Duplications  play a major role in shaping the genome. Duplication may range from extension of  short tandem repeats , to duplication of a cluster of genes, and all the way to duplication of entire chromosomes or even  entire genomes . Such duplications are probably fundamental to the creation of genetic novelty.


Horizontal gene transfer  is invoked to explain how there is often an extreme similarity between small portions of the genomes of two organisms that are otherwise very distantly related. Horizontal gene transfer seems to be common among many  microbes . Also,  eukaryotic cells  seem to have experienced a transfer of some genetic material from their  chloroplast  and  mitochondrial  genomes to their nuclear chromosomes. Recent empirical data suggest an important role of viruses and sub-viral RNA-networks to represent a main driving role to generate genetic novelty and natural genome editing.


Works of science fiction illustrate concerns about the availability of genome sequences.


Michael Crichton's 1990 novel  Jurassic Park  and the subsequent  film  tell the story of a billionaire who creates a theme park of cloned dinosaurs on a remote island, with disastrous outcomes. A geneticist extracts dinosaur DNA from the blood of ancient mosquitoes and fills in the gaps with DNA from modern species to create several species of dinosaurs. A chaos theorist is asked to give his expert opinion on the safety of engineering an ecosystem with the dinosaurs, and he repeatedly warns that the outcomes of the project will be unpredictable and ultimately uncontrollable. These warnings about the perils of using genomic information are a major theme of the book.


The 1997 film  Gattaca  is set in a futurist society where genomes of children are engineered to contain the most ideal combination of their parents' traits, and metrics such as risk of heart disease and predicted life expectancy are documented for each person based on their genome. People conceived outside of the eugenics program, known as "In-Valids" suffer discrimination and are relegated to menial occupations. The protagonist of the film is an In-Valid who works to defy the supposed genetic odds and achieve his dream of working as a space navigator. The film warns against a future where genomic information fuels prejudice and extreme class differences between those who can and cannot afford genetically engineered children. [ 52 ]


In  molecular biology ,  genome architecture mapping  (GAM) is a  cryosectioning  method to  map   colocalized   DNA  regions in a  ligation  independent manner. [ 1 ] [ 2 ]  It overcomes some limitations of  Chromosome conformation capture  (3C), as these methods have a reliance on digestion and  ligation  to capture interacting  DNA  segments. [ 3 ]  GAM is the first genome-wide method for capturing three-dimensional proximities between any number of  genomic loci  without ligation. [ 1 ]


The sections that are found using the cryosectioning method mentioned above are referred to as  nuclear profiles . The information that they provide relates to their coverage across a genome. A large set of values can be produced that represents the strength of nuclear profiles’ presence within a genome. Based on how large or small the coverage across a genome is, judgements can be made involving chromatin interactions, nuclear profile location within the nucleus being cryosectioned, and chromatin compaction levels. [ 4 ]


To be able to visualize this information, certain methods can be implemented using the raw data given by a table that shows whether or not nuclear profiles are detected in a genomic window, the genomic windows being represented within a certain chromosome. With a 1 representing a detection within a window and a 0 representing no detection, subsets of data can be obtained and interpreted by creating graphs, charts,  heatmaps , and other visualization methods that allow these subsets to be seen in ways other than binary detection methods. By using a more graphic approach to interpreting the data obtained with cryosectioning, it is possible to see interactions that would have otherwise not been seen before.


Some examples of how these visuals can be interpreted include bar graphs that show the radial position and chromatin compaction levels of nuclear profiles, they can be split into categories to give a generalization of how often nuclear profiles are detected within a genomic window. A  radar chart  is a circular graph that represents the percentages of occurrence within a number of variables. In the sense of genomic information, radar charts can be used to show how genomic windows are represented within “features” of the genome that are part of certain regions that make it up. These charts can be made to compare groups of nuclear profiles with each other and their differences in how they occur within these features is shown graphically. Heatmaps are another form of visual representation where individual values in a table are shown by cells that take on different colors based on their value. This allows for trends to be seen within a table by the display of groups of similar colors or the lack of.


The heatmap to the right represents the relationship between nuclear profiles based on a calculated  Jaccard Index  where the values ranging from 0-1 are the degree of similarity between two nuclear profiles. Showing this similarity can help to display where certain groups of nuclear profiles are more common within a genome. In this heatmap the diagonal white line of cells is expected because these cells indicate where nuclear profiles intersect themselves and are therefore the most similar as possible to each other, which gives them a value of 1. In addition to the white diagonal line of cells, a cluster of other lightly colored cells can be observed in the bottom right of the heatmap. This grouping of nuclear profiles display high similarity using the Jaccard Index. This means that the nuclear profiles are present in a greater number of genomic windows than others.


The bar graph to the right represents the percentage of nuclear profiles that belong to a category of radial position (with 5 being strongly equatorial and 1 being strongly apical). The cluster of nuclear profiles was calculated based on their similarity to each other using a  k-means clustering  method. To begin the process, three nuclear profiles were chosen at random as the ‘centers’ of the cluster. After the centers were chosen at random, every other nuclear profile is assigned to a cluster based on its distance from each center using a calculated distance value. New centers were then chosen to better represent the cluster. This process was repeated until the centers at the start matched the centers at the end. When the cluster centers have not changed, it could be interpreted that this means proper clusters have been chosen. Within each of these clusters the nuclear profiles are then given a value from 1 to 5 based on their radial position and this data is fed into a bar graph to give a visualization.


This radar chart to the right shows 3 clusters of nuclear profiles’ percentage of occurrence within certain features of the mouse genome. Each cluster of nuclear profiles was calculated using the k-means clustering technique described above, relating to the bar graph showing radial positions of nuclear profiles. Comparisons can be made between the clusters and how they show up more or less in certain features in contrast to each other. To calculate a cluster's presence within a certain feature, it is determined if a nuclear profile is present within a window that is detected within a feature. The percentage of how often nuclear profiles within a cluster occur within the same windows that are detected within a feature are then displayed by the radar chart.


Cryosections are produced according to the Tokuyasu method, involving stringent fixation to preserve nuclear and cellular architecture, cryoprotection with a sucrose-PBS solution, before freezing in liquid nitrogen. [ 5 ]  In Genome Architecture Mapping, sectioning is a necessary step for exploring the 3D topology of the genome, before Laser Microdissection. Then  laser microdissection  can isolate each nuclear profile, before DNA extraction and sequencing.


GAMtools is a collection of software utilities for Genome Architecture Mapping data developed by Robert Beagrie. [ 6 ]   Bowtie2  is required before running GAMtools. The input required for this program is in  Fastq format . This software has a variety of features and the exact commands to use will depend on what you want to do with it, however most features require generating segregation table, so for most users the first steps to take will be to download or create input data, and perform the sequence mapping. This will generate a segregation table, which can then be used to perform various other operations which are outlined below. For further information, view the GAMtools documentation. [ 7 ]


The GAMtools command  gamtools process_nps  can be used to perform the mapping. It maps the raw sequence data from the nuclear profiles. GAMtools also provides the option to perform quality control checks on the NPs. This option can be enabled by adding the flag  -c/--do-qc  to the previous command. When the quality control check is enabled, GAMtools will try to exclude poor quality nuclear profiles.


After the mapping has finished, GAMtools will compute the number of reads from each nuclear profile which overlap with each window in the background genome file. The default window size is 50 kb. This is all done by the same process_nps command. After this, it generates a segregation table.


The command for this process is  gamtools matrix . The input file is the segregation table that was calculated from the windows calling step. GAMtools calculates these matrices using the normalized linkage disequilibrium, which means that it looks at how many times each pair of windows are detected by the same NP, and then normalizes the results based on how many times each window was detected across all NPs. The figure below shows an example of a proximity matrix heatmap produced using GAMtools.


The GAMtools command  gamtools compaction  can be used to calculate an estimation of chromatin compaction.  Compaction is a value assigned to a gene that represents how large the gene is. The level of compaction is inversely proportional to the locus volume. Genomic loci with a low volume are said to have a high level of compaction, and loci with a high volume have a low level of compaction. As shown in the figure, loci with a low compaction level are expected to be intersected more often by the cryosection slices. GAMtools uses this information to assign a compaction value to each locus based on its detection frequency across many nuclear profiles. The compaction rate of these loci is not static, and will continually change throughout the life of the cell. Genomic loci are thought to be de-compacted when that gene is active. This allows a researcher to make assumptions about which genes are currently active in a cell, using the results of the GAMtools data. A locus with low compaction is also thought to be related to  transcriptional activity . The time-complexity of the compaction command is  O ( m  ×  n ), where  m  is the number of genomic windows, and  n  is the number of nuclear profiles.


GAMtools can be used to calculate the radial position of NPs. The radial position of an NP is a measure of how near or far that NP is from the equator or center of the nucleus. NPs that are close to the center of the nucleus are considered equatorial whereas NPs that are closer to the edge of the nucleus are considered apical. The GAMtools command to calculate radial positioning is  gamtools radial_pos . This requires that you have previously generated a segregation table. The radial position is estimated from the average size of NPs that contain a given chromatin region. Chromatin that are closer to the periphery will typically be intersected by smaller, more apical NPs, whereas central chromatin will be intersected by larger, equatorial NPs.


In order to estimate the size of each NP, GAMtools looks at the number of windows each NP saw, as NPs that saw more windows can be assumed to be larger in volume. This is very similar to the method used to estimate chromatin compaction. The figure to the right illustrates how GAMtools looks at each NP's detection rate to estimate the volume, in order to determine the compaction or the radial position. If we look at the first NP, we see that it intersects all three windows, so we can estimate that it is one of the largest NPs. The second NP intersects two out of the three windows, so we can estimate that it is smaller than the first NP. The third NP only intersects one out of the three windows, so we can estimate that it is the smallest NP.  Now that we have an estimation of the size of each NP, we can estimate the radial position. If we assume that the larger NPs are more equatorial, then we find that the first NP is the most equatorial, the second NP is the second most equatorial, and the third NP is the most apical.


