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{{Short description|Computational analysis of large, complex sets of biological data}}
{{cs1 config|name-list-style=vanc|display-authors=6}}▼
{{For|the journal|Bioinformatics (journal)}}
{{Not to be confused with|Biological computation|Genetic algorithm}}
▲{{cs1 config|name-list-style=vanc|display-authors=6}}
{{Use dmy dates|date=September 2020}}
[[File:WPP ___domain alignment.PNG|500px|thumbnail|right|Early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see {{Section link||Sequence analysis}} for further information.]]
[[Image:Genome viewer screenshot small.png|thumbnail|220px|Map of the human X chromosome (from the [[National Center for Biotechnology Information]] (NCBI) website)]]
'''Bioinformatics''' ({{IPAc-en|audio=en-us-bioinformatics.ogg|ˌ|b|aɪ|.|oʊ|ˌ|ɪ|n|f|ɚ|ˈ|m|æ|t|ɪ|k|s}}) is an [[interdisciplinary]] branch of [[science]] that develops methods and [[Bioinformatics software|software tool]]s for understanding [[biology|biological]] data, especially when the data sets are large and complex. Bioinformatics uses [[biology]], [[chemistry]], [[physics]], [[computer science]], [[computer programming]], [[Information engineering (field)|information engineering]], [[mathematics]] and [[statistics]] to analyze and interpret [[biological data]].<ref>{{cite book |last1=Gagniuc |first1=Paul |title=Algorithms in Bioinformatics: Theory and Implementation |date=17 August 2021 |publisher=Wiley |isbn=978-1-119-69796-1 |pages=1-528 |edition=1 |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119698005 |language=en}}</ref> The subsequent process of analyzing and interpreting data is referred to as [[computational biology]]. ▼
▲'''Bioinformatics''' ({{IPAc-en|audio=en-us-bioinformatics.ogg|ˌ|b|aɪ|.|oʊ|ˌ|ɪ|n|f|ɚ|ˈ|m|æ|t|ɪ|k|s}}) is an [[interdisciplinary]]
Computational, statistical, and computer programming techniques have been used for [[In silico|computer simulation]] analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of [[genomics]], such as by the identification of [[gene]]s and single [[nucleotide]] polymorphisms ([[Single-nucleotide polymorphism|SNPs]]). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. Bioinformatics also includes [[proteomics]], which tries to understand the organizational principles within [[nucleic acid]] and [[protein]] sequences.<ref>{{cite web |vauthors=Lesk AM |date=26 July 2013 |title=Bioinformatics |url=https://www.britannica.com/science/bioinformatics |website=Encyclopaedia Britannica |access-date=17 April 2017 |archive-date=14 April 2021 |archive-url=https://web.archive.org/web/20210414103621/https://www.britannica.com/science/bioinformatics |url-status=live }}</ref>▼
▲Computational, statistical, and computer programming techniques have been used for [[In silico|computer simulation]] analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of [[genomics]], such as by the identification of [[gene]]s and single [[nucleotide]] polymorphisms ([[
Image and [[signal processing]] allow extraction of useful results from large amounts of raw data. In the field of genetics, it aids in sequencing and annotating genomes and their observed [[mutation]]s. Bioinformatics includes [[text mining]] of biological literature and the development of biological and gene [[Ontology (information science)|ontologies]] to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in comparing, analyzing and interpreting genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of [[systems biology]]. In [[structural biology]], it aids in the simulation and modeling of DNA,<ref name=":0">{{cite journal | vauthors = Sim AY, Minary P, Levitt M | title = Modeling nucleic acids | journal = Current Opinion in Structural Biology | volume = 22 | issue = 3 | pages = 273–8 | date = June 2012 | pmid = 22538125 | pmc = 4028509 | doi = 10.1016/j.sbi.2012.03.012 }}</ref> RNA,<ref name=":0" /><ref>{{cite journal | vauthors = Dawson WK, Maciejczyk M, Jankowska EJ, Bujnicki JM | title = Coarse-grained modeling of RNA 3D structure | journal = Methods | volume = 103 | pages = 138–56 | date = July 2016 | pmid = 27125734 | doi = 10.1016/j.ymeth.2016.04.026 | doi-access = free }}</ref> proteins<ref>{{cite journal | vauthors = Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A | title = Coarse-Grained Protein Models and Their Applications | journal = Chemical Reviews | volume = 116 | issue = 14 | pages = 7898–936 | date = July 2016 | pmid = 27333362 | doi = 10.1021/acs.chemrev.6b00163 | doi-access = free }}</ref> as well as biomolecular interactions.