Bioinformatics
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{{Cleanup|date=November 2007}}
[[Image:Genome viewer screenshot small.png|thumbnail|right|220px|'''Map of the human X chromosome''' (from the [[National Center for Biotechnology Information|NCBI]] website). Assembly of the [[human genome]] is one of the greatest achievements of bioinformatics.]]
'''Bioinformatics''' and '''computational biology''' involve the use of techniques including [[applied mathematics]], [[informatics]], [[statistics]], [[computer science]], [[artificial intelligence]], [[chemistry]], and [[biochemistry]] to solve [[biology|biological]] problems usually on the [[molecular]] level. The core principle of these techniques is using computing resources in order to solve problems on scales of magnitude far too great for human discernment. Research in computational biology often overlaps with [[systems biology]]. Major research efforts in the field include [[sequence alignment]], [[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]].
==Introduction==
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 arising 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 [[NIH|National Institutes of Health]] in their [http://www.bisti.nih.gov/CompuBioDef.pdf 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. Bioinformatics is also often specified as an applied subfield of the more general discipline of [[Biomedical informatics]].
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]] to perform [[expression profiling]] using data from [[DNA microarray|microarrays]] or [[mass spectrometry]].
==Major research areas==
===Sequence analysis===
'''{{main|Sequence alignment|Sequence database}}'''
Since the [[Phi-X174 phage|Phage Φ-X174]] was [[sequencing|sequenced]] in 1977, the [[DNA sequence]]s 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 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 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 [[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 sequences for protein identification.
''See also:'' [[sequence analysis]], [[sequence profiling tool]], [[sequence motif]].
===Genome annotation===
{{main|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 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.
===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 factors important in bacterial [[speciation]],
*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]]s is sometimes confused with [[computational evolutionary biology]], but the two areas are unrelated.
===Measuring biodiversity===
[[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]]. 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. Specialized [[computer software|software]] programs are used to find, visualize, and analyze the information, and most importantly, communicate it to other people. 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]]). 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.
''Important projects:'' [http://www.sp2000.org/ Species 2000 project]; [http://www.ubio.org/ uBio Project].
===Analysis of gene expression===
The [[gene expression|expression]] of many genes can be determined by measuring [[mRNA]] levels with multiple techniques including [[DNA microarray|microarrays]], [[expressed sequence tag|expressed cDNA sequence tag]] (EST) sequencing, [[serial analysis of gene expression]] (SAGE) tag sequencing, [[massively parallel signature sequencing]] (MPSS), or various applications of multiplexed in-situ hybridization. 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 studies. Such 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 transcripts that are up-regulated and down-regulated in a particular population of cancer cells.
===Analysis of regulation===
Regulation is the complex orchestration of events starting with an extracellular signal such as a [[hormone]] and leading to an increase or decrease in the activity of one or more [[protein]]s. Bioinformatics techniques have been applied to explore various steps in this process. For example, [[promoter analysis]] involves the identification and study of [[sequence motif]]s in the DNA surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. 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. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented [[regulatory elements]].
===Analysis of protein expression===
[[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 approach faces similar problems as with 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.
===Analysis of mutations in cancer===
In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. Massive sequencing efforts are used to identify previously unknown [[point mutation]]s in a variety of [[gene]]s in [[cancer]]. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of [[human genome]] sequences and [[germline]] polymorphisms. New physical detection technology are employed, such as [[oligonucleotide]] microarrays to identify chromosomal gains and losses (called [[comparative genomic hybridization]]), and [[single nucleotide polymorphism]] arrays to detect known ''point mutations''. These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate [[terabyte]]s of data per experiment. Again the massive amounts and new types of data generate new opportunities for bioinformaticians. The data is often found to contain considerable variability, or [[noise]], and thus [[Hidden Markov model]] and [[change-point analysis]] methods are being developed to infer real [[copy number variation|copy number]] changes.
Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors
.
===Prediction of protein structure===
{{main|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 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 structure|secondary]]'', ''[[tertiary structure|tertiary]]'' and ''[[quaternary structure|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 (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 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.
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 the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.
Other techniques for predicting protein structure include protein threading and ''de novo'' (from scratch) physics-based modeling.
''See also:'' [[structural motif]] and [[structural domain]].
=== Comparative genomics ===
{{main|Comparative genomics}}
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.
Many of these studies are based on the homology detection and protein families computation.
