In soil antibiotic resistant bacterial communities, resistance-conferring genes have been found on mobile genetic elements. [ 4 ]  Similarly, in an analysis of the resistome in a water treatment plant,  plasmids  and other protein-coding  mobile genetic elements  were present at all levels of filtration, and these mobile elements harbored genes for resistance. [ 5 ]  These soil and water-based resistant communities are known as reservoirs, from which resistance can transfer to pathogenic bacteria. [ 12 ]  Metagenomic sequencing and short-read based assembly have revealed the exchange of antibiotic resistance genes between non-pathogenic environmental soil bacteria and clinical pathogens. [ 13 ]  The portions in the soil bacteria perfectly match the identity of several diverse human pathogens and contain resistance  cassettes  against five  classes of antibiotics . [ 13 ]  These resistance cassettes also contain sequences that reflect recent horizontal gene transfer and provide the mechanism for how that transfer occurred. [ 13 ]  These antibiotic resistant genes also retain their functionality even after they are entirely removed from the context of their original host, emphasizing their compatibility with a wide range of hosts, including pathogens. [ 13 ]  Interestingly, the high conservation of resistance gene identity also was observed in the human gut microbiome. [ 13 ]  Although the average amino acid similarity between human gut microbiota and resistant pathogens was only around 30.2 to 45.5%, their resistance genes perfectly matched those of the pathogenic bacteria, suggesting the resistomes of the human gastrointestinal tract, soil, and clinical pathogens are all connected. [ 13 ]  It should be noted, however, that the risk of transmission cannot simply be extrapolated from abundance of resistome genes in a population, and a multifaceted approach to risk analyses should be considered to fully understand the risks posed. [ 12 ]  For example, the mobility of antibiotic resistant genes has been observed to be dependent on if the population is pathogenic or not, with pathogen communities having far higher proportions of mobile genetic elements. [ 14 ]


When antibiotic resistance is present in the environment, it is important to consider how human pathogens are interacting or integrated into those environments, and how antibiotic resistance is being exchanged there. For example, mouth bacteria can reach other parts of the body through the digestive and blood systems, and our saliva readily transfers bacteria to other people, so there are several ways for antibiotic resistant bacteria in the oral micriobiome to readily transfer their resistance genes to other, potentially pathogenic bacterial communities. [ 10 ]   Additionally, soil and pathogenic resistomes have been observed not to be distinct, so it’s essential that we understand environmental resistance in aquatic and other environments with high likelihood of human pathogen interaction. [ 13 ]  In the hyper-antibiotic resistant  Pseudomonas aeruginosa  environmental stress key to the way its resistome is expressed; intrinsic, acquired, and adaptive forms of resistant gene expression occur under different environmental pressures and lead to significant challenges in developing effective treatments in response. [ 15 ]


Our understanding of how humans create additional positive selective pressure for antibiotic resistance in non-clinical environments is essential now more than ever. [ 4 ] [ 5 ]  The rise in antibiotic resistance has severely reduced the efficacy of antibiotic drugs, posing severe cause for concern in the realm of drug development and maintaining public health. [ 9 ]  Human-manufactured antibiotics are not the only source of antibiotic resistant pressure in the wild, as antibiotics are present at various concentrations and function as both defense and signaling mechanisms,  selecting  for antibiotic resistance naturally in the environment. [ 6 ]  For this reason, studying natural antibiotics and the patterns of antibiotic resistance that naturally arise in the wild may help us to predict and respond to antibiotic resistance in clinical settings. [ 6 ]  Analyses of metagenomic sequence data are useful tools for understanding how human impact influences the spread of resistance genes. [ 13 ]  An effect of the introduction of high levels of human-made antibiotics in the environment is the promotion of antibiotic resistance even in the absence of natural antibiotic production. [ 4 ]  Secondary stress conditions like heavy metal pollution cause higher HGT as a stress response, which also likely contributes to the dissemination of antibiotic resistant genes. [ 6 ]  Also, rapid increase in human populations without adequate wastewater treatment allows for more chance for human pathogens to be in contact with environmental resistance-carrying bacteria, [ 6 ]  so it's important to look to wastewater treatment as a source of HGT. [ 10 ]


The resistome also refers to an inherited set of genes used to resist infections. [ 3 ] [ 2 ]  This concept is also referred to as  innate immunity , and resistance genes within the resistome provide different functions for immune response, and are differentially transcribed. [ 3 ]  Interestingly, in one study of  Arabidopsis thaliana,  activated regions on  the chromosome for resistance against both bacteria and viruses are clustered together, likely meaning they are co-regulated. [ 3 ]


Comparing different mutations in the  germline  can be used to help define the size and position of the resistome, this set of genes conferring an inherited immune response. [ 2 ]  Because of mutation, the ‘universal resistome’, a set of resistance genes shared among all mice, similar to the concept of the pan-microbiome, [ 16 ]  is likely extremely small. [ 2 ]


