Editing Comparative genomics (section)

==Selected applications==

===Agriculture===
[[Agriculture]] is a field that reaps the benefits of comparative genomics. Identifying the [[Locus (genetics)|loci]] of advantageous genes is a key step in [[crop breeding|breeding crops]] that are optimized for greater [[crop yield|yield]], cost-efficiency, quality, and [[disease resistance]]. For example, one [[genome wide association study]] conducted on 517 rice [[landrace]]s revealed 80 loci associated with several categories of agronomic performance, such as grain weight, [[amylose]] content, and [[drought tolerance]]. Many of the loci were previously uncharacterized.<ref>{{cite journal | vauthors = Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B | title = Genome-wide association studies of 14 agronomic traits in rice landraces | journal = Nature Genetics | volume = 42 | issue = 11 | pages = 961–967 | date = November 2010 | pmid = 20972439 | doi = 10.1038/ng.695 | s2cid = 439442 }}</ref> Not only is this methodology powerful, it is also quick. Previous methods of identifying loci associated with agronomic performance required several generations of carefully monitored breeding of parent strains, a time-consuming effort that is unnecessary for comparative genomic studies.<ref>{{cite journal | vauthors = Morrell PL, Buckler ES, Ross-Ibarra J | title = Crop genomics: advances and applications | journal = Nature Reviews. Genetics | volume = 13 | issue = 2 | pages = 85–96 | date = December 2011 | pmid = 22207165 | doi = 10.1038/nrg3097 | s2cid = 13358998 }}</ref>

===Medicine===
====Vaccine development====
The medical field also benefits from the study of comparative genomics. In an approach known as [[reverse vaccinology]], researchers can discover candidate antigens for vaccine development by analyzing the genome of a [[pathogen]] or a family of pathogens.<ref>{{cite journal | vauthors = Seib KL, Zhao X, Rappuoli R | title = Developing vaccines in the era of genomics: a decade of reverse vaccinology | journal = Clinical Microbiology and Infection | volume = 18 | issue = Suppl 5 | pages = 109–116 | date = October 2012 | pmid = 22882709 | doi = 10.1111/j.1469-0691.2012.03939.x | hdl-access = free | doi-access = free | hdl = 10072/50260 }}</ref> Applying a comparative genomics approach by analyzing the genomes of several related pathogens can lead to the development of vaccines that are multi-protective. A team of researchers employed such an approach to create a universal vaccine for [[Streptococcus agalactiae|Group B Streptococcus]], a group of bacteria responsible for severe [[neonatal infection]].<ref>{{cite journal | vauthors = Maione D, Margarit I, Rinaudo CD, Masignani V, Mora M, Scarselli M, Tettelin H, Brettoni C, Iacobini ET, Rosini R, D'Agostino N, Miorin L, Buccato S, Mariani M, Galli G, Nogarotto R, Nardi-Dei V, Vegni F, Fraser C, Mancuso G, Teti G, Madoff LC, Paoletti LC, Rappuoli R, Kasper DL, Telford JL, Grandi G | title = Identification of a universal Group B streptococcus vaccine by multiple genome screen | journal = Science | volume = 309 | issue = 5731 | pages = 148–150 | date = July 2005 | pmid = 15994562 | pmc = 1351092 | doi = 10.1126/science.1109869 | bibcode = 2005Sci...309..148M }}</ref> Comparative genomics can also be used to generate specificity for vaccines against pathogens that are closely related to commensal microorganisms. For example, researchers used comparative genomic analysis of [[commensal]] and pathogenic strains of [[Escherichia coli|''E. coli'']] to identify pathogen-specific genes as a basis for finding antigens that result in immune response against pathogenic strains but not commensal ones.<ref>{{cite journal | vauthors = Rasko DA, Rosovitz MJ, Myers GS, Mongodin EF, Fricke WF, Gajer P, Crabtree J, Sebaihia M, Thomson NR, Chaudhuri R, Henderson IR, Sperandio V, Ravel J | title = The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates | journal = Journal of Bacteriology | volume = 190 | issue = 20 | pages = 6881–6893 | date = October 2008 | pmid = 18676672 | pmc = 2566221 | doi = 10.1128/JB.00619-08 |doi-access=free}}</ref> In May 2019, using the Global Genome Set, a team in the UK and Australia sequenced thousands of globally-collected isolates of [[Streptococcus#Group A|Group A Streptococcus]], providing potential targets for developing a vaccine against the pathogen, also known as [[Streptococcus pyogenes|''S. pyogenes'']].<ref>{{Cite web |date=28 May 2019 |url=https://www.genomeweb.com/sequencing/group-streptococcus-vaccine-target-candidates-identified-global-genome-set#.XRKFu_ZFxPY |title=Group a Streptococcus Vaccine Target Candidates Identified from Global Genome Set}}</ref>