<ref>{{cite book | vauthors = Wong KC |year=2016 |title=Computational Biology and Bioinformatics: Gene Regulation |publisher=CRC Press/Taylor & Francis Group |isbn=978-1-4987-2497-5 }}</ref><ref>{{cite journal | vauthors = Joyce AP, Zhang C, Bradley P, Havranek JJ | title = Structure-based modeling of protein: DNA specificity | journal = Briefings in Functional Genomics | volume = 14 | issue = 1 | pages = 39–49 | date = January 2015 | pmid = 25414269 | pmc = 4366589 | doi = 10.1093/bfgp/elu044 | doi-access = free }}</ref><ref>{{Cite book | vauthors = Spiga E, Degiacomi MT, Dal Peraro M |date=2014 |chapter=New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly | veditors = Karabencheva-Christova T |title=Biomolecular Modelling and Simulations |series=Advances in Protein Chemistry and Structural Biology |volume=96 |pages=77–111 |publisher=Academic Press |doi=10.1016/bs.apcsb.2014.06.008 |pmid=25443955 |isbn=978-0-12-800013-7 }}</ref><ref>{{cite journal | vauthors = Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S | title = Protein-peptide docking: opportunities and challenges | journal = Drug Discovery Today | volume = 23 | issue = 8 | pages = 1530–1537 | date = August 2018 | pmid = 29733895 | doi = 10.1016/j.drudis.2018.05.006 | doi-access = free }}</ref>▼
▲Image and [[signal processing]] allow extraction of useful results from large amounts of raw data.
== History ==
The first definition of the term ''bioinformatics'' was coined by [[Paulien Hogeweg]] and [[Ben Hesper]] in 1970, to refer to the study of information processes in biotic systems.<ref>{{cite journal |last1=Ouzounis |first1=C. A. |last2=Valencia |first2=A. |date=2003 |title=Early bioinformatics: the birth of a discipline—a personal view |journal=Bioinformatics |volume=19 |issue=17 |pages=2176–2190 | pmid=14630646 | doi=10.1093/bioinformatics/btg309| doi-access=free}}</ref><ref name="Hogeweg2011">{{cite journal |vauthors=Hogeweg P |title=The Roots of Bioinformatics in Theoretical Biology |journal=PLOS Computational Biology |volume=7 |issue=3 |pages=e1002021 |date=2011 |pmid=21483479 |pmc=3068925 | doi=10.1371/journal.pcbi.1002021 | bibcode = 2011PLSCB...7E2021H | doi-access = free }}</ref><ref>{{Cite journal| vauthors = Hesper B, Hogeweg P |year=1970|title=BIO-INFORMATICA: een werkconcept |trans-title=BIO-INFORMATICS: a working concept |language=nl |journal=Het Kameleon|volume=1 |issue=6| pages=28–29}}</ref><ref>{{cite arXiv |vauthors=Hesper B, Hogeweg P |eprint=2111.11832v1 |title=Bio-informatics: a working concept. A translation of "Bio-informatica: een werkconcept" by B. Hesper and P. Hogeweg |date=2021 |class=q-bio.OT}}</ref><ref>{{cite journal |vauthors = Hogeweg P |title=Simulating the growth of cellular forms |journal=Simulation |volume=31 |issue=3 |pages=90–96 |year=1978 |doi=10.1177/003754977803100305 |s2cid=61206099 }}</ref> This definition placed bioinformatics as a field parallel to [[biochemistry]] (the study of chemical processes in biological systems).<ref name="Hogeweg2011" />
Bioinformatics and computational biology involved the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the [[Human Genome Project]] and by rapid advances in DNA sequencing technology.{{cn|date=February 2025}}
Analyzing biological data to produce meaningful information involves writing and running software programs that use [[algorithm]]s from [[graph theory]], [[artificial intelligence]], [[soft computing]], [[data mining]], [[image processing]], and [[computer simulation]]. The algorithms in turn depend on theoretical foundations such as [[discrete mathematics]], [[control theory]], [[system theory]], [[information theory]], and [[statistics]].{{cn|date=May 2024}}
=== Sequences ===
[[File: Example DNA sequence.png|thumbnail|right|Sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually.]]