Restriction landmark genomic scanning  ( RLGS ) is a  genome  analysis method for rapid simultaneous visualization of thousands of landmarks, or  restriction sites . Using a combination of  restriction enzymes  some of which are specific to  DNA modifications , the technique can be used to visualize differences in  methylation  levels across the  genome  of a given organism. [ 1 ]  RLGS employs direct labeling of  DNA , which is first cut by a specific series of restriction enzymes, and then labeled by a  radioactive   isotope  (usually  phosphorus-32 ). A  two-dimensional   electrophoresis  process is then employed, yielding high-resolution results. The radioactive second-dimension gel is then allowed to expose a large sheet of  film . The radiation produced by the radioactive labeling will cause the film to be exposed wherever the restriction fragments have migrated during electrophoresis. The film is then developed, yielding a visual representation of the results in the form of an  autoradiograph . The same combination of restriction enzymes will produce the same pattern of 'spots' from samples from the same organisms, but different patterns for different types of organism. For example, human and mouse DNA will produce distinctly different patterns when treated with the same combination of enzymes. These finished auto-rads can be examined against each other, revealing any changes in gene expression that lead to visual differences in the film. Each autoradiograph contains thousands of spots, each corresponding to a labeled DNA restriction landmark.


RLGS becomes useful when doing  whole-genome  scans, and can effectively do the work of thousands of  polymerase chain reactions  at once. It readily detects alterations deviating from normal, and thus is exceptionally effective in identifying  hyper/hypomethylation  in  tumors ,  deletions  or  amplifications  of genes, or simply changes in  gene expression  throughout the development of an organism.


Ribonomics  is the study of ribonucleic acids ( RNAs ) associated with  RNA-binding proteins  (RBPs). The term was introduced by  Robert Cedergren  and colleagues [ 1 ]  who used a bioinformatic search tool to discover novel  ribozymes  and RNA motifs originally found in  HIV .
Ribonomics, like  genomics  or  proteomics , is the large-scale, high-throughput approach to identifying subsets of RNAs by their association with  proteins  in cells.  Since many  messenger RNAs  (mRNAs) are linked with multiple processes, this technique offers a facile mechanism to study the relationship of various  intracellular  systems.
 Prokaryotes  co-regulate genes common to cellular processes via a  polycistronic operon .  Since eukaryotic  transcription  produces mRNA encoding proteins in a  monocistronic  fashion, many gene products must be concomitantly expressed (see  gene expression ) and  translated  in a timed fashion.  RBPs are thought [ by whom? ]  to be the molecules which physically and biochemically organize these messages to different cellular locales where they may be translated, degraded or stored. The study of transcripts associated with RBPs is therefore thought [ by whom? ]  to be important in  eukaryotes  as a mechanism for coordinated  gene regulation .  The likely biochemical processes which account for this regulation are the expedited/delayed degradation of RNA.  In addition to the influence on RNA half-life, translation rates are also thought to be altered by RNA-protein interactions.
The  Drosophila  ELAV family, [ 2 ]  the Puf family [ 3 ]  in  yeast , and the human La, Ro, [ 4 ]  and FMR [ 5 ]  proteins are known examples of RBPs, showing the diverse species and processes with which post-transcriptional gene regulation is associated.


Ridges  ( r egions of  i ncrease d   g ene  e xpression) are domains of the  genome  with a high  gene expression ; the opposite of ridges are  antiridges . The term was first used by Caron et al. in 2001. [ 1 ]  Characteristics of ridges are: [ 1 ]


Clustering of genes in  prokaryotes  was known for a long time. Their genes are grouped in  operons , genes within operons share a common promoter unit. These genes are mostly functionally related. The genome of prokaryotes is relatively very simple and compact. In  eukaryotes  the genome is huge and only a small amount of it are functionally genes, furthermore the genes are not arranged in operons. Except for  nematodes  and  trypanosomes ; although their operons are different from the prokaryotic operons. In eukaryotes each gene has a transcription regulation site of its own. Therefore, genes don't have to be in close proximity to be co-expressed. Therefore, it was long assumed that eukaryotic genes were randomly distributed across the genome due to the high rate of chromosome rearrangements. But because the complete sequence of genomes became available it became possible to absolutely locate a gene and measure its distance to other genes.


