Applications of motif discovery in biological data

Sequential motif discovery, the ability to identify conserved patterns in ordered datasets without a priori knowledge of exactly what those patterns will be, is a frequently encountered and difficult problem in computational biology and biochemical engineering. The most prevalent example of such a problem is finding conserved DNA sequences in the upstream regions of genes that are believed to be coregulated. Other examples are as diverse as identifying conserved secondary structure in proteins and interpreting time-series data. This thesis creates a unified, generic approach to addressing these (and other) problems in sequential motif discovery and demonstrates the utility of that approach on a number of applications. A generic motif discovery algorithm was created for the purpose of finding conserved patterns in arbitrary data types. This approach and implementation, name Gemoda, decouples three key steps in the motif discovery process: comparison, clustering, and convolution. Since it decouples these steps, Gemoda is a modular algorithm; that is, any comparison metric can be used with any clustering algorithm and any convolution scheme. The comparison metric is a data-specific function that transforms the motif discovery problem into a solvable graph-theoretic problem that still adequately represents the important similarities in the data.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Chemical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Styczynski, Mark Philip-Walter

Advisor dc:contributor.advisor

• Gregory Stephanopoulos.

• Chemical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/38976

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/38976