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Comparative genomics is a branch of biological research that examines genome sequences across a spectrum of species, spanning from humans and mice to a diverse array of organisms from bacteria to chimpanzees.^[2]^[3] This large-scale holistic approach compares two or more genomes to discover the similarities and differences between the genomes and to study the biology of the individual genomes.^[4] Comparison of whole genome sequences provides a highly detailed view of how organisms are related to each other at the gene level. By comparing whole genome sequences, researchers gain insights into genetic relationships between organisms and study evolutionary changes.^[2] The major principle of comparative genomics is that common features of two organisms will often be encoded within the DNA that is evolutionarily conserved between them. Therefore, Comparative genomics provides a powerful tool for studying evolutionary changes among organisms, helping to identify genes that are conserved or common among species, as well as genes that give unique characteristics of each organism. Moreover, these studies can be performed at different levels of the genomes to obtain multiple perspectives about the organisms.^[4]

Table 1: Comparative genome sizes of humans and other model organisms
Organism	Estimated size (base pairs)	Chromosome number	Estimated gene number
Human (Homo sapiens)	3.1 billion	46	25,000
Mouse (Mus musculus)	2.9 billion	40	25,000
Bovine (Bos taurus)	2.7 billion	60	22,000
Fruit fly (Drosophila melanogater)	165 million	8	13,000
Plant (Arabidopsis thaliana)	157 million	10	25,000
Roundworm (Caenorhabditis elegans)	97 million	12	19,000
Yeast (Saccharomyces cerevisiae)	12 million	32	6,000
Bacteria (Escherichia coli)	4.6 million	1	3,200

The comparative genomic analysis begins with a simple comparison of the general features of genomes such as genome size, number of genes, and chromosome number. Table 1 presents data on several fully sequenced model organisms, and highlights some striking findings. For instance, while the tiny flowering plant Arabidopsis thaliana has a smaller genome than that of the fruit fly Drosophila melanogaster (157 million base pairs v. 165 million base pairs, respectively) it possesses nearly twice as many genes (25,000 v. 13,000). In fact, A. thaliana has approximately the same number of genes as humans (25,000). Thus, a very early lesson learned in the genomic era is that genome size does not correlate with evolutionary status, nor is the number of genes proportionate to genome size.^[5]

In comparative genomics, synteny is the preserved order of genes on chromosomes of related species indicating their descent from a common ancestor. Synteny provides a framework in which the conservation of homologous genes and gene order is identified between genomes of different species.^[6] Synteny blocks are more formally defined as regions of chromosomes between genomes that share a common order of homologous genes derived from a common ancestor.^[7]^[8] Alternative names such as conserved synteny or collinearity have been used interchangeably.^[9] Comparisons of genome synteny between and within species have provided an opportunity to study evolutionary processes that lead to the diversity of chromosome number and structure in many lineages across the tree of life^[10]^[11]; early discoveries using such approaches include chromosomal conserved regions in nematodes and yeast,^[12]^[13] evolutionary history and phenotypic traits of extremely conserved Hox gene clusters across animals and MADS-box gene family in plants,^[14]^[15] and karyotype evolution in mammals and plants.^[16]

Furthermore, comparing two genomes not only reveals conserved domains or synteny but also aids in detecting copy number variations, single nucleotide polymorphisms (SNPs) , indels, and other genomic structural variations.

Virtually started as soon as the whole genomes of two organisms became available (that is, the genomes of the bacteria Haemophilus influenzae and Mycoplasma genitalium) in 1995, comparative genomics is now a standard component of the analysis of every new genome sequence.^[2]^[17] With the explosion in the number of genome projects due to the advancements in DNA sequencing technologies, particularly the next-generation sequencing methods in late 2000s, this field has become more sophisticated, making it possible to deal with many genomes in a single study.^[18] Comparative genomics has revealed high levels of similarity between closely related organisms, such as humans and chimpanzees, and, more surprisingly, similarity between seemingly distantly related organisms, such as humans and the yeast Saccharomyces cerevisiae.^[19] It has also showed the extreme diversity of the gene composition in different evolutionary lineages.^[17]

History

See also: History of genomics

Comparative genomics has a root in the comparison of virus genomes in the early 1980s.^[17] For example, small RNA viruses infecting animals (picornaviruses) and those infecting plants (cowpea mosaic virus) were compared and turned out to share significant sequence similarity and, in part, the order of their genes.^[20] In 1986, the first comparative genomic study at a larger scale was published, comparing the genomes of varicella-zoster virus and Epstein-Barr virus that contained more than 100 genes each.^[21]

The first complete genome sequence of a cellular organism, that of Haemophilus influenzae Rd, was published in 1995.^[22] The second genome sequencing paper was of the small parasitic bacterium Mycoplasma genitalium published in the same year.^[23] Starting from this paper, reports on new genomes inevitably became comparative-genomic studies.^[17]

Microbial genomes. The first high-resolution whole genome comparison system of microbial genomes of 10-15kbp was developed in 1998 by Art Delcher, Simon Kasif and Steven Salzberg and applied to the comparison of entire highly related microbial organisms with their collaborators at the Institute for Genomic Research (TIGR). The system is called MUMMER and was described in a publication in Nucleic Acids Research in 1999. The system helps researchers to identify large rearrangements, single base mutations, reversals, tandem repeat expansions and other polymorphisms. In bacteria, MUMMER enables the identification of polymorphisms that are responsible for virulence, pathogenicity, and anti-biotic resistance. The system was also applied to the Minimal Organism Project at TIGR and subsequently to many other comparative genomics projects.