[[File:Muscle alignment view.png|thumb|369x369px|These are sequences being compared in a MUSCLE multiple sequence alignment (MSA). Each sequence name (leftmost column) is from various louse species, while the sequences themselves are in the second column.]]▼
There has been a tremendous advance in speed and cost reduction since the completion of the Human Genome Project, with some labs able to [[DNA sequencing|sequence]] over 100,000 billion bases each year, and a full genome can be sequenced for $1,000 or less.<ref>{{cite web | vauthors = Colby B | date = 2022 | work = Sequencing.com | title = Whole Genome Sequencing Cost | url = https://sequencing.com/education-center/whole-genome-sequencing/whole-genome-sequencing-cost | access-date = 8 April 2022 | archive-date = 15 March 2022 | archive-url = https://web.archive.org/web/20220315025036/https://sequencing.com/education-center/whole-genome-sequencing/whole-genome-sequencing-cost | url-status = live }}</ref>
Computers became essential in molecular biology when [[protein sequences]] became available after [[Frederick Sanger]] determined the sequence of [[insulin]] in the early 1950s.<ref name="Sanger1951">{{cite journal |vauthors=Sanger F, Tuppy H |title=The Amino-acid Sequence in the Phenylalanyl Chain of Insulin. I. The identification of lower peptides from partial hydrolysates |journal=Biochemical Journal |volume=49 |issue=4 |pages=463–81 |date=1951 |pmid=14886310 |doi=10.1042/bj0490463 |pmc=1197535 }}</ref><ref name="Sanger1953">{{cite journal |vauthors=Sanger F, Thompson EO |title=The Amino-acid Sequence in the Glycyl Chain of Insulin. I. The identification of lower peptides from partial hydrolysates |journal=Biochemical Journal |volume=53 |issue=3 |pages=353–66 |date=1953 |pmid=13032078 |doi=10.1042/bj0530353 |pmc=1198157 }}</ref> Comparing multiple sequences manually turned out to be impractical. [[Margaret Oakley Dayhoff]], a pioneer in the field,<ref>{{cite book | vauthors=Moody G |year=2004 |title=Digital Code of Life: How Bioinformatics is Revolutionizing Science, Medicine, and Business |publisher=John Wiley & Sons |___location=Hoboken, NJ, USA |isbn=978-0-471-32788-2 |url-access=registration |url=https://archive.org/details/digitalcodeoflif0000mood }}</ref> compiled one of the first protein sequence databases, initially published as books<ref name="Dayhoff1965">{{cite book |vauthors=Dayhoff MO, Eck RV, Chang MA, Sochard MR |date=1965 |title=ATLAS of PROTEIN SEQUENCE and STRUCTURE |publisher=National Biomedical Research Foundation |___location=Silver Spring, MD, USA |url=https://ntrs.nasa.gov/api/citations/19660014530/downloads/19660014530.pdf |lccn=65-29342 }}</ref> as well as methods of sequence alignment and [[molecular evolution]].<ref name="pmid17775169">{{cite journal |vauthors=Eck RV, Dayhoff MO |title= Evolution of the Structure of Ferredoxin Based on Living Relics of Primitive Amino Acid Sequences | journal = Science | volume = 152 | issue = 3720 | pages = 363–6 | date = April 1966 | pmid = 17775169 | doi = 10.1126/science.152.3720.363 | s2cid = 23208558 | bibcode = 1966Sci...152..363E }}</ref> Another early contributor to bioinformatics was [[Elvin A. Kabat]], who pioneered biological sequence analysis in 1970 with his comprehensive volumes of antibody sequences released online with Tai Te Wu between 1980 and 1991.<ref>{{cite journal | vauthors = Johnson G, Wu TT | title = Kabat database and its applications: 30 years after the first variability plot | journal = Nucleic Acids Research | volume = 28 | issue = 1 | pages = 214–8 | date = January 2000 | pmid = 10592229 | pmc = 102431 | doi = 10.1093/nar/28.1.214 }}</ref>
In the 1970s, new techniques for sequencing DNA were applied to bacteriophage MS2 and øX174, and the extended nucleotide sequences were then parsed with informational and statistical algorithms. These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were the proof of the concept that bioinformatics would be insightful.<ref>{{cite journal | vauthors = Erickson JW, Altman GG |title=A Search for Patterns in the Nucleotide Sequence of the MS2 Genome |journal=Journal of Mathematical Biology |date=1979 |volume=7 |issue=3 |pages=219–230 |doi=10.1007/BF00275725 |s2cid=85199492 }}</ref><ref>{{cite journal | vauthors = Shulman MJ, Steinberg CM, Westmoreland N | title = The coding function of nucleotide sequences can be discerned by statistical analysis | journal = Journal of Theoretical Biology | volume = 88 | issue = 3 | pages = 409–20 | date = February 1981 | pmid = 6456380 | doi = 10.1016/0022-5193(81)90274-5 | bibcode = 1981JThBi..88..409S }}</ref>
▲[[File:Muscle alignment view.png|thumb|369x369px|These are sequences being compared in a MUSCLE multiple sequence alignment (MSA). Each sequence name (leftmost column) is from various louse species, while the sequences themselves are in the second column.]]
