1. The
creation of databases allowing the storage and management of large biological
data sets.
2. The development of algorithms and statistics
to determine relationships among members of large data sets.
3. The
use of these tools for the analysis and interpretation of various types of
biological data, including DNA, RNA and protein sequences, protein structures,
gene expression profiles, and biochemicalpathways.
The
term bioinformatics first came into use in the 1990s and was originally
synonymous with the management and analysis of DNA, RNA and protein sequence
data. Computational tools for sequence analysis had been available since the
1960s, but this was a minority interest until advances in sequencing technology
led to a rapid expansion in the number of stored sequences in databases such as
GenBank. Now, the term has expanded to incorporate many other types of
biological data, for example protein structures, gene expression profiles and
protein interactions. Each of these areas requires its own set of databases,
algorithms and statistical methods. Bioinformatics is largely, although not
exclusively, a computer-based discipline. Computers are important in
bioinformatics for two reasons:
First,
many bioinformatics problems require the same task to be repeated millions of
times. For example, comparing a new sequence to every other sequence stored in
a database or comparing a group of sequences systematically to determine
evolutionary relationships. In such cases, the ability of computers to process
information and test alternative solutions rapidly is indispensable. 080415
Bibliotheca Alexandrina Updated by Mariam Salib & Marwa Abdelrassoul
Second,
computers are required for their problem-solving power. Typical problems that
might be addressed using bioinformatics could include solving the folding
pathways of protein given its amino acid sequence, or deducing a biochemical
pathway given a collection of RNA expression profiles. Computers can help with
such problems, but it is important to note that expert input and robust
original data are also required.
1 The
future of bioinformatics is integration. For example, integration of a wide variety
of data sources such as clinical and genomic data will allow us to use disease symptoms
to predict genetic mutations and vice versa. The integration of GIS data, such
as maps, weather systems, with crop health and genotype data, will allow us to predict
successful outcomes of agriculture experiments. Another future area of
research
in bioinformatics is large-scale comparative genomics. For example, the development
of tools that can do 10-way comparisons of genomes will push forward the
discovery rate in this field of bioinformatics. Along these lines, the modeling
and visualization of full networks of complex systems could be used in the
future to predict how the system (or cell) reacts to a drug for example. A
technical set of challenges faces bioinformatics and is being addressed by
faster computers, technological advances in disk storage space, and increased
bandwidth. Finally, a key research question for the future of bioinformatics
will be how to computationally compare complex biological observations, such as
gene expression patterns and protein networks. Bioinformatics is about
converting biological observations to a model that a computer will understand.
This is a very challenging task since biology
can be
very complex. This problem of how to digitize phenotypic data such as behavior,
electrocardiograms, and crop health into a computer readable form offers exciting
challenges for future bioinformaticians.
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