===Modeling biological systems===
{{main|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 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 (science)|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:
* high-throughput and high-fidelity quantification and sub-cellular localization ([[high-content screening]], [[cytohistopathology]])
* [[morphometrics]]
* clinical image analysis and visualization
* determining the real-time air-flow patterns in breathing lungs of living animals
* quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury
* making behavioral observations from extended video recordings of laboratory animals
* infrared measurements for metabolic activity determination
===Protein-protein docking===
{{main|Protein-protein docking}}
In the last two decades, tens of thousands of protein three-dimensional structures have been determined by [[X-ray crystallography]] and [[Protein nuclear magnetic resonance spectroscopy]] (protein NMR). One central question for the biological scientist is whether it is practical to predict possible protein-protein interactions only based on these 3D shapes, without doing [[protein-protein interaction]] experiments. A variety of methods have been developed to tackle the [[Protein-protein docking]] problem, though it seems that there is still much place to work on in this field.
===Software and Tools===
Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services. The computational biology tool best-known among biologists is probably [[BLAST]], an algorithm for determining the similarity of arbitrary sequences against other sequences, possibly from curated databases of protein or DNA sequences. The [[National Center for Biotechnology Information|NCBI]] provides a popular web-based implementation that searches their databases. BLAST is one of a number of [[List of sequence alignment software|generally available programs]] for doing sequence alignment.
===Web Services in Bioinformatics===
[[SOAP]] and [[REST]]-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. The main advantages lay in the end user not having to deal with software and database maintenance overheads[[[European Bioinformatics Institute]]] Basic bioinformatics services are classified by the [[EBI]] into three categories: [[Sequence alignment software|SSS]] (Sequence Search Services), [[Multiple sequence alignment|MSA]] (Multiple Sequence Alignment) and [[Bioinformatics#Sequence_analysis|BSA]] (Biological Sequence Analysis). The availability of these [[service-oriented]] bioinformatics resources demonstrate the applicability of web based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative, distributed and extensible [[bioinformatics workflow management systems]].
==See also==
===Related topics===
* [[Biocybernetics]]
* [[Bioinformatics companies]]
* [[Biologically-inspired computing]]
* [[Biomedical informatics]]
* [[Biowiki]]
* [[Computational biology]]
* [[Computational biomodeling]]
* [[Computational genomics]]
* [[Dot plot (bioinformatics)]]
* [[Dry Lab]]
* [[Metabolic network modelling]]
* [[Molecular modelling]]
* [[Morphometrics]]
* [[Natural computation]]
* [[Pharmaceutical company]]
* [[Protein-protein interaction prediction]]
* [[List of numerical analysis software]]
* [[List of scientific journals in Bioinformatics]]
===Related fields===
{|
| valign=top |
* [[Applied mathematics]]
* [[Artificial intelligence]]
* [[Biology]]
* [[Cheminformatics]]
* [[Clinomics]]
* [[Computational biology]]
* [[Computational epigenetics]]
| valign=top |
* [[Computational science]]
* [[Computer science]]
* [[Cybernetics]]
* [[Genomics]]
* [[Informatics]]
* [[Information theory]]
* [[Mathematical biology]]
| valign=top |
* [[Neuroinformatics]]
* [[Proteomics]]
* [[Scientific computing]]
* [[Statistics]]
* [[Structural biology]]
* [[Systems biology]]
* [[Theoretical biology]]
|}
==References==
{{reflist}}
{{Nofootnotes|date=February 2008}}
{{refbegin}}
* Achuthsankar S Nair [http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf Computational Biology & Bioinformatics - A gentle Overview], Communications of Computer Society of India, January 2007
* Aluru, Srinivas, ed. ''Handbook of Computational Molecular Biology''. Chapman & Hall/Crc, 2006. ISBN 1584884061 (Chapman & Hall/Crc Computer and Information Science Series)
* Baldi, P and Brunak, S, ''Bioinformatics: The Machine Learning Approach'', 2nd edition. MIT Press, 2001. ISBN 0-262-02506-X
* Barnes, M.R. and Gray, I.C., eds., ''Bioinformatics for Geneticists'', first edition. Wiley, 2003. ISBN 0-470-84394-2
* 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 0-471-47878-4
* Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds., ''Current Protocols in Bioinformatics''. Wiley, 2007. ISBN 0-471-25093-7
* Claverie, J.M. and C. Notredame, ''Bioinformatics for Dummies''. Wiley, 2003. ISBN 0-7645-1696-5
* Cristianini, N. and Hahn, M. [http://www.computational-genomics.net/ ''Introduction to Computational Genomics''], Cambridge University Press, 2006. (ISBN 9780521671910 | ISBN 0521671914)
* Durbin, R., S. Eddy, A. Krogh and G. Mitchison, ''Biological sequence analysis''. Cambridge University Press, 1998. ISBN 0-521-62971-3
* Gilbert, D. [http://bib.oxfordjournals.org/cgi/content/abstract/5/3/300 ''Bioinformatics software resources'']. Briefings in Bioinformatics, Briefings in Bioinformatics, 2004 5(3):300-304.
