Evolutionary development biology (evo-devo) approach compares cognitive and neuroanatomic development patterns between sets of species. Studies of human  fetus  brains reveal that almost a third of expressed genes are regionally differentiated, far more than non-human species. [ 4 ]  This finding could potentially explain variations in cognitive development between individuals. Neuroanatomical evo-devo studies have connected higher brain order to  brain lateralization  which, though present in other species, is highly ordered in humans.


Evolutionary phenotype biology (evo-pheno) approach examines phenotype expression between species. Evolutionary pathology biology (evo-patho) approach investigates disease prevalence between species.


In genomics, a gene being imaged and analyzed is referred to as a candidate gene. The ideal candidate genes for comparative genomic testing are genes that harbor well-defined functional polymorphisms with known effects on neuroanatomical and/or cognitive function. [ 2 ]  However, genes with either identified  single-nucleotide polymorphisms  or allele variations with potential functional implications on neuroanatomical systems suffice. [ 2 ]  The weaker the connection between the gene and the phenotype, the more difficult it is to establish causality through testing. [ 2 ]


Non-genetic factors such as age, illness, injury, or substance abuse can have significant effects on gene expression and phenotypic variance. [ 2 ]  The identification and contribution of genetic variation to specific phenotypes can only be performed when other potential contributing factors can be matched across genotype groups. [ 2 ]  In the case of neuroimaging during task performance such as in fMRI, groups are matched by performance level. Non-genetic factors have a particularly large potential effect on cognitive development. In the case of autism, non-genetic factors account for 62% of disease risk. [ 6 ]


In order to study the connection between a  candidate gene  and a proposed phenotype, a subject is often given a task to perform that elicits the behavioral phenotype while undergoing some form of  neuroimaging . Many behavioral tasks used for genomic studies are modified versions of classic behavioral and  neuropsychological  tests designed to investigate neural systems critical to particular behaviors. [ 2 ]


In 2003, the  Human Genome Project  produced the first complete human genome. [ 7 ]  Despite the project's success, very little is known about cognitive gene expression. [ 8 ]  Prior to 2003, any evidence concerning human brain connectivity was based on  post-mortem  observations. [ 9 ]  Due to ethical concerns, no invasive  in vivo  genomics studies have been performed on live humans. [ citation needed ]


As the closest genetic relatives to humans,  non-human primates  are the most preferable genomics imaging subjects. In most cases, primates are imaged while under  anesthesia . [ 8 ]  Due to the high cost of raising and maintaining primate populations, genomic testing on non-human primates is typically performed at primate research facilities.


Chimpanzees  ( Pan troglodytes ) are the closest genetic relatives to  humans , sharing 93.6% genetic similarity. [ 10 ]  It is believed that humans and chimpanzees shared a common genetic ancestor around 7 million years ago. [ 8 ]  The movement to sequence the chimpanzee genome began in 1998 and was given high priority by the US National Institutes of Health (NIH). [ 11 ]


Currently, human and chimpanzees have the only sequenced genomes in the extended family of primates. [ 12 ]  Some comparisons of autosomal intergenic non-repetitive DNA segments suggest as little as 1.24% genetic difference between humans and chimpanzees along certain sections. [ 13 ]  Despite the genetic similarity, 80% of proteins between the two species are different which understates the clear phenotypical differences. [ 14 ]


Rhesus macaques  ( Macaca mulatta ) exhibit a 93% genetic similarity to humans approximately. [ 15 ]  They are often used as an out-group in human/chimpanzee genomic studies. [ 8 ]  Humans and rhesus macaques shared a common ancestor an estimated 25 million years ago. [ 5 ]


Orangutans  ( Pongo pygmaeus ) and  gorillas  ( Gorilla gorilla ) have been used in genomics testing but are not common subjects due to cost. [ 8 ]


Despite what is sometimes reported, most behavioral or pathological phenotypes are not due to a single  gene mutation  but rather a complex genetic basis. [ 16 ]  However, there are some exceptions to this rule such as  Huntington's disease  which is caused by a single specific genetic disorder. [ 16 ]  The occurrence of neurobehavioral disorders is influenced by a number of factors, genetic and non-genetic.


Down syndrome is a genetic syndrome marked by  intellectual disability  and distinct cranio-facial features and occurs in approximately 1 in 800 live births. [ 17 ]  Experts believe the genetic cause for the syndrome is a lack of genes in the  21st chromosome . [ 17 ]  However, the gene or genes responsible for the cognitive phenotype have yet to be discovered.


Fragile-X syndrome  is caused by a mutation of the FRAXA gene located in the  X chromosome . [ 17 ]  The syndrome is marked by intellectual disability (moderate in males, mild in females), language deficiency, and some  autistic spectrum  behaviors. [ 17 ]


Alzheimer's disease is a neurodegenerative disorder that causes age-correlated progressive cognitive decline. [ 17 ]   animal model  using mice have investigated the pathophysiology and suggest possible treatments such as  immunization  with  amyloid beta  and peripheral administration of  antibodies  against amyloid beta. [ 17 ]  Studies have linked Alzheimer's with gene alterations causing SAMP8  protein  abnormalities. [ 18 ]


Autism is a pervasive developmental disorder characterized by abnormal social development, inability to empathize and effectively communicate, and restricted patterns of interest. [ 17 ]  A possible neuroanatomical cause is the presence of tubers in the temporal lobe. [ 17 ]  As mentioned previously, non-genetic factors account for 62% of autism development risk. [ 6 ]  Autism is a human-specific disorder. As such, the genetic cause has been implicated to highly ordered brain lateralization exhibited by humans. [ 4 ]  Two genes have been linked to autism and autism spectrum disorders (ASD): c3orf58 (a.k.a. Deleted In Autism-1 or DIA1) and cXorf36 (a.k.a.Deleted in Autism-1 Related or DIA1R). [ 19 ]


Major depressive disorder is a common  mood disorder  believed to be caused by irregular neural uptake of  serotonin . While the genetic cause is unknown, genomic studies of post-mortem MDD brains have discovered abnormalities in the  fibroblast growth factor  system which supports the theory of growth factors playing an important role in mood disorders. [ 20 ]


Other neurodegenerative disorders include  Rett syndrome ,  Prader–Willi syndrome ,  Angelman syndrome , and  Williams-Beuren syndrome .


Combined Annotation Dependent Depletion  (CADD) is a tool that evaluates the deleteriousness of  single nucleotide ,  insertion and deletion  variants in the human  genome . In contrast with other  annotation  tools that are restricted in scope, the CADD framework integrates multiple annotations into one metric. This is done by contrasting variants that survived natural selection with simulated mutations.


COMBREX  is a multifaceted project that includes a database of  gene annotations,  functional predictions  and recommendations based on  Active Learning  principles associated with millions of genes in  prokaryotic genomes . [ 1 ]


COMBREX is a multifaceted project that aims to bring together the computational and experimental communities of biologists in the interest of improving our understanding of microbial gene function and accelerating the annotation of microbial gene function. The COMBREX project was co-founded by Simon Kasif, Richard Roberts and Martin Steffen as an international consortium with a headquarters at Boston University and over 100 experimental and computational collaborators.  The project was inspired by a call for community action published in PLoS Biology by Richard J. Roberts.