Here is some pseudocode that illustrates how one might calculate the radial position of a list of NPs:


This pseudocode will create a list of radial positions that range from 0 - 1 that provide an estimation of the radial position, where 1 is the most equatorial and 0 is the most apical. The time complexity of this pseudocode is O( n * m ), where n is the number of NPs and m is the number of windows. The first for loop goes through n iterations, and it has an inner for loop which goes through m iterations, which means the time complexity of that for loop is O( n * m ). The second for loop has n iterations, so it has time complexity O( n ). Therefore, the overall time complexity of this code is O( n * m + n ), which can be reduced to O( n * m ).


The above flowchart shows a general process of how data may be derived from GAM analysis. Circles represent processes that may be performed, and squares represent pieces of data.


The first step of GAM analysis is the cryosectioning and examination of cells.  This process results in a collection of nucleus slices (nuclear profiles) which contain pieces of DNA (genomic windows).  These nuclear profiles are then examined so that a segregation table may be formed.  Segregation tables are the foundation of GAM analysis.  They contain information detailing which genomic loci appear within each nuclear profile.


An example of data analysis not given below would be clustering.  For example, nuclear profiles that contain similar genomic loci could be clustered together by  k-means clustering  or some variation.  K-means would work well for this particular problem in the sense that it would cluster every nuclear profile according to a  similarity  measure, but it also has drawbacks.  The time complexity of K-means clustering is O(tknd), where  t  is the number of iterations,  k  is the number of means,  n  is the number of data points, and  d  is the number of dimensions for each data point.  Such a complexity makes it  NP-hard . [ 8 ]   As such, it does not scale well to large data sets and is more suited to subsets of data.


For further analysis, GAMtools may be used. [ 6 ]   GAMtools is a suite of software tools which can be used to extrapolate data from the segregation table, some of the results of which will be discussed below.


Cosegregation, or linkage, can be determined by observing how often two genomic loci appear together in the same nuclear profile.  This data can show which loci are physically close to each other in 3D space, and which loci  interact  with each other regularly, which can help explain DNA transcription. [ 1 ]


SLICE is a method of predicting specific interactions among genomic loci. It uses statistical data derived from cosegregation data. [ 1 ]


Finally, graph analysis can be applied to the segregation table to locate communities.  Communities can be defined several ways, such as by  cliques , [ 9 ]  but in this article, community analysis will be focused on  centrality .  Centrality-based communities can be thought of as analogous to celebrities and their fan bases on a social media network.  The fans may not interact with each other very much, but they do interact with the celebrity, who is the “center.”


There are several different types of centrality, including but not limited to degree centrality,  eigenvector centrality , and betweenness centrality, which may all result in different communities being defined. Something of note is that in our social network analogy above, an eigenvector centrality may not be accurate because one person who follows many celebrities may not have any influence over them.  In that case, the graph may be seen as directed.  In GAM analysis, it is generally assumed that the graph is undirected, so that if eigenvector centrality were to be used it would be accurate. Both clique and centrality calculations are computationally complex. Similar to the clustering mentioned above, they do not scale well to large problems.


SLICE (StatisticaL Inference of Co-sEgregation) plays a key role in GAM data analysis. [ 1 ]  It was developed in the laboratory of Mario Nicodemi to provide a math model to identify the most specific interactions among loci from GAM cosegregation data. It estimates the proportion of specific interaction for each pair  loci at a given time. It is a kind of likelihood method. The first step of SLICE is to provide a function of  the expected proportion of GAM nuclear profiles. Then find the best probability result to explain the experimental data. [ 1 ]


The SLICE Model is based on a hypothesis that the probability of non-interacting loci falls into the same nuclear profile is predictable. The probability is dependent on the distance of these loci.
The SLICE Model considers a pair of loci as two types: one is interacting, the other is non-interacting. As per the hypothesis, the proportions of nuclear profiles state can be predicted by mathematical analysis. By deriving a function of the interaction probability, these GAM data can also be used to find prominent interactions and explore the sensitivity of GAM.


SLICE considers a pair of loci can be interaction or non-interaction across the cell population. The first step of this calculation is to describe a single locus. A pair of loci,  A  and  B , can have two possible states: one is that  A  and  B  have no interactions with each other. The other is that they have. The first problem is that whether a single locus can be found in a nuclear profile.


The mathematical expression is:


Single locus probability:

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As the equation above, the volume of the nuclear is a necessary value for calculation. The radii of these nuclear profiles can be used to estimate the nuclear radius. The SLICE prediction for radius matches  Monte Carlo simulations (more detail about this step will be updated after get the license of the figure in the original author's paper.). With the result of the estimated radius, the probability of two loci in a non-interacting state and the probability of these two loci in an interacting state can be estimated.


Here is the mathematical expression of non-interacting:


<

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 >,i = 0, 1, 2 represents: find 0, 1 or 2 loci of a pair of non-interacting loci.
Two loci in a non-interacting state:

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 {\displaystyle <u_{0}>=<v_{0}^{2}>,<u_{1}>=<v_{1}v_{0}>,<u_{2}>=<v_{1}^{2}>}

Here is the mathematical expression of interacting:
Estimation of two loci interaction state:

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 {\displaystyle t_{i}}

  probability

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With the results of previous processes, the occurrence probability of a pair of loci in one nuclear profile can be calculated by statistics method. A pair of loci can exist in three different states. Each of them has a probability of

 P

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 {\displaystyle P_{i},i=0,1,2}

Occurrence probability of pairs of loci in single nuclear profiles:

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 {\displaystyle P_{2},P_{1},P_{0}}

 P

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 {\displaystyle P_{2}}

 : probability of two pairs of loci are in a state of interaction;

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 : probability of one interacts the other, but the other does not interact;

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 : probability of the two not interact.
SLICE Statistical Analysis

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 {\displaystyle N_{0,0}/N=<t_{0}^{2}>P_{2}+<t_{0}u_{0}>P_{1}+<u_{0}^{2}>P_{0}}

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 N

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 j

 {\displaystyle N_{i,j}}

  represent: number i is for A. Number j is for B.(i and j are equal to 0, 1 or 2 loci).


As the number of experiments is limited, there should be some detection efficiency. Considering the detection efficiency can expand this SLICE model to accommodate additional complications. It is a statistical method to improve the calculation result. In this part, the GAM data is divided into two types: one is that the locus in the slice is found in the experiments, and the other is that the locus in the slice is not detected in the experiments.


Based on the estimated detection efficiency and the previous probability of

 u

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 ,the interaction probability of pairs can be calculated. The loci are detected by  next generation sequencing .


When mapping a genome, you can look at the co-segregation across different  genomic windows  and Nuclear Profiles (NPs) of a genome. Taking slices and samples of tissues derives nuclear profiles, and the ranges of windows found within a genome.  Co-segregation  in this instance is identifying the linkage between specified windows in a genome, as well as  linkage disequilibrium  and normalized linkage disequilibrium. One of the steps in calculating the co-segregation and linkage is finding each window's detection frequency. The detection frequency is the number of NPs present in the specified window divided by the total number of NPs. Each of the values calculated identify important differences and statistics for analyzing a genome.  Normalized linkage disequilibrium  is the final calculation which determines the real linkage between genomic windows. Once each of the values are calculated each result is used to calculate the normalized linkage equilibrium for each specified window in a genome. The normalized linkage value can be between 1.0 and -1.0, with 1.0 meaning the linkage between the two is high, and below 1.0 the linkage gets lower. Combining each windows normalized linkage value into a chart or matrix allows for the genome to be mapped and analyzed using a  heatmap  or another graph. The co-segregation and normalized linkage values can also be used for further calculations and analysis such as centrality and community detection which is discussed in the next section.


In order to find the co-segregation and linkages of windows, the following calculations must be completed: Detection frequency, co-segregation, linkage, and normalized linkage.


Each calculation step discussed above is displayed and explained in the table below.


L
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 {\displaystyle LM=min(fa*fb,(1-fa)*(1-fb))}

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 L
 =

 (

 L
 i
 n
 k
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 g
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 L
 M

 )

 {\displaystyle NL=\left({\frac {Linkage}{LM}}\right)}

 If Linkage is greater than 0:

 L
 M
 =
 m
 i
 n
 (
 f
 b
 ∗
 (
 1
 −
 f
 a
 )
 ,
 f
 a
 ∗
 (
 1
 −
 f
 b
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 )

 {\displaystyle LM=min(fb*(1-fa),fa*(1-fb))}

 N
 L
 =

 (

 L
 i
 n
 k
 a
 g
 e

 L
 M

 )

 {\displaystyle NL=\left({\frac {Linkage}{LM}}\right)}


Once all calculation steps in the previous step have been completed, a matrix can be created and then mapped. In a specified set of 81 windows in a genome, a normalized linkage can be filled into a  matrix  that is of size 81 by 81. This is due to the fact that each window will be compared to itself and every other window in order to calculate all normalized linkage values. As each window's linkage is calculated, the value should be inserted to its specified location in the matrix. For example, if the comparison is between the first and second window, the linkage value would be placed in the first column and the second row of the matrix. An example of a  heatmap  generated from a matrix of this size is shown below.


When analyzing the heatmap displayed from the normalized linkage matrix, the colors of each block are the key. Looking at the example heatmap above, the legend indicates that 1.00 linkage value corresponds to bright yellow within the heatmap. This is the highest linkage value, which is shown in the diagonal line of yellow blocks within the map where each window is compared against itself. This legend and heatmap allows for the linkages to be shown based on color, showing that there is a lower level of linkage between the first and last few windows in the matrix, where is a blue/green color. The heatmap is one of the easiest and clearest ways to analyze the linkage values between every window in a specified section of windows in a genome. This generated heatmap and normalized linkage matrix once created can be used for further analysis as described below.


Once cosegregation of all of the targeted genomic windows has been calculated, related subsets or "communities" within the set of windows can be approximated via graph analysis.


