<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 &quot;shotgun&quot; DNA [[sequencing]], and the prediction of [[gene regulation]] with data from [[Messenger RNA|mRNA]] [[DNA microarray|microarray]]s or [[mass spectrometry]]. </p>

''Main articles:'' [[Sequence alignment]], [[Sequence database]]</p>

<p>Since the [[Phi-X174 phage|Phage &amp;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.&nbsp; 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.&nbsp; 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.&nbsp; 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.&nbsp; 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>

''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.&nbsp; 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]].&nbsp; 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.&nbsp; Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA are constantly changing and improving.</p>

[[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 />

Future work endeavours to reconstruct the now more complex [[Evolutionary_tree|tree of life]].</p>

<p>The area of research within [[computer science]] that uses [[genetic algorithm|genetic algorithms]] is sometimes confused with [[computational evolutionary biology]].&nbsp; Work in this area involves using specialized [[computer software]] to improve equations, algorithms, or [[integrated circuit]] designs.&nbsp; It is inspired by [[evolutionary principles]] such as [[replication]], [[diversification]] through [[recombination]] or [[mutation]], [[fitness]], survival through [[selection]] or [[culling]], and [[iteration]], collectively called a [[Darwinian machine]] or [[Darwinian ratchet]].</p>

[[Biodiversity]] of an ecosystem might be defined as the total genomic complement of a particular environment, from all of the species present, whether it is a biofilm in an abandoned mine, a drop of sea water, a scoop of soil, or the entire [[biosphere]] of the planet [[Earth]].&nbsp; Databases are used to collect the [[species]] names, descriptions, distributions, genetic information, status and size of [[population]]s, [[Habitat (ecology)|habitat]] needs, and how each organism interacts with other species.&nbsp; Specialized [[computer software|software]] programs are used to find, visualize, and analyze the information, and most importantly, communicate it to other people.&nbsp; Computer simulations model such things as population dynamics, or calculate the cumulative genetic health of a breeding pool (in [[agriculture]]) or endangered population (in [[conservation ecology|conservation]]).&nbsp; One very exciting potential of this field is that entire [[DNA]] sequences, or [[genome]]s of [[endangered species]] can be preserved, allowing the results of Nature's genetic experiment to be remembered ''[[in silico]]'', and possibly reused in the future, even if that species is eventually lost.</p>

<p>''Important Projects:'' [http://www.sp2000.org/ Species 2000 project].</p>

<p>The [[expression]] of many genes can be determined by measuring [[mRNA]] levels with multiple techniques including [[DNA_microarray|microarrays]],&nbsp; [[expressed sequence tag|expressed cDNA sequence tag]] (EST) sequencing, [[Serial Analysis of Gene Expression|serial analysis of gene expression]] (SAGE) tag sequencing, [[massively parallel signature sequencing]] (MPSS), or various applications of multiplexed in-situ hybridization.&nbsp; All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate [[signal (information theory)|signal]] from [[noise]] in high-throughput gene expression (HT) studies. HT studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the proteins that are up-regulated and down-regulated in cancer cells.</p>

Regulation is the complex orchestra of events starting with an [[extra-cellular signal]] and ultimately leading to the increase or decrease in the [[protein activity|activity]] of one or more protein molecules.&nbsp; Bioinformatics techniques have been applied to explore various steps in this process.&nbsp; For example, [[promoter analysis]] involves the elucidation and study of [[sequence motif]]s in the genomic region surround the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA.&nbsp; Expression data can be used to infer gene regulation: one might compare [[microarray]] data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the [[cell cycle]], along with various stress conditions (heat shock, starvation, etc.). One can then apply [[cluster analysis|clustering algorithms]] to that expression data to determine which genes are co-expressed. Further analysis could take a variety of directions: one 2004 study analyzed the [[promoter]] sequences of co-expressed (clustered together) genes to find common [[regulatory elements]] and used [[machine learning]] techniques to identify the promoter elements involved in regulating each cluster{{ref|Beer_2004}}.</p>

Protein [[microarray]]s and high throughput (HT) [[mass spectrometry]] (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former involves a number of the same problems involve in examining microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete, peptides from each protein are detected.</p>

Massive sequencing efforts are currently underway to identify [[point mutation]]s in a variety of [[gene]]s in [[cancer]]. The sheer volume of data produced requires automated systems to read sequence data, and to compare the sequencing results to the known sequence of the [[human genome]], including known [[germline]] polymorphisms.</p>

<p>[[Oligonucleotide]] microarrays, including [[comparative genomic hybridization]] and [[single nucleotide polymorphism]] arrays, able to probe simultaneously up to several hundred thousand sites throughout the genome are being used to identify chromosomal gains and losses in cancer. [[Hidden Markov model]] and [[change-point analysis]] methods are being developed to infer real copy number changes from often noisy data. Further informatics approaches are being developed to understand the implications of lesions found to be recurrent across many tumors.</p>

<p>Some modern tools (e.g. [http://www.q-pharm.com/home/contents/drug_d/soft Quantum 3.1] ) provide tool for changing the protein sequence at specific sites through alterations to its amino acids and predict changes in the bioactivity after mutations.</p>