This evolving database consists of experimentally determined and computationally predicted functions for more than three million microbial genes. Searching for a gene or genes of interest may be an end in itself, or it may be a first step toward contributing information to or seeking a grant from COMBREX. The database presently consists of genes from over 1000 completely sequenced bacterial and archaeal genomes, supplemented with a number of individual genes whose biochemical function has been experimentally determined. The genes are organized into sequence-similar, and likely isofunctional, groups determined by NCBI, referred to as Protein Clusters.


A color-coding system is used to identify which genes have experimentally determined functions, which have computationally predicted functions, and which have no known or predicted function  (info) . By necessity, "predicted functions" may encompass a broad range of specificity, and one of our longer range goals is to quantify this specificity. (For example, the predicted function "valine decarboxylase" is significantly more specific, and more readily verifiable, than "lyase", or even "carboxy-lyase".)


Identification of genes whose products have been experimentally verified is also not a trivial task, and so we have embarked on a project to create a comprehensive, manually curated set of all such genes, which we refer to as the  Gold Standard Gene Database . This curated set is at present unique to the COMBREX database, and genes belonging to it are color-coded with a gold symbol.


The COMBREX database serves as a venue for computational biologists to publicize their most informative gene function predictions. A major effort within the bio-informatics field has been the computational prediction of gene function. There have been significant advances in this field over the last decade or so, but many of these efforts have not realized their full potential to advance biological knowledge due to the fact that predictions are rarely experimentally tested, and predicted functions for individual genes made by competing methods are rarely directly compared.


The COMBREX database, besides drawing information from familiar sources such as NCBI and UniProtKB, also displays gene function predictions submitted by individual laboratories. Such predictions may be generated in large scale using computational algorithms, or may be made for individual genes by experimental or computational biologists well acquainted with a particular protein family or biochemical pathway. Thus, predictions made by different methods may be easily compared, contrasted, and examined by experimental biologists. This side-by-side display of function predictions from many sources is the heart of the interaction between computational and experimental communities that COMBREX hopes to foster.


COMBREX uses simple principles as well as more sophisticated  Active Learning  methodologies to recommend the most informative experiments. These are experiments that are most likely to generate the most informative (in the mathematical sense of maximizing information gain) predictions for the largest number of proteins in the database. The most basic recommendations provide ranking of all proteins in a gene cluster in terms of their distance to other proteins.  In the simplest case proteins near the center of a cluster are judged to be most informative because their distance to the other proteins in the cluster is relatively small.  As a result, functional annotation of a "center" of a cluster is likely to result in the most accurate predictions for the other proteins in the cluster.  In evolutionary terms these "cluster centers" are closest to the evolutionary ancestor of all the proteins in the cluster.  Active Learning generalizes this intuition principle to produce recommendations for additional experiments that are likely to either produce accurate predictions or identify proteins that are not annotated correctly.


In addition to evolutionary analysis and Active Learning COMBREX also points to other criteria that might be considered in considering experiments. Such criteria include whether there is a structure available, conservation of the bacterial gene in the human genome (e.g. domain sharing), availability of computational or experimental evidence of gene function, phenotypical considerations (such as presence in a pathogen or relation to antibiotic resistance, pathogenicity or virulence) and others.


One of the missions of COMBREX is to issue small monetary grants for the experimental validation of specific gene predictions. The experimental determination of biochemical function for specific gene products serves to validate (or invalidate) the computational predictions made a priori. Thus, this experimental effort serves three goals: (1) it brings together directly the scientists who make gene function predictions and those who test them, (2) it evaluates computational methods based on how accurate their predictions are so they can be improved, and (3) it broadens the landscape of experimentally validated genes, improving our overall understanding of biology and of sequence-structure-function relationships.


The experimental investigation of the biochemical function of a single gene or small number of genes is often beneath the purview of large funding agencies. COMBREX is set up to issue small grants for exactly this type of work, and such grants are particularly suited for laboratories already familiar with the types of assays required for the intended experiments.


One of the current problems with gene and genome annotation is a lack of transparency with respect to source. It is often difficult to determine which functions have been determined experimentally and which are predicted computationally. Furthermore, for computationally predicted functions, the method used to make the prediction and the strength of the evidence are rarely stated. COMBREX has taken the first steps toward a more transparent system of annotation by (1) color-coding genes to distinguish observed from predicted functions, and (2) for many functions predicted by sequence similarity, identifying the experimentally validated "source gene" on which the prediction was based.


COMBREX is working towards a more completely traceable annotation system, in which every stated functional annotation is either experimentally determined, or is a prediction explicitly linked through a chain of evidence to an ultimate source of information. These sources will in many cases be experimentally validated genes, but in some cases will be annotations from existing databases whose sources are themselves not immediately apparent.


COMBREX is the first database that attempts to "computationally" identify the link to the experimental source of an annotation using homology. Other databases provide two types of evidence, e.g. inferred directly from experiments or inferred computationally. However, the inference cannot be typically traced to the experimental source of the annotation. COMBREX cannot guarantee that  the "traces"  it provides are accurate at this point but it enables biologists to make this determination directly by examining the link.


This system of identifying source genes and functions, and evidential links, will enable a dynamic system of annotation that is automatically updated as experimental evidence for new genes is determined, and as new predictive methods are developed. Such a dynamic system of gene functional annotation may help overcome the relatively high frequency of unannotated and misannotated genes that results from the static system used in many public databases. Furthermore, it will illuminate those genes whose biochemical functions are truly unknown, as opposed to those that are simply insufficiently annotated.


Making gene functional predictions transparent is important, but equally important is making them as accurate as possible. Predictions need to be commensurate with the strength of evidence for them, such that they are as specific as the evidence will allow. Those that are not specific enough do not lend themselves to experimental testing, and those that are too specific for the underlying evidence run a high risk of being inaccurate. COMBREX is actively working on developing algorithms for functional prediction that can identify genes with novel or interesting functions, and whose results can sit beside the high-quality predictions received from collaborating computational groups. COMBREX's relatively  conservative BLAST-based propagation of gene function  represents a simple first step towards this goal.


Through its funding decisions, COMBREX can help broaden as well as deepen our understanding of biochemical gene function by encouraging experimental investigation of specific genes. The choice of which genes to validate is an important one: little overall new knowledge is gained by validating closely similar relatives of isofunctional genes, and validating experiments for genes with no specific predicted functions are unlikely to succeed. Furthermore, the landscape of what is already known is uneven, with many validated examples of some functions and few or no examples of others.


COMBREX wishes to develop a new, integrative model of research in which experiments are prioritized to close the largest gaps in our overall predictive understanding of gene function. Such a model favors the validation of genes that provide relatively large increases in knowledge, for example because their validated function results in a large number of new predictions for other genes. At an early stage COMBREX will introduce lists of "high priority" genes, which may be identified as being of significant predictive or biomedical value, and to which COMBREX members may nominate candidates. As a longer-term goal, COMBREX is working towards the use of machine learning techniques such as active learning to optimize the selection of such genes.


