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Bioinformatics

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Bioinformatics ~~and computational biology involve~~  involves the use of techniques including applied mathematics, informatics, statistics, computer science, ~~artificial intelligence,~~  chemistry, and biochemistry to solve biological problems ~~usually on the molecular level. Research in computational biology often overlaps with systems biology~~. Major research efforts 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.  [[111]]<h2p>Introduction</h2p>

The terms bioinformatics and computational biology are often used interchangeably. However bioinformatics more properly refers to the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems inspired from the management and analysis of biological data. Computational biology, on the other hand, refers to hypothesis-driven investigation of a specific biological problem using computers, carried out with experimental or simulated data, with the primary goal of discovery and the advancement of biological knowledge. Put more simply, bioinformatics is concerned with the information while computational biology is concerned with the hypotheses. A similar distinction is made by National Institutes of Health in their working definitions of Bioinformatics and Computational Biology, where it is further emphasized that there is a tight coupling of developments and knowledge between the more hypothesis-driven research in computational biology and technique-driven research in bioinformatics.

A common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract useful information from data produced by high-throughput biological techniques such as genome sequencing. A representative problem in bioinformatics is the assembly of high-quality genome sequences from fragmentary "shotgun" DNA sequencing. Other common problems include the study of gene regulation using data from microarrays or mass spectrometry.

<h2p>Major research areas</h2p>

<h3>Sequence analysis</h3>

~~<dl><dd>~~

~~<div class="noprint">Main articles: Sequence alignment and Sequence database</div>~~

~~</dd></dl>~~

Since the Phage Φ-X174 was sequenced in 1977, the DNA sequences of hundreds of organisms have been decoded and stored in databases. The information is analyzed to determine genes that encode polypeptides, 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 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. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenzae) 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.

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 sequences for protein identification.

<h4>Genome annotation</h4>

~~<dl><dd>~~

~~<div class="noprint">Main article: Gene finding</div>~~

~~</dd></dl>~~

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 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. 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.

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:

<ul>

<li>trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone, </li> <li>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, </li> <li>build complex computational models of populations to predict the outcome of the system over time </li> <li>track and share information on an increasingly large number of species and organisms </li>

</ul>

Future work endeavours to reconstruct the now more complex tree of life.

<h3>Prediction of protein structure</h3>

~~<dl><dd>~~

~~<div class="noprint">Main article: Protein structure prediction</div>~~

~~</dd></dl>~~

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. (Of course, there are exceptions, such as the bovine spongiform encephalopathy - aka Mad Cow Disease - prion.) Knowledge of this structure is vital in understanding the function of the protein. For lack of better terms, structural information is usually classified as one of secondary, tertiary and quaternary structure. A viable general solution to such predictions remains an open problem. As of now, most efforts have been directed towards heuristics that work most of the time.

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: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. 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. In 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. This currently remains the only way to predict protein structures reliably.

<h3>Modeling biological systems</h3>

~~<dl><dd>~~

~~<div class="noprint">Main article: Systems biology</div>~~

~~</dd></dl>~~

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.

Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment an observer's ability to make 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. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research. Some examples are:

<ul>

<li>high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology) </li> <li>morphometrics </li> <li>clinical image analysis and visualization </li> <li>determining the real-time air-flow patterns in breathing lungs of living animals </li> <li>quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury </li> <li>making behavioral observations from extended video recordings of laboratory animals </li> <li>infrared measurements for metabolic activity determination </li>

</ul>

<h2p>Software tools</h2p>

First generation bioinformatics tools consisted of applications, usually with a text-based interface, which performed a specific task well. The computational biology tool best-known among biologists is probably BLAST, an algorithm for searching large databases of protein or DNA sequences. The NCBI provides a popular web-based implementation that searches their massive sequence databases. Also fairly early on, due to the amassing of sequence and annotation data, keyword search engines which were able to resolve gene and protein synonyms were important. Computer scripting languages such as Perl (thanks to its regular expressions handling facilities) and Python are often used to interface with biological databases and parse output from bioinformatics programs written in languages such as C or C++. Communities of bioinformatics programmers have set up free open source bioinformatics projects to develop and distribute the tools and modules they produce.