Eukaryote genomes. Saccharomyces cerevisiae, the baker's yeast, was the first eukaryote to have its complete genome sequence published in 1996.^[24] After the publication of the roundworm Caenorhabditis elegans genome in 1998^[25] and together with the fruit fly Drosophila melanogaster genome in 2000,^[26] Gerald M. Rubin and his team published a paper titled "Comparative Genomics of the Eukaryotes", in which they compared the genomes of the eukaryotes D. melanogaster, C. elegans, and S. cerevisiae, as well as the prokaryote H. influenzae.^[27] At the same time, Bonnie Berger, Eric Lander, and their team published a paper on whole-genome comparison of human and mouse.^[28]

With the publication of the large genomes of vertebrates in the 2000s, including human, the Japanese pufferfish Takifugu rubripes, and mouse, precomputed results of large genome comparisons have been released for downloading or for visualization in a genome browser. Instead of undertaking their own analyses, most biologists can access these large cross-species comparisons and avoid the impracticality caused by the size of the genomes.^[29]

Next-generation sequencing methods, which were first introduced in 2007, have produced an enormous amount of genomic data and have allowed researchers to generate multiple (prokaryotic) draft genome sequences at once. These methods can also quickly uncover single-nucleotide polymorphisms, insertions and deletions by mapping unassembled reads against a well annotated reference genome, and thus provide a list of possible gene differences that may be the basis for any functional variation among strains.^[18]

Evolutionary principles

One character of biology is evolution, evolutionary theory is also the theoretical foundation of comparative genomics, and at the same time the results of comparative genomics unprecedentedly enriched and developed the theory of evolution. When two or more of the genome sequence are compared, one can deduce the evolutionary relationships of the sequences in a phylogenetic tree. Based on a variety of biological genome data and the study of vertical and horizontal evolution processes, one can understand vital parts of the gene structure and its regulatory function.

Similarity of related genomes is the basis of comparative genomics. If two creatures have a recent common ancestor, the differences between the two species genomes are evolved from the ancestors' genome. The closer the relationship between two organisms, the higher the similarities between their genomes. If there is close relationship between them, then their genome will display a linear behaviour (synteny), namely some or all of the genetic sequences are conserved. Thus, the genome sequences can be used to identify gene function, by analyzing their homology (sequence similarity) to genes of known function.

Human FOXP2 gene and evolutionary conservation is shown in and multiple alignment (at bottom of figure) in this image from the UCSC Genome Browser. Note that conservation tends to cluster around coding regions (exons).

Orthologous sequences are related sequences in different species: a gene exists in the original species, the species divided into two species, so genes in new species are orthologous to the sequence in the original species. Paralogous sequences are separated by gene cloning (gene duplication): if a particular gene in the genome is copied, then the copy of the two sequences is paralogous to the original gene. A pair of orthologous sequences is called orthologous pairs (orthologs), a pair of paralogous sequence is called collateral pairs (paralogs). Orthologous pairs usually have the same or similar function, which is not necessarily the case for collateral pairs. In collateral pairs, the sequences tend to evolve into having different functions.

Comparative genomics exploits both similarities and differences in the proteins, RNA, and regulatory regions of different organisms to infer how selection has acted upon these elements. Those elements that are responsible for similarities between different species should be conserved through time (stabilizing selection), while those elements responsible for differences among species should be divergent (positive selection). Finally, those elements that are unimportant to the evolutionary success of the organism will be unconserved (selection is neutral).

One of the important goals of the field is the identification of the mechanisms of eukaryotic genome evolution. It is however often complicated by the multiplicity of events that have taken place throughout the history of individual lineages, leaving only distorted and superimposed traces in the genome of each living organism. For this reason comparative genomics studies of small model organisms (for example the model Caenorhabditis elegans and closely related Caenorhabditis briggsae) are of great importance to advance our understanding of general mechanisms of evolution.^[30]^[31]

Comparative Genomics and the Role of CNVs in Evolution

See also: Copy number variation

Comparative genomics plays a crucial role in identifying copy number variations (CNVs) and understanding their significance in evolution. CNVs, which involve deletions or duplications of large segments of DNA, are recognized as a major source of genetic diversity, influencing gene structure, dosage, and regulation. While single nucleotide polymorphisms (SNPs) are more common, CNVs impact larger genomic regions and can have profound effects on phenotype and diversity. Recent studies suggest that CNVs constitute around 4.8–9.5% of the human genome and have a substantial functional and evolutionary impact. In mammals, CNVs contribute significantly to population diversity, influencing gene expression and various phenotypic traits. Comparative genomics analyses of human and chimpanzee genomes have revealed that CNVs may play a greater role in evolutionary change compared to single nucleotide changes. Research indicates that CNVs affect more nucleotides than individual base-pair changes, with about 2.7% of the genome affected by CNVs compared to 1.2% by SNPs. Moreover, while many CNVs are shared between humans and chimpanzees, a significant portion is unique to each species. Additionally, CNVs have been associated with genetic diseases in humans, highlighting their importance in human health. Despite this, many questions about CNVs remain unanswered, including their origin and contributions to evolutionary adaptation and disease. Ongoing research aims to address these questions using techniques like comparative genomic hybridization, which allows for a detailed examination of CNVs and their significance. When investigators examined the raw sequence data of the human and chimpanzee

Significance of comparative genomics

Comparative genomics holds profound significance across various fields, including medical research, basic biology, and biodiversity conservation. Genomic advancements have not only facilitated breakthroughs in understanding diseases but also aided in combating illegal poaching and safeguarding endangered species. For instance, in medical research, predicting how genomic variants limited ability to predict which genomic variants lead to changes in organism-level phenotypes, such as increased disease risk in humans, remains challenging due to the immense size of the genome, comprising about three billion nucleotides.