== Goals ==
In order to study how normal cellular activities are altered in different disease states, raw biological data must be combined to form a comprehensive picture of these activities. Therefore{{When|date=June 2023}}, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This also includes nucleotide and [[amino acid sequence]]s, [[protein ___domain]]s, and [[protein structure]]s.<ref>{{Cite book|title=Essential Bioinformatics|url=https://archive.org/details/essentialbioinfo00xion|url-access=limited| vauthors = Xiong J |publisher=Cambridge University Press|year=2006|isbn=978-0-511-16815-4|___location=Cambridge, United Kingdom|pages=[https://archive.org/details/essentialbioinfo00xion/page/n13 4]|via=Internet Archive}}</ref>
Important sub-disciplines within bioinformatics and [[computational biology]] include:
* Development and implementation of computer programs to efficiently access, manage, and use various types of information.
* Development of new mathematical algorithms and statistical measures to assess relationships among members of large data sets. For example, there are methods to locate a [[gene]] within a sequence, to predict protein structure and/or function, and to [[Cluster analysis|cluster]] protein sequences into families of related sequences.
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==Sequence analysis==
{{main|Sequence alignment|Sequence database|Alignment-free sequence analysis}}
Since the bacteriophage [[Phi X 174|Phage Φ-X174]] was [[
[[File:Sequencing analysis steps.png|center|600px|Image: 450 pixels Sequencing analysis steps]]▼
===DNA sequencing===
{{main|DNA sequencing}}
Before sequences can be analyzed, they are obtained from a data storage bank, such as GenBank. [[DNA sequencing]] is still a non-trivial problem as the raw data may be noisy or affected by weak signals. [[Algorithm]]s have been developed for [[base calling]] for the various experimental approaches to DNA sequencing.
▲[[File:Sequencing analysis steps.png|center|600px|Image: 450 pixels Sequencing analysis steps]]
===Sequence assembly===
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{{main|Gene prediction}}
In [[genomics]], [[Genome project#Genome annotation|annotation]] refers to the process of marking the stop and start regions of genes and other biological features in a sequenced DNA sequence. Many genomes are too large to be annotated by hand. As the rate of [[DNA sequencing|sequencing]] exceeds the rate of genome annotation, genome annotation has become the new bottleneck in bioinformatics.{{When|date=June 2023}}
Genome annotation can be classified into three levels: the [[nucleotide]], protein, and process levels.
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Understanding the function of genes and their products in the context of cellular and organismal physiology is the goal of process-level annotation. An obstacle of process-level annotation has been the inconsistency of terms used by different model systems. The Gene Ontology Consortium is helping to solve this problem.<ref>{{cite journal |title=Genome annotation: from sequence to biology |journal=Nature |year=2001 |doi=10.1038/35080529|last1=Stein |first1=Lincoln |volume=2 |issue=7 |pages=493–503 |pmid=11433356 |s2cid=12044602 }}</ref>
The first description of a comprehensive annotation system was published in 1995<ref name="pmid7542800" /> by [[The Institute for Genomic Research]], which performed the first complete sequencing and analysis of the genome of a free-living (non-[[
Following the goals that the Human Genome Project left to achieve after its closure in 2003, the [[ENCODE]] project was developed by the [[National Human Genome Research Institute]]. This project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy (base call error) and fidelity (assembly error).
