Computers and information technology have become indispensable tools for most of us. This is particularly true in biological research, where scientists increasingly apply information technology to biological problems. The recent flood of data from genome sequences and functional genomics has given rise to new field, Bioinformatics, which combines elements of biology and computer science. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development.
WHAT IS BIOINFORMATICS?
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to study and process biological data.
The science of bioinformatics actually develops algorithms and biological software of computer to analyze and record the data related to biology for example the data of genes, proteins, drug ingredients and metabolic pathways. As biological data is always in raw form and there is a need of certain storage house in which the data can be stored, organized and manipulated. Bioinformatics provides central, globally accessible databases that enable scientists to submit, search and analyse information. It offers analysis software for data studies and comparisons and provides tools for modelling, visualising, exploring and interpreting data.
Nevertheless, bioinformatics has been defined in many different ways, since practitioners do not always agree upon the scope of its use within the biological and computer sciences, but it is always considered a combination of both sciences, along with other contributing disciplines.
GOALS OF BIOINFORMATICS
The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, and the modeling of evolution.
APPLICATIONS OF BIOINFORMATICS
Bioinformatics joins mathematics, statistics, and computer science and information technology to solve complex biological problems. These problems are usually the molecular level which cannot be solved by other means. This interesting field of science has many applications and research areas where it can be applied including :
- Molecular medicine
- Personalised medicine
- Preventative medicine
- Gene therapy
- Drug development
- Microbial genome applications
- Waste cleanup
- Climate change Studies
- Alternative energy sources
- Biotechnology
- Antibiotic resistance
- Forensic analysis of microbes
- The reality of bioweapon creation
- Evolutionary studies
- Crop improvement
- Insect resistance
- Development of Drought resistance varieties
- Veterinary Science
- Comparative Studies
BIOINFORMATICS SOFTWARES
Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions. They are designed for extracting the meaningful information from the mass of molecular biology / biological databases & to carry out sequence or structural analysis. Some examples of bioinformatics tools are :
1. BLAST:
1. BLAST:
BLAST ( Basic Local Alignment Search Tool) comes under the category of homology and similarity tools. It is a set of search programs designed for the Windows platform and is used to perform fast similarity searches regardless of whether the query is for protein or DNA. Comparison of nucleotide sequences in a database can be performed. Also a protein database can be searched to find a match against the queried protein sequence.
2. FASTA:
FAST homology search A ll sequences. An alignment program for protein sequences created by Pearsin and Lipman in 1988. The program is one of the many heuristic algorithms proposed to speed up sequence comparison. The basic idea is to add a fast prescreen step to locate the highly matching segments between two sequences, and then extend these matching segments to local alignments using more rigorous algorithms such as Smith-Waterman.
3. EMBOSS:
EMBOSS (European Molecular Biology Open Software Suite) is a software-analysis package. It can work with data in a range of formats and also retrieve sequence data transparently from the Web. Extensive libraries are also provided with this package, allowing other scientists to release their software as open source. It provides a set of sequence-analysis programs, and also supports all UNIX platforms.
4. Clustalw:
It is a fully automated sequence alignment tool for DNA and protein sequences. It returns the best match over a total length of input sequences, be it a protein or a nucleic acid.
5. RasMol:
It is a powerful research tool to display the structure of DNA, proteins, and smaller molecules. Protein Explorer, a derivative of RasMol, is an easier to use program.
6. PROSPECT:
PROSPECT (PROtein Structure Prediction and Evaluation Computer ToolKit) is a protein-structure prediction system that employs a computational technique called protein threading to construct a protein's 3-D model.
7. PatternHunter :
PatternHunter, based on Java, can identify all approximate repeats in a complete genome in a short time using little memory on a desktop computer. Its features are its advanced patented algorithm and data structures, and the java language used to create it. The Java language version of PatternHunter is just 40 KB, only 1% the size of Blast, while offering a large portion of its functionality.
8. COPIA :
COPIA (COnsensus Pattern Identification and Analysis) is a protein structure analysis tool for discovering motifs (conserved regions) in a family of protein sequences. Such motifs can be then used to determine membership to the family for new protein sequences, predict secondary and tertiary structure and function of proteins and study evolution history of the sequences