To tackle this challenge, comparative genomics offers a solution by pinpointing nucleotide positions that have remained unchanged over millions of years of evolution. These conserved regions indicate potential sites where genetic alterations could have detrimental effects on an organism's fitness, thus guiding the search for disease-causing variants. Moreover, comparative genomics holds promise in unraveling the mechanisms of gene evolution, environmental adaptations, gender-specific differences, and population variations across vertebrate lineages.

Furthermore, comparative studies enable the identification of genomic signatures of selection—regions in the genome that have undergone preferential increase and fixation in populations due to their functional significance in specific processes. For instance, in animal genetics, indigenous cattle exhibit superior disease resistance and environmental adaptability but lower productivity compared to exotic breeds. Through comparative genomic analyses, significant genomic signatures responsible for these unique traits can be identified. Using insights from this signature, breeders can make informed decisions to enhance breeding strategies and promote breed development.

Methods

Computational approaches are necessary for genome comparisons, given the large amount of data encoded in genomes. Many tools are now publicly available, ranging from whole genome comparisons to gene expression analysis.^[32] This includes approaches from systems and control, information theory, string analysis and data mining.^[33] Computational approaches will remain critical for research and teaching, especially when information science and genome biology is taught in conjunction.^[34]

Comparative genomics starts with basic comparisons of genome size and gene density. For instance, genome size is important for coding capacity and possibly for regulatory reasons. High gene density facilitates genome annotation, analysis of environmental selection. By contrast, low gene density hampers the mapping of genetic disease as in the human genome.

Sequence alignment

Alignments are used to capture information about similar sequences such as ancestry, common evolutionary descent, or common structure and function. Alignments can be done for both genetic and protein sequences.^[36]^[37] Alignments consist of local or global pairwise alignments, and multiple sequence alignments. One way to find global alignments is to use a dynamic programming algorithm known as Needleman-Wunsch algorithm. This algorithm can be modified and used to find local alignments.

Whole-genome alignment

Whole-genome alignment (WGA) involves predicting evolutionary relationships at the nucleotide level between two or more genomes. It integrates elements of colinear sequence alignment and gene orthology prediction, presenting a greater challenge due to the vast size and intricate nature of whole genomes. Despite its complexity, numerous methods have emerged to tackle this problem because WGAs play a crucial role in various genome-wide analyses, such as phylogenetic inference, genome annotation, and function prediction. Thereby, SyRI (Synteny and Rearrangement Identifier) is one such method that utilizes whole genome alignment and it is designed to identify both structural and sequence differences between two whole-genome assemblies. By taking WGAs as input, SyRI initially scans for disparities in genome structures. Subsequently, it identifies local sequence variations within both rearranged and non-rearranged (syntenic) regions ^[38].

Phylogenetic reconstruction

Another computational method for comparative genomics is phylogenetic reconstruction. It is used to describe evolutionary relationships in terms of common ancestors. The relationships are usually represented in a tree called a phylogenetic tree. Similarly, coalescent theory is a retrospective model to trace alleles of a gene in a population to a single ancestral copy shared by members of the population. This is also known as the most recent common ancestor. Analysis based on coalescence theory tries predicting the amount of time between the introduction of a mutation and a particular allele or gene distribution in a population. This time period is equal to how long ago the most recent common ancestor existed. The inheritance relationships are visualized in a form similar to a phylogenetic tree. Coalescence (or the gene genealogy) can be visualized using dendrograms.^[39]

Example of synteny block and break. Genes located on chromosomes of two species are denoted in letters. Each gene is associated with a number representing the species they belong to (species 1 or 2). Orthologous genes are connected by dashed lines and genes without an orthologous relationship are treated as gaps in synteny programs.^[40]

Genome maps

An additional method in comparative genomics is genetic mapping. In genetic mapping, visualizing synteny is one way to see the preserved order of genes on chromosomes. It is usually used for chromosomes of related species, both of which result from a common ancestor.^[41] This and other methods can shed light on evolutionary history. A recent study used comparative genomics to reconstruct 16 ancestral karyotypes across the mammalian phylogeny. The computational reconstruction showed how chromosomes rearranged themselves during mammal evolution. It gave insight into conservation of select regions often associated with the control of developmental processes. In addition, it helped to provide an understanding of chromosome evolution and genetic diseases associated with DNA rearrangements.^{[citation needed]}

Tools

Computational tools for analyzing sequences and complete genomes are developing quickly due to the availability of large amount of genomic data. At the same time, comparative analysis tools are progressed and improved. In the challenges about these analyses, it is very important to visualize the comparative results.^[42]

