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Bioinformatics which is
coming with HGP brings together the fields of life science, computer science
and statistics and strives to understand medical and biological systems by the creative
application of statistics and computer analysis.

Bioinformatics is the
use of computer technology to help scientists keep track of the genetic
information they find. Using computers, researchers can gather, store, analyze
and compare biological data with great speed and accuracy.

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Imagine studying gene
structures without the help of a computer. It would take many years to compare
the 15,000 genes of Arabidopsis to the genes of a similar plant. And keeping
track of the 100,000 genes of a human being would be inconceivable. With
computers, the process of comparison is automated. By storing information as it
is discovered, computers ease the immense job of genome mapping. But computers
can analyze as well as store information. They can be used to construct models
that reduce the need for experimentation.

In this way,
biotechnology has become more efficient. Scientists are able to use fairly
reliable computer-assisted predictions of test results on genetic
modifications. This complements the time-consuming process involved in growing
out every modified plant in the laboratory or green house to test for the
desired modification.











Bioinformatics today
has entered every major discipline in biology. In genomics, Bioinformatics has
aided in genome sequencing, and has shown its success in locating the genes, in
phylogenetic comparison and in the detection of transcription factor binding
sites of the genes (Liu et al., 1995; This G. et al., 2002), just to name a
few. Microarray technology has opened the world of transcript me in front
‘biologists (Spellman et al., 1998; Eisen et al., 1998). Bioinformatics
provides analytical tools for microarray data. These tools range from image the
processing techniques that read out the data, to the visualization tools that
provide a first-sight hint to the biologists; from preprocessing techniques
(Durbin et al., 2002) that remove the systematic noise in the data to the
clustering methods (Eisen et al., 1998; Sheng et al., 2003) that reveal genes
that behave similarly under different experimental conditions. In proteomics,
bioinformatics helps in the study of protein structures and the discovery of sequence
sites where protein-protein interactions take place. To help understanding
biology at the system level, bioinformatics begins to show promise in
unraveling genetic networks (Segal et al., 2003). Finally, in the study of metabolize,
bioinformatics is used to study the dynamics in a cell, and thus to simulate
the cellular interactions.
















Plant life plays
important and diverse roles in our society, our economy, and our global
environment. Especially crop is the most important plants to us. Feeding the
increasing world population is a challenge for modern plant biotechnology. Crop
yields have increased during the last century and will continue to improve as
agronomy re-assorting the enhanced breeding and develop new
biotechnological-engineered strategies. The onset of genomics is providing
massive information to improve crop phenotypes. The accumulation of sequence
data allows detailed genome analysis by using friendly database access and
information retrieval. Genetic and molecular genome co linearity allows
efficient transfer of data revealing extensive conservation of genome
organization between species. The goals of genome research are the
identification of the sequenced genes and the deduction of their functions by
metabolic analysis and reverses genetic screens of gene knockouts. Over 20% of
the predicted genes occur as cluster of related genes generating a considerable
proportion of gene families. Multiple alignments provide a method to estimate
the number of genes in gene families allowing the identification of previously
un described genes. This information enables new strategies to study gene
expression patterns in plants. Available information from news technologies, as
the database stored DNA microarray expression data, will help plant biology
functional genomics. Expressed sequence tags (ESTs) also give the opportunity
to perform “digital northern” comparison of gene expression levels providing
initial clues toward unknown regulatory phenomena. Crop plant networks
collections of databases and bioinformatics resources for crop plants genomics
have been built to harness the extensive work in genome mapping. This resource
facilitates the identification of ergonomically important genes, by comparative
analysis between crop plants and model species, allowing the genetic
engineering of crop plants selected by the quality of the resulting products.
Bioinformatics resources have evolved beyond expectation, developing new
nutritional genomics biotechnology tools to genetically modify and improve food
supply, for an ever-increasing world population. So bioinformatics can now be
leveraged to accelerate the translation of basic discovery to agriculture. The
predictive manipulation of plant growth will affect agriculture at a time when
food security, diminution of lands available for agricultural use, stewardship
of the environment, and climate change are all issues of growing public





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