'''Personalized Medicine'''

[[Personalized medicine|Personalized Medicine]], enabled by Comparative Genomics, represents a revolutionary approach in healthcare, tailoring medical treatment and disease prevention to the individual patient's genetic makeup.<ref>{{cite journal | vauthors = Sadee W | title = Genomics and personalized medicine | journal = International Journal of Pharmaceutics | volume = 415 | issue = 1–2 | pages = 2–4 | date = August 2011 | pmid = 21539903 | doi = 10.1016/j.ijpharm.2011.04.048 }}</ref> By analyzing [[genetic variation]]s across populations and comparing them with an individual's genome, clinicians can identify specific [[genetic marker]]s associated with disease susceptibility, [[drug metabolism]], and treatment response. By identifying genetic variants associated with drug metabolism pathways, drug targets, and [[adverse reaction]]s, personalized medicine can optimize medication selection, dosage, and treatment regimens for individual patients. This approach minimizes the risk of adverse drug reactions, enhances treatment efficacy, and improves patient outcomes.

'''Cancer'''

[[Cancer genomics|Cancer Genomics]] represents a cutting-edge field within oncology that leverages comparative genomics to revolutionize [[cancer]] diagnosis, treatment, and prevention strategies. Comparative genomics plays a crucial role in cancer research by identifying [[driver mutations]], and providing comprehensive analyses of [[mutation]]s, [[Copy number variation|copy number]] alterations, structural variants, [[gene expression]], and [[DNA methylation]] profiles in large-scale studies across different cancer types. By analyzing the genomes of cancer cells and comparing them with healthy cells, researchers can uncover key genetic alterations driving [[tumorigenesis]], tumor progression, and [[metastasis]]. This deep understanding of the genomic landscape of cancer has profound implications for precision [[oncology]]. Moreover, Comparative Genomics is instrumental in elucidating mechanisms of [[drug resistance]]—a major challenge in cancer treatment.

[[File:New Mouse and Human Comparison (2).png|thumb|upright=1.15|[[T-cell receptor|TCR]] loci from humans (H, top) and mice (M, bottom) are compared, with TCR elements in red, non-TCR genes in purple, and V segments in orange, other TCR elements in red. M6A, a putative [[methyltransferase]]; ZNF, a [[zinc-finger protein]]; OR, [[olfactory receptor]] genes; DAD1, defender against [[cell death]]; The sites of species-specific, processed pseudogenes are shown by gray triangles. See also [[GenBank]] accession numbers AE000658-62. Modified after Glusman et al. 2001.<ref name="glusman2001" /> ]]

====Mouse models in immunology====
[[T cells]] (also known as a T lymphocytes or a thymocytes) are [[immune cells]] that grow from stem cells in the bone marrow. They assist to defend the body from infection and may aid in the fight against cancer. Because of their morphological, physiological, and genetic resemblance to humans, mice and rats have long been the preferred species for biomedical research [[animal model]]s. Comparative Medicine Research is built on the ability to use information from one species to understand the same processes in another. We can get new insights into molecular pathways by comparing human and mouse T cells and their effects on the immune system utilizing comparative genomics. In order to comprehend its TCRs and their genes, Glusman conducted research on the sequencing of the human and mouse T cell receptor loci. TCR genes are well-known and serve as a significant resource for supporting functional genomics and understanding how genes and intergenic regions of the genome contribute to biological processes.<ref name=glusman2001/>

T-cell immune receptors are important in seeing the world of pathogens in the cellular immune system. One of the reasons for sequencing the human and mouse TCR loci was to match the orthologous gene family sequences and discover conserved areas using comparative genomics. These, it was thought, would reflect two sorts of biological information: (1) exons and (2) [[regulatory sequence]]s. In fact, the majority of V, D, J, and C exons could be identified in this method. The variable regions are encoded by multiple unique DNA elements that are rearranged and connected during T cell (TCR) differentiation: variable (V), diversity (D), and joining (J) elements for the and polypeptides; and V and J elements for the and polypeptides.[Figure 1] However, several short noncoding conserved blocks of the genome had been shown. Both human and mouse motifs are largely clustered in the 200 bp [Figure 2], the known 3′ [[Enhancer (genetics)|enhancer]]s in the TCR/ were identified, and a conserved region of 100 bp in the mouse J intron was subsequently shown to have a regulatory function.