COMBREX encourages the development of new technologies and cost-effective assays for gene function determination. The experimental validation effort described above amounts to a massively parallel application of low-throughput experiments via many small-scale grants. High-throughput assays that can analyze many gene products in parallel may result in the determination of function for many genes simultaneously, and may help make large strides in our overall understanding of gene function. [ 2 ]


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 ]


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. [ 9 ]  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. [ 10 ] [ 11 ]  Alternative names such as conserved synteny or  collinearity  have been used interchangeably. [ 12 ]  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; [ 13 ] [ 14 ]  early discoveries using such approaches include chromosomal conserved regions in  nematodes  and  yeast , [ 15 ] [ 16 ]  evolutionary history and phenotypic traits of extremely conserved  Hox gene  clusters across animals and  MADS-box  gene family in plants, [ 17 ] [ 18 ]  and  karyotype  evolution in mammals and plants. [ 19 ]


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 ] [ 20 ]  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. [ 21 ]  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 . [ 22 ]  It has also showed the extreme diversity of the gene composition in different evolutionary lineages. [ 20 ]


See also :  History of genomics


Comparative genomics has a root in the comparison of  virus  genomes in the early 1980s. [ 20 ]  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. [ 23 ]  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. [ 24 ]


The first complete genome sequence of a cellular organism, that of  Haemophilus influenzae  Rd, was published in 1995. [ 25 ]  The second genome sequencing paper was of the small parasitic bacterium  Mycoplasma genitalium  published in the same year. [ 26 ]  Starting from this paper, reports on new genomes inevitably became comparative-genomic studies. [ 20 ]


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. [ 27 ]  After the publication of the roundworm  Caenorhabditis elegans  genome in 1998 [ 15 ]  and together with the fruit fly  Drosophila melanogaster  genome in 2000, [ 28 ]   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 . [ 29 ]  At the same time,  Bonnie Berger ,  Eric Lander , and their team published a paper on whole-genome comparison of human and mouse. [ 30 ]


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. [ 31 ]


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. [ 21 ]


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.


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. [ 32 ] [ 33 ]


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. [ 34 ]  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 . [ 35 ]  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. [ 36 ]


Comparative genomics holds profound significance across various fields, including medical research, basic biology, and biodiversity conservation. 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. [ 37 ] [ 38 ] [ 39 ]


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. [ 40 ]


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. [ 41 ]  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. [ 42 ]


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. [ 43 ]  This includes approaches from systems and control, information theory, string analysis and data mining. [ 44 ]  Computational approaches will remain critical for research and teaching, especially when information science and genome biology is taught in conjunction. [ 45 ]


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.


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 nucleotide and protein sequences. [ 47 ] [ 48 ]  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 whereas  Smith–Waterman algorithm  used to find local alignments. With the exponential growth of sequence databases and the emergence of longer sequences, there's a heightened interest in faster, approximate, or  heuristic alignment  procedures. Among these, the  FASTA  and  BLAST  algorithms are prominent for local pairwise alignment. Recent years have witnessed the development of programs tailored to aligning lengthy sequences, such as  MUMmer  (1999),  BLASTZ  (2003), and  AVID  (2003). While BLASTZ adopts a local approach, MUMmer and AVID are geared towards global alignment. To harness the benefits of both local and global alignment approaches, one effective strategy involves integrating them. Initially, a rapid variant of BLAST known as  BLAT  is employed to identify homologous "anchor" regions. These anchors are subsequently scrutinized to identify sets exhibiting conserved order and orientation. Such sets of anchors are then subjected to alignment using a global strategy.


Additionally, ongoing efforts focus on optimizing existing algorithms to handle the vast amount of genome sequence data by enhancing their speed. Furthermore,  MAVID  stands out as another noteworthy pairwise alignment program specifically designed for aligning multiple genomes.


Pairwise Comparison:  The Pairwise comparison of genomic sequence data is widely utilized in comparative gene prediction. Many studies in comparative functional genomics lean on pairwise comparisons, wherein traits of each gene are compared with traits of other genes across species. his method yields many more comparisons than unique observations, making each comparison dependent on others. [ 49 ] [ 50 ]


Multiple comparisons:  The comparison of multiple genomes is a natural extension of pairwise inter-specific comparisons. Such comparisons typically aim to identify conserved regions across two phylogenetic scales: 1. Deep comparisons, often referred to as  phylogenetic footprinting [ 51 ]  reveal conservation across higher taxonomic units like vertebrates. [ 52 ]  2. Shallow comparisons, recently termed
 Phylogenetic shadowing , [ 53 ]  probe conservation across a group of closely related species.


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. [ 54 ]  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. [ 55 ]


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 . [ 56 ]


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. [ 58 ]  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 ]


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. [ 59 ]


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. [ 59 ]


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. [ 59 ]


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. [ 74 ]  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. [ 75 ]


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. [ 76 ]  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 . [ 77 ]  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. [ 78 ]  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 . [ 79 ]


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. [ 80 ]  By analyzing  genetic variations  across populations and comparing them with an individual's genome, clinicians can identify specific  genetic markers  associated with disease susceptibility,  drug metabolism , and treatment response. By identifying genetic variants associated with drug metabolism pathways, drug targets, and  adverse reactions , 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  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  mutations ,  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.


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. [ 81 ]


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. [ 81 ]


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. [ 82 ]


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. [ 83 ]  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. [ 84 ]


Computational genomics  refers to the use of computational and statistical analysis to decipher biology from  genome sequences  and related data, [ 1 ]  including both  DNA  and  RNA  sequence as well as other "post-genomic" data (i.e., experimental data obtained with technologies that require the genome sequence, such as genomic  DNA microarrays ). These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of  bioinformatics  and  computational biology , but with a focus on using whole genomes (rather than individual genes) to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond.  With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery. [ 2 ]


The roots of computational genomics are shared with those of  bioinformatics . During the 1960s,  Margaret Dayhoff  and others at the National Biomedical Research Foundation assembled databases of homologous protein sequences for evolutionary study. [ 3 ]   Their research developed a  phylogenetic tree  that determined the evolutionary changes that were required for a particular protein to change into another protein based on the underlying  amino acid  sequences.  This led them to create a scoring matrix that assessed the likelihood of one protein being related to another.


Beginning in the 1980s, databases of genome sequences began to be recorded, but this presented new challenges in the form of searching and comparing the databases of gene information.  Unlike text-searching algorithms that are used on websites such as Google or Wikipedia, searching for sections of genetic similarity requires one to find strings that are not simply identical, but similar.  This led to the development of the  Needleman-Wunsch algorithm , which is a  dynamic programming  algorithm for comparing sets of amino acid sequences with each other by using scoring matrices derived from the earlier research by Dayhoff.  Later, the  BLAST  algorithm was developed for performing fast, optimized searches of gene sequence databases.  BLAST and its derivatives are probably the most widely used algorithms for this purpose. [ 4 ]


The emergence of the phrase "computational genomics" coincides with the availability of complete sequenced genomes in the mid-to-late 1990s. The first meeting of the Annual Conference on Computational Genomics was organized by scientists from  The Institute for Genomic Research  (TIGR) in 1998, providing a forum for this speciality and effectively distinguishing this area of science from the more general fields of  Genomics  or  Computational Biology . [ citation needed ]   The first use of this term in scientific literature, according to  MEDLINE  abstracts, was just one year earlier in  Nucleic Acids Research . [ 5 ]   The final Computational Genomics conference was held in 2006, featuring a keynote talk by Nobel Laureate  Barry Marshall , co-discoverer of the link between  Helicobacter pylori  and stomach ulcers.  As of 2014, the leading conferences in the field include  Intelligent Systems for Molecular Biology  (ISMB) and  Research in Computational Molecular Biology  (RECOMB).