Once a cosegregation matrix has been established, the process of converting it to an adjacency matrix to represent a graph is a relatively simple process. Each cell of the cosegregation matrix must be compared to a threshold value between 0.0 and 1.0. This value can be adjusted depending on the desired specificity of the graph. If a higher value is chosen as the threshold, then the graph will generally have fewer edges, as high thresholds require the two windows to be strongly linked. If a lower value is chosen, then the graph will generally have more edges, as windows will not need to be as strongly linked to be classified as an edge. A reasonable starting point to set this value to is the mean value of the cosegregation graph. However, if the simple mean is used, then the threshold may be higher than intended. This is because the cosegregation value of any window to itself will be a value of 1.0. Since the adjacency matrix being constructed is non-reflexive, meaning that a window cannot share an edge with itself, the diagonal of the adjacency must be all zeroes, and the diagonal of the cosegregation matrix is not relevant. To compensate for this, one can simply discount the values along the diagonal of the cosegregation matrix to normalize the mean. To see the effect of this adjustment, see the attached figure.
Once the threshold value is set, the translation becomes rather direct. If the cell of the cosegregation matrix is along the main diagonal, then its respective cell in the adjacency matrix will be 0 as previously mentioned. Otherwise, it is compared with the threshold. If the value is lower than the threshold, then the respective cell in the adjacency matrix will be a 0, otherwise it will be a 1.


Once the adjacency matrix has been established, then the windows can be assessed via several different measures of   centrality . One such measure is degree centrality. Degree centrality is calculated by dividing the number of edges a given node of the graph (one of the genomic windows) has by the quantity of the total number of nodes minus one. See the included figure for an example of this calculation. The centrality of a node can be a good indicator of that individual node's potential to be strongly influential in the dataset based upon its relatively high amount of connections.


Once centrality values have been calculated, it becomes possible to infer related subsets of the data. These related subsets of the data are called "communities" which are clusters in the data that are closely linked within, but not as closely linked to the rest of the data outside. While one of the most common applications of community detection is in regards to social media and mapping social connections, [ 11 ]  it can be applied to problems such as genomic interactions.
A relatively simple method of approximating communities is to isolate several significant nodes based on centrality measures, such as degree centrality, and to then build communities from them. A community of a node will be the full set of nodes immediately linked to it, as well as the node itself. For instance, in the figure to the left, the community around node  C  would be all four nodes of the graph, while the community of  D  would just be nodes  C  and  D . Detection of communities in genomic windows may highlight potential chromatin interactions, or other interactions not previously expected or understood, and provide a target for further study.


In comparison with 3C based methods, GAM provides three key advantages. [ 12 ]


The completion of the human  genome  sequencing in the early 2000s was a turning point in genomics research. [ 1 ]  Scientists have conducted series of research into the activities of genes and the genome as a whole. The human genome contains around 3 billion  base pairs  nucleotide, and the huge quantity of data created necessitates the development of an accessible tool to explore and interpret this information in order to investigate the genetic basis of disease, evolution, and biological processes. [ 2 ]  The field of genomics has continued to grow, with new sequencing technologies and computational tool making it easier to study the genome.


The  genome browser  is an important tool for studying the genome. In  bioinformatics , a genome browser is a graphical interface for displaying  information from a  biological database  for genomic data. [ 2 ]  It is a software tool that displays genetic data in graphical form. Genome browsers enable users to visualize and browse entire genomes with annotated data, including gene prediction, gene structure, protein, expression, regulation, variation, and comparative analysis. Annotated data is usually from multiple diverse sources. They differ from ordinary biological databases in that they display data in a graphical format, with genome coordinates on one axis with annotations or space-filling graphics to show analyses of the genes, such as the frequency of the genes and their expression profiles. [ 1 ]  The software allows users to navigate the genome, view numerous features, analyze and investigate the relationships between various genomic elements.


The first genome browser, known as the  Ensembl Genome Browser , was developed as part of the   Human Genome Project  by a group of researchers from the  European Bioinformatics Institute  (EBI). It was created with the aim  of providing a complete resource for the human genome sequence, with focus on gene annotation. It is a user-friendly interface for exploring the human genome and other organism's genomes. Several more genome browsers have been created, including the  UCSC Genome Browser , developed in 2000 by  Jim Kent  and   David Haussler , and the  NCBI 's  Genome Data Viewer. [ 2 ] [ 3 ]


These genome browsers may support multiple genomes, however, other genome browsers may be specific for particular species. These browsers may provide summary of data from genomic databases and comparative assessment of different genetic sequences across multiple species, and allow the data to be visualized in various ways to facilitate assessment and interpretation of these complex data. [ 4 ] [ 5 ]


Genome Assembly and Annotation: Give access to the reference genome assembly, serving as a framework for overlaying and analyzing other genomic data. They also include gene annotations that provide information about gene locations, transcripts, and functional elements. There is no specific browser that is considered the "best" for genome annotation and assembly as it ultimately depends on the specific needs of the user and the type of analysis being performed. Integrative Genomics Viewer (IGV): IGV is a popular browser for visualizing and annotating genomic data, including genomic variation, gene expression, and chromatin structure. It supports a wide range of file formats and provides advanced tools for data analysis.


Data Overlay and Integration: Allow users to overlay and integrate diverse genomic data types, such as DNA sequencing data, gene expression data, and epigenetic data, onto the reference genome. This enables researchers to study relationships between different genomic features and datasets.The choice of the most suitable genome annotation and assembly browser varies depending on the specific analysis needs and preferences of the user. However, one popular option for visualizing and annotating genomic data is the Integrative Genomics Viewer (IGV), which offers a wide range of data analysis tools and supports various file formats, including genomic variation, gene expression, and chromatin structure data.


Visualization Tools: Offer visualization tools that enable users to visualize genomic data in various formats, such as heatmaps, line plots, bar plots, and genomic tracks. These tools facilitate exploration and interpretation of complex genomic data in a graphical format. The UCSC Genome Browser is a popular and comprehensive genome browser that offers a wide range of visualization tools for genomic data, such as genetic variation, gene expression, and epigenetic modifications. Additionally, it provides access to numerous publicly available datasets for comparative genomics research. [ 6 ]


Zooming and Navigation: Provide zooming and navigation tools that allow users to explore genomic data at different scales, from the whole genome down to individual nucleotides. This facilitates navigation and focus on specific genomic regions of interest. Again UCSC is a great browser for navigation, however the NCBI in Figure 1 as featured in the figure below has logical navigation and user interface .


Search and Retrieval: Include search and retrieval features that allow users to search for specific genes, genomic regions, or functional elements. This simplifies the process of locating and retrieving relevant genomic data for analysis. The NCBI browser  [ 7 ]  is a valuable tool for genomics research due to its extensive database, user-friendly interface, and integration with other NCBI tools. It provides access to a large and diverse set of biological databases, including the GenBank database, making it easier for users to search and retrieve genomic data. Additionally, the user-friendly interface and advanced search options allow for more efficient searches, while the integration with other NCBI tools ensures a seamless search and retrieval experience.


Comparative Genomics: Some genomic browsers include features for comparing and analyzing genomic data from different species or strains. This enables researchers to study evolutionary relationships, identify conserved regions, and compare gene orthologs. Ensembl  [ 8 ]  offers advanced comparative genomics tools, including the ability to compare gene structures, genome alignments, and synteny between different organisms.


Customization and Annotation: Can allow users to customize the display of genomic data by adding their own annotations, tracks, or visualizations. This enables researchers to tailor the browser interface to their specific research needs and hypotheses.


Data Sharing and Collaboration: Contain features for data sharing and collaboration, such as the ability to share browser sessions, save customizations, or collaborate with other researchers in real-time. This promotes collaboration and data sharing among researcher. GMOD: GMOD (Generic Model Organism Database) is a collection of open-source tools for building and sharing genome databases. It provides a framework for integrating genomic data with other biological data types, such as proteomics and metabolomics, and allows for the sharing of data and analysis with collaborators.


Analysis Tools: Some browsers provide analysis tools, such as tools for identifying differentially expressed genes, predicting functional elements, or performing other computational analyses on the genomic data directly within the browser environment.


↵The two images  show the features and inputs of the NCBI Genomic Browser which is one of many. The right image displays the Chr1 region of the human gene. The box at the bottom highlighted in red shows the customizable options such as BLAST, track by accession, assembly details, history, and tracks/user data. These features can be different across different genomic platforms.


The genome browser displays the genome as a series of tracks or layers that can be toggled on or off based on the needs of the user. Each track represents a unique genomic feature such as genes, transcripts, regulatory region, or sequence variations. The user can zoom in and out of a certain genome region to view different level of detail or additional information, as well as navigate to specific regions using a search function or by clicking on a specific feature.


Aside from gene annotations, genome browsers can display a variety of different data types, such as:


DNA Sequence: This can be shown as a single linear track or as several tracks, with different colors signifying distinct features (for example,  exons ,  introns , and repetitions).


Variation Data: This includes information on  Single-nucleotide polymorphism  (SNPs), insertions/deletions (indels), and structural variants.


Transcriptomics: This contains information on gene expression levels, alternative splicing, and non-coding RNAs.


Proteomics: This includes information on protein expression levels, post-translational modifications, and protein-protein interactions.


Genome browsers are used in a variety of research fields, including bioinformatics, genetics, and clinical genomics. They allow researchers to investigate the genetic basis of disease, evolution, and other biological processes. Here are some instances of how genome browsers are being used in various fields:


Evolutionary Biology : Genome browsers are used to study and compare the genomes of various organisms to identify similarities and differences in gene structure, regulatory element, function and repetitive sequence. This can provide evolutionary insight into the relationship between different species and also help identify genetic alteration that underpin adaptation and speciation, as well as provide evolutionary insight into relationship between different species.


Clinical Genomics: Genome browsers are used to study the genetic basis of disease. By examining the genome of a patient, researchers can identify genetic  mutation  that may be responsible for the disease. Genome browsers enable researchers to investigate these mutations' possible impact on gene expression and protein function by visualizing them in the context of the genome and how proteins work.