The first eukaryote genome ever sequenced was that of  Saccharomyces cerevisiae , or budding yeast, in 1996. Half a year after that Velculescu et al. (1997) published a research in which they had integrated  SAGE  data with the now available genome map. During a cell cycle different genes are active in a cell. Therefore, they used SAGE data from three moments of the cell cycle (log phase,  S phase -arrested and  G2 / M -phase arrested cells). Because in yeast all genes have a promoter unit of their own it was not suspected that genes would cluster near to each other but they did. Clusters were present on all 16 yeast chromosomes. [ 2 ]
A year later Cho et al. also reported (although in more detail) that certain genes are located near to each other in yeast. [ 3 ]


Cho et al. were the first who determined that clustered genes have the same expression levels. They identified transcripts that show cell-cycle dependent periodicity. Of those genes 25% was located in close proximity to other genes which were transcript in the same cell cycle. Cohen et al. (2000) also identified clusters of co-expressed genes.


Caron et al. (2001) made a human transcriptome map of 12 different tissues (cancer cells) and concluded that genes are not randomly distributed across the chromosomes. Instead, genes tend to cluster in groups of sometimes 39 genes in close proximity. Clusters were not only gene dense. They identified 27 clusters of genes with very high expression levels and called them RIDGEs. A common RIDGE counts 6 to 30 genes per centiray. However, there were great exceptions, 40 to 50% of the RIDGEs were not that gene dense; just like in yeast these RIDGEs were located in the  telomere  regions. [ 1 ]


Lercher et al. (2002) pointed to some weaknesses in Caron's approach. Clusters of genes in close proximity and high transcription levels can easily been generated by tandem duplicates. Genes can generate duplicates of themselves which are incorporated in their neighborhood. These duplicates can either became a functional part of the pathway of their parent gene, or (because they are no longer favored by natural selection) gain deleterious mutations and turn into pseudogenes. Because these duplicates are false positives in the search for gene clusters they have to be excluded. Lercher excluded neighboring genes with high resemblance to each other, after that he searched with a sliding window for regions with 15 neighboring genes. [ 4 ]


It was clear that gene dense regions existed. There was a striking correlation between gene density and a high CG content. Some clusters indeed had high expression levels. But most of the highly expressed regions consisted of housekeeping genes; genes that are highly expressed in all tissues because they code for basal mechanisms. Only a minority of the clusters contained genes that were restricted to specific tissues.


Versteeg et al. (2003) tried, with a better human genome map and better SAGE taqs, to determine the characteristics of RIDGEs more specific.  Overlapping genes  were treated as one gene, and genes without introns were rejected as pseudogenes. They determined that RIDGEs are very gene dense, have a high gene expression, short introns, high SINE repeat density and low LINE repeat density. Clusters containing genes with very low transcription levels had characteristics that were the opposite of RIDGEs, therefore those clusters were called antiridges. [ 5 ]  LINE repeats are junk DNA which contains a cleavage site of endonuclease (TTTTA). Their scarcity in RIDGEs can be explained by the fact that natural selection favors the scarcity of LINE repeats in ORFs because their endonuclease sites can cause deleterious mutation to the genes. Why SINE repeats are abundant is not yet understood.


Versteeg et al. also concluded that, contrary to Lerchers analysis, the transcription levels of many genes in RIDGEs (for example a cluster on chromosome 9) can vary strongly between different tissues. Lee et al. (2003) analyzed the trend of gene clustering between different species. They compared  Saccharomyces cerevisiae ,  Homo sapiens ,  Caenorhabditis elegans ,  Arabidopsis thaliana  and  Drosophila melanogaster , and found a degree of clustering, as fraction of genes in loose clusters, of respectively (37%), (50%), (74%), (52%) and (68%). They concluded that pathways of which the genes are clusters across many species are rare. They found seven universally clustered pathways:  glycolysis ,  aminoacyl-tRNA biosynthesis ,  ATP synthase ,  DNA polymerase ,  hexachlorocyclohexane degradation ,  cyanoamino acid metabolism , and  photosynthesis  ( ATP  synthesis in non plant species). Not surprisingly these are basic cellular pathways. [ 6 ]


Lee et al. used very diverse groups of animals. Within these groups clustering is conserved, for example the clustering motifs of Homo sapiens and Mus musculus are more or less the same. [ 7 ]


Spellman and Rubin (2002) made a transcriptome map of  Drosophila . Of all assayed genes 20% was clustered. Clusters consisted of 10 to 30 genes over a group size of about 100 kilobases. The members of the clusters were not functionally related and the location of clusters didn't correlate with know chromatin structures. [ 8 ]


This study also showed that within clusters the expression levels of on average 15 genes was much the same across the many experimental conditions which were used. These similarities were so striking that the authors reasoned that the genes in the clusters are not individually regulated by their personal promoter but that changes in the chromatin structure were involved. A similar co-regulation pattern was published in the same year by Roy et al. (2002) in C. elegans. [ 9 ]


Many genes which are grouped into clusters show the same expression profiles in human invasive ductal breast carcinomas. Roughly 20% of the genes show a correlation with their neighbors. Clusters of co-expressed genes were divided by regions with less correlation between genes. These clusters could cover an entire chromosome arm.