The development of computer-assisted mathematics (using products such as  Mathematica  or  Matlab ) has helped engineers, mathematicians and computer scientists to start operating in this domain, and a public collection
of case studies and demonstrations is growing, ranging from whole genome comparisons to  gene expression  analysis. [ 6 ]  This has increased the introduction of different ideas, including concepts from systems and control, information theory, strings analysis and data mining. It is anticipated that computational approaches will become and remain a standard topic for research and teaching, while students fluent in both topics start being formed in the multiple courses created in the past few years.


Contributions of computational genomics research to biology include: [ 2 ]


Computational tools have been developed to assess the similarity of genomic sequences. Some of them are  alignment -based distances such as  Average Nucleotide Identity . [ 7 ]  These methods are highly specific, while being computationally slow.
Other, alignment-free methods, include statistical and probabilistic approaches. One example is Mash, [ 8 ]  a probabilistic approach using  minhash . In this method, given a number k, a genomic sequence is transformed into a shorter sketch through a random  hash function  on the possible  k-mers . For example, if

 k
 =
 2

 {\displaystyle k=2}

 , sketches of size 4 are being constructed and given the following hash function


the sketch of the sequence


CTGACCTTAACGGGAGACTATGATGACGACCGCAT


is {0,1,1,2} which are the smallest hash values of its k-mers of size 2. These sketches are then compared to estimate the fraction of shared k-mers ( Jaccard index ) of the corresponding sequences.
It is worth noticing that a hash value is a binary number. In a real genomic setting a useful size of k-mers ranges from 14 to 21, and the size of the sketches would be around 1000. [ 8 ]


By reducing the size of the sequences, even hundreds of times, and comparing them in an alignment-free way, this method reduces significantly the time of estimation of the similarity of sequences.


Clustering  data is a tool used to simplify statistical analysis of a genomic sample. For example, in [ 9 ]  the authors developed a tool (BiG-SCAPE) to analize sequence similarity networks of  biosynthetic gene clusters  (BGC). In  [ 10 ]  successive layers of clusterization of biosynthetic gene clusters are used in the automated tool BiG-MAP, both to filter redundant data and identify gene clusters families. This tool profiles the abundance and expressions levels of BGC's in microbiome samples.


Bioinformatic tools have been developed to predict, and determine the abundance and expression of, this kind of gene cluster in microbiome samples, from metagenomic data. [ 11 ]  Since the size of metagenomic data is considerable, filtering and clusterization thereof are important parts of these tools. These processes can consist of dimensionality -reduction techniques, such as  Minhash , [ 8 ]  and clusterization algorithms such as  k-medoids  and  affinity propagation . Also several metrics and similarities have been developed to compare them.


Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).
BiG-SLiCE (Biosynthetic Genes Super-Linear Clustering Engine), a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion.


Satria et. al, 2021 [ 12 ]  across BiG-SLiCE demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential, opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. [ 12 ]


In  academia ,  computational immunology  is a  field of science  that encompasses high-throughput  genomic  and  bioinformatics  approaches to  immunology . The field's main aim is to convert immunological data into computational problems, solve these problems using  mathematical  and computational approaches and then convert these results into immunologically meaningful interpretations.


The  immune system  is a complex system of the human body and understanding it is one of the most challenging topics in biology. Immunology research is important for understanding the mechanisms underlying the defense of human body and to develop drugs for immunological diseases and maintain health. Recent findings in genomic and proteomic technologies have transformed the immunology research drastically. Sequencing of the  human  and other  model organism   genomes  has produced increasingly large volumes of data relevant to immunology research and at the same time huge amounts of functional and clinical data are being reported in the scientific literature and stored in clinical records. Recent advances in  bioinformatics  or  computational biology  were helpful to understand and organize these large-scale data and gave rise to new area that is called  Computational immunology  or  immunoinformatics .


Computational immunology is a branch of bioinformatics and it is based on similar concepts and tools, such as  sequence alignment  and  protein structure  prediction tools.  Immunomics  is a discipline like  genomics  and  proteomics . It is a science, which specifically combines  immunology  with  computer science ,  mathematics ,  chemistry , and  biochemistry  for large-scale analysis of immune system functions. It aims to study the complex  protein–protein interactions  and networks and allows a better understanding of  immune responses  and their role during normal, diseased and reconstitution states. Computational immunology is a part of immunomics, which is focused on analyzing large-scale experimental data. [ 1 ] [ 2 ]


Computational immunology began over 90 years ago with the theoretic modeling of malaria epidemiology. At that time, the emphasis was on the use of mathematics to guide the study of disease transmission. Since then, the field has expanded to cover all other aspects of immune system processes and diseases. [ 3 ]


After the recent advances in sequencing and proteomics technology, there have been many fold increase in generation of molecular and immunological data. The data are so diverse that they can be categorized in different databases according to their use in the research. Until now there are total 31 different immunological databases noted in the  Nucleic Acids Research (NAR) Database Collection , which are given in the following table, together with some more immune related databases. [ 4 ]  The information given in the table is taken from the database descriptions in  NAR Database Collection .


Online resources for allergy information are also available on  http://www.allergen.org . Such data is valuable for investigation of cross-reactivity between known allergens and analysis of potential allergenicity in proteins. The  Structural Database of Allergen Proteins  (SDAP) stores information of allergenic proteins. The  Food Allergy Research and Resource Program (FARRP) Protein Allergen -Online Database contains sequences of known and putative allergens derived from scientific literature and public databases.  Allergome  emphasizes the annotation of allergens that result in an IgE-mediated disease.


A variety of computational, mathematical and statistical methods are available and reported. These tools are helpful for collection, analysis, and interpretation of immunological data. They include  text mining , [ 33 ]  information management, [ 34 ] [ 35 ]  sequence analysis, analysis of molecular interactions, and mathematical models that enable advanced simulations of immune system and immunological processes. [ 36 ] [ 37 ]
Attempts are being made for the extraction of interesting and complex patterns from non-structured text documents in the immunological domain, such as categorization of allergen cross-reactivity information, [ 33 ]  identification of cancer-associated gene variants and the classification of immune epitopes.


Immunoinformatics is using the basic bioinformatics tools such as ClustalW, [ 38 ]  BLAST, [ 39 ]  and TreeView, as well as specialized immunoinformatics tools, such as EpiMatrix, [ 40 ] [ 41 ]  IMGT/V-QUEST for IG and TR sequence analysis, IMGT/ Collier-de-Perles and IMGT/StructuralQuery [ 42 ]  for IG variable domain structure analysis. [ 43 ]  Methods that rely on sequence comparison are diverse and have been applied to analyze HLA sequence conservation, help verify the origins of human immunodeficiency virus (HIV) sequences, and construct homology models for the analysis of hepatitis B virus polymerase resistance to lamivudine and emtricitabine.


There are also some computational models which focus on protein–protein interactions and networks. There are also tools which are used for T and B cell epitope mapping, proteasomal cleavage site prediction, and TAP– peptide prediction. [ 44 ]  The experimental data is very much important to design and justify the models to predict various molecular targets. Computational immunology tools is the game between experimental data and mathematically designed computational tools.


Allergies , while a critical subject of immunology, also vary considerably among individuals and sometimes even among genetically similar individuals. The assessment of protein allergenic potential focuses on three main aspects: (i) immunogenicity; (ii) cross-reactivity; and (iii) clinical symptoms. [ 45 ]  Immunogenicity is due to responses of an  IgE   antibody -producing B cell and/or of a  T cell  to a particular  allergen . Therefore, immunogenicity studies focus mainly on identifying recognition sites of  B-cells  and T-cells for allergens. The three-dimensional structural properties of allergens control their allergenicity.


