Probabilistic Model and Algorithms Design for Motif Detection

Authors

Yuan Xue, University of Texas-Pan American

Date of Award

8-2013

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Bin Fu

Second Advisor

Dr. Zhixiang Chen

Third Advisor

Dr. Artem Chebotko

Abstract

In this thesis, a natural probabilistic model has been used to test the quality of motif discovery programs. In this model, there are k background sequences, in which each character is a random character from Σ. Motif is a string G = g1g2 . . . gm. Each background sequence is implanted a probabilistically generated approximate copy of G. For each copy b1b2 . . . bm of G, every character bi is probabilistically generated such that the probability for bi 6= gi is at most α. Based on this model, two randomized algorithms ,one deterministic algorithm and one enumerative algorithm are designed, which can handle any motif patterns, and run much faster than those before, one can even run in sublinear time. These methods have been implemented in software.

Comments

Copyright 2013 Yuan Xue. All Rights Reserved.

https://www.proquest.com/dissertations-theses/probabilistic-model-algorithms-design-motif/docview/1426707440/se-2

Granting Institution

University of Texas-Pan American