Contrary to previous discussed reports Johnidis et al. (2005) have discovered that (at least some) genes within clusters are not co-regulated. Aire is a transcription factor which has an up- and down-regulation effect on various genes. It functions in negative selection of thymocytes, which responds to the organisms own epitopes, by medullary cells. [ 10 ]


The genes that were controlled by aire clustered. 53 of the genes most activated by aire had an aire-activated neighbor within 200 Kb or less, and 32 of the genes most repressed by aire had an aire-repressed neighbor within 200 Kb; this is less than expected by change. They did the same screening for the transcriptional regulator CIITA.


These transcription regulators didn't have the same effect on al genes in the same cluster. Genes that were activated and repressed or unaffected were sometimes present in the same cluster. In this case, it's impossible that aire-regulated genes were clustered because they were all co-regulated.


So it is not very clear if domains are co-regulated or not. A very effective way to test this would be by insert synthetic genes into RIDGEs, antiridges and/or random places in the genome and determine their expression. Those expression levels must be compared to each other. Gierman et al. (2007) were the first who proved co-regulation using this approach. As an insertion construct they used a fluorescing  GFP  gene driven by the ubiquitously expressed human phosphoglycerate kinase (PGK) promoter. They integrated this construct in 90 different positions in the genome of human  HEK293 cells . They found that the expression of the construct in Ridges was indeed higher than those inserted in antiridges (while all constructs have the same promoter). [ 11 ]


They investigated if these differences in expressions were due to genes in the direct neighborhood of the constructs or by the domain as a whole. They found that constructs next to highly expressed genes were slightly more expressed than others. But when to enlarged the window size to the surrounding 49 genes (domain level) they saw that constructs located in domains with an overall high expression had a more than 2-fold higher expression then those located in domains with a low expression level.


They also checked if the construct was expressed at similar levels as neighboring genes, and if that tight co-expression was present solely within RIDGEs. They found that the expressions were highly correlated within RIDGEs, and almost absent near the end and outside the RIDGEs.


Previous observations and the research of Gierman et al. proved that the activity of a domain has great impact on the expression of the genes located in it. And the genes within a RIDGE are co-expressed. However the constructs used by Gierman et al. were regulated by al full-time active promoter. The genes of the research of Johnidis et al. were dependent of the present of the aire transcription factor. The strange expression of the aire regulated genes could partly have been caused by differences in expression and conformation of the aire transcription factor itself.


It was known before the genomic era that clustered genes tend to be functionally related. Abderrahim et al. (1994) had shown that all the genes of the major histocompatibility complex were clustered on the 6p21 chromosome. Roy et al. (2002) showed that in the nematode  C. elegans  genes that are solely expressed in muscle tissue during the larval stage tend to cluster in small groups of 2–5 genes. They identified 13 clusters.


Yamashita et al. (2004) showed that genes related to specific functions in organs tend to cluster. Six liver related domains contained genes for xenobiotic, lipid and alcohol metabolism. Five colon-related domains had genes for apoptosis, cell proliferation, ion transporter and mucin production. These clusters were very small and expression levels were low. Brain and breast related genes didn't cluster. [ 12 ]


This shows that at least some clusters consist of functionally related genes. However, there are great exceptions. Spellman and Rubin have shown that there are clusters of co-expressed genes that are not functionally related. It seems like that clusters appear in very different forms.


Cohen et al. found that of a pair of co-expressed genes only one promoter has an  Upstream Activating Sequence  (UAS) associated with that expression pattern. They suggested that UASs can activate genes that are not in immediate adjacency to them. This explanation could explain the co-expression of small clusters, but many clusters contain to many genes to be regulated by a single UAS.


Chromatin  changes are a plausible explanation for the co-regulation seen in clusters. Chromatin consists of the DNA strand and  histones  that are attached to the DNA. Regions were chromatin is very tightly packed are called heterochromatin. Heterochromatin consists very often of remains of viral genomes,  transposons  and other junk DNA. Because of tight packing the DNA is almost unreachable for the transcript machinery, covering deleterious DNA with proteins is the way in which the cell can protect itself. Chromatin which consists of functional genes is often an open structure were the DNA is accessible. However, most of the genes are not needed to be expressed all the time.


DNA with genes that aren't needed can be covered with histones. When a gene must be expressed special proteins can alter the chemical that are attached to the histones (histone modifications) that cause the histones to open the structure. When the chromatin of one gene is opened, the chromatin of the adjacent genes is also until this modification meets a boundary element. In that way genes is close proximity are expressed on the same time. So, genes are clustered in “expression hubs”. In comparison with this model Gilbert et al. (2004) showed that RIDGEs are mostly present in open chromatin structures. [ 13 ] [ 14 ]


However Johnidis et al. (2005) have shown that genes in the same cluster can be very differently expressed. How eukaryotic gene regulation, and associated chromatin changes, precisely works is still very unclear and there is no consensus about it. In order to get a clear picture about the mechanism of gene clusters first the workings chromatin and gene regulation needs to be illuminated.
Furthermore, most papers that identified clusters of co-regulated genes focused on transcription levels whereas few focused on clusters regulated by the same transcription-factors. Johnides et al. discovered strange phenomena when they did.


