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Computational biology

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[[Bioinformatics]] and Compuational Biology

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.

~~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~~ sometimes 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]].

Major research areas

Sequence analysis

~~''Main articles:'' [[Sequence alignment]], [[Sequence database]]~~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.

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.

''See also:'' [[sequence analysis]], [[sequence profiling tool]], [[sequence motif]].

Genome annotation

~~''Main articles:'' [[Gene finding]]~~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.

Computational evolutionary biology

[[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:

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

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

The area of research within [[computer science]] that uses [[genetic algorithm|genetic algorithms]] is sometimes confused with [[computational evolutionary biology]].  Work in this area involves using specialized [[computer software]] to improve equations, algorithms, or [[integrated circuit]] designs.  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]].

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.

Structure prediction

~~''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. 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.)  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.

One of the key ideas in bioinformatics research is the notion of [[homology (biology)|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 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.

See also [[comparative genomics]], [[bayesian network]] and [[protein family]].

Modeling biological systems

~~''Main article:'' [[Systems biology]]~~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.  [[Artificial life]] or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

High-throughput image analysis

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.  A fully developed analysis system may completely replace the observer.  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.  Some examples:   * high-throughput and high-fidelity quantification and sub-cellular localization ([[high-content screening]], [[cytohistopathology]]) *  [[morphometrics]] are used to analyze pictures of [[embryo]]s to track and to predict the fate of cell clusters during [[morphogenesis]] *  clinical image analysis and visualization *  determine the real-time air-flow patterns in breathing lungs of living individuals before and during challenge *  quantify occlusion size in real-time imagery from the development of and recovery during arterial injury *  making behavioural observations from extended video recordings of laboratory animals *  infrared measurements for metabolic activity determination

Software tools

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.

See also

*  [[Biomedical informatics]] *  [[Biologically-inspired computing]] *  [[List_of_publications_in_biology#Bioinformatics|List of publications in bioinformatics]] *  [[Molecular modelling]] *  [[Morphometrics]] *  [[Metabolic network]] *  [[Biocybernetics]] *  [[Computational biomodeling]]

Related fields

* [[applied mathematics]] &mdash; [[biology]] &mdash; [[computer science]] &mdash; [[informatics]] &mdash; [[mathematical biology]] &mdash; [[theoretical biology]] &mdash; [[Scientific computing]] &mdash; [[cheminformatics]] &mdash; [[computational science]]

External links

[http://bioinformatics.ws Bioinformatics Wiki Site]