==== Gene function prediction ====
While genome annotation is primarily based on sequence similarity (and thus [[Homology (biology)|homology]]), other properties of sequences can be used to predict the function of genes. In fact, most ''gene'' function prediction methods focus on ''protein'' sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic [[amino acid]]s predicts [[Transmembrane ___domain|transmembrane segments]] in proteins. However, protein function prediction can also use external information such as gene (or protein) [[Gene expression|expression]] data, [[protein structure]], or [[
===Computational evolutionary biology===
{{further|Computational phylogenetics}}
[[Evolutionary biology]] is the study of the origin and descent of [[species]], as well as their change over time. [[
* trace the evolution of a large number of organisms by measuring changes in their [[DNA]], rather than through physical taxonomy or physiological observations alone,
* compare entire [[genomes]], which permits the study of more complex evolutionary events, such as [[gene duplication]], [[horizontal gene transfer]], and the prediction of factors important in bacterial [[speciation]],
* build complex computational [[population genetics]] models to predict the outcome of the system over time<ref>{{cite journal | vauthors = Carvajal-Rodríguez A | title = Simulation of genes and genomes forward in time | journal = Current Genomics | volume = 11 | issue = 1 | pages = 58–61 | date = March 2010 | pmid = 20808525 | pmc = 2851118 | doi = 10.2174/138920210790218007 }}</ref>
* track and share information on an increasingly large number of species and organisms
===Comparative genomics===
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The core of comparative genome analysis is the establishment of the correspondence between [[genes]] ([[Homology (biology)#Orthology|orthology]] analysis) or other genomic features in different organisms. Intergenomic maps are made 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.<ref>{{cite book | vauthors = Brown TA |title=Genomes |date=2002 |publisher=Oxford |___location=Manchester (UK) |edition=2nd |chapter=Mutation, Repair and Recombination}}</ref> Entire genomes are involved in processes of hybridization, polyploidization and [[endosymbiosis]] that lead to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum 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 detection of [[sequence homology]] to assign sequences to [[
===Pan genomics===
{{main|Pan-genome}}
Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular [[
===Genetics of disease===
{{main|Genome-wide association studies}}
As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple [[Mendelian inheritance]] has been observed for over 3,000 disorders that have been identified at the [[Online Mendelian Inheritance in Man]] database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as [[infertility]],<ref name="Demerec1945">{{cite journal | vauthors = Aston KI | title = Genetic susceptibility to male infertility: news from genome-wide association studies | journal = Andrology | volume = 2 | issue = 3 | pages = 315–21 | date = May 2014 | pmid = 24574159 | doi = 10.1111/j.2047-2927.2014.00188.x | s2cid = 206007180 | doi-access = free }}</ref> [[breast cancer]]<ref name="Véron2013">{{cite journal | vauthors = Véron A, Blein S, Cox DG | title = Genome-wide association studies and the clinic: a focus on breast cancer | journal = Biomarkers in Medicine | volume = 8 | issue = 2 | pages = 287–96 | year = 2014 | pmid = 24521025 | doi = 10.2217/bmm.13.121 }}</ref> and [[Alzheimer's disease]]<ref name="Tosto2013">{{cite journal | vauthors = Tosto G, Reitz C | title = Genome-wide association studies in Alzheimer's disease: a review | journal = Current Neurology and Neuroscience Reports | volume = 13 | issue = 10 | pages = 381 | date = October 2013 | pmid = 23954969 | pmc = 3809844 | doi = 10.1007/s11910-013-0381-0 }}</ref>), rather than a single cause.<ref name="Londin2013">{{Cite book |vauthors=Londin E, Yadav P, Surrey S, Kricka LJ, Fortina P |chapter=Use of linkage analysis, genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations |title=Pharmacogenomics |volume=1015 |pages=127–46 |year=2013 |pmid=23824853 |doi=10.1007/978-1-62703-435-7_8 |isbn=978-1-62703-434-0 |series=Methods in Molecular Biology }}</ref><ref>{{cite journal | vauthors = Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA | title = Potential etiologic and functional implications of genome-wide association loci for human diseases and traits | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 23 | pages = 9362–7 | date = June 2009 | pmid = 19474294 | pmc = 2687147 | doi = 10.1073/pnas.0903103106 | doi-access = free | bibcode = 2009PNAS..106.9362H }}</ref> There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides.
