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| '''Bioinformatics''' or '''computational biology''' is the use of | |||
| ⚫ | ] and ] techniques, including ], to solve ] problems, usually by creating or using ]s, ]s or both. One of the main areas of bioinformatics is the ] and analysis of the data gathered by the various ]s. Other areas are ], ], ], ] and ]. | ||
| As a summary, the various genome projects produce many long lists of letters and one of the roles of bioinformatics is to attempt to determine the words, grammar, sentences and ultimately, meaning (functional significance) of those letters. There are many who hope that developments in this field will ultimately help in the discoveries of ]s for various diseases including ]. | As a summary, the various genome projects produce many long lists of letters and one of the roles of bioinformatics is to attempt to determine the words, grammar, sentences and ultimately, meaning (functional significance) of those letters. There are many who hope that developments in this field will ultimately help in the discoveries of ]s for various diseases including ]. | ||
Revision as of 00:18, 14 December 2004
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Bioinformatics or computational biology is the use of mathematical and informational techniques, including statistics, to solve biological problems, usually by creating or using computer programs, mathematical models or both. One of the main areas of bioinformatics is the data mining and analysis of the data gathered by the various genome projects. Other areas are sequence alignment, protein structure prediction, systems biology, protein-protein interactions and virtual evolution. As a summary, the various genome projects produce many long lists of letters and one of the roles of bioinformatics is to attempt to determine the words, grammar, sentences and ultimately, meaning (functional significance) of those letters. There are many who hope that developments in this field will ultimately help in the discoveries of cures for various diseases including cancer.
Sequence analysis
Main articles: Sequence alignment, Sequence database
Since the Epstein-Barr virus was sequenced in 1984, the DNA sequence of more and more organisms is stored in electronic databases. These data are analyzed to determine genes that code for proteins, as well as regulatory sequences. 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 becomes impossible to analyze DNA sequences manually. Today, computer programs are used to find similar sequences in the genome of dozens of organisms, within billions of nucleotides. These programs can 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. The so-called shotgun sequencing (that was used, for example, by Celera Genomics to sequence the human genome) does not give a sequential list of nucleotides, but instead the sequences of thousands of small DNA fragments (each about 600 nucleotides long). The ends of these fragments overlap and, aligned in the right way, make up the complete genome. Shotgun sequencing yields sequence data quickly, but the task to re-align the fragments can be quite complicated for larger genomes. In the case of the Human Genome Project, it took several months on a supercomputer array to align them correctly. Shotgun sequencing is generally preferred for smaller genomes, such as bacteria, and often used at least partially on organisms with much larger genomes.
Another aspect of bioinformatics in sequence analysis is the automatic search for genes and regulatory sequences within a genome. Not all of the nucleotides within a genome are genes. Within the genome of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects, for example in the use of DNA sequence for protein identification.
Bioinformatics tools
Computer scripting languages such as Perl and Python are often used to interface with biological databases and parse output from bioinformatics programs. Communities of bioinformatics programmers have set up free/open source projects such as EMBOSS, EnsEMBL, BioPerl, BioPython, BioRuby, and BioJava which develop and distribute shared programming tools and objects (as program modules) that make bioinformatics easier.
Bioinformatics and structural biology
Main article: Protein structure prediction
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. But, the protein can only function correctly if it is folded in a very special and individual way (if it has the correct secondary, tertiary and quaternary structure). The prediction of this folding just by looking at the amino acid sequence is quite difficult. Several methods for computer predictions of protein folding are currently (as of 2004) under development.
One of the key principles in bioinformatics is homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene. If gene A is homologous to gene B of which the function is known, it is likely to have a similar function. In the structural branch of bioinformatics homology is used to determine which parts of the protein are important in structure formation and interaction with other proteins. In a technique called homology modelling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. This currently remains the only way to predict protein structures reliably.
One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in both organisms. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.
Modeling biological systems
Main article: Systems biology
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.
Other applications
Morphometrics is used to analyze pictures of embryos to track and to predict the fate of cell clusters during morphogenesis.
See also
- sequence analysis, sequence motif, structural motif, protein structure prediction, biologically-inspired computing, morphometrics, metabolic network, Important publications in bioinformatics
Related fields
Bibliography
- R. Durbin, S. Eddy, A. Krogh and G. Mitchison, Biological sequence analysis. Cambridge University Press, 1998. ISBN 0521629713
- Mount, David W. "Bioinformatics: Sequence and Genome Analysis" Spring Harbor Press, May 2002. ISBN 0879696087
- JM. Claverie, C. Notredame, Bioinformatics for Dummies. Wiley, 2003. ISBN 0764516965
External links
- Software projects
- Organizations
- Directories
- Other
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