Visualization of sequence conservation is a tough task of comparative sequence analysis. As we know, it is highly inefficient to examine the alignment of long genomic regions manually. Internet-based genome browsers provide many useful tools for investigating genomic sequences due to integrating all sequence-based biological information on genomic regions. When we extract large amount of relevant biological data, they can be very easy to use and less time-consuming.^[42]

UCSC Browser: This site contains the reference sequence and working draft assemblies for a large collection of genomes.^[43]
Ensembl: The Ensembl project produces genome databases for vertebrates and other eukaryotic species, and makes this information freely available online.^[44]
MapView: The Map Viewer provides a wide variety of genome mapping and sequencing data.^[45]
VISTA is a comprehensive suite of programs and databases for comparative analysis of genomic sequences. It was built to visualize the results of comparative analysis based on DNA alignments. The presentation of comparative data generated by VISTA can easily suit both small and large scale of data.^[46]
BlueJay Genome Browser: a stand-alone visualization tool for the multi-scale viewing of annotated genomes and other genomic elements.^[47]

An advantage of using online tools is that these websites are being developed and updated constantly. There are many new settings and content can be used online to improve efficiency.^[42]

Selected applications

Agriculture

Agriculture is a field that reaps the benefits of comparative genomics. Identifying the loci of advantageous genes is a key step in breeding crops that are optimized for greater yield, cost-efficiency, quality, and disease resistance. For example, one genome wide association study conducted on 517 rice landraces 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.^[48] 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.^[49]

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.^[50] 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 Group B Streptococcus, a group of bacteria responsible for severe neonatal infection.^[51] 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 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.^[52] In May 2019, using the Global Genome Set, a team in the UK and Australia sequenced thousands of globally-collected isolates of Group A Streptococcus, providing potential targets for developing a vaccine against the pathogen, also known as S. pyogenes.^[53]

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 models. 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.^[54]

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 sequences. 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′ enhancers 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.

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.^[54]

Research

Comparative genomics also opens up new avenues in other areas of research. As DNA sequencing technology has become more accessible, the number of 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, differential gene expression, and evolutionary dynamics in primates that were indiscernible using previous data and methods.^[55]

Great Ape Genome Project

The Great Ape Genome Project used comparative genomic methods to investigate genetic variation with reference to the six great ape species, finding healthy levels of variation in their gene pool despite shrinking population size.^[56] 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.^[57]