The use of immunoinformatics tools can be useful to predict protein allergenicity and will become increasingly important in the screening of novel foods before their wide-scale release for human use. Thus, there are major efforts under way to make reliable broad based allergy databases and combine these with well validated prediction tools in order to enable the identification of potential allergens in genetically modified drugs and foods. Though the developments are on primary stage, the World Health organization and Food and Agriculture Organization have proposed guidelines for evaluating allergenicity of genetically modified foods. According to the  Codex alimentarius , [ 46 ]  a protein is potentially allergenic if it possesses an identity of ≥6 contiguous amino acids or ≥35% sequence similarity over an 80 amino acid window with a known allergen. Though there are rules, their inherent limitations have started to become apparent and exceptions to the rules have been well reported  [ 47 ]


In the study of infectious diseases and host responses, the mathematical and computer models are a great help. These models were very useful in characterizing the behavior and spread of infectious disease, by understanding the dynamics of the pathogen in the host and the mechanisms of host factors which aid pathogen persistence. Examples include  Plasmodium falciparum [ 48 ]  and nematode infection in ruminants. [ 49 ]


Much has been done in understanding immune responses to various pathogens by integrating genomics
and proteomics with bioinformatics strategies. Many exciting developments in large-scale screening of pathogens are currently taking place.  National Institute of Allergy and Infectious Diseases (NIAID)  has initiated an endeavor for systematic mapping of B and T cell epitopes of category A-C pathogens. These pathogens include  Bacillus anthracis  (anthrax),  Clostridium botulinum  toxin (botulism),  Variola major  (smallpox),  Francisella tularensis  (tularemia), viral hemorrhagic fevers,  Burkholderia pseudomallei ,  Staphylococcus enterotoxin  B, yellow fever, influenza, rabies, Chikungunya virus etc. Rule-based systems have been reported for the automated extraction and curation of influenza A records. [ 50 ]


This development would lead to the development of an algorithm which would help to identify the conserved regions of pathogen sequences and in turn would be useful for vaccine development. This would be helpful in limiting the spread of infectious disease. Examples include a method for identification of vaccine targets from protein regions of conserved HLA binding [ 51 ]  and computational assessment of cross-reactivity of broadly neutralizing antibodies against viral pathogens. [ 52 ]  These examples illustrate the power of immunoinformatics applications to help solve complex problems in public health. Immunoinformatics could accelerate the discovery process dramatically and potentially shorten the time required for vaccine development. Immunoinformatics tools have been used to design the vaccine against SARS-CoV-2, [ 53 ]  Dengue virus  [ 54 ]  and Leishmania. [ 55 ]


Using this technology it is possible to know the model behind immune system. It has been used to model T-cell-mediated suppression, [ 56 ]  peripheral lymphocyte migration, [ 57 ]  T-cell memory, [ 58 ]  tolerance, [ 59 ]  thymic function, [ 60 ]  and antibody networks. [ 61 ]  Models are helpful to predicts dynamics of pathogen toxicity and T-cell memory in response to different stimuli. There are also several models which are helpful in understanding the nature of specificity in immune network and immunogenicity.


For example, it was useful to examine the functional relationship between TAP peptide transport and HLA class I antigen presentation. [ 62 ]  TAP is a transmembrane protein responsible for the transport of antigenic peptides into the endoplasmic reticulum, where MHC class I molecules can bind them and presented to T cells. As TAP does not bind all peptides equally, TAP-binding affinity could influence the ability of a particular peptide to gain access to the MHC class I pathway. Artificial neural network (ANN), a computer model was used to study peptide binding to human TAP and its relationship with MHC class I binding. The affinity of HLA-binding peptides for TAP was found to differ according to the HLA supertype concerned using this method. This research could have important implications for the design of peptide based immuno-therapeutic drugs and vaccines. It shows the power of the modeling approach to understand complex immune interactions. [ 62 ]


There exist also methods which integrate peptide prediction tools with computer simulations that can provide detailed information on the immune response dynamics specific to the given pathogen's peptides
. [ 63 ]


Cancer is the result of somatic mutations which provide cancer cells with a selective growth advantage. Recently it has been very important to determine the novel mutations. Genomics and proteomics techniques are used worldwide to identify mutations related to each specific cancer and their treatments. Computational tools are used to predict growth and surface antigens on cancerous cells. There are publications explaining a targeted approach for assessing mutations and cancer risk. Algorithm CanPredict was used to indicate how closely a specific gene resembles known cancer-causing genes. [ 64 ]  Cancer immunology has been given so much importance that the data related to it is growing rapidly. Protein–protein interaction networks provide valuable information on tumorigenesis in humans. Cancer proteins exhibit a network topology that is different from normal proteins in the human interactome. [ 65 ] [ 66 ]  Immunoinformatics have been useful in increasing success of tumour vaccination. Recently, pioneering works have been conducted to analyse the host immune system dynamics in response to artificial immunity induced by vaccination strategies. [ 67 ] [ 68 ] [ 69 ]  Other simulation tools use predicted cancer peptides to forecast immune specific anticancer responses that is dependent on the specified HLA. [ 37 ]
These resources are likely to grow significantly in the near future and immunoinformatics will be a major growth area in this domain.


Conservation Genomics  is the use of  genomic  study to aide in the preservation and viability of different and diverse organisms and populations. Genomics can be utilized in order to classify or argue diversity, hybridization, and history as well as identity different and similar species. Genomics can evaluate how these measures relate to  effective population size  as well as other ideas under the umbrella of  conservation genetics , and overall biological  conservation . [ 1 ]  Genomic analysis can evaluate the extent to which alleles at certain loci interact with one and other to display nuanced ways which the genome may be intertwined. [ 2 ]


Genetic diversity is a measure of the number of different alleles or combinations of alleles present in a population. This may be measured by the amount of heterozygosity measured compared to the expected amount of heterozygosity predicted by  Hardy-Weinberg Equilibrium . Evaluating genetic diversity in the genomes of populations can inform us about levels of biodiversity and allele frequencies. Genetics play a large role in the extinction of species [ 3 ]  and understanding how certain alleles accumulate and interact at the genome level is crucial to the preservation of those species.


One of the most important measures of genomic health is the ratio of expected heterozygosity to measured heterozygosity. Generally, an individual or species with more heterozygous alleles have a higher chance of survival. This is known as  heterosis . [ 4 ]  Low levels of heterozygosity is a sign of possible concern for a species or organism. Evaluating the genomes of organisms that exist in an endangered species or population segment can provide insight to the severity of their endangerment. This method can be used to classify and rank species as well.


Linkage disequilibrium  is a concept that defines the non-random association between two alleles at different loci in the genome. Measures of linkage disequilibrium are useful tools for gene and genome mapping. [ 5 ]  A linkage between two genes may be due to their positions relative to each other in the genome or it may be due to selection acting to favor certain combinations of alleles. On a genomic scale, linkage disequilibrium plays a large analytical role. The term linkage disequilibrium concedes that there may also be a disruption or lack of linkage between two alleles. Any sort of deviation from the expected linkage between two alleles is considered linkage disequilibrium. In the age of genetic analysis in the use of conservation, understanding how alleles interact with each other is imperative to understanding how genomic diversity, and conservation of such, can be aided.