The 2000s witnessed an explosion of  genome sequencing  and mapping in evolutionarily diverse species. While full genome sequencing of  mammals  is rapidly progressing, the ability to assemble and align  orthologous  whole chromosomal regions from more than a few species is not yet possible. The intense focus on the building of comparative maps for domestic (dogs and cats), laboratory (mice and rats) and agricultural (cattle) animals has traditionally been used to understand the underlying basis of disease-related and healthy  phenotypes .


These maps also provide an unprecedented opportunity to use multispecies analysis as a tool to infer  karyotype  evolution. Comparative  chromosome painting  and related techniques are very powerful approaches in comparative genome studies. Homologies can be identified with high accuracy using molecularly defined DNA probes for  fluorescence  in situ  hybridization  (FISH) on chromosomes of different species. Chromosome painting data are now available for members of nearly all mammalian orders.


It was found that in most orders, there are species with rates of chromosome evolution that can be considered as 'default' rates. It needs to be noted that the number of rearrangements that have become fixed in evolutionary history seems relatively low, due to 180 million years of the mammalian radiation. Thus a record of the history of karyotype changes that have occurred during evolution have been attained through comparative chromosome maps.


Modern mammals (class  Mammalia ) are divided into  Monotremes ,  Marsupials , and  Placentals . The subclass  Prototheria  ( Monotremes ) comprises the five species of egg-laying mammals:  platypus  and four  echidna  species. The infraclasses  Metatheria  ( Marsupials ) and  Eutheria  ( Placentals ) together form the subclass  Theria . [ 4 ]


In the 2000s understanding of the relationships among eutherian mammals has experienced a virtual revolution. Molecular phylogenomics, new fossil finds and innovative morphological interpretations now group the more than 4600 extant species of eutherians into four major super-ordinal  clades :  Euarchontoglires  (including  Primates ,  Dermoptera ,  Scandentia ,  Rodentia , and  Lagomorpha ),  Laurasiatheria  ( Cetartiodactyla ,  Perissodactyla ,  Carnivora ,  Chiroptera ,  Pholidota , and  Eulipotyphla ),  Xenarthra , and  Afrotheria  ( Proboscidea ,  Sirenia ,  Hyracoidea ,  Afrosoricida ,  Tubulidentata , and  Macroscelidea ). [ 4 ]  This tree is very useful in unifying the parts of a puzzle in comparative mammalian cytogenetics.


Each gene maps to the same chromosome in every cell.  Linkage  is determined by the presence of two or more  loci  on the same chromosome. The entire chromosomal set of a species is known as a karyotype.


A seemingly logical consequence of descent from common ancestors is that more closely related species should have more chromosomes in common. However, it is now widely thought that species may have  phenetically  similar karyotypes due to genomic conservation. Therefore, in comparative cytogenetics, phylogenetic relationships should be determined on the basis of the polarity of chromosomal differences (derived traits).


Mammalian comparative cytogenetics, an indispensable part of  phylogenomics , has evolved in a series of steps from pure description to the more heuristic science of the genomic era. Technical advances have marked the various developmental steps of cytogenetics.


The first step of the  Human Genome Project  took place when  Tjio  and  Levan , in 1956, reported the accurate  diploid  number of human chromosomes as 2n = 46. [ 6 ]


During this phase, data on the karyotypes of hundreds of mammalian species (including information on diploid numbers, relative length and morphology of chromosomes, presence of  B chromosomes ) were described. Diploid numbers (2n) were found to vary from 2n = 6–7 in the  Indian muntjac [ 7 ]  to over 100 in some rodents. [ 8 ]


The second step derived from the invention of C-, G-, R- and other banding techniques and was marked by the Paris Conference (1971), which led to a standard nomenclature to recognize and classify each human chromosome. [ 9 ]


The most widely used banding methods are  G-banding  (Giemsa-banding) and R-banding (reverse-banding). These techniques produce a characteristic pattern of contrasting dark and light transverse bands on the chromosomes. Banding makes it possible to identify homologous chromosomes and construct chromosomal nomenclatures for many species. Banding of homologous chromosomes allows chromosome segments and rearrangements to be identified. The banded karyotypes of 850 mammalian species were summarized in the  Atlas of Mammalian Chromosomes . [ 10 ]


Karyotype variability in mammals is mainly due to the varying amount of  heterochromatin  in each mammal. Once the amount of heterochromatin is subtracted from total genome content, all mammals have very similar genome sizes.


Mammalian species differ considerably in heterochromatin content and location. Heterochromatin is most often detected using C-banding. [ 12 ]  Early studies using C-banding showed that differences in the fundamental number (i.e., the number of chromosome arms) could be entirely due to the addition of heterochromatic chromosome arms. Heterochromatin consists of different types of repetitive DNA, not all seen with C-banding that can vary greatly between karyotypes of even closely related species. The differences of the amount of heterochromatin among congeneric rodent species may reach 33% of nuclear DNA in  Dipodomys  species, [ 13 ]  36% in  Peromyscus  species, [ 14 ]  42% in  Ammospermophilus [ 15 ]  and 60% in  Thomomys  species where  C-value  (haploid DNA content) ranges between 2.1 and 5.6 pg. [ 16 ] [ 17 ]


The  red viscacha rat  ( Tympanoctomys barrerae ) has a record C-value among mammals—9.2 pg. [ 18 ]  Although tetrapoidy was first proposed to be a reason for its high genome size and diploid chromosome number, Svartman et al. [ 19 ]  showed that the high genome size was due to the enormous amplification of heterochromatin. Although one single copy  gene was found to be duplicated in its genome, [ 20 ]  data on absence of large genome segment duplications (single paints of most  Octodon degu  probes) and repetitive DNA hybridization evidence rules against tetraploidy. The study of heterochromatin composition, repeated DNA amount and its distribution on chromosomes of octodontids is absolutely necessary to define exactly what heterochromatin fraction is responsible for the large genomes of the red viscacha rat. [ 21 ]


In comparative cytogenetics, chromosome homology between species was proposed on the basis of similarities in banding patterns. Closely related species often had very similar banding pattern and after 40 years of comparing bands it seems safe to generalize that karyotype divergence in most taxonomic groups follows their phylogenetic relationship, despite notable exceptions. [ 10 ] [ 22 ]


The conservation of large chromosomal segments makes comparison between species worthwhile. Chromosome banding has been a reliable indicator of chromosome homology overall, i.e. that the chromosome identified on the basis of banding actually carries the same genes. This relationship may fail for phylogenetically distant species or species that have experienced extremely rapid chromosome evolution. Banding is still morphological and is not always a foolproof indicator of DNA content. [ 21 ]


The third step occurred when molecular techniques were incorporated into cytogenetics. These techniques use DNA probes of diverse sizes to compare chromosomes at the DNA level. Homology can be confidently compared even between phylogenetically distant species or highly rearranged species (e.g.,  gibbons ). Using cladistic analysis rearrangements that have diversified the mammalian karyotype are more precisely mapped and placed in a phylogenomic perspective. "Comparative chromosomics" defines the field of cytogenetics dealing with molecular approaches, [ 30 ]  although "chromosomics" was originally introduced to define the research of chromatin dynamics and morphological changes in interphase chromosome structures. [ 31 ]


Chromosome painting or Zoo-FISH was the first technique to have a wide-ranging impact. [ 32 ] [ 33 ] [ 34 ] [ 35 ] [ 36 ]  With this method the homology of chromosome regions between different species are identified by hybridizing DNA probes of an individual, whole chromosomes of one species to  metaphase  chromosomes of another species. Comparative chromosome painting allows a rapid and efficient comparison of many species and the distribution of homologous regions makes it possible to track the translocation of chromosomal evolution. When many species covering different mammalian orders are compared, this analysis can provide information on trends and rates of chromosomal evolution in different branches.


However, homology is only detected qualitatively, and resolution is limited by the size of visualized regions. Thus, the method does not detect all minuscule homologous regions from multiple rearrangements (as between mouse and human). The method also fails to report internal inversions within large segments. Another limitation is that painting across great phylogenetic distance often results in a decreased efficiency. Nevertheless, the use of painting probes derived from different species combined with comparative sequencing projects help to increase the resolution of the method. [ 21 ]


In addition to sorting,  microdissection  of chromosomes and chromosome regions was also used to obtain probes for chromosome painting. Best results were obtained when a series of microdissection probes covering the total human genome were localized on anthropoid primate chromosomes via multicolor banding (MCB). [ 37 ] [ 38 ]  However a limitation of MCB is that it can only be used within a group of closely related species ("phylogenetic" resolution is too low). Spectral karyotyping (SKY) and MFISH—the ratio labeling and simultaneous hybridization of a complete chromosomal set have similar drawbacks and little application outside of clinical studies. [ 21 ]


Comparative genomics  data including chromosome painting confirmed the substantial conservation of mammalian chromosomes. [ 36 ]  Total human chromosomes or their arms can efficiently paint extended chromosome regions in many placentals down to  Afrotheria  and  Xenarthra . Gene localization data on human chromosomes can be extrapolated to the homologous chromosome regions of other species with high reliability. Usefully, humans express conserved syntenic chromosome organization similar to the ancestral condition of all placental mammals.


After the Human Genome Project researchers focused on evolutionary comparisons of the genome structures of different species. The whole genome of any species can be sequenced completely and repeatedly to obtain a comprehensive single-nucleotide map. This method makes it possible to compare genomes for any two species regardless of their taxonomic distance.


Sequencing efforts provided a variety of products useful in molecular cytogenetics. Fluorescence  in situ  hybridization (FISH) with DNA clones ( BAC  and  YAC  clones,  cosmids ) allowed the construction of chromosome maps at a resolution of several megabases that could detect relatively small chromosome rearrangements. A resolution of several kilobases can be achieved on interphase chromatin. A limitation is that hybridization efficiencies decrease with increasing phylogenetic distance.