The first models which tried to explain the clustering of genes were, of course, focused on operons because they were discovered before eukaryote gene clusters were. In 1999 Lawrence proposed a model for the origin operons. This  selfish operon model  suggests that individual genes were grouped together by vertical en horizontal transfer and were preserved as a single unit because that was beneficial for the genes, not per se for the organism. This model predicts that the gene clusters must have conserved between species. This is not the case for many operons and gene clusters seen in eukaryotes. [ 15 ]


According to Eichler and Sankoff the two mean processes in eukaryotic chromosome evolution are 1) rearrangements of chromosomal segments and 2) localized duplication of genes. Clustering could be explained by reasoning that all genes in a cluster are originated from tandem duplicates of a common ancestor. If all co-expressed genes in a cluster were evolved from a common ancestral gene it would have been expected that they're co-expressed because they all have comparable promoters. However, gene clustering is a very common tread in genomes and it isn't clear how this duplication model could explain all of the clustering. Furthermore, many genes that are present in clusters are not homologous.


How did evolutionary non-related genes come in close proximity in the first place? Either there is a force that brings functionally related genes near to each other, or the genes came near by change. Singer et al. proposed that genes came in close proximity by random recombination of genome segments. When functionally related genes came in close proximity to each other, this proximity was conserved. They determined all possible recombination sites between genes of human and mouse. After that, they compared the clustering of the mouse and human genome and looked if recombination had occurred at the potentially recombination sites. It turned out that recombination between genes of the same cluster was very rare. So, as soon as a functional cluster is formed recombination is suppressed by the cell. On sex chromosomes, the amount of clusters is very low in both human and mouse. The authors reasoned this was due to the low rate of chromosomal rearrangements of sex chromosomes.


Open chromatin regions are active regions. It is more likely that genes will be transferred to these regions. Genes from organelle and virus genome are inserted more often in these regions. In this way non-homologous genes can be pressed together in a small domain. [ 16 ]


It is possible that some regions in the genome are better suited for important genes. It is important for the cell that genes that are responsible for basal functions are protected from recombination. It has been observed in yeast and worms that essential genes tend to cluster in regions with a small replication rate. [ 17 ]


It is possible that genes came in close proximity by change. Other models have been proposed but none of them can explain all observed phenomena. It's clear that as soon as clusters are formed they are conserved by natural selection. However, a precise model of how genes came in close proximity is still lacking.


The bulk of the present clusters must have formed relatively recent because only seven clusters of functionally related genes are conserved between phyla. Some of these differences can be explained by the fact that gene expression is very differently regulated by different phyla. For example, in vertebrates and plants DNA methylation is used, whereas it is absent in yeast and flies. [ 18 ]


The Scripps Genomic Health Initiative  (SGHI) is a large scale study aimed at understanding how personal  genetic testing  influences and improves health. [ 1 ]


Led by Dr.  Eric Topol , director of the San Diego–based Scripps Translational Science Institute, the 20-year initiative will determine whether patients make an effort to improve their lifestyle and get regular checkups after learning their genetic predisposition for many common diseases. Researchers will also assess the psychological impact of genomic testing, and whether those who do it are able to prevent or delay disease by taking action after getting their results. [ 2 ]


The study was launched in October 2008 and will follow more than 10,000 adults. A consortium of health care, technology and research leaders have joined forces in the first-of-its-kind research study, including genetic test provider  Navigenics  Inc.,  Affymetrix  and  Microsoft  Corp. [ 3 ]


Study participants receive a scan of their genome and a detailed analysis of their genetic risk for more than 20 health conditions that may be changed by lifestyle, including type 2 diabetes, Alzheimer's disease, heart attack, obesity, and several types of cancer. [ 4 ]


Said Peter Neupert, corporate VP for the Health Solutions Group at Microsoft:


"Personalized medicine stands to change the way people approach their health and wellness, as well as open up new genetic research opportunities." [ 5 ]


Sequence graph , also called an  alignment graph ,  breakpoint graph , or  adjacency graph,  are bidirected graphs used in  comparative genomics . The structure consists of multiple graphs or  genomes  with a series of edges and vertices represented as  adjacencies  between segments in a  genome [ 1 ]  and  DNA  segments respectively. Traversing a connected component of segments and adjacency edges (called a  thread ) yields a sequence, which typically represents a genome or a section of a genome. The segments can be thought of as  synteny  blocks, with the edges dictating how to arrange these blocks in a particular genome, and the labelling of the adjacency edges representing bases that are not contained in synteny blocks.


