Abstract

Leishmania major, a member of the Kinetoplastida family, is a primitive protozoan that causes a human disease, called leishmaniases, affecting numerous people worldwide. The identification of new drug targets to combat leishmaniases necessitates a thorough understanding of how genomic instructions are transformed into functional proteins. It requires not only the prediction and categorization of all the genes, but also a profound understanding of their regulation. Much of gene regulation may occur through a process known as frarcs-splicing. Trans-splicing, which is mechanistically similar to cissplicing, is the process of cleaving a large polycistronic transcript into smaller monocistronic components.

The goal of this project was to establish a model to accurately predict sites where /rans-splicing occurs. After carefully analyzing the data set, a second-order log odds ratio model was created. This method achieved an overall accuracy of 89% in predicting transsplice sites.

Furthermore, this new method has been applied to a small data set with alternative trans-splice sites. Of the 70 EST-indicated alternative frans-splice sites 60 were identified as such. This represents the first computational method for the prediction of alternative splice sites. In addition, we have found the first real evidence for the branch point signal which plays an essential role in the ^raws-splicing process.

Library of Congress Subject Headings

Leishmania--Genetics; RNA splicing--Mathematical models; Leishmaniasis--Treatment

Publication Date

2007

Document Type

Thesis

Student Type

Graduate

Degree Name

Bioinformatics (MS)

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences (COS)

Advisor

Shuba Gopal

Advisor/Committee Member

James Halavin

Advisor/Committee Member

Paul Tymann

Comments

Physical copy available from RIT's Wallace Library at QL368.K5 H32 2007

Campus

RIT – Main Campus

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