As the data sources expanded and diversified, both in content and geography, bioinformatic meta search engines, such as Sequence profiling tools, emerged to help find relevant information from several databases. These meta search engines might index data from a local server or even from a panel of third party services.

More recently, SOAP-based interfaces have been developed for a wide variety of bioinformatics applications allowing an application running on one computer in one part of the world to use algorithms, data and computing resources on servers in other parts of the world. A large availability of these SOAP-based bioinformatics web services, along with the open source bioinformatics collections, lead to the next generation of bioinformatics tools: the integrated bioinformatics platform. These tools range from a collection of standalone tools with a common data format under a single, slick standalone or web-based interface, to integrative and extensible bioinformatics workflow development environments.

<h2p>See also</~~span~~font></~~h2>~~<pspan>~~ ~~

<h3>Related topics</h3>

<ul>

<li>Biocybernetics </li> <li>Bioinformatics companies </li> <li>Biologically-inspired computing </li> <li>Biomedical informatics </li> <li>Computational biology </li> <li>Computational biomodeling </li> <li>Computational genomics </li> <li>Dot plot (bioinformatics) </li>

</ul>

</td>

<ul>

<li>Metabolic network modelling </li> <li>Molecular modelling </li> <li>Morphometrics </li> <li>Natural computation </li> <li>Pharmaceutical company </li> <li>Protein-protein interaction prediction </li> <li>List of numerical analysis software </li>

</ul>

</td>

<ul>

<li>Applied mathematics </li> <li>Artificial intelligence </li> <li>Biology </li> <li>Cheminformatics </li> <li>Computational biology </li>

</ul>

</td>

<ul>

<li>Computational science </li> <li>Computer science </li> <li>Cybernetics </li> <li>Informatics </li> <li>Mathematical biology </li>

</ul>

</td>

<ul>

<li>Neuroinformatics </li> <li>Scientific computing </li> <li>Statistics </li> <li>Systems biology </li> <li>Theoretical biology </li>

