequence motifs we built binary classifiers for each chromatic state to eval- uate whether a given genomic sequence is a good candidate for belonging to a particular chromatin state. Of four classification algorithms (C4.5, Naive Bayes, Random Forest, and SVM) used for this purpose, the decision tree based classifiers (C4.5 and Random Forest) yielded best results among those we evaluated. Our results suggest that in general these models lack sufficient predictive power, although for four chromatin states (insulators, het- erochromatin, and two types of copy number variation) we found that presence of certain motifs in DNA sequences does imply an increased probability that such a sequence is one of these chromatin states.
| Date of Award | Jun 2013 |
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| Original language | English (US) |
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| Awarding Institution | - Computer, Electrical and Mathematical Sciences and Engineering
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| Supervisor | Vladimir Bajic (Supervisor) |
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- chromatin
- machine learning
- motifs