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<p>'''Bioinformatics''' or '''computational <strong>Computational biology''' </strong> is the use biology that tries to solve biological problems using large amount of computational power and techniques from [[applied mathematics]], [[informatics]], [[statistics]], and [[computer science]] to solve [[. <br /><br /><strong>Bioinformatics and Compuational Biology</strong><br />Bioinformatics is an independant dscipline that was formed around mid 1990s. While computational biology is a more generic field of biology|where much computing resource is necessary. They are different from each other in many ways. Biological informatics is essentially the reverse of Computational Biology as it is an informatics field where the major application is biological]] problems. <br /><br />Research in computational biology often overlaps with [[systems biology]]. Major research efforts in the field include [[sequence alignment]], [[Gene_finding|gene finding]], genome assembly, [[ protein structural alignment | protein structure alignment]], [[protein structure prediction]], prediction of [[gene expression]] and [[protein-protein interactions]], and the modeling of [[evolution]]. The terms ''bioinformatics'' and ''computational biology'' are often used interchangeably, although the latter typically focuses on algorithm development and specific computational methods. (In the biology-mathematics-computer science triad, bioinformatics will intimately involve all three components while computational biology will focus on biology and mathematics.) Due to interest from computer scientists and mathematicians and the popularity of computational techniques in the field of genomics, it is commonly referred to as ''computational biology''; a more accurate term is computational genomics. There are also lesser known but equally important areas of computational [[biochemistry]] and computational [[biophysics]], that are also a part of computational biology. (For working definitions of Bioinformatics and Computational Biology used by [[NIH|National Institutes of Health]] please see [http://www.bisti.nih.gov/CompuBioDef.pdf this link].) A common thread in projects in bioinformatics and computational genomics is the use of mathematical tools to extract useful information from [[noise|noisy]] data produced by high-throughput biological techniques. (The field of [[data mining]] overlaps with computational biology in this regard.) Representative problems in computational biology include the assembly of high-quality [[DNA]] sequences from fragmentary "shotgun" DNA [[sequencing]], and the prediction of [[gene regulation]] with data from [[Messenger RNA|mRNA]] [[DNA microarray|microarray]]s or [[mass spectrometry]]. </p>
<p>[[image:dna-split.png|thumbnail|right|150px|Making sense of the huge amounts of DNA data (pictured) produced by gene sequencing projects is just one of the tasks faced by bioinformatics.]]</p>
<p><strong>==Major research areas==</strong></p><p><strong>===Sequence analysis===</strong><br />''Main articles:'' [[Sequence alignment]], [[Sequence database]]</p>
<p>Since the [[Phi-X174 phage|Phage &Phi;-X174]] was [[sequencing|sequenced]] in 1977, the [[DNA sequence]]s of more and more organisms have been decoded and stored in electronic databases. This data is analyzed to determine genes that code for [[protein]]s, 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 tree]]s). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, [[computer program]]s are used to search the [[genome]] of thousands of organisms, containing billions of [[nucleotide]]s. 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]] technique (which was used, for example, by [[The Institute for Genomic Research]] to sequence the first bacterial genome, ''Haemophilus influenza'') does not give a sequential list of nucleotides, but instead the sequences of thousands of small DNA fragments (each about 600-800 nucleotides long). The ends of these fragments overlap and, when aligned in the right way, make up the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. 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.</p>
<p>Another aspect of bioinformatics in sequence analysis is the automatic [[gene finding|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.</p>
<p>''See also:'' [[sequence analysis]], [[sequence profiling tool]], [[sequence motif]].</p>
<p><strong>====Genome annotation====</strong><br />''Main articles:'' [[Gene finding]]</p>
<p>In the context of genomics, '''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 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]]. Dr. White built a software system to find the genes (places in the DNA sequence that encode a protein), the transfer RNA, and other features, and to make initial assignments of function to those genes. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA are constantly changing and improving.</p>
<p>===Computational evolutionary biology===<br />[[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:<br />*trace the evolution of a large number of organisms by measuring changes in their [[DNA]], rather than through [[physical taxonomy]] or physiological observations alone,<br />*more recently, compare entire [[genomes]], which permits the study of more complex evolutionary events, such as [[gene duplication]], [[lateral gene transfer]], and the prediction of bacterial [[speciation factors]],<br />*build complex computational models of populations to predict the outcome of the system over time<br />*track and share information on an increasingly large number of species and organisms<br />Future work endeavours to reconstruct the now more complex [[Evolutionary_tree|tree of life]].</p>