Genomics and genomic analysis provide a bigger picture explanation of aggregate smaller genetic evaluations. Different relationships and reasoning can be drawn from the data presented by genomics than compared to that data that can be drawn from genetics. Genomics is beginning its use in the field of invasive species where it is thought that understanding how genomes drive invasive processes can help better protect native species from their devastating effects. [ 6 ]  These studies are aimed at tackling the major issue of habitat damage and disrupted ecosystems that invasive species have caused.


Under historical contexts, understanding genomic analysis can teach us much about patterns and effects of natural processes. Historical genomic evaluations have been performed on animals like Hominids, [ 7 ]  and even diseases like  Multiple Sclerosis [ 8 ]  and the  Black Death [ 9 ]  in order to determine their origins and evolutionary histories. In the case of diseases this information is often used in eradication efforts, as it would be used with invasive species. However, this data can be used in the preservation of species as well. Understanding the history of the genomics in a population is an important step in making decisions about how to correctly interpret and apply genomic analysis for conservation today.


In conservational contexts specifically, genomics can be used to research and understand how the relationships amongst the factors listed above affect an organism's fitness. The study of organisms through a genomic lens can lead to knowledge about their history that can be applied today.


Hybridization  practices, as they become a larger player in conservation may be impacted by genomic research. Hybridization can create entirely new allelic relationships and  disequilibria . Understanding these factors is important in the interests of effective conservation efforts. Furthermore, studying these new relationships in hybrids can continue to provide insight in the efforts to conserve species. Hybridization still has many critics and has been shown to cause reduced fitness amongst natural populations. There are still many questions about the consequences of hybridization on evolution and genetic health of species. [ 10 ]  Genomic studies may help to draw conclusions about new allelic relationships and how they may effect the outcomes for those species.


The current landscape of conservation genomics is still in its infancy. New ways to apply and understand genomics for the use of conservation are arising, and there is much thought that the understanding of gene interactions plays an important role in conservation. Currently, despite genomics being valuable to the conservation sphere, there is not enough of a connection between the researchers who study it and those with the means to apply it. [ 11 ]


A  contig  (from  contiguous ) is a set of overlapping DNA segments that together represent a  consensus region of DNA . [ 1 ]
In  bottom-up sequencing  projects, a contig refers to overlapping sequence data ( reads ); [ 2 ]  in  top-down sequencing  projects, contig refers to the overlapping clones that form a  physical map  of the genome that is used to guide sequencing and  assembly . [ 3 ]  Contigs can thus refer both to overlapping DNA sequences and to overlapping physical segments (fragments) contained in clones depending on the context.


In 1980, Staden  [ 4 ]  wrote:  In order to make it easier to talk about our data gained by the shotgun method of sequencing we have invented the word "contig". A contig is a set of gel readings that are related to one another by overlap of their sequences. All gel readings belong to one and only one contig, and each contig contains at least one gel reading. The gel readings in a contig can be summed to form a contiguous consensus sequence and the length of this sequence is the length of the contig.


A sequence contig is a continuous (not contiguous) sequence resulting from the reassembly of the small DNA fragments generated by  bottom-up sequencing  strategies. This meaning of contig is consistent with the original definition by  Rodger Staden  (1979). [ 5 ]  The bottom-up  DNA sequencing  strategy involves shearing genomic DNA into many small fragments ("bottom"), sequencing these fragments, reassembling them back into contigs and eventually the entire genome ("up"). Because current technology allows for the direct sequencing of only relatively short DNA fragments (300–1000 nucleotides), genomic DNA must be fragmented into small pieces prior to sequencing. [ 6 ]  In bottom-up sequencing projects,  amplified  DNA is sheared randomly into fragments appropriately sized for sequencing. The subsequent sequence reads, which are the data that contain the sequences of the small fragments, are put into a database. The  assembly software [ 6 ]  then searches this database for pairs of overlapping reads. Assembling the reads from such a pair (including, of course, only one copy of the identical sequence) produces a longer contiguous read (contig) of sequenced DNA. By repeating this process many times, at first with the initial short pairs of reads but then using increasingly longer pairs that are the result of previous assembly, the DNA sequence of an entire chromosome can be determined.


Today, it is common to use  paired-end sequencing  technology where both ends of  consistently sized  longer DNA fragments are sequenced. Here, a contig still refers to any contiguous stretch of sequence data created by read overlap.  Because the fragments are of known length, the distance between the two end reads from each fragment is known. [ 7 ]  This gives additional information about the orientation of contigs constructed from these reads and allows for their assembly into  scaffolds  in a process called  scaffolding .


Scaffolds consist of overlapping contigs separated by gaps of known length. The new constraints placed on the orientation of the contigs allows for the placement of highly repeated sequences in the genome. If one end read has a repetitive sequence, as long as its  mate pair  is located within a contig, its placement is known. [ 7 ]  The remaining gaps between the contigs in the scaffolds can then be sequenced by a variety of methods, including PCR amplification followed by sequencing (for smaller gaps) and  BAC  cloning methods followed by sequencing for larger gaps. [ 2 ]


Contig can also refer to the overlapping  clones  that form a  physical map  of a chromosome when the  top-down  or  hierarchical  sequencing strategy is used. [ 1 ]   In this sequencing method, a low-resolution  map  is made prior to sequencing in order to provide a framework to guide the later assembly of the sequence reads of the genome. This map identifies the relative positions and overlap of the clones used for sequencing.  Sets of overlapping clones that form a contiguous stretch of DNA are called contigs; the minimum number of clones that form a contig that covers the entire chromosome comprise the tiling path that is used for sequencing. Once a tiling path has been selected, its component BACs are sheared into smaller fragments and sequenced. Contigs therefore provide the framework for hierarchical sequencing. [ 3 ]


The assembly of a contig map involves several steps.  First, DNA is sheared into larger (50–200kb) pieces, which are cloned into  BACs  or  PACs  to form a BAC  library .  Since these clones should cover the entire genome/chromosome, it is theoretically possible to assemble a contig of BACs that covers the entire chromosome. [ 1 ]  Reality, however, is not always ideal. Gaps often remain, and a scaffold—consisting of contigs and gaps—that covers the map region is often the first result. [ 1 ]  The gaps between contigs can be closed by various methods outlined below.


BAC contigs are constructed by aligning BAC regions of known overlap via a variety of methods. One common strategy is to use  sequence-tagged site  (STS) content mapping to detect unique DNA sites in common between BACs.  The degree of overlap is roughly estimated by the number of STS markers in common between two clones, with more markers in common signifying a greater overlap. [ 2 ]  Because this strategy provides only a very rough estimate of overlap,  restriction digest  fragment analysis, which provides a more precise measurement of clone overlap, is often used. [ 2 ]   In this strategy, clones are treated with one or two  restriction enzymes  and the resulting fragments separated by  gel electrophoresis .  If two clones, they will likely have restriction sites in common, and will thus share several fragments. [ 3 ]   Because the number of fragments in common and the length of these fragments is known (the length is judged by comparison to a size standard), the degree of overlap can be deduced to a high degree of precision.


