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Automation has assaulted several levels of biological research generating vast
 

 
amounts of data. Great leaps in DNA sequencing technology and automation led
 

 
to a dramatic increase in sequence throughput thus giving birth to the genome
 

 
sequencing projects, with the culmination this year of the near completion of
 

 
the human genome sequence.

 

 
In addition, advances in the development of high throughput display methods
 

 
such as microarrays and technical developments in the field of proteomics have
 

 
enhanced sensitivity and throughput in expression analysis and protein identification.
 

 
Collectively, these, and other, developments have given rise to the accumulation
 

 
of vast amounts of biological data at an unprecedented and still fast growing
 

 
rate.

 

 

There has therefore been a need for the development of tools that facilitate
 

 
the gathering and deposition into specialized databanks of biological data as
 

 
well as their efficient management and retrieval. In itself, data does not necessarily
 

 
translate into knowledge. Tools are required to sift through the vast amounts
 

 
of data in order to extract information in a form that is meaningful to the
 

 
practitioners in the field, in a process that has now been termed data mining.Beyond
 

 
data management and analysis, there is a need in modern biology in keeping pace
 

 
with the rapidly expanding knowledge, for the integration of complex data sets
 

 
from different sources and at multiple levels. This would require the codification
 

 
of all biological information, such as cellular pathways, biomolecules and gene
 

 
data, into a standard syntax, or ontology, that can be used in modeling and
 

 
simulating biological processes in aiding discovery.

 

 

Bionformatics is the scientific discipline that has evolved to fulfill these
 

 
requirements. Bionformatics can therefore be defined as encompassing “all
 

 
aspects of biological information acquisition, processing, storage, distribution
 

 
and analysis”, combining the methods of mathematics, computer science and
 

 
biology (Benton, 1996). In practical terms, bioinformatics involves the application
 

 
of information technology for the gathering, management and analysis of biological
 

 
data, which translates into databases and their search and analysis software
 

 
tools that have become an essential part of today’s biological research
 

 
environment.

EXECUTIVE SUMMARY

1. INTRODUCTION

1.1. What is Bioinformatics?

1.2. Historical Perspective

1.3. What is Bioinformatics Used For?

2. APPROACHES AND TOOLS

2.1. DNA Sequence Assembly

2.2. Expression Profiling and Clustering

2.3. Sequence Alignment and Similarity Tools: BLAST and FASTA

2.4. Gene Identification

2.5. Protein Sequence Analysis

2.6. Tools for Protein Pattern Identification and Analysis

3. DATABASES

3.1. DNA Sequence Databases

3.2. EST and Expression Databases

3.3. Protein Sequence Databases

3.4. Protein Structure Databases

3.5. Protein Pattern and Alignment Databases

3.6. ENTREZ

4. BIOINFORMATICS IN DRUG DISCOVERY AND DISEASE

4.1. New Drug Target Identification

4.2. Computational Modeling in Target Identification

4.3. Single Nucleotide Polymorphisms (SNPs)

4.4. Cheminformatics

5. INDUSTRY REPRESENTATIVE PROFILES

5.1. Core Bioinformatics Companies

5.2. Genomics Companies

5.3. Bioinformatics in the Big Pharmas

5.4. Big Pharma Bioinformatics Collaborations

6. BIOINFORMATICS COMPANIES

6.1. Methodology

6.2. Company Listing

7. BIOINFORMATICS PATENTS

7.1. Methodology

7.2. Concise Patent Listing

7.3. Extended Patent Listing

8. THE FUTURE OF BIOINFORMATICS

8.1. Bioinformatics in the Post-Genomic Era

8.2. Data Mining

8.3. Current Challenges