|last58=Rotter |first58=Jerome I. |last59=Natarajan |first59=Pradeep |last60=Peloso |first60=Gina M. |last61=Li |first61=Zilin |last62=Lin |first62=Xihong |title=Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies |journal=Nature Genetics |date=January 2023 |volume=55 |issue=1 |pages=154–164 |doi=10.1038/s41588-022-01225-6|pmid=36564505 |pmc=10084891 |s2cid=255084231 }}</ref>
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{{main|Oncogenomics}}
In [[cancer]], the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to [[single-nucleotide polymorphism]] arrays identifying [[point mutation]]s that cause cancer, [[oligonucleotide]] microarrays can be used to identify chromosomal gains and losses (called [[comparative genomic hybridization]]). These detection methods generate [[terabyte]]s of data per experiment.<ref>{{cite arXiv | last1=Tsourakakis | first1=Charalampos E. | last2=Tolliver | first2=David | last3=Tsiarli | first3=Maria A. | last4=Shackney | first4=Stanley | last5=Schwartz | first5=Russell | title=CGHTRIMMER: Discretizing noisy Array CGH Data | date=2010 | class=q-bio.GN | eprint=1002.4438 }}</ref> 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 [[
Two important principles can be used to identify cancer by mutations in the [[exome]]. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.<ref>{{cite journal | vauthors = Vazquez M, de la Torre V, Valencia A | title = Chapter 14: Cancer genome analysis | journal = PLOS Computational Biology | volume = 8 | issue = 12 | pages = e1002824 | date = 2012-12-27 | pmid = 23300415 | pmc = 3531315 | doi = 10.1371/journal.pcbi.1002824 | bibcode = 2012PLSCB...8E2824V | doi-access = free }}</ref>
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===Analysis of gene expression===
The [[gene expression|expression]] of many genes can be determined by measuring [[
[[File:MIcroarray vs RNA-Seq.png|thumb|center|400px|MIcroarray vs RNA-Seq]]
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===Analysis of regulation===
[[
For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of [[sequence motif]]s in the DNA surrounding the protein-coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. [[Enhancer (genetics)|Enhancer]] elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of [[chromosome conformation capture]] experiments.
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===Protein localization===
Finding the ___location of proteins allows us to predict what they do. This is called [[protein function prediction]]. For instance, if a protein is found in the [[Cell nucleus|nucleus]] it may be involved in [[
===Nuclear organization of chromatin===
{{main|Nuclear organization}}
Data from high-throughput [[chromosome conformation capture]] experiments, such as [[
==Structural bioinformatics==<!-- this is specific enough, compared to general knowledge in previous paragraph, that it would be nice to have a source or example -->
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=== Amino acid sequence ===
The linear [[amino acid]] sequence of a protein is called the [[primary structure]]. The primary structure can be easily determined from the sequence of [[codons]] on the DNA gene that codes for it. In most proteins, the primary structure uniquely determines the 3-dimensional structure of a protein in its native environment. An exception is the
=== 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 structural bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. [[Homology modeling]] is used to predict the structure of an unknown protein from existing homologous proteins.
One example of this is hemoglobin in humans and the hemoglobin in legumes ([[leghemoglobin]]), which are distant relatives from the same [[protein superfamily]]. Both serve the same purpose of transporting oxygen in the organism. Although both of these proteins have
Other techniques for predicting protein structure include protein threading and ''de novo'' (from scratch) physics-based modeling.