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^ Stein LD, Bao Z, Blasiar D, Blumenthal T, Brent MR, Chen N, et al. (November 2003). "The genome sequence of Caenorhabditis briggsae: a platform for comparative genomics". PLOS Biology. 1 (2): E45. doi:10.1371/journal.pbio.0000045. PMC 261899. PMID 14624247.
^ "Newly Sequenced Worm a Boon for Worm Biologists". PLOS Biology. 1 (2): e4. 2003. doi:10.1371/journal.pbio.0000044. PMC 261884.
^ Cristianini N, Hahn M (2006). Introduction to Computational Genomics. Cambridge University Press. ISBN 978-0-521-67191-0.
^ Pratas D, Silva RM, Pinho AJ, Ferreira PJ (May 2015). "An alignment-free method to find and visualise rearrangements between pairs of DNA sequences". Scientific Reports. 5: 10203. Bibcode:2015NatSR...510203P. doi:10.1038/srep10203. PMC 4434998. PMID 25984837.
^ Via A, De Las Rivas J, Attwood TK, Landsman D, Brazas MD, Leunissen JA, et al. (October 2011). "Ten simple rules for developing a short bioinformatics training course". PLOS Computational Biology. 7 (10): e1002245. Bibcode:2011PLSCB...7E2245V. doi:10.1371/journal.pcbi.1002245. PMC 3203054. PMID 22046119.
^ ^a ^b Damas J, Corbo M, Kim J, Turner-Maier J, Farré M, Larkin DM, et al. (October 2022). "Evolution of the ancestral mammalian karyotype and syntenic regions". Proceedings of the National Academy of Sciences of the United States of America. 119 (40): e2209139119. Bibcode:2022PNAS..11909139D. doi:10.1073/pnas.2209139119. PMC 9550189. PMID 36161960.
^ Altschul SF, Pop M (2017). "Sequence Alignment". In Rosen KH, Shier DR, Goddard W (eds.). Handbook of Discrete and Combinatorial Mathematics (2nd ed.). Boca Raton (FL): CRC Press/Taylor & Francis. ISBN 978-1-58488-780-5. PMID 29206392. Retrieved 2022-12-18.
^ Prjibelski AD, Korobeynikov AI, Lapidus AL (2019-01-01). "Sequence Analysis". In Ranganathan S, Gribskov M, Nakai K, Schönbach C (eds.). Encyclopedia of Bioinformatics and Computational Biology. Oxford: Academic Press. pp. 292–322. doi:10.1016/b978-0-12-809633-8.20106-4. ISBN 978-0-12-811432-2. S2CID 226247797.
^ https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1911-0
^ Haubold B, Wiehe T (September 2004). "Comparative genomics: methods and applications". Die Naturwissenschaften. 91 (9): 405–421. Bibcode:2004NW.....91..405H. doi:10.1007/s00114-004-0542-8. PMID 15278216. S2CID 2041895.
^ Liu D, Hunt M, Tsai IJ (January 2018). "Inferring synteny between genome assemblies: a systematic evaluation". BMC Bioinformatics. 19 (1): 26. doi:10.1186/s12859-018-2026-4. PMC 5791376. PMID 29382321.
^ Duran C, Edwards D, Batley J (2009). "Genetic Maps and the Use of Synteny". Plant Genomics. Methods in Molecular Biology. Vol. 513. pp. 41–55. doi:10.1007/978-1-59745-427-8_3. ISBN 978-1-58829-997-0. PMID 19347649.
^ ^a ^b ^c Bergman NH (2007). Bergman NH (ed.). Comparative Genomics: Volumes 1 and 2. Totowa, New Jersey: Humana Press. ISBN 978-193411-537-4. PMID 21250292.
^ "UCSC Browser".
^ "Ensembl Genome Browser". Archived from the original on 2013-10-21.
^ "Map Viewer".
^ "VISTA tools".
^ Soh J, Gordon PM, Sensen CW (March 2012). "The Bluejay genome browser". Current Protocols in Bioinformatics. 37. John Wiley & Sons, Inc.: Unit10.9. doi:10.1002/0471250953.bi1009s37. ISBN 9780471250951. PMID 22389011. S2CID 34553139.
^ Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, et al. (November 2010). "Genome-wide association studies of 14 agronomic traits in rice landraces". Nature Genetics. 42 (11): 961–967. doi:10.1038/ng.695. PMID 20972439. S2CID 439442.
^ Morrell PL, Buckler ES, Ross-Ibarra J (December 2011). "Crop genomics: advances and applications". Nature Reviews. Genetics. 13 (2): 85–96. doi:10.1038/nrg3097. PMID 22207165. S2CID 13358998.
^ Seib KL, Zhao X, Rappuoli R (October 2012). "Developing vaccines in the era of genomics: a decade of reverse vaccinology". Clinical Microbiology and Infection. 18 Suppl 5 (SI): 109–116. doi:10.1111/j.1469-0691.2012.03939.x. hdl:10072/50260. PMID 22882709.
^ Maione D, Margarit I, Rinaudo CD, Masignani V, Mora M, Scarselli M, et al. (July 2005). "Identification of a universal Group B streptococcus vaccine by multiple genome screen". Science. 309 (5731): 148–150. Bibcode:2005Sci...309..148M. doi:10.1126/science.1109869. PMC 1351092. PMID 15994562.
^ Rasko DA, Rosovitz MJ, Myers GS, Mongodin EF, Fricke WF, Gajer P, et al. (October 2008). "The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates". Journal of Bacteriology. 190 (20): 6881–6893. doi:10.1128/JB.00619-08. PMC 2566221. PMID 18676672.
^ "Group a Streptococcus Vaccine Target Candidates Identified from Global Genome Set". 28 May 2019.
^ ^a ^b ^c ^d Glusman G, Rowen L, Lee I, Boysen C, Roach JC, Smit AF, et al. (September 2001). "Comparative genomics of the human and mouse T cell receptor loci". Immunity. 15 (3): 337–349. doi:10.1016/s1074-7613(01)00200-x. PMID 11567625.
^ Rogers J, Gibbs RA (May 2014). "Comparative primate genomics: emerging patterns of genome content and dynamics". Nature Reviews. Genetics. 15 (5): 347–359. doi:10.1038/nrg3707. PMC 4113315. PMID 24709753.
^ Prado-Martinez J, Sudmant PH, Kidd JM, Li H, Kelley JL, Lorente-Galdos B, et al. (July 2013). "Great ape genetic diversity and population history". Nature. 499 (7459): 471–475. Bibcode:2013Natur.499..471P. doi:10.1038/nature12228. PMC 3822165. PMID 23823723.
^ Zeng J, Konopka G, Hunt BG, Preuss TM, Geschwind D, Yi SV (September 2012). "Divergent whole-genome methylation maps of human and chimpanzee brains reveal epigenetic basis of human regulatory evolution". American Journal of Human Genetics. 91 (3): 455–465. doi:10.1016/j.ajhg.2012.07.024. PMC 3511995. PMID 22922032.

External links

Genomes OnLine Database (GOLD)
Genome News Network
JCVI Comprehensive Microbial Resource
Pathema: A Clade Specific Bioinformatics Resource Center
CBS Genome Atlas Database Archived 2016-05-16 at the Portuguese Web Archive
The UCSC Genome Browser
The U.S. National Human Genome Research Institute
Ensembl The Ensembl Genome Browser
Genolevures, comparative genomics of the Hemiascomycetous yeasts
Phylogenetically Inferred Groups (PhIGs), a recently developed method incorporates phylogenetic signals in building gene clusters for use in comparative genomics.
Metazome Archived 2006-08-10 at the Wayback Machine, a resource for the phylogenomic exploration and analysis of Metazoan gene families.
IMG The Integrated Microbial Genomes system, for comparative genome analysis by the DOE-JGI.
Dcode.org Dcode.org Comparative Genomics Center.
SUPERFAMILY Protein annotations for all completely sequenced organisms
Comparative Genomics
Blastology and Open Source: Needs and Deeds
Alignment-free comparative Genomics tool

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[29] Ureta-Vidal A, Ettwiller L, Birney E (April 2003). "Comparative genomics: genome-wide analysis in metazoan eukaryotes". Nature Reviews. Genetics. 4 (4): 251–262. doi:10.1038/nrg1043. PMID 12671656. S2CID 2037634.