Radiation hybrid (RH) genome mapping  is another efficient approach. This method includes the irradiation of cells to disrupt the genome into the desired number of fragments that are subsequently fused with  Chinese hamster  cells. The resulting somatic cell hybrids contain individual fragments of the relevant genome. Then, 90–100 (sometimes, more) clones covering the total genome are selected, and the sequences of interest are localized on the cloned fragments via the  polymerase chain reaction  (PCR) or direct DNA–DNA hybridization. To compare the genomes and chromosomes of two species, RHs should be obtained for both species. [ 21 ]


In contrast to many other taxa, therian mammals and birds are characterized by highly conserved systems of genetic sex determination that lead to special chromosomes, i.e. the  sex chromosomes . Although the XX/XY sex chromosome system is the most common among eutherian species, it is not universal. In some species X-autosomal translocations result in the appearance of "additional Y" chromosomes (for example, XX/XY1Y2Y3 systems in  black muntjac ). [ 39 ] [ 40 ]


In other species Y-autosomal translocations lead to appearance of additional X chromosomes (for example, in some  New World primates  such as  howler monkeys ). Regarding this aspect, rodents again represent a peculiar derived group, comprising the record number of species with non-classical sex chromosomes such as the  wood lemming , the  collared lemming , the creep vole, the spinous country rat, the  Akodon  and the  bandicoot rat . [ 41 ]


Genome evolution  is the process by which a  genome  changes in structure (sequence) or size over time. The study of genome evolution involves multiple fields such as structural analysis of the genome, the study of genomic parasites,  gene  and ancient genome duplications,  polyploidy , and  comparative genomics . Genome evolution is a constantly changing and evolving field due to the steadily growing number of sequenced genomes, both prokaryotic and eukaryotic, available to the scientific community and the public at large.


Since the first sequenced genomes became available in the late 1970s, [ 1 ]  scientists have been using comparative genomics to study the differences and similarities between various genomes.  Genome sequencing  has progressed over time to include more and more complex genomes including the eventual sequencing of the entire  human genome  in 2001. [ 2 ]  By comparing genomes of both close relatives and distant ancestors the stark differences and similarities between species began to emerge as well as the mechanisms by which genomes are able to evolve over time. [ citation needed ]


Prokaryotic  genomes have two main mechanisms of evolution:  mutation  and  horizontal gene transfer . [ 3 ]  A third mechanism,  sexual reproduction , is prominent in eukaryotes and also occurs in bacteria.  Prokaryotes can acquire novel genetic material through the process of  bacterial conjugation  in which both plasmids and whole chromosomes can be passed between organisms. An often cited example of this process is the transfer of antibiotic resistance utilizing plasmid DNA. [ 4 ]  Another mechanism of genome evolution is provided by  transduction  whereby bacteriophages introduce new DNA into a bacterial genome.  The main mechanism of sexual interaction is  natural genetic transformation  which involves the transfer of DNA from one prokaryotic cell to another though the intervening medium.  Transformation is a common mode of DNA transfer and at least 67 prokaryotic species are known to be competent for transformation. [ 5 ]


Genome evolution in bacteria is well understood because of the thousands of completely sequenced bacterial genomes available. Genetic changes may lead to both increases or decreases of genomic complexity due to adaptive genome streamlining and purifying selection. [ 6 ]  In general, free-living bacteria have evolved larger genomes with more genes so they can adapt more easily to changing environmental conditions. By contrast, most parasitic bacteria have reduced genomes as their hosts supply many if not most nutrients, so that their genome does not need to encode for enzymes that produce these nutrients themselves. [ 7 ] [ page needed ]


Eukaryotic genomes are generally larger than that of the prokaryotes. While the  E. coli  genome is roughly 4.6Mb in length, [ 9 ]  in comparison the Human genome is much larger with a size of approximately 3.2Gb. [ 10 ]  The eukaryotic genome is linear and can be composed of multiple chromosomes, packaged in the nucleus of the cell. The non-coding portions of the gene, known as  introns , which are largely not present in prokaryotes, are removed by RNA splicing before  translation  of the protein can occur. Eukaryotic genomes evolve over time through many mechanisms including sexual reproduction which introduces much greater genetic diversity to the offspring than the usual prokaryotic process of replication in which the offspring are theoretically genetic clones of the parental cell. [ citation needed ]


Genome size  is usually measured in base pairs (or bases in  single-stranded DNA  or  RNA ). The  C-value  is another measure of genome size. Research on prokaryotic genomes shows that there is a significant positive correlation between the  C-value  of prokaryotes and the amount of genes that compose the genome. [ 11 ]   This indicates that gene number is the main factor influencing the size of the prokaryotic genome. In  eukaryotic  organisms, there is a paradox observed, namely that the number of genes that make up the genome does not correlate with genome size.  In other words, the genome size is much larger than would be expected given the total number of protein coding genes. [ 12 ]


Genome size can increase by  duplication ,  insertion , or  polyploidization .  Recombination  can lead to both DNA loss or gain. Genomes can also shrink because of  deletions . A famous example for such gene decay is the genome of  Mycobacterium leprae , the causative agent of  leprosy .  M. leprae  has lost many once-functional genes over time due to the formation of  pseudogenes . [ 13 ]  This is evident in looking at its closest ancestor  Mycobacterium tuberculosis . [ 14 ]   M. leprae  lives and replicates inside of a host and due to this arrangement it does not have a need for many of the genes it once carried which allowed it to live and prosper outside the host. Thus over time these genes have lost their function through mechanisms such as mutation causing them to become pseudogenes. It is beneficial to an organism to rid itself of non-essential genes because it makes replicating its DNA much faster and requires less energy. [ 15 ]


An example of increasing genome size over time is seen in filamentous plant pathogens. These plant pathogen genomes have been growing larger over the years due to repeat-driven expansion. The repeat-rich regions contain genes coding for host interaction proteins. With the addition of more and more repeats to these regions the plants increase the possibility of developing new virulence factors through mutation and other forms of genetic recombination. In this way it is beneficial for these plant pathogens to have larger genomes. [ 16 ]


The  evolution of genomes  can be impressively shown by the change of chromosome number and structure over time. For instance, the ancestral chromosomes corresponding to  chimpanzee chromosomes 2A and 2B fused to produce human chromosome 2 . Similarly, the chromosomes of more distantly related species show chromosomes that have been broken up into more parts over the course of evolution. This can be demonstrated by  Fluorescence in situ hybridization . [ 17 ]


Gene duplication  is the process by which a region of DNA coding for a gene is duplicated. This can occur as the result of an error in  recombination  or through a  retrotransposition  event. Duplicate genes are often immune to the  selective pressure  under which genes normally exist. As a result, a large number of mutations may accumulate in the duplicate gene code. This may render the gene non-functional or in some cases confer some benefit to the organism. [ 18 ] [ 19 ]


Similar to gene duplication, whole genome duplication is the process by which an organism's entire genetic information is copied, once or multiple times which is known as  polyploidy . [ 20 ]  This may provide an evolutionary benefit to the organism by supplying it with multiple copies of a gene thus creating a greater possibility of functional and selectively favored genes. However, tests for enhanced rate and innovation in teleost fishes with duplicated genomes compared with their close relative holostean fishes (without duplicated genomes) found that there was little difference between them for the first 150 million years of their evolution. [ 21 ]


In 1997, Wolfe & Shields gave evidence for an ancient duplication of the  Saccharomyces cerevisiae  ( Yeast ) genome. [ 22 ]  It was initially noted that this yeast genome contained many individual gene duplications. Wolfe & Shields hypothesized that this was actually the result of an entire genome duplication in the yeast's distant evolutionary history. They found 32 pairs of homologous chromosomal regions, accounting for over half of the yeast's genome. They also noted that although  homologs  were present, they were often located on different  chromosomes . Based on these observations, they determined that  Saccharomyces cerevisiae  underwent a whole genome duplication soon after its evolutionary split from  Kluyveromyces , a genus of ascomycetous yeasts. Over time, many of the duplicate genes were deleted and rendered non-functional. A number of chromosomal rearrangements broke the original duplicate chromosomes into the current manifestation of homologous chromosomal regions. This idea was further solidified in looking at the genome of yeast's close relative  Ashbya gossypii . [ 23 ]    Whole genome duplication is common in fungi as well as plant species.  An example of extreme genome duplication is represented by the Common Cordgrass ( Spartina anglica ) which is a dodecaploid, meaning that it contains 12 sets of chromosomes, [ 24 ]  in stark contrast to the human diploid structure in which each individual has only two sets of 23 chromosomes.


Transposable elements  are regions of DNA that can be inserted into the genetic code through one of two mechanisms. These mechanisms work similarly to "cut-and-paste" and "copy-and-paste" functionalities in word processing programs. The "cut-and-paste" mechanism works by excising DNA from one place in the genome and inserting itself into another location in the code. The "copy-and-paste" mechanism works by making a genetic copy or copies of a specific region of DNA and inserting these copies elsewhere in the code. [ 25 ] [ 26 ]  The most common transposable element in the  human genome  is the  Alu sequence , which is present in the genome over one million times. [ 27 ]


Spontaneous  mutations  often occur which can cause various changes in the genome. [ 28 ]  Mutations can either change the identity of one or more nucleotides, or result in the addition or deletion of one or more  nucleotide bases . Such changes can lead to a  frameshift mutation , causing the entire code to be read in a different order from the original, often resulting in a protein becoming non-functional. [ 29 ]  A mutation in a  promoter region , enhancer region or  transcription factor  binding region can also result in either a loss of function, or an up or downregulation in the  transcription  of the gene targeted by these regulatory elements. Mutations are constantly occurring in an organism's genome and can cause either a negative effect, positive effect or neutral effect (no effect at all). [ 30 ] [ 31 ]


Often a result of spontaneous  mutation ,  pseudogenes  are dysfunctional genes derived from previously functional gene relatives. There are many mechanisms by which a functional gene can become a pseudogene including the deletion or insertion of one or multiple  nucleotides . This can result in a shift of  reading frame , causing the gene to no longer code for the expected protein, introduce a premature  stop codon  or a mutation in the  promoter  region. [ 32 ]