Another aspect of structural bioinformatics include the use of protein structures for [[Virtual screening|Virtual Screening]] models such as [[
A 2021 [[deep-learning]] algorithms-based software called [[AlphaFold]], developed by Google's [[DeepMind]], greatly outperforms all other prediction software methods,<ref>{{Cite journal |last1=Jumper |first1=John |last2=Evans |first2=Richard |last3=Pritzel |first3=Alexander |last4=Green |first4=Tim |last5=Figurnov |first5=Michael |last6=Ronneberger |first6=Olaf |last7=Tunyasuvunakool |first7=Kathryn |last8=Bates |first8=Russ |last9=Žídek |first9=Augustin |last10=Potapenko |first10=Anna |last11=Bridgland |first11=Alex |last12=Meyer |first12=Clemens |last13=Kohl |first13=Simon A. A. |last14=Ballard |first14=Andrew J. |last15=Cowie |first15=Andrew |date=August 2021 |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |language=en |volume=596 |issue=7873 |pages=583–589 |bibcode=2021Natur.596..583J |doi=10.1038/s41586-021-03819-2 |issn=1476-4687 |pmc=8371605 |pmid=34265844}}</ref>{{How|date=June 2023}}
==Network and systems biology==
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===Molecular interaction networks===
[[File:The protein interaction network of Treponema pallidum.png|200px|thumbnail|right|Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from ''[[Treponema pallidum]]'', the causative agent of [[syphilis]] and other diseases.<ref>{{cite journal | vauthors = Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P | title = The binary protein interactome of Treponema pallidum--the syphilis spirochete | journal = PLOS ONE | volume = 3 | issue = 5 | pages = e2292 | date = May 2008 | pmid = 18509523 | pmc = 2386257 | doi = 10.1371/journal.pone.0002292 | bibcode = 2008PLoSO...3.2292T | veditors = Hall N | doi-access = free }}</ref>]]▼
{{main|Protein–protein interaction prediction|interactome}}
▲[[File:The protein interaction network of Treponema pallidum.png|200px|thumbnail|right|Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from ''[[Treponema pallidum]]'', the causative agent of [[syphilis]] and other diseases.<ref>{{cite journal | vauthors = Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P | title = The binary protein interactome of Treponema pallidum--the syphilis spirochete | journal = PLOS ONE | volume = 3 | issue = 5 | pages = e2292 | date = May 2008 | pmid = 18509523 | pmc = 2386257 | doi = 10.1371/journal.pone.0002292 | bibcode = 2008PLoSO...3.2292T | veditors = Hall N | doi-access = free }}</ref>]]
Tens of thousands of three-dimensional protein structures have been determined by [[X-ray crystallography]] and [[protein nuclear magnetic resonance spectroscopy]] (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing [[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 work to be done in this field.
Other interactions encountered in the field include Protein–ligand (including drug) and [[protein–peptide]]. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational [[algorithm]]s, termed docking algorithms, for studying [[interactome|molecular interactions]].
==Biodiversity informatics==
{{main|Biodiversity informatics}}
Biodiversity informatics deals with the collection and analysis of [[biodiversity]] data, such as [[taxonomic database]]s, or [[microbiome]] data. Examples of such analyses include [[phylogenetics]], [[niche modelling]], [[species richness]] mapping, [[DNA barcoding]], or [[Speciesism|species]] identification tools. A growing area is also [[Macroecology|macro-ecology]], i.e. the study of how biodiversity is connected to [[ecology]] and human impact, such as [[climate change]].
==Others==▼
▲==Others==
===Literature analysis===
{{main|Text mining|Biomedical text mining}}
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===High-throughput single cell data analysis===
{{main|Flow cytometry bioinformatics}}
Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained from [[flow cytometry]]. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition.
===Ontologies and data integration===
Biological ontologies are [[directed acyclic graph]]s of [[
The [[OBO Foundry]] was an effort to standardise certain ontologies. One of the most widespread is the [[Gene ontology]] which describes gene function. There are also ontologies which describe phenotypes.
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==Databases==
{{main|List of biological databases|Biological database}}
Databases are essential for bioinformatics research and applications. Databases exist for many different information types, including DNA and protein sequences, molecular structures, phenotypes and biodiversity. Databases can contain both empirical data (obtained directly from experiments) and predicted data (obtained from analysis of existing data). They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. Databases can have different formats, access mechanisms, and be public or private.
Some of the most commonly used databases are listed below:
* Used in biological sequence analysis: [[Genbank]], [[UniProt]]
* Used in structure analysis: [[Protein Data Bank]] (PDB)
* Used in finding Protein Families and [[Sequence motif|Motif]] Finding: [[InterPro]], [[Pfam]]
* Used for Next Generation Sequencing: [[Sequence Read Archive]]
* Used in Network Analysis: Metabolic Pathway Databases ([[KEGG]], [[
* Used in design of synthetic genetic circuits: [[GenoCAD]]
{{Citation needed|date=June 2023}}
==Software and tools==
[[List of bioinformatics software|Software tools for bioinformatics]] include simple command-line tools, more complex graphical programs, and standalone web-services. They are made by [[
===Open-source bioinformatics software===
{{Main
{{See also|List of bioinformatics software}}
Many [[free and open-source software]] tools have existed and continued to grow since the 1980s.<ref name="obf-main">{{cite web |title=Open Bioinformatics Foundation: About us |url=http://www.open-bio.org/wiki/Main_Page |website=Official website |publisher=[[Open Bioinformatics Foundation]] |access-date=10 May 2011 |archive-date=12 May 2011 |archive-url=https://web.archive.org/web/20110512022059/http://open-bio.org/wiki/Main_Page |url-status=live }}</ref> The combination of a continued need for new [[algorithm]]s for the analysis of emerging types of biological readouts, the potential for innovative ''[[in silico]]'' experiments, and freely available [[open code]] bases have created opportunities for research groups to contribute to both bioinformatics regardless of [[Funding of science|funding]]. The open source tools often act as incubators of ideas, or community-supported [[Plug-in (computing)|plug-ins]] in commercial applications. They may also provide ''[[de facto]]'' standards and shared object models for assisting with the challenge of bioinformation integration.