[[File:Mouse and Human Comparison (2) (2).png|thumb|upright=1.15|[Figure 2] Gene structure of the human (top) and mouse (bottom) V, D, J, and C gene segments. The arrows represent the transcriptional direction of each TCR gene. The squares and circles represent going in a direct and reverse direction. Modified after Glusman et al. 2001.<ref name=glusman2001/>]]

Comparisons of the genomic sequences within each physical site or location of a specific gene on a chromosome (locs) and across species allow for research on other mechanisms and other regulatory signals. Some suggest new hypotheses about the evolution of TCRs, to be tested (and improved) by comparison to the TCR gene complement of other vertebrate species. A comparative genomic investigation of humans and mice will obviously allow for the discovery and annotation of many other genes, as well as identifying in other species for regulatory sequences.<ref name=glusman2001>{{cite journal | vauthors = Glusman G, Rowen L, Lee I, Boysen C, Roach JC, Smit AF, Wang K, Koop BF, Hood L | title = Comparative genomics of the human and mouse T cell receptor loci | journal = Immunity | volume = 15 | issue = 3 | pages = 337–349 | date = September 2001 | pmid = 11567625 | doi = 10.1016/s1074-7613(01)00200-x | doi-access = free }}</ref>

===Research===

Comparative genomics also opens up new avenues in other areas of research. As DNA sequencing technology has become more accessible, the number of [[genome sequencing|sequenced genomes]] has grown. With the increasing reservoir of available genomic data, the potency of comparative genomic inference has grown as well.

A notable case of this increased potency is found in recent [[primate]] research. Comparative genomic methods have allowed researchers to gather information about [[genetic variation]], [[Gene expression profiling|differential gene expression]], and evolutionary dynamics in primates that were indiscernible using previous data and methods.<ref>{{cite journal | vauthors = Rogers J, Gibbs RA | title = Comparative primate genomics: emerging patterns of genome content and dynamics | journal = Nature Reviews. Genetics | volume = 15 | issue = 5 | pages = 347–359 | date = May 2014 | pmid = 24709753 | pmc = 4113315 | doi = 10.1038/nrg3707 }}</ref>

====Great Ape Genome Project====
The '''Great Ape Genome Project''' used comparative genomic methods to investigate genetic variation with reference to the six [[Hominidae|great ape]] species, finding healthy levels of variation in their gene pool despite shrinking population size.<ref>{{cite journal | vauthors = Prado-Martinez J, Sudmant PH, Kidd JM, Li H, Kelley JL, Lorente-Galdos B, Veeramah KR, Woerner AE, O'Connor TD, Santpere G, Cagan A, Theunert C, Casals F, Laayouni H, Munch K, Hobolth A, Halager AE, Malig M, Hernandez-Rodriguez J, Hernando-Herraez I, Prüfer K, Pybus M, Johnstone L, Lachmann M, Alkan C, Twigg D, Petit N, Baker C, Hormozdiari F, Fernandez-Callejo M, Dabad M, Wilson ML, Stevison L, Camprubí C, Carvalho T, Ruiz-Herrera A, Vives L, Mele M, Abello T, Kondova I, Bontrop RE, Pusey A, Lankester F, Kiyang JA, Bergl RA, Lonsdorf E, Myers S, Ventura M, Gagneux P, Comas D, Siegismund H, Blanc J, Agueda-Calpena L, Gut M, Fulton L, Tishkoff SA, Mullikin JC, Wilson RK, Gut IG, Gonder MK, Ryder OA, Hahn BH, Navarro A, Akey JM, Bertranpetit J, Reich D, Mailund T, Schierup MH, Hvilsom C, Andrés AM, Wall JD, Bustamante CD, Hammer MF, Eichler EE, Marques-Bonet T | title = Great ape genetic diversity and population history | journal = Nature | volume = 499 | issue = 7459 | pages = 471–475 | date = July 2013 | pmid = 23823723 | pmc = 3822165 | doi = 10.1038/nature12228 | bibcode = 2013Natur.499..471P |doi-access=free}}</ref> Another study showed that patterns of DNA methylation, which are a known regulation mechanism for gene expression, differ in the prefrontal cortex of humans versus chimps, and implicated this difference in the evolutionary divergence of the two species.<ref>{{cite journal | vauthors = Zeng J, Konopka G, Hunt BG, Preuss TM, Geschwind D, Yi SV | title = Divergent whole-genome methylation maps of human and chimpanzee brains reveal epigenetic basis of human regulatory evolution | journal = American Journal of Human Genetics | volume = 91 | issue = 3 | pages = 455–465 | date = September 2012 | pmid = 22922032 | pmc = 3511995 | doi = 10.1016/j.ajhg.2012.07.024 |doi-access=free}}</ref>