</ul>

</td>

<h2p>References</h2p>

<ul>

<li>Aluru, Srinivas, ed. Handbook of Computational Molecular Biology. Chapman & Hall/Crc, 2006. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=1584884061">ISBN 1584884061</a> (Chapman & Hall/Crc Computer and Information Science Series) </li> <li>Baldi, P and Brunak, S, Bioinformatics: The Machine Learning Approach, 2nd edition. MIT Press, 2001. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=026202506X">ISBN 0-262-02506-X</a> </li> <li>Barnes, M.R. and Gray, I.C., eds., Bioinformatics for Geneticists, first edition. Wiley, 2003. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0470843942">ISBN 0-470-84394-2</a> </li> <li>Baxevanis, A.D. and Ouellette, B.F.F., eds., Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, third edition. Wiley, 2005. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0471478784">ISBN 0-471-47878-4</a> </li> <li>Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds., Current Protocols in Bioinformatics. Wiley, 2007. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0471250937">ISBN 0-471-25093-7</a> </li> <li>Claverie, J.M. and C. Notredame, Bioinformatics for Dummies. Wiley, 2003. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0764516965">ISBN 0-7645-1696-5</a> </li> <li>Cristianini, N. and Hahn, M. <a class="external text" title="http://www.computational-genomics.net/" rel="nofollow" href="http://www.computational-genomics.net/">Introduction to Computational Genomics</a>, Cambridge University Press, 2006. (<a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=9780521671910">ISBN 9780521671910</a> | <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0521671914">ISBN 0521671914</a>) </li> <li>Durbin, R., S. Eddy, A. Krogh and G. Mitchison, Biological sequence analysis. Cambridge University Press, 1998. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0521629713">ISBN 0-521-62971-3</a> </li> <li>Gilbert, D. <a class="external text" title="http://bib.oxfordjournals.org/cgi/content/abstract/5/3/300" rel="nofollow" href="http://bib.oxfordjournals.org/cgi/content/abstract/5/3/300">Bioinformatics software resources</a>. Briefings in Bioinformatics, Briefings in Bioinformatics, 2004 5(3):300-304. </li> <li>Keedwell, E., Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems. Wiley, 2005. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0470021756">ISBN 0-470-02175-6</a> </li> <li>Kohane, et al. Microarrays for an Integrative Genomics. The MIT Press, 2002. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=026211271X">ISBN 0-262-11271-X</a> </li> <li>Lund, O. et al. Immunological Bioinformatics. The MIT Press, 2005. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0262122804">ISBN 0-262-12280-4</a> </li> <li>Michael S. Waterman, Introduction to Computational Biology: Sequences, Maps and Genomes. CRC Press, 1995. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0412993910">ISBN 0-412-99391-0</a> </li> <li>Mount, David W. Bioinformatics: Sequence and Genome Analysis Spring Harbor Press, May 2002. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0879696087">ISBN 0-87969-608-7</a> </li> <li>Pachter, Lior and <a title="Bernd Sturmfels" href="http://en.wikipedia.org/wiki/Bernd_Sturmfels">Sturmfels, Bernd</a>. "Algebraic Statistics for Computational Biology" Cambridge University Press, 2005. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0521857007">ISBN 0-521-85700-7</a> </li> <li>Pevzner, Pavel A. Computational Molecular Biology: An Algorithmic Approach The MIT Press, 2000. <a class="internal" href="http://en.wikipedia.org/w/index.php?title=Special:Booksources&isbn=0262161974">ISBN 0-262-16197-4</a> </li>

</ul>

~~<a id="External_links" name="External_links"></a>~~ <h2p>External links</h2p>

</div>

</div>

<li>

<ul>

<li>[http://bioinformatics.ws Bioinformatics.ws]: Bioinformatics wiki site.</li> <li>[http://biomatics.org Biomatics.org]</li> <li><a class="external text" title="http://bioinformatics.org/" rel="nofollow" href="http://bioinformatics.org/">Bioinformatics Organization (Bioinformatics.Org): The Open-Access Institute</a> </li> <li><a class="external text" title="http://www.embnet.org/" rel="nofollow" href="http://www.embnet.org/">EMBnet</a> </li> <li><a class="external text" title="http://www.ebi.ac.uk/" rel="nofollow" href="http://www.ebi.ac.uk/">European Bioinformatics Institute</a> </li> <li><a class="external text" title="http://www.embl.org/" rel="nofollow" href="http://www.embl.org/">European Molecular Biology Laboratory</a> </li> <li><a class="external text" title="http://www.iscb.org/" rel="nofollow" href="http://www.iscb.org/">The International Society for Computational Biology</a> </li> <li><a class="external text" title="http://www.ncbi.nlm.nih.gov/" rel="nofollow" href="http://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> </li> <li><a class="external text" title="http://www.nih.gov" rel="nofollow" href="http://www.nih.gov/">National Institutes of Health homepage</a> </li> <li><a class="external text" title="http://www.open-bio.org/" rel="nofollow" href="http://www.open-bio.org/">Open Bioinformatics Foundation: umbrella non-profit organization supporting certain open-source projects in bioinformatics</a> </li> <li><a title="Swiss Institute of Bioinformatics" href="http://en.wikipedia.org/wiki/Swiss_Institute_of_Bioinformatics">Swiss Institute of Bioinformatics</a> </li> <li><a title="Wellcome Trust Sanger Institute" href="http://en.wikipedia.org/wiki/Wellcome_Trust_Sanger_Institute">Wellcome Trust Sanger Institute</a> </li>