*  [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). *  Major Societies *[http://www.iscb.org/ The International Society for Computational Biology]*  Major Organizations **[http://bioinformatics.org/ Bioinformatics Organization (Bioinformatics.Org): The Open-Access Institute] **[http://www.embnet.org/ EMBnet is a science-based group of collaborating nodes throughout Europe and a number of nodes outside Europe] **[http://www.cbse.ucsc.edu/ UCSC Center for Biomolecular Science and Engineering] **[http://www.ebi.ac.uk/ European Bioinformatics Institute] **[http://www.embl.org/ European Molecular Biology Laboratory] **[http://www.girinst.org/ Genetic Information Research Institute] **[http://www.ncbi.nlm.nih.gov/ National Center for Biotechnology Information] **[http://www.open-bio.org/ Open Bioinformatics Foundation: umbrella non-profit organization supporting certain open-source projects in bioinformatics] **[http://ncbo.us National Center for Biomedical Ontology] **[http://www.jgi.doe.gov/ US Department of Energy Joint Genome Institute]* Software projects **[http://amos.sourceforge.net/ AMOS: a modular, open-source genome assembler] **[http://www.biosimgrid.org/ BioSimGrid: a distributed database for biomolecular simulations] **[http://www.bioconductor.org/ Bioconductor] **[http://www.cbi.cnptia.embrapa.br/SMS Diamond STING] **[http://bioinformatics.upmc.edu/ UPMC Bioinformatics Web Tools] **[http://www.biojava.org/ BioJava] **[http://biomap.org/ BIOMAP Project: Creating a Unified Global Map of various Macromolecular Biological Structures] **[http://www.bind.ca/ Biomolecular Interaction Network Database] **[http://www.bioperl.org/ BioPerl] **[http://www.biophp.org/ BioPHP] **[http://www.biolinux.fac.org.ar/ BioLinux] **[http://www.biopython.org/ BioPython] **[http://www.bioruby.org/ BioRuby] **[http://www.phylo.org/ CIPRES Project: The Cyber-Infrastructure for Phylogenetic Research] **[http://emboss.sourceforge.net/ EMBOSS] **[http://www.ensembl.org/ Ensembl] **[http://www.gmod.org/ GMOD: The Generic Model Organism Database Project] **[http://harvester.embl.de/ HARVESTER: bioinformatic meta search engine for proteins in human, mouse and rat] **[http://manatee.sourceforge.net/ MANATEE: a web-based system for genome annotation and curation] **[http://proteomeontology.org/ Proteome Ontology Project: An effort to build a Protein Ontology Specification, a part of BIOMAP Project] **[http://bioinformatics.georgetown.edu/Sequerome.htm Sequerome] **[http://seqhound.blueprint.org/ Seqhound] **[http://sidhe.cs.uni.edu/marbl.html MARBL: Text Indexing & Retrieval from Bioinformatics Libraries GPL open source software package to search Genbank] **[http://www.cs.uni.edu/~okane/source/IDF/idf.html Inverse Document Frequency Weighted Genomic Sequence Retrieval] **[http://bio.macfast.org/bide/ BiDE - The Bioinformatics Desktop Environment - An Open Source project to develop a  compact Red Hat Linux-based single CD installation, which provides all the flavours of bioinformatics to your desktop] **[http://www.biocircle.org/bide BioCircle BiDE Page] **[http://www.ebioinformatics.org/ eBiotools: A software package that brings most of the Bioinformatics programs to the MacOSX]*Comprehensive, Reviewed, Third-Party Course Lists **[http://wbiomed.curtin.edu.au/teach/biochem/resources/Bioinformatics.html A long list of courses world wide]. **[http://www.ebi.ac.uk/training/ Training courses] at the [[European Bioinformatics Institute]]. **[http://www.ensembl.org/info/courses.html Courses] given about [[Ensembl]].*Major Journals **[http://compbiol.plosjournals.org PLoS Computational Biology] **[http://www.nature.com/msb/index.html Nature Molecular Systems Biology] **[http://bioinformatics.oupjournals.org/ Bioinformatics journal] **[http://www.biomedcentral.com/bmcbioinformatics BMC Bioinformatics journal] **[http://www.la-press.com/caninfo.htm Cancer Informatics Open Access journal] **[http://www.comcen.com.au/~journals/bioinfo.htm Online Journal of Bioinformatics ] **[http://www.embnet.org/download/embnetnews/index.html EMBnet.News Online  Journal]*Other Important Sites**[http://www.biocircle.org/ OpenSource Bioinformatics / Computational Systems Biology portal] **[http://www.bionews.in/ Bioinformatics News] **[http://www.bioinfo-online.net/ Bioinfo-Online News] **[http://bio.oreilly.com/ Books and articles on Bioinformatics from O'Reilly] **[http://gchelpdesk.ualberta.ca/servers/servers.php Genome Canada: Canadian Bioinformatics Help Desk] **[http://www.ornl.gov/TechResources/Human_Genome/research/informatics.html Human Genome Project and Bioinformatics] **[http://ontology.buffalo.edu/smith Barry Smith's biomedical ontology site] **[http://www.microbesonline.org Virtual Insitute of Microbial Stress and Survival (VIMSS)]

Notes & references

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

Bibliography

*  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 *  Claverie, J.M. and C. Notredame, ''Bioinformatics for Dummies''. Wiley, 2003. ISBN 0764516965 *  Durbin, R., S. Eddy, A. Krogh and G. Mitchison, ''Biological sequence analysis''. Cambridge University Press, 1998. ISBN 0521629713 *  Kohane, et al. ''Microarrays for an Integrative Genomics.'' The MIT Press, 2002. ISBN 026211271X *  Michael S. Waterman, ''Introduction to Computational Biology: Sequences, Maps and Genomes''. CRC Press, 1995. ISBN 0412993910 *  Mount, David W.  ''Bioinformatics: Sequence and Genome Analysis'' Spring Harbor Press, May 2002. ISBN 0879696087 *  Pevzner, Pavel A. ''Computational Molecular Biology: An Algorithmic Approach'' The MIT Press, 2000. ISBN 0262161974

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