Often cited examples of pseudogenes within the human genome include the once functional  olfactory  gene families. Over time, many olfactory genes in the human genome became pseudogenes and were no longer able to produce functional proteins, explaining the poor sense of smell humans possess in comparison to their mammalian relatives. [ 33 ] [ 34 ]


Similarly, bacterial pseudogenes commonly arise from  adaptation  of free-living bacteria to  parasitic  lifestyles, so that many metabolic genes become superfluous as these species become adapted to their host. Once a parasite obtains nutrients (such as  amino acids  or  vitamins ) from its host it has no need to produce these nutrients itself and often loses the genes to make them. [ citation needed ]


Exon shuffling  is a mechanism by which new genes are created. This can occur when two or more  exons  from different genes are combined or when exons are duplicated. Exon shuffling results in new genes by altering the current intron-exon structure. This can occur by any of the following processes:  transposon  mediated shuffling, sexual recombination or non-homologous recombination (also called  illegitimate recombination ). Exon shuffling may introduce new genes into the genome that can be either selected against and deleted or selectively favored and conserved. [ 35 ] [ 36 ] [ 37 ]


Many species exhibit genome reduction when subsets of their genes are not needed anymore. This typically happens when organisms adapt to a parasitic life style, e.g. when their nutrients are supplied by a host. As a consequence, they lose the genes needed to produce these nutrients. In many cases, there are both free living and parasitic species that can be compared and their lost genes identified. Good examples are the genomes of  Mycobacterium tuberculosis  and  Mycobacterium leprae , the latter of which has a dramatically reduced genome (see figure under  pseudogenes  above).


Another beautiful example are  endosymbiont  species. For instance,  Polynucleobacter necessarius  was first described as a cytoplasmic endosymbiont of the ciliate  Euplotes aediculatus . The latter species dies soon after being cured of the endosymbiont. In the few cases in which  P. necessarius  is not present, a different and rarer bacterium apparently supplies the same function. No attempt to grow symbiotic  P. necessarius  outside their hosts has yet been successful, strongly suggesting that the relationship is obligate for both partners. Yet, closely related free-living relatives of P. necessarius have been identified. The endosymbionts have a significantly reduced genome when compared to their free-living relatives (1.56 Mbp vs. 2.16 Mbp). [ 38 ]


A major question of evolutionary biology is how genomes change to create new species.  Speciation  requires changes in  behavior ,  morphology ,  physiology , or  metabolism  (or combinations thereof). The evolution of genomes during speciation has been studied only very recently with the availability of  next-generation sequencing  technologies. For instance,  cichlid fish  in African lakes differ both morphologically and in their behavior. The genomes of 5 species have revealed that both the sequences but also the expression pattern of many genes has quickly changed over a relatively short period of time (100,000 to several million years). Notably, 20% of  duplicate gene  pairs have gained a completely new  tissue-specific expression  pattern, indicating that these genes also obtained new functions. Given that gene expression is driven by short  regulatory sequences , this demonstrates that relatively few mutations are required to drive speciation. The cichlid genomes also showed increased evolutionary rates in  microRNAs  which are involved in gene expression. [ 39 ] [ 40 ]


Mutations can lead to changed gene function or, probably more often, to changed gene expression patterns. In fact, a study on 12  animal species provided strong evidence that tissue-specific gene expression was largely conserved between orthologs in different species. However, paralogs within the same species often have a different expression pattern. That is, after duplication of genes they often change their expression pattern, for instance by getting expressed in another tissue and thereby adopting new roles. [ 41 ]


The genetic code is made up of sequences of four  nucleotide  bases:  Adenine ,  Guanine ,  Cytosine  and  Thymine , commonly referred to as A, G, C, and T. The GC-content is the percentage of G & C bases within a genome. GC-content varies greatly between different organisms. [ 42 ]  Gene coding regions have been shown to have a higher GC-content and the longer the gene is, the greater the percentage of G and C bases that are present. A higher GC-content confers a benefit because a Guanine-Cytosine bond is made up of three  hydrogen bonds  while an Adenine-Thymine bond is made up of only two. Thus the three hydrogen bonds give greater stability to the DNA strand. So, it is not surprising that important genes often have a higher GC-content than other parts of an organism's genome. [ 43 ]  For this reason, many species living at very high temperatures such as the ecosystems surrounding hydrothermal vents, have a very high GC-content. High GC-content is also seen in regulatory sequences such as promoters which signal the start of a gene. Many promoters contain  CpG islands , areas of the genome where a cytosine nucleotide occurs next to a guanine nucleotide at a greater proportion.  It has also been shown that a broad distribution of GC-content between species within a genus shows a more ancient ancestry. Since the species have had more time to evolve, their GC-content has diverged further apart. [ citation needed ]


Amino acids are made up of three base long  codons  and both  Glycine  and  Alanine  are characterized by codons with Guanine-Cytosine bonds at the first two codon base positions. This GC bond gives more stability to the DNA structure. It has been hypothesized that as the first organisms evolved in a high-heat and pressure environment they needed the stability of these GC bonds in their genetic code. [ 44 ]


Novel genes can arise from  non-coding DNA . De novo origin of (protein-coding) genes only requires two features, namely the generation of an  open reading frame , and the creation of a  transcription factor  binding site. For instance, Levine and colleagues reported the origin of five new genes in the  D. melanogaster  genome from noncoding DNA. [ 45 ] [ 46 ]  Subsequently,  de novo  origin of genes  has been also shown in other organisms such as  yeast , [ 47 ]   rice [ 48 ]  and  humans . [ 49 ]  For instance, Wu et al. (2011) reported 60 putative de novo human-specific genes all of which are short consisting of a single  exon  (except one). [ 50 ]  In bacteria, 'grounded'  prophages  (i.e. integrated phage that cannot produce new phage) are buffer zones which would tolerate variations thereby increasing the probability of de novo gene formation. [ 51 ]  These grounded prophages and other such genetic elements are sites where genes could be acquired through  horizontal gene transfer  (HGT).


In order to understand how the genome arose, knowledge is required of the chemical pathways that permit formation of the key building blocks of the genome under plausible  prebiotic conditions .  According to the  RNA world  hypothesis free-floating ribonucleotides were present in the primitive soup.  These were the fundamental molecules that combined in series to form the original  RNA  genome.  Molecules as complex as RNA must have arisen from small molecules whose reactivity was governed by physico-chemical processes.  RNA is composed of  purine  and  pyrimidine  nucleotides, both of which are necessary for reliable information transfer, and thus Darwinian natural selection and  evolution . Nam et al. [ 52 ]  demonstrated the direct condensation of nucleobases with ribose to give ribonucleosides in aqueous microdroplets, a key step leading to formation of the RNA genome. Also, a plausible prebiotic process for synthesizing pyrimidine and purine ribonucleotides leading to genome formation using wet-dry cycles was presented by Becker et al. [ 53 ]


Leafy seadragons  are widely recognized for their strikingly derived characters, including leaf-like appendages, extreme spinal curvature, elongated craniofacial bones, and large body size.  These make them phenotypic outliers in the  teleost  fish clade (family  Syngnathidae ).  Up until recently, the genome of leafy seadragons was unknown; but in 2022, their genome was fully annotated, along with  weedy seadragons .  Several significant features have been found in the leafy seadragon and weedy seadragon genomes, including divergent noncoding regions near a developmental gene responsible for integumentary growth; high genome wide repetitive DNA density; and recent  transposable element  expansions.  Genetic diversity in leafy seadragons and weedy seadragons is relatively low as they have low-dispersal  life history .  Most recently, another species of seadragon was discovered and genetically sequenced: the  ruby seadragon , which differs genetically and phenotypically from the two other seadragon species.


Seadragons are known for their remarkable derived traits, including leaf-like appendages; extreme spinal curvature ( kyphosis  and  lordosis ); elongated craniofacial bones; large body size and unique body plan; and elaborate skin coloration, presumably adapted for  crypsis . [ 1 ]  They also have a toothless tubular mouth, bony armour covering the body, and an absence of pelvic fins and scales. [ 2 ]  Seadragons belong to the family  Syngnathidae , a lineage of  teleost  fishes renowned for having evolved male pregnancy. [ 1 ]   All syngnathid fishes have evolved complex, pouch like structures that maintain homeostasis during pregnancy. [ 3 ]   Syngnathidae  are found in temperate and tropical oceans, and include 321 species of  seahorses ,  pipefishes , and seadragons. [ 4 ]  Three known species comprise the seadragon clade, including  Phycodurus eques  (leafy seadragon),  Phyllopteryx taeniolatus  (weedy, or common seadragon), and the most recently discovered,  Phyllopteryx dewysea  (ruby seadragon). [ 1 ]


The southern Australian coastline changed significantly during the  Last Glacial Maximum  (around 20,000 years ago).  Sea levels lowered 100 meters, [ 5 ]  which resulted in new continental shelves and displacement of subtidal organisms in affected areas.  A land bridge, called the historical Bassian Isthmus, connected mainland Australia to Tasmania; and this caused extensive habitat changes. [ 5 ]  The seaway opened once again ca. 14,000 years ago, and is now called the  Bass Strait .  However, evidence of historical isolation is still present in seadragons.  Populations of weedy seadragons west of the Bassian Isthmus are more diverse, while populations east of the Bassian Isthmus have low genetic diversity. [ 5 ]   During  interglacial periods , postglacial flooding resulted in rising sea levels, which produced variable differences in colonizing leafy seadragon populations. [ 6 ]   In the West, populations exhibit shallow genetic structure, weak expansion signals, and a westward  cline  in genetic diversity, most likely because shallow seabeds did not survive the  Last Glacial Maximum . [ 6 ]   This indicates that leafy seadragons recolonized the western regions following a sea level rise. [ 6 ]   In the East, shallow seabeds persisted during the  Last Glacial Maximum , and increased after postglacial flooding.  This resulted in strong demographic expansions, deeper genetic structure, and higher genetic diversity. [ 6 ]


