Editing Computational biology (section)

=== Genomics ===
{{main|Computational genomics}}
{{See also|Earth BioGenome Project}}
[[File:Genome viewer screenshot small.png|thumbnail|right|A partially sequenced genome]]
Computational genomics is the study of the [[genome]]s of [[Cell (biology)|cells]] and [[organism]]s. The [[Human Genome Project]] is one example of computational genomics. This project looks to sequence the entire human genome into a set of data. Once fully implemented, this could allow for doctors to analyze the genome of an individual [[patient]].<ref>{{cite magazine|title=Genome Sequencing to the Rest of Us|url=http://www.scientificamerican.com/article.cfm?id=personal-genome-sequencing|magazine=Scientific American}}</ref>  This opens the possibility of personalized medicine, prescribing treatments based on an individual's pre-existing genetic patterns. Researchers are looking to sequence the genomes of animals, plants, [[bacteria]], and all other types of life.<ref name="Koonin 2001 155–158">{{cite journal|last=Koonin|first=Eugene|title=Computational Genomics|date=6 March 2001|volume=11|issue=5|pages=155–158|doi=10.1016/S0960-9822(01)00081-1|pmid=11267880|journal=Curr. Biol.|s2cid=17202180|doi-access=free|bibcode=2001CBio...11.R155K }}</ref>

One of the main ways that genomes are compared is by [[sequence homology]]. Homology is the study of biological structures and nucleotide sequences in different organisms that come from a common [[ancestor]]. Research suggests that between 80 and 90% of genes in newly sequenced [[Prokaryote|prokaryotic]] genomes can be identified this way.<ref name="Koonin 2001 155–158"/>

[[Sequence alignment]] is another process for comparing and detecting similarities between biological sequences or genes. Sequence alignment is useful in a number of bioinformatics applications, such as computing the [[Longest common subsequence problem|longest common subsequence]] of two genes or comparing variants of certain [[disease]]s.{{fact|date=August 2024}}

An untouched project in computational genomics is the analysis of intergenic regions, which comprise roughly 97% of the human genome.<ref name="Koonin 2001 155–158" /> Researchers are working to understand the functions of non-coding regions of the human genome through the development of computational and statistical methods and via large consortia projects such as [[ENCODE]] and the [[Epigenome#Roadmap epigenomics project|Roadmap Epigenomics Project]].

Understanding how individual [[gene]]s contribute to the [[biology]] of an organism at the [[Molecule|molecular]], [[Cell (biology)|cellular]], and organism levels is known as [[Gene Ontology|gene ontology]]. The [[Gene Ontology Consortium]]'s mission is to develop an up-to-date, comprehensive, computational model of [[biological system]]s, from the molecular level to larger pathways, cellular, and organism-level systems. The Gene Ontology resource provides a computational representation of current scientific knowledge about the functions of genes (or, more properly, the [[protein]] and non-coding [[RNA]] molecules produced by genes) from many different organisms, from humans to bacteria.<ref>{{Cite web |title=Gene Ontology Resource |url=http://geneontology.org/ |access-date=2022-04-18 |website=Gene Ontology Resource}}</ref>

3D genomics is a subsection in computational biology that focuses on the organization and interaction of genes within a [[Eukaryotic Cell|eukaryotic cell]]. One method used to gather 3D genomic data is through [[Genome architecture mapping|Genome Architecture Mapping]] (GAM). GAM measures 3D distances of [[chromatin]] and DNA in the genome by combining [[cryosectioning]], the process of cutting a strip from the nucleus to examine the DNA, with laser microdissection. A nuclear profile is simply this strip or slice that is taken from the nucleus. Each nuclear profile contains genomic windows, which are certain sequences of [[nucleotide]]s - the base unit of DNA. GAM captures a genome network of complex, multi enhancer chromatin contacts throughout a cell.<ref>{{Cite journal |last1=Beagrie |first1=Robert A. |last2=Scialdone |first2=Antonio |last3=Schueler |first3=Markus |last4=Kraemer |first4=Dorothee C. A. |last5=Chotalia |first5=Mita |last6=Xie |first6=Sheila Q. |last7=Barbieri |first7=Mariano |last8=de Santiago |first8=Inês |last9=Lavitas |first9=Liron-Mark |last10=Branco |first10=Miguel R. |last11=Fraser |first11=James |date=March 2017 |title=Complex multi-enhancer contacts captured by genome architecture mapping |journal=Nature |language=en |volume=543 |issue=7646 |pages=519–524 |bibcode=2017Natur.543..519B |doi=10.1038/nature21411 |issn=1476-4687 |pmc=5366070 |pmid=28273065}}</ref>