Before constructing a sequence graph, there must be at least two genomes represented as directed graphs with edges as threads (adjacency edges) and vertices as DNA segments. The genomes should be labeled  P  and  Q,  while the sequence graph is labeled as BreakpointGraph( P, Q ). [ 2 ]


The directional vertices of Q and their edges are arranged in the order of P. Once completed, the edges of Q are reconnected to their original vertices. After all edges have been matched the vertex directions are removed and instead each vertex is labeled as v h  (vertex head) and v t  (vertex tail).


Similarity between genomes is represented by the number of cycles (independent systems) within the sequence graph. The number of cycles is equal to cycles (P, Q). The max number of cycles possible is equal to the number of vertices in the sequence graph.


Figure example.


Upon receiving genomes P (+a +b -c) and Q (+a +b -c), [ 1 ]  Q should be realigned to follow the direction edges (red) of P. The vertices should be renamed from a, b, c to a h  a t , b h  b t , c h  c t  and the edges of P and Q should be connected to their original vertices (P edges = black, Q edges = green). Remove the directional edges (red). The number of cycles in G(P, Q) is 1 while the max possible is 3.


Alekseyev and Pevzner use sequence graphs to create their own algorithm to study the genome rearrangement history of several mammals, as well as a way to overcome problems with current ancestral reconstruction of genomes. [ 1 ]


Sequence graphs can be used to represent  multiple sequence alignments  with the addition of a new kind of edge representing  homology  between segments. [ 3 ]  For a set of genomes, one can create an acyclic breakpoint graph with a thread for each genome. For two segments

 (
 a
 ,
 b
 )

 {\displaystyle (a,b)}

  and

 (
 c
 ,
 d
 )

 {\displaystyle (c,d)}

 , where

 a

 {\displaystyle a}

 ,

 b

 {\displaystyle b}

 ,

 c

 {\displaystyle c}

 , and

 d

 {\displaystyle d}

  represent the endpoints of the two segments, homology edges can be created from

 a

 {\displaystyle a}

  to

 c

 {\displaystyle c}

  and

 b

 {\displaystyle b}

  to

 d

 {\displaystyle d}

  or from

 a

 {\displaystyle a}

  to

 d

 {\displaystyle d}

  and

 b

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  to

 c

 {\displaystyle c}

  - representing the two possible orientations of the homology. The advantage of representing a multiple sequence alignment this way is that it is possible to include inversions and other structural rearrangements that wouldn't be allowable in a matrix representation.


If there are multiple possible paths when traversing a thread in a sequence graph, multiple sequences can be represented by the same thread. This means it is possible to create a sequence graph that represents a population of individuals with slightly different genomes - with each genome corresponding to one path through the graph. These graphs have been proposed as a replacement for the  reference   human genome . [ 4 ]


A  sequence related amplified polymorphism  ( SRAP ) is a  molecular technique , developed by G. Li and C. F. Quiros in 2001, [ 1 ]  for detecting  genetic  variation in the  open reading frames  (ORFs) of  genomes  of  plants  and related  organisms . [ 2 ]


A  sequence-tagged site  (or  STS ) is a short (200 to 500 base pair)  DNA  sequence that has a single occurrence in the  genome  and whose location and base sequence are known. [ 1 ]


STSs can be easily detected by the  polymerase chain reaction  (PCR) using specific  primers . For this reason they are useful for constructing  genetic and physical maps  from sequence data reported from many different laboratories. They serve as landmarks on the developing  physical map  of a genome.


When STS  loci  contain genetic polymorphisms (e.g. simple sequence length polymorphisms,  SSLPs , single nucleotide polymorphisms), they become valuable genetic markers, i.e. loci which can be used to distinguish individuals.


They are used in  shotgun sequencing , specifically to aid  sequence assembly .


STSs are very helpful for detecting microdeletions in some genes. For example, some STSs can be used in screening by  PCR  to detect microdeletions in  Azoospermia  (AZF)  genes  in  infertile  men.


Single cell epigenomics  is the study of  epigenomics  (the complete set of  epigenetic  modifications on the  genetic material  of a cell) in individual cells by  single cell sequencing . [ 2 ] [ 1 ] [ 3 ]  Since 2013, methods have been created including whole-genome single-cell  bisulfite sequencing  to measure  DNA methylation , whole-genome  ChIP-sequencing  to measure  histone  modifications, whole-genome  ATAC-seq  to measure  chromatin  accessibility and  chromosome conformation capture .


