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[[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]] field of [[science]] that develops methods and [[Bioinformatics software|software tool]]s for understanding [[biological]] data, especially when the data sets are large and complex. Bioinformatics uses [[biology]], [[chemistry]], [[physics]], [[computer science]], [[data science]], [[computer programming]], [[information engineering]], [[mathematics]] and [[statistics]] to analyze and interpret [[biological data]].
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 ([[SNPs]]). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (
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=
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}}
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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-[[symbiotic]]) organism, the bacterium ''[[Haemophilus influenzae]]''.<ref name="pmid7542800" /> The system identifies the genes encoding all proteins, transfer RNAs, ribosomal RNAs, in order to make initial functional assignments. The [[GeneMark]] program trained to find protein-coding genes in ''[[Haemophilus influenzae]]'' is constantly changing and improving.
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 [[protein-protein interactions]].<ref>{{cite journal |
===Computational evolutionary biology===
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* 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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{{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 [[copy number]] changes.<ref>{{
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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=== 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.
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Another aspect of structural bioinformatics include the use of protein structures for [[Virtual screening|Virtual Screening]] models such as [[Quantitative Structure-Activity Relationship]] models and proteochemometric models (PCM). Furthermore, a protein's crystal structure can be used in simulation of for example ligand-binding studies and ''in silico'' mutagenesis studies.
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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=== BioCompute and BioCompute Objects ===
In 2014, the [[US Food and Drug Administration]] sponsored a conference held at the [[National Institutes of Health]] Bethesda Campus to discuss reproducibility in bioinformatics.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|title=Advancing Regulatory Science – Sept. 24–25, 2014 Public Workshop: Next Generation Sequencing Standards|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=14 November 2017|archive-url=https://web.archive.org/web/20171114200347/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|url-status=
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==
[[MOOC]] platforms also provide online certifications in bioinformatics and related disciplines, including [[Coursera]]'s Bioinformatics Specialization at the [[University of California, San Diego]], Genomic Data Science Specialization at [[Johns Hopkins University]], and [[EdX]]'s Data Analysis for Life Sciences XSeries at [[Harvard University]].
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* [[Computational genomics]]
* [[Cyberbiosecurity]]
* [[Earth BioGenome Project]]
* [[Functional genomics]]
* [[Gene Disease Database]]
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