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Bioinformatics

In the early eighties, the word “Bioinformatics” was not that familiar. There was a small band of biologists who realized the immense potential of applying computer technology to model and analyze biological data. A collaboration was forged between mathematicians, physicists, biologists and computer scientists to form a fledging bioinformatics community. During the 90’s this interesting field was transformed beyond recognition with the establisment of internet as the worldwide standard network and advances in sequence technology. Bioinformatics involves the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems. Major research activities in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution. The science of Bioinformatics, which is the melding of molecular biology with computer science, is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery. In recognition of this, many universities, government institutions and pharmaceutical firms have formed bioinformatics groups, consisting of computational biologists and bioinformatics computer scientists.

The terms bioinformatics and computational biology are often used interchangeably. Bioinformatics is concerned with the information while computational biology is concerned with the hypotheses. A common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract significant information from data produced by high-throughput biological techniques such as genome sequencing. The ultimate aim of Bioinformatics is complete understanding of the organism-given its genome. It requires characterization and modelling of extremely complex systems: not only within the cell but also including the fantastic network of cell-cell interaction that go to make up an organism.

Applications
Sequence analysis
A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, computer programs are used to search the genome of thousands of organisms, containing billions of nucleotides. These programs would compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, in order to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. In the case of the Human Genome Project, it took several months of CPU time (on a circa-2000 vintage DEC Alpha computer) to assemble the fragments. Shotgun sequencing is the method of choice for virtually all genomes sequenced today, and genome assembly algorithms are a critical area of bioinformatics research.

Biodiversity
In addition to species and physical measures, bioinformatics provides a genetic measure of biodiversity. Eventually, it is the genetic diversity in a biosphere that will provide the correct measure of its extent. Computer simulations model such things as population dynamics, or calculate the cumulative genetic health of a breeding pool (in agriculture) or endangered population (in conservation).

Genome annotation
Annotation is the process of marking the genes and other biological features in a DNA sequence. The first genome annotation software system was designed in 1995 by Dr. Owen White, who was part of the team that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA are constantly changing and improving.

Risk Assessment
More research on human genome can help in assessing individual’s risk on exposure to toxic elements as resistance to external agents. It can also help to reduce the likelihood of heritable mutations.

Evolutionary Biology
Evolutionary biology is the study of the origin and descent of species, as well as their change over time. Informatics has assisted evolutionary biologists in several key ways; it has enabled researchers to:

  • trace the evolution of a large number of organisms by measuring changes in their DNA
  • compare entire genomes, permitting the study of complex evolutionary events, such as gene duplication, lateral gene transfer, and the prediction of factors important in organism speciation
  • build complex computational models of populations to predict the outcome of the system over time
  • track and share information on an increasingly large number of species and organisms.

Analysis of Gene Expression
A physical or chemical change in a living system is not caused by a single gene but by the combined action of several genes. Understanding the action of many genes of a single condition will, one day, provide a genetic basis for disease and change control.

Analysis of Regulation
Regulation is the chain of events starting with an extracellular signal such as temperature or a hormone and leading to an increase or decrease in the activity of one or more proteins. Bioinformatics techniques have been applied to explore various steps in this process. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. The analysis of what promotes and regulates the activity of genes and proteins forms a part of this study.

Microbial Genomics
The genomes of bacteria can help to throw light on different energy resources, environmental monitoring detecting pollutants, find disease producing properties of genes and improve industrial efficiency.

Prediction of Protein Structure
Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determines a structure in its native environment. Knowledge of this structure is vital in understanding the function of the protein. One of the key ideas in bioinformatics is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene. In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. A technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known.

Comparative Genomics
By comparing the genes of different organisms, it is possible to trace evolutionary pathways by which one organism could have evoved into another. Such studies can not only throw new light on evolution, but also provide evidence for the migration of species, thereby bringing new evidence to historical and morphological studies.

Modeling Biological Systems
Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

High-throughput Image Analysis
Computational technologies accelerate and automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment a researcher’s ability to do measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. These systems are becoming more important for both diagnostics and research.

 

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DATABASE
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genome Genome Projects
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