Single cell DNA genome sequencing quantifies  DNA methylation . This is similar to single cell genome sequencing, but with the addition of a  bisulfite  treatment before sequencing. Forms include whole genome bisulfite sequencing, [ 4 ] [ 5 ]  and  reduced representation bisulfite sequencing [ 6 ] [ 7 ]


ATAC-seq stands for Assay for Transposase-Accessible Chromatin with high throughput sequencing. [ 9 ]  It is a technique used in molecular biology to  identify accessible DNA regions, equivalent to DNase I hypersensitive sites. [ 9 ]  Single cell ATAC-seq has been performed since 2015, using methods ranging from  FACS  sorting,  microfluidic  isolation of single cells, to combinatorial indexing. [ 8 ]  In initial studies, the method was able to reliably separate cells based on their cell types, uncover sources of cell-to-cell variability, and show a link between chromatin organization and cell-to-cell variation. [ 8 ]


ChIP-sequencing, also known as ChIP-seq, is a method used to analyze  protein  interactions with  DNA . [ 9 ]  ChIP-seq combines  chromatin immunoprecipitation  (ChIP) with massively parallel  DNA sequencing  to identify the  binding sites  of DNA-associated proteins. [ 9 ]  In epigenomics, this is often used to assess  histone  modifications (such as  methylation ). [ 9 ]  ChIP-seq is also often used to determine  transcription factor   binding sites . [ 9 ]


Single-cell ChIP-seq is extremely challenging due to background noise caused by nonspecific antibody pull-down, [ 1 ]  and only one study so far has performed it successfully. This study used a droplet-based microfluidics approach, and the low coverage required thousands of cells to be sequenced in order to assess cellular heterogeneity. [ 10 ] [ 1 ]


Chromosome conformation capture techniques  (often abbreviated to 3C technologies or 3C-based methods [ 11 ] ) are a set of molecular biology methods used to analyze the spatial organization of chromatin in a cell. These methods quantify the number of interactions between genomic loci that are nearby in three dimensional space, even if the loci are separated by many kilobases [ 12 ]  in the linear genome.


Currently, 3C methods start with a similar set of steps, performed on a sample of cells. [ 11 ]  First, the cells are  cross-linked , which introduces bonds between proteins, and between proteins and nucleic acids, [ 12 ]  that effectively "freeze" interactions between genomic loci. [ 11 ]  The genome is then cut digested into fragments through the use of  restriction enzymes . Next, proximity based  ligation  is performed, creating long regions of hybrid DNA. [ 11 ]  Lastly, the hybrid DNA is sequenced to determine genomic loci that are in close proximity to each other. [ 11 ]


Single-cell Hi-C is a modification of the original  Hi-C  protocol, which is an adaptation of the 3C method, that allows you to determine proximity of different regions of the genome in a single cell. [ 13 ]  This method was made possible by performing the digestion and ligation steps in individual nuclei, [ 13 ]  as opposed to the original Hi-C protocol, where ligation was performed after cell lysis in a pool containing crosslinked chromatin complexes. [ 14 ]  In single cell Hi-C, after ligation, single cells are isolated and the remaining steps are performed in separate compartments, [ 13 ] [ 15 ]  and hybrid DNA is tagged with a compartment specific barcode.  High-throughput sequencing  is then performed on the pool of the hybrid DNA from the single cells. Although the recovery rate of sequenced interactions (hybrid DNA) can be as low as 2.5% of potential interactions, [ 16 ]  it has been possible to generate three dimensional maps of entire genomes using this method. [ 17 ] [ 18 ]  Additionally, advances have been made in the analysis of Hi-C data,  allowing for the enhancement of HiC datasets to generate even more accurate and detailed contact maps and 3D models. [ 15 ]


Single-molecule real-time  ( SMRT )  sequencing  is a parallelized single molecule  DNA sequencing  method. Single-molecule real-time sequencing utilizes a  zero-mode waveguide  (ZMW). [ 1 ]  A single  DNA polymerase  enzyme is affixed at the bottom of a ZMW with a single molecule of DNA as a template. The ZMW is a structure that creates an illuminated observation volume that is small enough to observe only a single  nucleotide  of DNA being incorporated by  DNA polymerase . Each of the four DNA bases is attached to one of four different fluorescent dyes. When a nucleotide is incorporated by the DNA polymerase, the fluorescent tag is cleaved off and diffuses out of the observation area of the ZMW where its fluorescence is no longer observable. A detector detects the fluorescent signal of the nucleotide incorporation, and the base call is made according to the corresponding fluorescence of the dye. [ 2 ]


The DNA sequencing is done on a chip that contains many ZMWs. Inside each ZMW, a single active DNA polymerase with a single molecule of single stranded DNA template is immobilized to the bottom through which light can penetrate and create a visualization chamber that allows monitoring of the activity of the DNA polymerase at a single molecule level. The signal from a phospho-linked nucleotide incorporated by the DNA polymerase is detected as the DNA synthesis proceeds which results in the DNA sequencing in real time.