[30] Stein LD, Bao Z, Blasiar D, Blumenthal T, Brent MR, Chen N, et al. (November 2003). "The genome sequence of Caenorhabditis briggsae: a platform for comparative genomics". PLOS Biology. 1 (2): E45. doi:10.1371/journal.pbio.0000045. PMC 261899. PMID 14624247.

[31] "Newly Sequenced Worm a Boon for Worm Biologists". PLOS Biology. 1 (2): e4. 2003. doi:10.1371/journal.pbio.0000044. PMC 261884.

[32] Cristianini N, Hahn M (2006). Introduction to Computational Genomics. Cambridge University Press. ISBN 978-0-521-67191-0.

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[36] Altschul SF, Pop M (2017). "Sequence Alignment". In Rosen KH, Shier DR, Goddard W (eds.). Handbook of Discrete and Combinatorial Mathematics (2nd ed.). Boca Raton (FL): CRC Press/Taylor & Francis. ISBN 978-1-58488-780-5. PMID 29206392. Retrieved 2022-12-18.

[37] Prjibelski AD, Korobeynikov AI, Lapidus AL (2019-01-01). "Sequence Analysis". In Ranganathan S, Gribskov M, Nakai K, Schönbach C (eds.). Encyclopedia of Bioinformatics and Computational Biology. Oxford: Academic Press. pp. 292–322. doi:10.1016/b978-0-12-809633-8.20106-4. ISBN 978-0-12-811432-2. S2CID 226247797.

[38] ttps://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1911-0

[39] Haubold B, Wiehe T (September 2004). "Comparative genomics: methods and applications". Die Naturwissenschaften. 91 (9): 405–421. Bibcode:2004NW.....91..405H. doi:10.1007/s00114-004-0542-8. PMID 15278216. S2CID 2041895.

[40] Liu D, Hunt M, Tsai IJ (January 2018). "Inferring synteny between genome assemblies: a systematic evaluation". BMC Bioinformatics. 19 (1): 26. doi:10.1186/s12859-018-2026-4. PMC 5791376. PMID 29382321.

[41] Duran C, Edwards D, Batley J (2009). "Genetic Maps and the Use of Synteny". Plant Genomics. Methods in Molecular Biology. Vol. 513. pp. 41–55. doi:10.1007/978-1-59745-427-8_3. ISBN 978-1-58829-997-0. PMID 19347649.

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[43] "UCSC Browser".

[44] "Ensembl Genome Browser". Archived from the original on 2013-10-21.

[45] "Map Viewer".

[46] "VISTA tools".

[47] Soh J, Gordon PM, Sensen CW (March 2012). "The Bluejay genome browser". Current Protocols in Bioinformatics. 37. John Wiley & Sons, Inc.: Unit10.9. doi:10.1002/0471250953.bi1009s37. ISBN 9780471250951. PMID 22389011. S2CID 34553139.

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[49] Morrell PL, Buckler ES, Ross-Ibarra J (December 2011). "Crop genomics: advances and applications". Nature Reviews. Genetics. 13 (2): 85–96. doi:10.1038/nrg3097. PMID 22207165. S2CID 13358998.

[50] Seib KL, Zhao X, Rappuoli R (October 2012). "Developing vaccines in the era of genomics: a decade of reverse vaccinology". Clinical Microbiology and Infection. 18 Suppl 5 (SI): 109–116. doi:10.1111/j.1469-0691.2012.03939.x. hdl:10072/50260. PMID 22882709.

[51] Maione D, Margarit I, Rinaudo CD, Masignani V, Mora M, Scarselli M, et al. (July 2005). "Identification of a universal Group B streptococcus vaccine by multiple genome screen". Science. 309 (5731): 148–150. Bibcode:2005Sci...309..148M. doi:10.1126/science.1109869. PMC 1351092. PMID 15994562.

[52] Rasko DA, Rosovitz MJ, Myers GS, Mongodin EF, Fricke WF, Gajer P, et al. (October 2008). "The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates". Journal of Bacteriology. 190 (20): 6881–6893. doi:10.1128/JB.00619-08. PMC 2566221. PMID 18676672.

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[glusman2001-54] Glusman G, Rowen L, Lee I, Boysen C, Roach JC, Smit AF, et al. (September 2001). "Comparative genomics of the human and mouse T cell receptor loci". Immunity. 15 (3): 337–349. doi:10.1016/s1074-7613(01)00200-x. PMID 11567625.

[55] Rogers J, Gibbs RA (May 2014). "Comparative primate genomics: emerging patterns of genome content and dynamics". Nature Reviews. Genetics. 15 (5): 347–359. doi:10.1038/nrg3707. PMC 4113315. PMID 24709753.

[56] Prado-Martinez J, Sudmant PH, Kidd JM, Li H, Kelley JL, Lorente-Galdos B, et al. (July 2013). "Great ape genetic diversity and population history". Nature. 499 (7459): 471–475. Bibcode:2013Natur.499..471P. doi:10.1038/nature12228. PMC 3822165. PMID 23823723.

[57] Zeng J, Konopka G, Hunt BG, Preuss TM, Geschwind D, Yi SV (September 2012). "Divergent whole-genome methylation maps of human and chimpanzee brains reveal epigenetic basis of human regulatory evolution". American Journal of Human Genetics. 91 (3): 455–465. doi:10.1016/j.ajhg.2012.07.024. PMC 3511995. PMID 22922032.