Gaps often remain after initial BAC contig construction.  These gaps occur if the  Bacterial Artificial Chromosome  (BAC) library screened has low complexity, meaning it does not contain a high number of STS or restriction sites, or if certain regions were less stable in cloning hosts and thus underrepresented in the library. [ 1 ]   If gaps between contigs remain after STS landmark mapping and restriction fingerprinting have been performed, the sequencing of contig ends can be used to close these gaps.  This end-sequencing strategy essentially creates a novel STS with which to screen the other contigs.  Alternatively, the end sequence of a contig can be used as a primer to  primer walk  across the gap. [ 2 ]


Copy number variation  ( CNV ) is a phenomenon in which sections of the genome are repeated and the number of repeats in the genome varies between individuals. [ 1 ]  Copy number variation is a type of  structural variation : specifically, it is a type of  duplication  or  deletion  event that affects a considerable number of base pairs. [ 2 ]  Approximately two-thirds of the entire human genome may be composed of repeats [ 3 ]  and 4.8–9.5% of the human genome can be classified as copy number variations. [ 4 ]  In  mammals , copy number variations play an important role in generating necessary variation in the population as well as disease phenotype. [ 1 ]


Copy number variations can be generally categorized into two main groups: short repeats and long repeats. However, there are no clear boundaries between the two groups and the classification depends on the nature of the  loci  of interest. Short repeats include mainly  dinucleotide  repeats (two repeating nucleotides e.g. A-C-A-C-A-C...) and trinucleotide repeats. Long repeats include repeats of entire genes. This classification based on size of the repeat is the most obvious type of classification as size is an important factor in examining the types of mechanisms that most likely gave rise to the repeats, [ 5 ]  hence the likely effects of these repeats on phenotype.


One of the most well known examples of a short copy number variation is the trinucleotide repeat of the CAG base pairs in the  huntingtin gene  responsible for the neurological disorder  Huntington's disease . [ 6 ]  For this particular case, once the CAG trinucleotide repeats more than 36 times in a  trinucleotide repeat expansion , Huntington's disease will likely develop in the individual and it will likely be inherited by his or her offspring. [ 6 ]  The number of repeats of the CAG trinucleotide is inversely correlated with the  age of onset  of Huntington's disease. [ 7 ]  These types of short repeats are often thought to be due to errors in  polymerase  activity during  replication  including polymerase slippage, template switching, and fork switching which will be discussed in detail later. The short repeat size of these copy number variations lends itself to errors in the polymerase as these repeated regions are prone to misrecognition by the polymerase and replicated regions may be replicated again, leading to extra copies of the repeat. [ 8 ]  In addition, if these trinucleotide repeats are in the same  reading frame  in the  coding  portion of a gene, it may lead to a long chain of the same  amino acid , possibly creating  protein aggregates  in the cell, [ 7 ]  and if these short repeats fall into the non-coding portion of the gene, it may affect  gene expression  and regulation. On the other hand, a variable number of repeats of entire genes is less commonly identified in the genome. One example of a whole gene repeat is the  alpha-amylase  1 gene ( AMY1 ) that encodes alpha-amylase which has a significant copy number variation between different populations with different diets. [ 9 ]  Although the specific mechanism that allows the  AMY1  gene to increase or decrease its copy number is still a topic of debate, some hypotheses suggest that the  non-homologous end joining  or the  microhomology-mediated end joining  is likely responsible for these whole gene repeats. [ 9 ]  Repeats of entire genes has immediate effects on expression of that particular gene, and the fact that the copy number variation of the  AMY1  gene has been related to diet is a remarkable example of recent human evolutionary adaptation. [ 9 ]  Although these are the general groups that copy number variations are grouped into, the exact number of base pairs copy number variations affect depends on the specific loci of interest. Currently, using data from all reported copy number variations, the mean size of copy number variant is around 118kb, and the median is around 18kb. [ 10 ]


In terms of the structural architecture of copy number variations, research has suggested and defined hotspot regions in the genome where copy number variations are four times more enriched. [ 2 ]  These hotspot regions were defined to be regions containing long repeats that are 90–100% similar known as  segmental duplications  either  tandem  or interspersed and most importantly, these hotspot regions have an increased rate of  chromosomal rearrangement . [ 2 ]  It was thought that these large-scale chromosomal rearrangements give rise to normal  variation  and  genetic diseases , including copy number variations. [ 1 ]  Moreover, these copy number variation hotspots are consistent throughout many populations from different continents, implying that these hotspots were either independently acquired by all the populations and passed on through generations, or they were acquired in early human evolution before the populations split, the latter seems more likely. [ 1 ]  Lastly, spatial biases of the location at which copy number variations are most densely distributed does not seem to occur in the genome. [ 1 ]  Although it was originally detected by  fluorescent in situ hybridization  and  microsatellite analysis  that copy number repeats are localized to regions that are highly repetitive such as  telomeres ,  centromeres , and  heterochromatin , [ 11 ]  recent  genome-wide studies  have concluded otherwise. [ 2 ]  Namely, the  subtelomeric  regions and pericentromeric regions are where most chromosomal rearrangement hotspots are found, and there is no considerable increase in copy number variations in that region. [ 2 ]  Furthermore, these regions of chromosomal rearrangement hotspots do not have decreased gene numbers, again, implying that there is minimal spatial bias of the genomic location of copy number variations. [ 2 ]


Copy number variation was initially thought to occupy an extremely small and negligible portion of the genome through  cytogenetic  observations. [ 12 ]  Copy number variations were generally associated only with small tandem repeats or specific genetic disorders, [ 13 ]  therefore, copy number variations were initially only examined in terms of specific loci. However, technological developments led to an increasing number of highly accurate ways of identifying and studying copy number variations. Copy number variations were originally studied by cytogenetic techniques, which are techniques that allow one to observe the physical structure of the chromosome. [ 12 ]  One of these techniques is  fluorescent in situ hybridization  (FISH) which involves inserting fluorescent probes that require a high degree of  complementarity  in the genome for binding. [ 10 ]   Comparative genomic hybridization  was also commonly used to detect copy number variations by  fluorophore  visualization and then comparing the length of the chromosomes. [ 10 ]


Recent advances in  genomics  technologies gave rise to many important methods that are of extremely high genomic resolution and as a result, an increasing number of copy number variations in the genome have been reported. [ 10 ]  Initially these advances involved using  bacterial artificial chromosome  (BAC) array with around 1 megabase of intervals throughout the entire gene, [ 14 ]  BACs can also detect copy number variations in rearrangement hotspots allowing for the detection of 119 novel copy number variations. [ 2 ]   High throughput genomic sequencing  has revolutionized the field of human genomics and  in silico  studies have been performed to detect copy number variations in the genome. [ 2 ]  Reference sequences have been compared to other sequences of interest using  fosmids  by strictly controlling the fosmid clones to be 40kb. [ 15 ]  Sequencing end reads would provide adequate information to align the reference sequence to the sequence of interest, and any misalignments are easily noticeable thus concluded to be copy number variations within that region of the clone. [ 15 ]  This type of detection technique offers a high genomic resolution and precise location of the repeat in the genome, and it can also detect other types of structural variation such as inversions. [ 10 ]


In addition, another way of detecting copy number variation is using  single nucleotide polymorphisms  (SNPs). [ 10 ]  Due to the abundance of the human SNP data, the direction of detecting copy number variation has changed to utilize these SNPs. [ 16 ]  Relying on the fact that human  recombination  is relatively rare and that many recombination events occur in specific regions of the genome known as recombination hotspots,  linkage disequilibrium  can be used to identify copy number variations. [ 16 ]  Efforts have been made in associating copy number variations with specific  haplotype  SNPs by analyzing the linkage disequilibrium, using these associations, one is able to recognize copy number variations in the genome using SNPs as markers.  Next-generation sequencing  techniques including short and long read sequencing are nowadays increasingly used and have begun to replace array-based techniques to detect copy number variations. [ 17 ] [ 18 ]


There are two main types of molecular mechanism for the formation of copy number variations:  homologous  based and non-homologous based. [ 5 ]  Although many suggestions have been put forward, most of these theories are speculations and conjecture. There is no conclusive evidence that correlates a specific copy number variation to a specific mechanism.