Open-source bioinformatics software includes [[Bioconductor]], [[BioPerl]], [[Biopython]], [[BioJava]], [[BioJS]], [[BioRuby]], [[Bioclipse]], [[EMBOSS]], .NET Bio, [[Orange (software)|Orange]] with its bioinformatics add-on, [[Apache Taverna]], [[UGENE]] and [[GenoCAD]].
The non-profit [[Open Bioinformatics Foundation]]<ref name="obf-main" /> and the annual [[Bioinformatics Open Source Conference]] promote open-source bioinformatics software.<ref name="obf-bosc">{{cite web |title=Open Bioinformatics Foundation: BOSC |url=http://www.open-bio.org/wiki/BOSC |website=Official website |publisher=[[Open Bioinformatics Foundation]] |access-date=10 May 2011 |archive-date=18 July 2011 |archive-url=https://web.archive.org/web/20110718175922/http://www.open-bio.org/wiki/BOSC |url-status=live }}</ref>
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{{main|Bioinformatics workflow management systems}}
A [[
* provide an easy-to-use environment for individual application scientists themselves to create their own workflows,
* provide interactive tools for the scientists enabling them to execute their workflows and view their results in real-time,
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=== BioCompute and BioCompute Objects ===
In 2014, the [[
It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|title=Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and encouraging interoperability|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=26 January 2018|archive-url=https://web.archive.org/web/20180126133504/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|url-status=
In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a [[BioCompute Object]], an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.<ref>{{cite journal | vauthors = Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, Hayes A, Suresh A, Purkayastha A, King CH, Taylor D, Johanson E, Thompson EE, Donaldson E, Morizono H, Tsang H, Vora JK, Goecks J, Yao J, Almeida JS, Keeney J, Addepalli K, Krampis K, Smith KM, Guo L, Walderhaug M, Schito M, Ezewudo M, Guimera N, Walsh P, Kahsay R, Gottipati S, Rodwell TC, Bloom T, Lai Y, Simonyan V, Mazumder R | title = Enabling precision medicine via standard communication of HTS provenance, analysis, and results | journal = PLOS Biology | volume = 16 | issue = 12 | pages = e3000099 | date = December 2018 | pmid = 30596645 | doi = 10.1371/journal.pbio.3000099 | pmc = 6338479 | doi-access = free }}</ref><ref>{{Citation|title=BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification Document: user manual for understanding and creating B.|date=2017-09-03|url=https://github.com/biocompute-objects/HTS-CSRS|publisher=biocompute-objects|access-date=30 November 2017|archive-date=27 June 2018|archive-url=https://web.archive.org/web/20180627081221/https://github.com/biocompute-objects/HTS-CSRS|url-status=live}}</ref>
==Education platforms==
[[
==Conferences==
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== See also ==
{{Columns-list|colwidth=30em|
* [[Biodiversity informatics]]
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* [[Computational genomics]]
* [[Cyberbiosecurity]]
* [[Earth BioGenome Project]]
* [[Functional genomics]]
* [[Gene Disease Database]]▼
* [[Health informatics]]
* [[International Society for Computational Biology]]
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* [[List of bioinformatics journals]]
* [[Metabolomics]]
* [[MitoMap]]
* [[Nucleic acid sequence]]
* [[Phylogenetics]]
* [[Proteomics]]
▲* [[Gene Disease Database]]
}}
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== Further reading ==
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{{Library resources box}}
{{refbegin|35em}}
* Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator(DAOA ), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.
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== External links ==
<!-- Please use the talk page to propose any additions to this section. If you do not do this, the link will almost certainly be deleted. Also, do not list bioinformatics research groups or centers.-->
* [http://expasy.org Bioinformatics Resource Portal (SIB)]
{{Bioinformatics}}
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{{Computer science}}
{{Health informatics}}
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