Weedy seadragons span the entire southern coast of  Australia , from  Western Australia  to  New South Wales  and  Tasmania ; whereas leafy seadragons span a more restricted region from  Western Australia  to  South Australia . Leafy seadragon have more elaborate appendages than the weedy seadragons. [ 2 ]  Both species exhibit low genetic diversity.  Weedy seadragons are weak swimmers, so young seadragons stay close to home, hiding themselves in kelp and seaweed with  crypsis .  Four genetically distinct populations are recognized in weedy seadragons, in central  New South Wales , southern  New South Wales ,  Victoria , and  Tasmania . [ 4 ]   Similarly, leafy seadragons have a low dispersal life history.  Males brood eggs on the underside of their tails, which prohibits a dispersive egg or larval phase.  Additionally, adult leafy seadragons are slow swimmers; therefore, they tend to stick close to home.  They occur in two genetically distinct populations, one in the Western part of their range and the other on the Southern part of their range. [ 7 ]


Both weedy and leafy seadragons exhibit genomic features that help explain their unusual phenotypic traits, including highly repetitive DNA sequences for their size.  Transposable elements  accumulate nucleotide substitutions over evolutionary time, and are prone to genetic rearrangements. [ 8 ]   Often,  transposable elements  have significant mutational effects on their hosts, some even contributing to organismic evolution. [ 8 ]   Transposable element  density is enriched near expanded gene families, and low near contracted gene families. [ 1 ]  For example, BovB and Tc1 transposable elements are enriched near a copy number expansion of  Copb2 , a coatamer complex gene that forms part of a protein complex involved in retrograde vesicle budding from the Golgi apparatus.  It is also important for secretion of macromolecule cargo, like collagen, which is important for bone and connective tissue development. [ 1 ]  Weedy and leafy seadragons have elaborated bony exoskeletons, stiff bodies, connective tissue dense leafy-like appendages, and kinked axial skeletons, likely as a result of  Copb2  proliferation. [ 1 ]


Another gene family influenced by transposable elements is the fgf family.   Fgf3  and  Fgf4  are lost in weedy and leafy seadragons, which is shocking considering their importance in pharyngeal arches, teeth, brain, cranial placodes, epidermal appendages, limbs, and segmental axis development. [ 1 ]  Specifically in weedy seadragons, the  secretory calcium-binding phosphoprotein  5 is missing, which is essential for oral and pharyngeal teeth. [ 9 ]   Evolutionary loss of these genes could have led to their strange craniofacial features. [ 1 ]


Specifically in weedy seadragons, 433 genes are highly expressed in the leaf-like appendages. [ 9 ]  Though they look like fins, they lack fin rays and are mostly bone matrix and connective tissue. [ 9 ]  Of these genes, 20 of them include genes that likely provide strength and elasticity (like  collagens ,  osteocalcin , and  keratins ), in additions to genes related to immune response (like  lectins ,  B2-macroglobulin , and  apolipoprotein E ).  It's thought that these genes were evolved to help protect their accident-prone leaf-like appendages. [ 9 ]  Several inflammation and injury repair genes, like tnfrsfs,  chemokines ,  chemokine receptors , and  protocadherins , are highly expressed in the appendages, suggesting that these genes could help the leaf-like appendages heal and regenerate after predator attacks. [ 9 ]   Another gene expansion is  MHC I , which is highly expressed in the leaf-like appendages, skin, and gills.  It is suggested that  MHC I  might provide additional immune protection. [ 9 ]


In both weedy and leafy seadragons,  GTPase  of the immunity-associated protein genes is another contracted gene family.  It is thought that contraction of this family could promote immunologic tolerance in embryos, before they develop their own adaptive immune system. [ 1 ]


The most recently discovered species is the ruby seadragon, whose morphological features distinguish it from the other two species.  It is red in color, with pink vertical bars that extend halfway it its body.  This uniform color differs from weedy and leafy seadragons, which are multicolored and blotchy. [ 2 ]  It is thought that in low light depths, cryptic red coloration is an efficient camouflage strategy. [ 10 ]   Its body shape is reminiscent of the weedy seadragon, but not of the leafy seadragon. [ 2 ]  It has an enlarge pectoral area, even larger than weedy seadragons; and has 18 trunk vertebrae instead of 17-18 in leafy seadragons and 17 in weedy seadragons. Additionally, the new species has a forward pointing pair of dorsal spines in contrast to the backwards facing pair in leafy and weedy seadragons. [ 2 ]


Ruby seadragons possess enlarged bony spines to which leafy appendages would attach in weedy and leafy seadragons, but the appendages themselves are absent.  It is thought that appendages would serve very little camouflaging purpose and would ultimately cause drag or fluid resistance. [ 10 ]  Ruby seadragon also have prehensile tails, while leafy seadragons and weedy seadragons lack these tails. [ 10 ]  One explanation is that this absence in the leafy weedy seadragons evolved in each species independently, and that the prehensile tail in the ruby seadragon was maintained from a common ancestor of  Syngnathidae . [ 10 ]   Another explanation could be that the common ancestor of all seadragons lost their prehensile tail, and the ruby seadragon re-acquired a prehensile tail. [ 10 ]  Ultimately, these phenotypic differences could be due to genetic divergences in mitochondrial and nuclear sequences between this newly discovered species and the other two species. [ 2 ]


Genome Research Foundation (게놈연구재단) also known as Genome Foundation is a non-profit research foundation and institute in Korea under the approval of the ministry of Education, Science, and Technology of Korea. It is a public research foundation with over 30 researchers and staff in 2011. It is a government accredited public charity organization.


GRF was established on 19 April 2010 and since then it has taken research in  genomics  and  bioinformatics . Its main research institute is PGI (Personal Genomics Institute) which performs research in genomics area for the general public and by the general public. In 2017, GRF has established a Cardiovasculomics Institute (CVI) to research on the omics of cardiovascular diseases as a sister institute of PGI.


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Genome size  is the total amount of  DNA  contained within one copy of a single complete  genome . It is typically measured in terms of  mass  in picograms (trillionths or10 −12  of a  gram , abbreviated pg) or less frequently in  daltons , or as the total number of  nucleotide   base pairs , usually in megabases (millions of base pairs, abbreviated Mb or Mbp). One picogram is equal to 978 megabases. [ 1 ]  In  diploid   organisms , genome size is often used interchangeably with the term  C-value .


An organism's complexity is not directly proportional to its genome size; total DNA content is widely variable between biological taxa. Some single-celled organisms have much more DNA than humans, for reasons that remain unclear (see  Junk DNA  and  C-value ).


Since the 1950s, with the emergence of various molecular techniques, the genome sizes of thousands of  eukaryotes  have been analyzed, and these data are available in online databases for animals, plants, and fungi (see external links). Nuclear genome size is typically measured in eukaryotes using either  densitometric  measurements of  Feulgen -stained nuclei (previously using specialized densitometers, now more commonly using computerized  image analysis [ 2 ] ) or  flow cytometry . In  prokaryotes ,  pulsed field gel electrophoresis  and complete  genome sequencing  are the predominant methods of genome size determination.


Nuclear genome sizes are well known to vary enormously among eukaryotic species. In animals they range more than 3,300-fold, and in land plants they differ by a factor of about 1,000. [ 3 ] [ 4 ]   Protist  genomes have been reported to vary more than 300,000-fold in size, but the high end of this range ( Amoeba ) has been called into question. [ by whom? ]


Genome size in eukaryotes was thought to be proportional to the complexity of an organism but by the mid-20th century it became apparent that closely-related species could differ substantially in the size of their genomes. This counter-intuitive observation gave rise to what became known as the " C-value paradox ."


By the end of 1960s this "paradox" was resolved by the discovery of  repetitive DNA  and the realization that much of the differences in genomes sizes was due to the presence or absence of large amounts of repetitive DNA. Further advances at that time indicated that most of the DNA in large genomes was non-functional  junk DNA  and only a small fraction corresponded to functional DNA, including the functional parts of genes.


Genome size correlates with a range of measurable characteristics at the  cell  and organism levels, including cell size,  cell division  rate, and, depending on the  taxon , body size,  metabolic rate , developmental rate,  organ  complexity, geographical distribution, or  extinction  risk. [ 3 ] [ 4 ]  Based on currently available completely sequenced genome data (as of April 2009), log-transformed gene number forms a linear correlation with log-transformed genome size in bacteria, archaea, viruses, and organelles combined, whereas a nonlinear (semi-natural logarithm) correlation is seen for eukaryotes. [ 5 ]  Although the latter contrasts with the previous view that no correlation exists for the eukaryotes, the observed nonlinear correlation for eukaryotes may reflect disproportionately fast-increasing  junk DNA  in increasingly large eukaryotic genomes. Although sequenced genome data are practically biased toward small genomes, which may compromise the accuracy of the empirically derived correlation, and ultimate proof of the correlation remains to be obtained by sequencing some of the largest eukaryotic genomes, current data do not seem to rule out a possible correlation.


In humans, the total female  diploid   nuclear genome  per cell extends for 6.37 Gigabase pairs (Gbp), is 208.23 cm long and weighs 6.51 picograms (pg). [ 6 ]  Male values are 6.27 Gbp, 205.00 cm, 6.41 pg. [ 6 ]  Each DNA polymer can contain hundreds of millions of nucleotides, such as in  chromosome 1 . Chromosome 1 is the largest human  chromosome  with approximately 220 million  base pairs , and would be  85 mm  long if straightened. [ 7 ]


In  eukaryotes , in addition to  nuclear DNA , there is also  mitochondrial DNA  (mtDNA) which encodes certain proteins used by the mitochondria. The mtDNA is usually relatively small in comparison to the nuclear DNA. For example, the  human mitochondrial DNA  forms closed circular molecules, each of which contains 16,569 [ 8 ] [ 9 ]  DNA base pairs, [ 10 ]  with each such molecule normally containing a full set of the mitochondrial genes. Each human mitochondrion contains, on average, approximately 5 such mtDNA molecules. [ 10 ]  Each human  cell  contains approximately 100 mitochondria, giving a total number of mtDNA molecules per human cell of approximately 500. [ 10 ]  However, the amount of mitochondria per cell also varies by cell type, and an  egg cell  can contain 100,000 mitochondria, corresponding to up to 1,500,000 copies of the mitochondrial genome (constituting up to 90% of the DNA of the cell). [ 11 ]


Genome reduction , also known as  genome degradation , is the process by which an organism's genome shrinks relative to that of its ancestors. Genomes fluctuate in size regularly, and  genome size reduction  is most significant in  bacteria .


