DESIGN AND DEVELOPMENT OF THE AFRICAN PLASMODIUM FALCIPARUM DATABASE – (afriPFdb)

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TABLE OF CONTENT

Title Page

Chapter One: Introduction
1.1       Background Information
1.2       Statement of the Problem
1.3       Aim and Objectives
1.4       Methodology
1.5       Significance of Study
1.6       Scope of Study
1.7       Limitation of Study
1.8       Expected Contribution
1.9       Arrangement of Outline

Chapter Two: Literature Review
2.1       What is Database?
2.2       Database Management Systems
2.3       Biological Databases
2.4       Review of Existing Biological Database
2.5       What is Plasmodium?

Chapter Three: Analysis and Design
3.1       System Architechture
3.2       The Modules
3.3       Database Design

Chapter Four: Implementation
4.1       The System Requirements
4.2       The Implementation Tools Used
4.3       Description of the System

Chapter Five: Summary and Conclusion
5.1       Summary
5.2       Recommendation
5.3       Future Work
5.4       Conclusion
References


CHAPTER ONE

INTRODUCTION





1.1       BACKGROUND INFORMATION


Malaria is one of the planet's deadliest diseases and one of the leading causes of sickness and death in the developing world. Africa has suffered and is still suffering from the adverse socio-economic effects of malaria; It is intimately connected with poverty. Judged as both a root cause and a consequence of poverty, it is most intractable for the poorest countries in the world. According to GSK’s (GlaxoSmithKline) Corporate Responsibility Report 2007, it affects the health and economic growth of nations and individuals alike and is costing Africa about $12 billion a year in economic output. It has a greater impact on Africa's human resources than simple lost earnings. Another indirect cost of malaria is the human pain and suffering caused by the disease. It also hampers children's schooling and social development through both absenteeism and permanent neurological and other damage associated with severe episodes of the disease (© 2008 Millennium Promise). According to WHO’s (World Health Organization) World Malaria Report 2008, there were an estimated 247 million malaria cases worldwide in 2006, of which 91% or 230 million were due to Plasmodium falciparum. The vast majority of cases (86%) were in the African Region, followed by the South-East Asia (9%) and Eastern Mediterranean regions (3%) as shown in figure 1.1.



There are four major human malaria strains (Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, Plasmodium malariae). Plasmodium falciparum is the most common and deadly form. The percentage of malaria cases due to Plasmodium falciparum exceeded 75% in most African countries but only in a few countries outside Africa. In Africa, Nineteen of the most populous countries accounted for 90% of estimated cases in 2006 with Nigeria having the highest percent of the 90% estimated cases (WHO’s World Malaria Report 2008) as shown in figure 1.2.

This calls for increased speed in the search for more potent/effective cure that will preserve the lives of many. Chloroquine and most anti-malaria drugs are fast becoming in-effective as the parasite has grown resistance to them. Therefore, there is a huge need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria (Bulashevska, S. et al; 2007).

This project work is aimed at building a publicly accessible database through the use of a relational database architecture that will house Plasmodium falciparum genome sequence data and the results that we obtained by applying in-silico tools for quick access such that researchers can integrate this resource with other relevant data sets, and exploit the resulting information for functional studies, including identification of novel drug targets and candidate vaccine antigens.

We believe that the experimental results that will be obtained from our data will drive work in malaria research that will quicken the discovery pipeline of drugs and vaccines.



1.2       STATEMENT OF THE PROBLEM


KEGG database is mainly developed using in-silico analysis, while BioCyc is experimentally curated but strongly in many cases confirmed the in-silico results displayed on the metabolic....


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