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 The comparative genomic analysis begins with a simple comparison of the general features of genomes such as genome size, number of genes, and chromosome number. Table 1 presents data on several fully sequenced model organisms, and highlights some striking findings. For instance, while the tiny flowering plant ''Arabidopsis thaliana'' has a smaller genome than that of the fruit fly ''Drosophila melanogaster'' (157 million base pairs v. 165 million base pairs, respectively) it possesses nearly twice as many genes (25,000 v. 13,000). In fact, ''A. thaliana'' has approximately the same number of genes as humans (25,000). Thus, a very early lesson learned in the '''genomic era''' is that genome size does not correlate with evolutionary status, nor is the number of genes proportionate to genome size.<ref>{{Cite journal |last=BENNETT |first=MICHAEL D. |last2=LEITCH |first2=ILIA J. |last3=PRICE |first3=H. JAMES |last4=JOHNSTON |first4=J. SPENCER |date=2003-04 |title=Comparisons with Caenorhabditis (∼100 Mb) and Drosophila (∼175 Mb) Using Flow Cytometry Show Genome Size in Arabidopsis to be ∼157 Mb and thus ∼25 % Larger than the Arabidopsis Genome Initiative Estimate of ∼125 Mb |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242247/ |journal=Annals of Botany |volume=91 |issue=5 |pages=547–557 |doi=10.1093/aob/mcg057 |issn=0305-7364 |pmc=4242247 |pmid=12646499}}</ref>
+In comparative genomics, [[synteny]] is the preserved order of genes on [[chromosomes]] of related species indicating their descent from a [[common ancestor]]. Synteny provides a framework in which the conservation of [[Sequence homology | homologous genes]] and [[gene order]] is identified between genomes of different species.<ref>{{Cite journal |last=Liu |first=Dang |last2=Hunt |first2=Martin |last3=Tsai |first3=Isheng J |date=2018-01-30 |title=Inferring synteny between genome assemblies: a systematic evaluation |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5791376/ |journal=BMC Bioinformatics |volume=19 |pages=26 |doi=10.1186/s12859-018-2026-4 |issn=1471-2105 |pmc=5791376 |pmid=29382321}}</ref> Synteny blocks are more formally defined as regions of chromosomes between genomes that share a common order of homologous genes derived from a common ancestor.<ref>{{Cite journal |last=Vergara |first=Ismael A |last2=Chen |first2=Nansheng |date=2010-09-24 |title=Large synteny blocks revealed between Caenorhabditis elegans and Caenorhabditis briggsae genomes using OrthoCluster |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2997010/ |journal=BMC Genomics |volume=11 |pages=516 |doi=10.1186/1471-2164-11-516 |issn=1471-2164 |pmc=2997010 |pmid=20868500}}</ref><ref>{{Cite journal |last=Tang |first=Haibao |last2=Lyons |first2=Eric |last3=Pedersen |first3=Brent |last4=Schnable |first4=James C |last5=Paterson |first5=Andrew H |last6=Freeling |first6=Michael |date=2011-04-18 |title=Screening synteny blocks in pairwise genome comparisons through integer programming |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3088904/ |journal=BMC Bioinformatics |volume=12 |pages=102 |doi=10.1186/1471-2105-12-102 |issn=1471-2105 |pmc=3088904 |pmid=21501495}}</ref> Alternative names such as conserved synteny or [[collinearity]] have been used interchangeably.<ref>{{Cite journal |last=Ehrlich |first=J. |last2=Sankoff |first2=D. |last3=Nadeau |first3=J. H. |date=1997-09 |title=Synteny Conservation and Chromosome Rearrangements during Mammalian Evolution |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1208112/ |journal=Genetics |volume=147 |issue=1 |pages=289–296 |issn=0016-6731 |pmc=1208112 |pmid=9286688}}</ref> Comparisons of genome synteny between and within species have provided an opportunity to study evolutionary processes that lead to the diversity of chromosome number and structure in many lineages across the tree of life<ref>{{Cite journal |last=Zhang |first=Guojie |last2=Li |first2=Bo |last3=Li |first3=Cai |last4=Gilbert |first4=M Thomas P |last5=Jarvis |first5=Erich D |last6=Wang |first6=Jun |date=2014-12-11 |title=Comparative genomic data of the Avian Phylogenomics Project |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4322804/ |journal=GigaScience |volume=3 |pages=26 |doi=10.1186/2047-217X-3-26 |issn=2047-217X |pmc=4322804 |pmid=25671091}}</ref><ref>{{Cite journal |last=Howe |first=Kevin L. |last2=Bolt |first2=Bruce J. |last3=Cain |first3=Scott |last4=Chan |first4=Juancarlos |last5=Chen |first5=Wen J. |last6=Davis |first6=Paul |last7=Done |first7=James |last8=Down |first8=Thomas |last9=Gao |first9=Sibyl |last10=Grove |first10=Christian |last11=Harris |first11=Todd W. |last12=Kishore |first12=Ranjana |last13=Lee |first13=Raymond |last14=Lomax |first14=Jane |last15=Li |first15=Yuling |date=2016-01-04 |title=WormBase 2016: expanding to enable helminth genomic research |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702863/ |journal=Nucleic Acids Research |volume=44 |issue=Database issue |pages=D774–D780 |doi=10.1093/nar/gkv1217 |issn=0305-1048 |pmc=4702863 |pmid=26578572}}</ref>; early discoveries using such approaches include chromosomal conserved regions in [[nematodes]] and [[yeast|yeast,]]<ref>{{Cite journal |last=C. elegans Sequencing Consortium |date=1998-12-11 |title=Genome sequence of the nematode C. elegans: a platform for investigating biology |url=https://pubmed.ncbi.nlm.nih.gov/9851916/ |journal=Science (New York, N.Y.) |volume=282 |issue=5396 |pages=2012–2018 |doi=10.1126/science.282.5396.2012 |issn=0036-8075 |pmid=9851916}}</ref><ref>{{Cite journal |last=Wong |first=Simon |last2=Wolfe |first2=Kenneth H. |date=2005-07 |title=Birth of a metabolic gene cluster in yeast by adaptive gene relocation |url=https://pubmed.ncbi.nlm.nih.gov/15951822/ |journal=Nature Genetics |volume=37 |issue=7 |pages=777–782 |doi=10.1038/ng1584 |issn=1061-4036 |pmid=15951822}}</ref> evolutionary history and phenotypic traits of extremely conserved [[Hox]] gene clusters across animals and [[MADS-box]] gene family in plants,<ref>{{Cite journal |last=Luebeck |first=E. Georg |date=2010-10-28 |title=Cancer: Genomic evolution of metastasis |url=https://pubmed.ncbi.nlm.nih.gov/20981088/ |journal=Nature |volume=467 |issue=7319 |pages=1053–1055 |doi=10.1038/4671053a |issn=1476-4687 |pmid=20981088}}</ref><ref>{{Cite journal |last=Ruelens |first=Philip |last2=de Maagd |first2=Ruud A. |last3=Proost |first3=Sebastian |last4=Theißen |first4=Günter |last5=Geuten |first5=Koen |last6=Kaufmann |first6=Kerstin |date=2013 |title=FLOWERING LOCUS C in monocots and the tandem origin of angiosperm-specific MADS-box genes |url=https://pubmed.ncbi.nlm.nih.gov/23955420/ |journal=Nature Communications |volume=4 |pages=2280 |doi=10.1038/ncomms3280 |issn=2041-1723 |pmid=23955420}}</ref> and [[karyotype]] evolution in mammals and plants.<ref>{{Cite journal |last=Kemkemer |first=Claus |last2=Kohn |first2=Matthias |last3=Cooper |first3=David N |last4=Froenicke |first4=Lutz |last5=Högel |first5=Josef |last6=Hameister |first6=Horst |last7=Kehrer-Sawatzki |first7=Hildegard |date=2009-04-24 |title=Gene synteny comparisons between different vertebrates provide new insights into breakage and fusion events during mammalian karyotype evolution |url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2681463/ |journal=BMC Evolutionary Biology |volume=9 |pages=84 |doi=10.1186/1471-2148-9-84 |issn=1471-2148 |pmc=2681463 |pmid=19393055}}</ref>
-In comparative genomics, [[synteny]] is the preserved order of genes on [[chromosomes]] of related species indicating their descent from a [[common ancestor]]. Synteny provides a framework in which the conservation of [[Sequence homology | homologous genes]] and [[gene order]] is identified between genomes of different species. Synteny blocks are more formally defined as regions of chromosomes between genomes that share a common order of homologous genes derived from a common ancestor. Alternative names such as conserved synteny or [[collinearity]] have been used interchangeably. Comparisons of genome synteny between and within species have provided an opportunity to study evolutionary processes that lead to the diversity of chromosome number and structure in many lineages across the tree of life; early discoveries using such approaches include chromosomal conserved regions in [[nematodes]] and [[yeast]], evolutionary history and phenotypic traits of extremely conserved [[Hox]] gene clusters across animals and [[MADS-box]] gene family in plants, and [[karyotype]] evolution in mammals and plants.
 Furthermore, comparing two genomes not only reveals conserved domains or synteny but also aids in detecting [[copy number variations]], [[Single-nucleotide polymorphism | single nucleotide polymorphisms (SNPs) ]], [[indels]], and other [[Structural variation | genomic structural variations]].

v t e Genomics
Fields	Cognitive genomics Computational genomics Comparative genomics Functional genomics Genome project Human Genome Project Metagenomics Human Microbiome Project Pangenomics Personal genomics Population genomics Sociogenomics Structural genomics
Bioinformatics	Biochip Cheminformatics Chemogenomics Connectomics Human Connectome Project Epigenomics Human Epigenome Project Glycomics Immunomics Lipidomics Metabolomics Microbiomics Nutrigenomics Paleopolyploidy Pharmacogenetics Pharmacogenomics Systems biology Toxicogenomics Transcriptomics
Structural biology	Proteomics Human proteome project Call-map proteomics Structure-based drug design Expression proteomics
Research tools	2-D electrophoresis Mass spectrometer Electrospray ionization Matrix-assisted laser desorption ionization Matrix-assisted laser desorption ionization-time of flight mass spectrometer Microfluidic-based tools Isotope affinity tags Chromosome conformation capture
Organizations	DNA Data Bank of Japan (JP) European Molecular Biology Laboratory (EU) National Institutes of Health (USA) Wellcome Sanger Institute (UK)
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