* Keedwell, E., ''Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems''. Wiley, 2005. ISBN 0-470-02175-6
* Kohane, et al. ''Microarrays for an Integrative Genomics.'' The MIT Press, 2002. ISBN 0-262-11271-X
* Lund, O. et al. ''Immunological Bioinformatics.'' The MIT Press, 2005. ISBN 0-262-12280-4
* Michael S. Waterman, ''Introduction to Computational Biology: Sequences, Maps and Genomes''. CRC Press, 1995. ISBN 0-412-99391-0
* Mount, David W. ''Bioinformatics: Sequence and Genome Analysis'' Spring Harbor Press, May 2002. ISBN 0-87969-608-7
* Pachter, Lior and [[Bernd Sturmfels|Sturmfels, Bernd]]. "Algebraic Statistics for Computational Biology" Cambridge University Press, 2005. ISBN 0-521-85700-7
* Pevzner, Pavel A. ''Computational Molecular Biology: An Algorithmic Approach'' The MIT Press, 2000. ISBN 0-262-16197-4
* Tisdall, James. "Beginning Perl for Bioinformatics" O'Reilly, 2001. ISBN 0-596-00080-4
* [http://publishing.royalsociety.org/bioinformatics Dedicated issue of ''Philosophical Transactions B'' on Bioinformatics freely available]
*[http://www.nap.edu/catalog/11480.html Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report]
*[http://www.nap.edu/catalog/2121.html Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)]
*[http://ocw.mit.edu/OcwWeb/Biology/7-91JSpring2004/LectureNotes/index.htm Foundations of Computational and Systems Biology MIT Course]
*[http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-895Fall-2005/CourseHome/index.htm Computational Biology: Genomes, Networks, Evolution Free MIT Course]
*[http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-096Spring-2005/CourseHome/index.htm Algorithms for Computational Biology Free MIT Course]
{{refend}}
==External links==
{{External links}}
{{Wiktionarypar|bioinformatics}}
{{WVD}}
* Major Organizations
**[http://bioinformatics.org/ Bioinformatics Organization (Bioinformatics.Org): The Open-Access Institute]
**[http://www.embnet.org/ EMBnet]
**[http://www.ebi.ac.uk/ European Bioinformatics Institute]
**[http://www.embl.org/ European Molecular Biology Laboratory]
**[http://www.iscb.org/ The International Society for Computational Biology]
**[http://www.ncbi.nlm.nih.gov/ National Center for Biotechnology Information]
**[http://www.nih.gov National Institutes of Health homepage]
**[http://www.open-bio.org/ Open Bioinformatics Foundation: umbrella non-profit organization supporting certain open-source projects in bioinformatics]
**[[Swiss Institute of Bioinformatics]]
**[[Wellcome Trust Sanger Institute]]
*Major Journals
**[http://www.almob.org/ Algorithms in Molecular Biology]
**[http://bioinformatics.oupjournals.org/ Bioinformatics]
**[http://www.biomedcentral.com/bmcbioinformatics BMC Bioinformatics]
**[http://bib.oxfordjournals.org/ Briefings in Bioinformatics]
**[http://www.la-press.com/evolbio.htm Evolutionary Bioinformatics]
**[http://www.genome.org Genome Research]
**[http://www.bepress.com/ijb/ The International Journal of Biostatistics]
**[http://www.liebertpub.com/publication.aspx?pub_id=31 Journal of Computational Biology]
**[http://la-press.com/journal.php?pa=description&journal_id=10 Cancer Informatics]
**[http://publishing.royalsociety.org/index.cfm?page=1058 Journal of the Royal Society Interface]
**[http://www.nature.com/msb/index.html Molecular Systems Biology]
**[http://compbiol.plosjournals.org PLoS Computational Biology]
**[http://www.bepress.com/sagmb/ Statistical Applications in Genetic and Molecular Biology]
**[http://www.computer.org/tcbb/ Transactions on Computational Biology and Bioinformatics - IEEE/ACM]
**[http://www.inderscience.com/browse/index.php?journalcode=ijbra International Journal of Bioinformatics Research and Applications]
**[http://www.bioinformatics.fr/journals.php List of Bioinformatics journals] at Bioinformatics.fr
**[http://www.embnet.org EMBnet.News] at EMBnet.org
*Other sites
**[http://www.biostatsresearch.com/repository/ The Collection of Biostatistics Research Archive]
**[http://www.ornl.gov/TechResources/Human_Genome/research/informatics.html Human Genome Project and Bioinformatics]
**[http://www.bioinformatics.fr/laboratories.php List of Bioinformatics Research Groups] at Bioinformatics.fr
**{{dmoz|Science/Biology/Bioinformatics/Research_Groups/|List of Bioinformatics Research Groups}}
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