Date of Degree

2-2014

Document Type

Dissertation

Degree Name

Ph.D.

Program

Computer Science

Advisor(s)

Dina Sokol

Subject Categories

Bioinformatics | Computer Sciences

Keywords

clustering, core promoter elements, human genome, ngram, promoter, tandem repeat

Abstract

DNA motifs include repeat elements, promoter elements and gene regulator elements, and play a critical role in the human genome. This thesis describes a genome-wide computational study on two groups of motifs: tandem repeats and core promoter elements.

Tandem repeats in DNA sequences are extremely relevant in biological phenomena and diagnostic tools. Computational programs that discover tandem repeats generate a huge volume of data, which can be difficult to decipher without further organization. A new method is presented here to organize and rank detected tandem repeats through clustering and classification. Our work presents multiple ways of expressing tandem repeats using the n-gram model with different clustering distance measures. Analysis of the clusters for the tandem repeats in the human genome shows that the method yields a well-defined grouping in which similarity among repeats is apparent. Our new, alignment-free method facilitates the analysis of the myriad of tandem repeats replete in the human genome. We believe that this work will lead to new discoveries on the roles, origins, and significance of tandem repeats.

As with tandem repeats, promoter sequences of genes contain binding sites for proteins that play critical roles in mediating expression levels. Promoter region binding proteins and their co-factors influence timing and context of transcription. Despite the critical regulatory role of these non-coding sequences, computational methods to identify and predict DNA binding sites are extremely limited. The work reported here analyzes the relative occurrence of core promoter elements (CPEs) in and around transcription start sites. We found that out of all the data sets 49\%-63\% upstream regions have either TATA box or DPE elements. Our results suggest the possibility of predicting transcription start sites through combining CPEs signals with other promoter signals such as CpG islands and clusters of specific transcription binding sites.

 
 

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