</ul>

</li>

<li>Major Journals

<ul>

<li><a class="external text" title="http://www.almob.org/" rel="nofollow" href="http://www.almob.org/">Algorithms in Molecular Biology</a> </li> <li><a class="external text" title="http://bioinformatics.oupjournals.org/" rel="nofollow" href="http://bioinformatics.oupjournals.org/">Bioinformatics journal</a> </li> <li><a class="external text" title="http://www.biomedcentral.com/bmcbioinformatics" rel="nofollow" href="http://www.biomedcentral.com/bmcbioinformatics">BMC Bioinformatics journal</a> </li> <li><a class="external text" title="http://bib.oxfordjournals.org/" rel="nofollow" href="http://bib.oxfordjournals.org/">Briefings in Bioinformatics</a> </li> <li><a class="external text" title="http://www.la-press.com/evolbio.htm" rel="nofollow" href="http://www.la-press.com/evolbio.htm">Evolutionary Bioinformatics</a> </li> <li><a class="external text" title="http://www.genome.org" rel="nofollow" href="http://www.genome.org/">Genome Research</a> </li> <li><a class="external text" title="http://www.bepress.com/ijb/" rel="nofollow" href="http://www.bepress.com/ijb/">The International Journal of Biostatistics</a> </li> <li><a class="external text" title="http://www.liebertpub.com/publication.aspx?pub_id=31" rel="nofollow" href="http://www.liebertpub.com/publication.aspx?pub_id=31">Journal of Computational Biology</a> </li> <li><a class="external text" title="http://www.nature.com/msb/index.html" rel="nofollow" href="http://www.nature.com/msb/index.html">Molecular Systems Biology</a> </li> <li><a class="external text" title="http://compbiol.plosjournals.org" rel="nofollow" href="http://compbiol.plosjournals.org/">PLoS Computational Biology</a> </li> <li><a class="external text" title="http://www.bepress.com/sagmb/" rel="nofollow" href="http://www.bepress.com/sagmb/">Statistical Applications in Genetic and Molecular Biology</a> </li>

</ul>

</li>

<li>Other sites

<ul>

<li><a class="external text" title="http://www.biostatsresearch.com/repository/" rel="nofollow" href="http://www.biostatsresearch.com/repository/">The Collection of Biostatistics Research Archive</a> </li> <li><a class="external text" title="http://www.ornl.gov/TechResources/Human_Genome/research/informatics.html" rel="nofollow" href="http://www.ornl.gov/TechResources/Human_Genome/research/informatics.html">Human Genome Project and Bioinformatics</a> </li> <li><a class="external text" title="http://dmoz.org/Science/Biology/Bioinformatics/Research_Groups/" rel="nofollow" href="http://dmoz.org/Science/Biology/Bioinformatics/Research_Groups/">List of Bioinformatics Research Groups</a> at the <a title="Open Directory Project" href="http://en.wikipedia.org/wiki/Open_Directory_Project">Open Directory Project</a> </li>

</ul>

</li>

~~[[생정보학]], [[생명정보학]], [[생물정보학]] 혹은 [[전산생물학]]은 [[응용수학]], [[전산학]], [[통계학]], [[물리학]] 등의 방법론을 빌려와 생물학적 문제를 해결하는 분야로, 생명계학과 공통분모를 지니고 있다. ~~주로 [[서열 정렬]], [[유전자 탐색]], [[게놈 조합]], [[단백질 구조 정렬]], [[단백질 구조 예측]], [[유전자 발현 예측]], [[단백질간 상호작용]], [[진화 모델링]] 등이 연구 분야에 속한다. ~~생정보학과 전산생물학은 혼용되어 자주 사용되는데, 후자가 [[알고리즘]] 개발과 특수한 전산적 방법론에 더 비중을 둔다고 말할 수 있다.~~

[http://bioinformatics.ws Bioinformatics.ws] | [http://biomics.org Biomics.org]

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