Bioinformatics

Information about Bioinformatics

Map of the human X chromosome (from the 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 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.

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 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. 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 using data from microarrays or mass spectrometry.

Major research areas

Sequence analysis

Main articles: Sequence alignment and Sequence database

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.

See also: sequence analysis, sequence profiling tool, sequence motif.

Genome annotation

Main article: 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 tree of life.

The area of research within computer science that uses genetic algorithms 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 populations, habitat needs, and how each organism interacts with other species. Specialized 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). One very exciting potential of this field is that entire DNA sequences, or genomes 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: Species 2000 project; uBio Project.

Analysis of gene expression

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, 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 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 proteins. Bioinformatics techniques have been applied to explore various steps in this process. For example, promoter analysis involves the identification and study of sequence motifs 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 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 microarrays 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 mutations in a variety of genes 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 terabytes 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 changes.

Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent across many tumors .

Prediction of protein structure

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 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.

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

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.

See also comparative genomics, bayesian network and protein family.

Modeling biological systems

Main article: Systems biology

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.

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, 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 article: Protein-protein docking

In the last two decades, tens of thousands of protein three-dimensional structures are 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 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 NCBI provides a popular web-based implementation that searches their databases.

SOAP-based (Service Oriented Architecture Protocol) 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 availability of these SOAP-based bioinformatics web services through systems such as the BioMoby service register demonstrate the applicability of web based bioinformatics solutions. These tools range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative and extensible bioinformatics workflow management systems.

References

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. 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. 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 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

External links

The external links in this article or section may require cleanup to comply with Wikipedia's content policies.
Please [ improve this article] by removing excessive or inappropriate external links. Please remove this tag when this is done. (talk)

At Wikiversity you can learn more and teach others about Bioinformatics at:

Genomics topics

Genome project | Paleopolyploidy | Glycomics | Human Genome Project | Proteomics

Bioinformatics | Cheminformatics | Systems biology

• • [ e] General subfields within biology
Anatomy - Astrobiology - Biochemistry - Bioinformatics - Botany - Cell biology - Ecology - Developmental biology - Evolutionary biology - Genetics - Genomics - Marine biology - Human biology - Microbiology - Molecular biology - Origin of life - Paleontology - Parasitology - Pathology - Physiology - Taxonomy - Zoology

• • [ e] Major fields of technology
Applied science	Artificial intelligence • Ceramic engineering • Computing technology • Electronics • • Energy storage • Engineering physics • Environmental technology • Materials science & engineering • Microtechnology • Nanotechnology • Nuclear technology • Optical engineering
Information and communication	Communication • Graphics • Music technology • Speech recognition • Visual technology
Industry	Construction • Financial engineering • Manufacturing • Machinery • Mining
Military	Bombs • Guns and Ammunition • Military technology and equipment • Naval engineering
Domestic	Domestic appliances • Domestic technology • Educational technology • Food technology
Engineering	Aerospace • Agricultural • Architectural • Bioengineering • Biochemical • Biomedical • Ceramic • Chemical • Civil • Computer • Construction • Cryogenic • Electrical • Electronic • Environmental • Food • Industrial • Materials • Mechanical • Mechatronics • Metallurgical • Mining • Naval • Nuclear • Petroleum • Software • Structural • Systems • Textile • Tissue
Health and safety	Biomedical engineering • Bioinformatics • Biotechnology • Cheminformatics • Fire protection engineering • Health technologies • Pharmaceuticals • Safety engineering • Sanitary engineering
Transport	Aerospace • Aerospace engineering • Marine engineering • Motor vehicles • Space technology • Transport

Applied mathematics is a branch of mathematics that concerns itself with the mathematical techniques typically used in the application of mathematical knowledge to other domains.
..... Click the link for more information.

Informatics includes the science of information, the practice of information processing, and the engineering of information systems. Informatics studies the structure, behavior, and interactions of natural and artificial systems that store, process and communicate information.
..... Click the link for more information.

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities.
..... Click the link for more information.

Computer science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems.
..... Click the link for more information.

artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.
..... Click the link for more information.

Editing of this page by unregistered or newly registered users is currently disabled due to vandalism.
If you are prevented from editing this page, and you wish to make a change, please discuss changes on the talk page, request unprotection, log in, or .
..... Click the link for more information.