The most evolutionarily significant cases of genome reduction may be observed in the eukaryotic  organelles  known to be derived from bacteria:  mitochondria  and  plastids . These organelles are descended from primordial  endosymbionts , which were capable of surviving within the host cell and which the host cell likewise needed for survival. Many present-day mitochondria have less than 20 genes in their entire genome, whereas a modern free-living bacterium generally has at least 1,000 genes. Many genes have apparently been transferred to the host  nucleus , while others have simply been lost and their function replaced by host processes.


Other bacteria have become endosymbionts or obligate intracellular  pathogens  and experienced extensive genome reduction as a result. This process seems to be dominated by  genetic drift  resulting from small  population  size, low  recombination  rates, and high  mutation  rates, as opposed to  selection  for smaller genomes. [ citation needed ]  Some free-living marine bacterioplanktons also shows signs of genome reduction, which are hypothesized to be driven by natural selection. [ 12 ] [ 13 ] [ 14 ]  In contrast, terrestrial prokaryotes appear to have larger genome sizes than both aquatic and host-associated prokaryotes (average of 3.7 Mbp for terrestrial, 3.1 Mbp for aquatic and 3.0 Mbp for host-associated). [ 15 ]


Obligate endosymbiotic species are characterized by a complete inability to survive external to their  host  environment. These species have become a considerable threat to human health, as they are often capable of evading human immune systems and manipulating the host environment to acquire nutrients. A common explanation for these manipulative abilities is their consistently compact and efficient genomic structure. These small genomes are the result of massive losses of extraneous DNA, an occurrence that is exclusively associated with the loss of a free-living stage. As much as 90% of the genetic material can be lost when a species makes the evolutionary transition from a free-living to an obligate intracellular lifestyle. During this process the future parasite subjected to an environment rich of metabolite where somehow needs to hide within the host cell, those factors reduce the retention and increase the genetic drift leading to an acceleration of the loss of non-essential genes. [ 16 ] [ 17 ] [ 18 ]  Common examples of species with reduced genomes include  Buchnera aphidicola ,  Rickettsia  prowazekii , and  Mycobacterium leprae . One obligate endosymbiont of  leafhoppers ,  Nasuia deltocephalinicola , has the smallest genome currently known among cellular organisms at 112 kb. [ 19 ]  Despite the pathogenicity of most endosymbionts, some obligate intracellular species have positive fitness effects on their hosts.


The  reductive evolution model  has been proposed as an effort to define the genomic commonalities seen in all obligate endosymbionts. [ 20 ]  This model illustrates four general features of reduced genomes and obligate intracellular species:


Based on this model, it is clear that endosymbionts face different adaptive challenges than free-living species and, as emerged from the analysis between different parasites, their genes inventories are extremely different, leading us to the conclusion that the genome miniaturization follows a different pattern for the different symbionts. [ 22 ] [ 23 ] [ 24 ]


or simply:


In 1991,  John W. Drake  proposed a general rule: that the mutation rate within a genome and its size are inversely correlated. [ 25 ]  This rule has been found to be approximately correct for simple genomes such as those in  DNA viruses  and unicellular organisms. Its basis is unknown.


It has been proposed that the small size of  RNA viruses  is locked into a three-part relation between replication fidelity, genome size, and genetic complexity. The majority of RNA viruses lack an RNA proofreading facility, which limits their replication fidelity and hence their genome size. This has also been described as the  Eigen paradox . [ 26 ]  An exception to the rule of small genome sizes in RNA viruses is found in the  Nidoviruses . These viruses appear to have acquired a  3′-to-5′ exoribonuclease  (ExoN) which has allowed for an increase in genome size. [ 27 ]


In 1972 Michael David Bennett [ 28 ]  hypothesized that there was a correlation with the DNA content and the nuclear volume while  Commoner  and  van’t Hoff  and Sparrow before him postulated that even cell size and cell-cycle length were controlled by the amount of DNA. [ 29 ] [ 30 ]  More recent theories have brought us to discuss about the possibility of the presence of a mechanism that constrains physically the development of the genome to an optimal size. [ 31 ]


Those explanations have been disputed by  Cavalier-Smith ’s article [ 32 ]   where the author pointed that the way to understand the relation between genome size and cell volume was related to the skeletal theory. The nucleus of this theory is related to the cell volume, determined by an adaptation balance between advantages and disadvantages of bigger cell size, the optimization of the ratio nucleus:cytoplasm (karyoplasmatic ratio) [ 33 ] [ 34 ]  and the concept that larger genomes provides are more prone to the accumulation of duplicative transposons as consequences of higher content of non-coding skeletal DNA. [ 32 ]  Cavalier-Smith also proposed that, as consequent reaction of a cell reduction, the nucleus will be more prone to a selection in favor for the deletion compared to the duplication. [ 32 ]


From the economic way of thinking, since phosphorus and energy are scarce, a reduction in the DNA should be always the focus of the evolution, unless a benefit is acquired. The random deletion will be then mainly deleterious and not selected due to the reduction of the gained fitness but occasionally the elimination will be advantageous as well. This trade-off between economy and accumulation of non-coding DNA is the key to the maintenance of the karyoplasmatic ratio.


The base question behind the process of genome miniaturization is whether it occurs through large steps or due to a constant erosion of the gene content. In order to assess the evolution of this process is necessary to compare an ancestral genome with the one where the shrinkage is supposed to be occurred. Thanks to the similarity among the gene content of  Buchnera aphidicola  and the enteric bacteria  Escherichia coli , 89% identity for the 16S rDNA and 62% for  orthologous  genes was possible to shed light on the mechanism of genome miniaturization. [ 35 ]  The genome of the  endosymbiont   B. aphidicola  is characterized by a genome size that is seven times smaller than  E. coli  (643 kb compared to 4.6 Mb) [ 36 ] [ 37 ]  and can be view as a subset of the enteric bacteria gene inventory. [ 37 ]  From the confrontation of the two genomes emerged that some genes persist as partially degraded. [ 37 ]  indicating that the function was lost during the process and that consequent events of erosion shortened the length as documented in  Rickettsia . [ 38 ] [ 39 ] [ 40 ]  This hypothesis is confirmed by the analysis of the  pseudogenes  of  Buchnera  where the number of deletions was more than ten times higher compared to the insertion. [ 40 ]


In  Rickettsia prowazekii , as with other small genome bacteria, this mutualistic endosymbiont has experienced a vast reduction of functional activity with a major exception compared to other parasites  still retain the bio-synthetic ability of production of amino acid needed by its host. [ 41 ] [ 42 ] [ 37 ]  The common effects of the genome shrinking between this endosymbiont and the other parasites are the reduction of the ability to produce phospholipids, repair and recombination and an overall conversion of the composition of the gene to a richer A-T [ 43 ]  content due to mutation and substitutions. [ 16 ] [ 41 ]  Evidence of the deletion of the function of repair and recombination is the loss of the gene  rec A, gene involved in the  recombinase  pathway. This event happened during the removal of a larger region containing ten genes for a total of almost 10 kb. [ 37 ] [ 41 ]  Same faith occurred  uvr A,  uvr B and  uvr C, genes encoding for excision enzymes involved in the repair of damaged DNA due to UV exposure. [ 35 ]


One of the most plausible mechanisms for the explanation of the genome shrinking is the chromosomal rearrangement because insertion/deletion of larger portion of sequence are more easily to be seen in during homologous recombination compared to the illegitimate, therefore the spread of the  transposable elements  will positively affect the rate of deletion. [ 32 ]  The loss of those genes in the early stages of miniaturization not only this function but must played a role in the evolution of the consequent deletions. Evidences of the fact that larger event of removal occurred before smaller deletion emerged from the comparison of the genome of  Bucknera  and a reconstructed ancestor, where the gene that have been lost are in fact not randomly dispersed in the ancestor gene but aggregated and the negative relation between number of lost genes and length of the spacers. [ 35 ]  The event of small local indels plays a marginal role on the genome reduction [ 44 ]  especially in the early stages where a larger number of genes became superfluous. [ 45 ] [ 35 ]


Single events instead occurred due to the lack of selection pressure for the retention of genes especially if part of a pathway that lost its function during a previous deletion. An example for this is the deletion of  rec F, gene required for the function of  rec A, and its flanking genes. [ 46 ]  One of the consequences of the elimination of such amount of sequences affected even the regulation of the remaining genes. The loss of large section of genomes could in fact lead to a loss in promotor sequences. This could in fact pushed the selection for the evolution of  polycistronic regions  with a positive effect for both size reduction [ 47 ]  and transcription efficiency. [ 48 ]


One example of the miniaturization of the genome occurred in the  microsporidia , an anaerobic intracellular parasite of arthropods evolved from aerobic fungi.


During this process the  mitosomes [ 49 ]  was formed consequent to the reduction of the mitochondria to a relic voided of genomes and metabolic activity except to the production of iron sulfur centers and the capacity to enter into the host cells. [ 50 ] [ 51 ]  Except for the  ribosomes , miniaturized as well, many other organelles have been almost lost during the process of the formation of the smallest genome found in the eukaryotes. [ 32 ]   From their possible ancestor, a  zygomycotine  fungi, the microsporidia shrunk its genome eliminating almost 1000 genes and reduced even the size of protein and protein-coding genes. [ 52 ]  This extreme process was possible thanks to the advantageous selection for a smaller cell size imposed by the parasitism.


Another example of miniaturization is represented by the presence of  nucleomorphs , enslaved nuclei, inside of the cell of two different algae,  cryptophytes  and  chlorarachneans . [ 53 ]