<p>''Main article:'' [[Protein structure prediction]]</p>

<p>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 determine a structure in its native environment. (Of course, there are exceptions, such as the [[bovine spongiform encephalopathy]] - aka Mad Cow Disease - prion.)&nbsp; Knowledge of this structure is vital in understanding the function of the protein. For lack of better terms, structural information are usually classified as one of ''[[secondary structure|secondary]]'', ''[[tertiary structure|tertiary]]'' and ''[[quaternary structure|quaternary]]'' structures. 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.</p>

<p>Other techniques for predicting protein structure include protein threading and ''de novo'' (from scratch) physics-based modeling.</p>

<p>See also [[structural motif]] and [[structural domain]].</p>

<p>The core of comparative genome analysis is the establishment of the correspondence between [[genes]] (orthology analysis) or other genomic features in different organisms. It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and [[endosymbiosis]], often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectra of algorithmic, statistical and mathematical techniques, ranging from exact, [[heuristics]], fixed parameter and [[approximation algorithms]] for problems based on parsimony models to [[Markov Chain Monte Carlo]] algorithms for [[Bayesian analysis]] of problems based on probabilistic models. </p>

<p>Many of these studies are based on the [[homology]] detection and protein families computation.&nbsp; [http://phigs.jgi-psf.org Phylogenetically Inferred Groups (PhIGs)], a recently developed method incorporates phylogenetic signals in building gene clusters for use in comparative genomics.</p>

<p>See also [[comparative genomics]], [[bayesian network]] and [[protein family]].</p>

''Main article:'' [[Systems biology]]</p>

<p>Systems biology involves the use of [[computer simulation]]s of [[cell (biology)|cellular]] subsystems (such as the [[metabolic network|networks of metabolites]] and [[enzyme]]s which comprise [[metabolism]], [[signal transduction]] pathways and [[gene regulatory network]]s) to both analyze and visualize the complex connections of these cellular processes.&nbsp; [[Artificial life]] or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.</p>

Computational technologies are also 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 the observers ability to make measurements from a large or complex set of images, by improving [[accuracy]], [[objectivity]], or speed.&nbsp; A fully developed analysis system may completely replace the observer.&nbsp; While these systems are not unique to biology related imagery, their application to biologic problems continue to provide unique challenges and solutions, placing several imagery application under the umbrella of Bioinformatics. These systems are in the process of becoming more important for both [[diagnostics]] and research.&nbsp; Some examples: <br />

* high-throughput and high-fidelity quantification and sub-cellular localization ([[high-content screening]], [[cytohistopathology]])<br />

* making behavioural observations from extended video recordings of laboratory animals<br />

* infrared measurements for metabolic activity determination</p>

<p>The computational biology tool best-known among biologists is probably [[BLAST]], an algorithm for searching large sequence (protein, DNA) databases. [[NCBI]] provides a popular implementation that searches their massive sequence databases.<br />

Bioinformatic meta search engines ([[Entrez]], [[Bioinformatic Harvester]]) help finding relevant information from several databases.&nbsp; There are also free Web-based software designed for [[structural bioinformatics]] such as [http://www.cbi.cnptia.embrapa.br/SMS/] [[STING]].</p>

[http://www.q-pharm.com&nbsp; Quantum 3.1] is an example of the bioinformatics post-[[QSAR]]&nbsp; technology applying quantum and molecular physics instead of statistical methods. </p>

<p><!-- Please do not add advertisements for commerical tools here. Objective descriptions of noteworthy commercial tools are fine, but ads are not. --></p>

<p>* [[Biomedical informatics]]<br />

* [[Biologically-inspired computing]]<br />

* [[Biocybernetics]]<br />

* [[Computational biomodeling]]</p>

<p>* [[applied mathematics]] &amp;mdash; [[biology]] &amp;mdash; [[computer science]] &amp;mdash; [[informatics]] &amp;mdash; [[mathematical biology]] &amp;mdash; [[theoretical biology]] &amp;mdash; [[Scientific computing]] &amp;mdash; [[cheminformatics]] &amp;mdash; [[computational science]]</p>

<p>* [http://wikiomics.org Wikiomics.org: bioinformatics wiki] for users and developers of bioinformatics worldwide. Focused on practical questions and pointers towards both academic publications and software resources (opened November 2005). <!-- please use it instead of cluttering Wikipedia with links; that's the right place for most of the stuff below --></p>

<p>* Major Societies<br />

**[http://ontology.buffalo.edu/smith Barry Smith's biomedical ontology site]<br />

**[http://www.microbesonline.org Virtual Insitute of Microbial Stress and Survival (VIMSS)]</p>

# {{note|Beer_2004}} Beer MA, Tavazoie S. &quot;[http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=pubmed&amp;dopt=Abstract&amp;list_uids=15084257 Predicting gene expression from sequence].&quot; In ''Cell''. 2004 Apr 16;117(2):185-98.]</p>

* Baxevanis, A.D. and Ouellette, B.F.F., eds., ''Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins'', third edition. Wiley, 2005. ISBN 0471478784<br />

* Claverie, J.M. and C. Notredame, ''Bioinformatics for Dummies''. Wiley, 2003. ISBN 0764516965<br />