Biochemistry is the study of the chemical processes in living organisms.^[1] The word "biochemistry" comes from the Greek word βιοχημεία biochēmeia, which means "the chemistry of life.
..... Click the link for more information.

Biology (from Greek: βίος, bio, "life"; and λόγος, logos, "knowledge"), also referred to as the biological sciences, is the scientific study of life.
..... Click the link for more information.

molecule is defined as a sufficiently stable electrically neutral group of at least two atoms in a definite arrangement held together by strong chemical bonds.^[1]^[2] In organic chemistry and biochemistry, the term molecule
..... Click the link for more information.

Systems biology, a field of study in the biosciences, focuses on the systematic study of complex interactions in biological systems. Particularly from 2000 onwards, the term is used widely in the biosciences, and in a variety of contexts.
..... Click the link for more information.

In bioinformatics, a sequence alignment is a way of arranging the primary sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
..... Click the link for more information.

Gene finding typically refers to the area of computational biology that is concerned with algorithmically identifying stretches of sequence, usually genomic DNA, that are biologically functional.
..... Click the link for more information.

Structural alignment is a form of sequence alignment based on comparison of shape. These alignments attempt to establish equivalences between two or more polymer structures based on their shape and three-dimensional conformation.
..... Click the link for more information.

Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry. Its aim is the prediction of the three-dimensional structure of proteins from their amino acid sequences, sometimes including additional relevant information such as
..... Click the link for more information.

For vocabulary, see Glossary of gene expression terms

Gene expression is the process by which the inheritable information in a gene, such as the DNA sequence, is made into a functional gene product, such as protein or RNA.
..... Click the link for more information.

Protein-protein interactions refer to the association of protein molecules and the study of these associations from the perspective of biochemistry, signal transduction and networks.

The interactions between proteins are important for many biological functions.
..... Click the link for more information.

National Institutes of Health (NIH) is an agency of the United States Department of Health and Human Services and is the primary agency of the United States government responsible for biomedical research.
..... Click the link for more information.

Biomedical informatics is a term used to describe the broad discipline that encompasses such subdomains as bioinformatics, clinical informatics, public health informatics, etc, and is most commonly used in this way in the USA^[1].
..... Click the link for more information.

The term DNA sequencing encompasses biochemical methods for determining the order of the nucleotide bases, adenine, guanine, cytosine, and thymine, in a DNA oligonucleotide.
..... Click the link for more information.

sequencing means to determine the primary structure (or primary sequence) of an unbranched biopolymer. Sequencing results in a symbolic linear depiction known as a sequence which succinctly summarizes much of the atomic-level structure of the sequenced molecule.
..... Click the link for more information.

Regulation of gene expression (or gene regulation) refers to the cellular control of the amount and timing of changes to the appearance of the functional product of a gene.
..... Click the link for more information.

DNA microarray (also commonly known as gene or genome chip, DNA chip, or gene array) is a collection of microscopic DNA spots, commonly representing single genes, arrayed on a solid surface by covalent attachment to chemically suitable matrices.
..... Click the link for more information.

Mass spectrometry (previously called mass spectroscopy ()^[1] or informally, "mass-spec" and MS) is an analytical technique used to measure the mass-to-charge ratio of ions.
..... Click the link for more information.

In the field of bioinformatics, a sequence database is a large collection of DNA, protein, or other sequences stored on a computer. A database can include sequences from only one organism, as in databases including all the proteins in Saccharomyces cerevisiae, or it can include
..... Click the link for more information.

The phi X 174 (or phi X) bacteriophage was the first organism to have its DNA-based genome sequenced by Fred Sanger and his team in 1977.^[1] In 1962, Walter Fiers had already demonstrated the physical, covalently closed circularity of phi X 174 DNA.
..... Click the link for more information.

DNA sequence or genetic sequence is a succession of letters representing the primary structure of a real or hypothetical DNA molecule or strand, with the capacity to carry information.
..... Click the link for more information.

Peptides (from the Greek πεπτίδια, "small digestibles") are short polymers formed from the linking, in a defined order, of α-amino acids.
..... Click the link for more information.

This article is copied from an article on Wikipedia.org - the free encyclopedia created and edited by online user community. The text was not checked or edited by anyone on our staff. Although the vast majority of the wikipedia encyclopedia articles provide accurate and timely information please do not assume the accuracy of any particular article. This article is distributed under the terms of GNU Free Documentation License.

Herod_Archelaus

iPedia Free Information Center