Publication Date

Fall 2013

Degree Type

Master's Project

Department

Computer Science

Abstract

Traditional Position Weight Matrices (PWMs) that are used to model Transcription Factor Binding Sites (TFBS) assume independence among different positions in the binding site. In reality, this may not necessarily be the case. A better way to model TFBS is to consider the distribution of dinucleotides or trinucleotides instead of just mononucleotides, thus taking neighboring nucleotides into account. We can therefore, extend the single nucleotide PWM to a dinucleotide PWM or an even higher-order PWM to correctly estimate the dependencies among the nucleotides in a given sequence. The purpose of this project is to develop an algorithm to implement higher-order PWMs to detect the TFBS and other biological motifs in DNA, RNA, and proteins.

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