One of the best-recognized theories that leads to copy number variations as well as  deletions  and inversions is  non-allelic homologous recombinations . [ 19 ]  During  meiotic recombination , homologous chromosomes pair up and form two ended double-stranded breaks leading to  Holliday junctions . However, in the aberrant mechanism, during the formation of Holliday junctions, the double-stranded breaks are misaligned and the  crossover  lands in non-allelic positions on the same chromosome. When the Holliday junction is resolved, the unequal crossing over event allows transfer of genetic material between the two homologous chromosomes, and as a result, a portion of the DNA on both the homologues is repeated. [ 19 ]  Since the repeated regions are no longer  segregating independently , the duplicated region of the chromosome is inherited. Another type of homologous recombination based mechanism that can lead to copy number variation is known as break induced replication. [ 20 ]  When a double stranded break occurs in the genome unexpectedly the cell activates pathways that mediate the repair of the break. [ 20 ]  Errors in repairing the break, similar to non-allelic homologous recombination, can lead to an increase in copy number of a particular region of the genome. During the repair of a double stranded break, the broken end can invade its homologous chromosome instead of rejoining the original strand. [ 20 ]  As in the non-allelic homologous recombination mechanism, an extra copy of a particular region is transferred to another chromosome, leading to a duplication event. Furthermore,  cohesin  proteins are found to aid in the repair system of double stranded breaks through clamping the two ends in close proximity which prevents interchromosomal invasion of the ends. [ 21 ]  If for any reason, such as activation of  ribosomal RNA , cohesin activity is affected then there may be local increase in double stranded break repair errors. [ 21 ]


The other class of possible mechanisms that are hypothesized to lead to copy number variations is non-homologous based. To distinguish between this and homologous based mechanisms, one must understand the concept of homology. Homologous pairing of chromosomes involved using DNA strands that are highly similar to each other (~97%) and these strands must be longer than a certain length to avoid short but highly similar pairings. [ 5 ]  Non-homologous pairings, on the other hand, rely on only few base pairs of similarity between two strands, therefore it is possible for genetic materials to be exchanged or duplicated in the process of non-homologous based double stranded repairs. [ 5 ]


One type of non-homologous based mechanism is the non-homologous end joining or  micro-homology end joining  mechanism. [ 22 ]  These mechanisms are also involved in repairing double stranded breaks but require no homology or limited micro-homology. [ 5 ]  When these strands are repaired, oftentimes there are small deletions or insertions added into the repaired strand. It is possible that  retrotransposons  are inserted into the genome through this repair system. [ 22 ]  If retrotransposons are inserted into a non-allelic position on the chromosome, meiotic recombination can drive the insertion to be recombined into the same strand as an already existing copy of the same region. Another mechanism is the break-fusion-bridge cycle which involves  sister chromatids  that have both lost its telomeric region due to double stranded breaks. [ 23 ]  It is proposed that these sister chromatids will fuse together to form one  dicentric chromosome , and then segregate into two different nuclei. [ 23 ]  Because pulling the dicentric chromosome apart causes a double stranded break, the end regions can fuse to other double stranded breaks and repeat the cycle. [ 23 ]  The fusion of two sister chromatids can cause inverted duplication and when these events are repeated throughout the cycle, the inverted region will be repeated leading to an increase in copy number. [ 23 ]  The last mechanism that can lead to copy number variations is polymerase slippage, which is also known as template switching. [ 24 ]  During normal DNA replication, the polymerase on the  lagging strand  is required to unclamp and re-clamp the replication region continuously. [ 24 ]  When small scale repeats in the DNA sequence exist already, the polymerase can be 'confused' when it re-clamps to continue replication and instead of clamping to the correct base pairs, it may shift a few base pairs and replicate a portion of the repeated region again. [ 24 ]  Note that although this has been experimentally observed and is a widely accepted mechanism, the molecular interactions that led to this error remains unknown. In addition, because this type of mechanism requires the polymerase to jump around the DNA strand and it is unlikely that the polymerase can re-clamp at another locus some kilobases apart, therefore this is more applicable to short repeats such as dinucleotide or trinucleotide repeats. [ 25 ]


Amylase is an  enzyme  in saliva that is responsible for the breakdown of  starch  into  monosaccharides , and one type of amylase is encoded by the alpha-amylase gene ( AMY1 ). [ 9 ]  The  AMY1  locus, as well as the amylase enzyme, is one of the most extensively studied and sequenced genes in the human genome. Its homologs are also found in other primates and therefore it is likely that the  primate   AMY1  gene is ancestral to the human  AMY1  gene and was adapted early in primate evolution. [ 9 ]   AMY1  is one of the most well studied genes which has wide range of variable numbers of copies throughout different human populations. [ 9 ]  The  AMY1  gene is also one of the few genes that had been studied that displayed convincing evidence which correlates its protein function to its copy number. [ 9 ]  Copy number is known to alter  transcription  as well as  translation  levels of a particular gene, however research has shown that the relationship between protein levels and copy number is variable. [ 26 ]  In the  AMY1  genes of European Americans it is found that the concentration of salivary amylase is closely correlated to the copy number of the  AMY1  gene. [ 9 ]  As a result, it was hypothesized that the copy number of the  AMY1  gene is closely correlated with its protein function, which is to digest starch. [ 9 ]


The  AMY1  gene copy number has been found to be correlated to different levels of starch in diets of different populations. [ 9 ]  Eight populations from different continents were categorized into high starch diets and low starch diets and their  AMY1  gene copy number was visualized using high resolution FISH and  qPCR . [ 9 ]  It was found that the high starch diet populations which consists of the Japanese, Hadza, and European American populations had a significantly higher (two times higher) average  AMY1  copy number than the low starch diet populations including Biaka, Mbuti, Datog and Yakut populations. [ 9 ]  It was hypothesized that the levels of starch in one’s regular diet, the substrate for AMY1, can directly affect the copy number of the  AMY1  gene. [ 9 ]  Since it was concluded that the copy number of  AMY1  is directly correlated with salivary amylase, [ 9 ]  the more starch present in the population’s daily diet, the more evolutionarily favorable it is to have multiple copies of the  AMY1  gene. The  AMY1  gene was the first gene to provide strong evidence for evolution on a  molecular genetic  level. [ 26 ]  Moreover, using  comparative genomic hybridization , copy number variations of the entire genomes of the Japanese population was compared to that of the Yakut population. [ 9 ]  It was found that the copy number variation of the  AMY1  gene was significantly different from the copy number variation in other genes or regions of the genome, suggesting that the  AMY1  gene was under a strong selective pressure that had little or no influence on the other copy number variations. [ 9 ]  Finally, the variability of length of 783  microsatellites  between the two populations was compared to copy number variability of the  AMY1  gene. It was found that the  AMY1  gene copy number range was larger than that of over 97% of the microsatellites examined. [ 9 ]  This implies that  natural selection  played a considerable role in shaping the average number of  AMY1  genes in these two populations. [ 9 ]  However, as only six populations were studied, it is important to consider the possibility that there may be other factors in their diet or culture that influenced the  AMY1  copy number other than starch.