To prepare the library, DNA fragments are put into a circular form using hairpin adapter ligations. [ 3 ]


For each of the nucleotide bases, there is a corresponding fluorescent dye molecule that enables the detector to identify the base being incorporated by the DNA polymerase as it performs the  DNA synthesis . The fluorescent dye molecule is attached to the phosphate chain of the nucleotide. When the nucleotide is incorporated by the DNA polymerase, the fluorescent dye is cleaved off with the phosphate chain as a part of a natural  DNA synthesis  process during which a  phosphodiester bond  is created to elongate the DNA chain. The cleaved fluorescent dye molecule then diffuses out of the detection volume so that the fluorescent signal is no longer detected. [ 4 ]


The zero-mode waveguide (ZMW) is a  nanophotonic  confinement structure that consists of a circular hole in an aluminum cladding film deposited on a clear silica substrate. [ 5 ]


The ZMW holes are ~70 nm in diameter and ~100 nm in depth. Due to the behavior of light when it travels through a small aperture, the optical field decays exponentially inside the chamber. [ 6 ] [ 7 ]


The observation volume within an illuminated ZMW is ~20 zeptoliters (20 X 10 −21  liters). Within this volume, the activity of DNA polymerase incorporating a single nucleotide can be readily detected. [ 4 ] [ 8 ]


Sequencing performance can be measured in read length, accuracy, and total throughput per experiment. PacBio sequencing systems using ZMWs have the advantage of long read lengths, although error rates are on the order of 5-15% and sample throughput is lower than  Illumina  sequencing platforms. [ 9 ]


On 19 Sep 2018,  Pacific Biosciences  [PacBio] released the Sequel 6.0 chemistry, synchronizing the chemistry version with the software version. Performance is contrasted for large-insert libraries with high molecular weight DNA versus shorter-insert libraries below ~15,000 bases in length. For larger templates average read lengths are up to 30,000 bases. For shorter-insert libraries, average read length are up to 100,000 bases while reading the same molecule in a circle several times. The latter shorter-insert libraries then yield up to 50 billion bases from a single SMRT Cell. [ 10 ]


Pacific Biosciences  (PacBio) commercialized SMRT sequencing in 2011, [ 11 ]  after releasing a beta version of its RS instrument in late 2010. [ 12 ]


At commercialization, read length had a normal distribution with a mean of about 1100 bases. A new chemistry kit released in early 2012 increased the sequencer's read length; an early customer of the chemistry cited mean read lengths of 2500 to 2900 bases. [ 13 ]


The XL chemistry kit released in late 2012 increased average read length to more than 4300 bases. [ 14 ] [ 15 ]


On August 21, 2013, PacBio released a new DNA polymerase Binding Kit P4. This P4 enzyme has average read lengths of more than 4,300 bases when paired with the C2 sequencing chemistry and more than 5,000 bases when paired with the XL chemistry. [ 16 ]  The enzyme’s accuracy is similar to C2, reaching QV50 between 30X and 40X coverage. The resulting P4 attributes provided higher-quality assemblies using fewer SMRT Cells and with improved variant calling. [ 16 ]  When coupled with input DNA size selection (using an electrophoresis instrument such as BluePippin) yields average read length over 7 kilobases. [ 17 ]


On October 3, 2013, PacBio released new reagent combination for PacBio RS II, the P5 DNA polymerase with C3 chemistry (P5-C3). Together, they extend sequencing read lengths to an average of approximately 8,500 bases, with the longest reads exceeding 30,000 bases. [ 18 ]  Throughput per SMRT cell is around 500 million bases demonstrated by sequencing results from the CHM1 cell line. [ 19 ]


On October 15, 2014, PacBio announced the release of new chemistry P6-C4 for the RS II system, which represents the company's 6th generation of polymerase and 4th generation chemistry--further extending the average read length to 10,000 - 15,000 bases, with the longest reads exceeding 40,000 bases. The throughput with the new chemistry was estimated between 500 million to 1 billion bases per SMRT Cell, depending on the sample being sequenced. [ 20 ] [ 21 ]  This was the final version of chemistry released for the RS instrument.


Throughput per experiment for the technology is both influenced by the read length of DNA molecules sequenced as well as total multiplex of a SMRT Cell. The prototype of the SMRT Cell contained about 3000 ZMW holes that allowed parallelized DNA sequencing. At commercialization, the SMRT Cells were each patterned with 150,000 ZMW holes that were read in two sets of 75,000. [ 22 ]  In April 2013, the company released a new version of the sequencer called the "PacBio RS II" that uses all 150,000 ZMW holes concurrently, doubling the throughput per experiment. [ 23 ] [ 24 ]  The highest throughput mode in November 2013 used P5 binding, C3 chemistry, BluePippin size selection, and a PacBio RS II officially yielded 350 million bases per SMRT Cell though a human  de novo  data set released with the chemistry averaging 500 million bases per SMRT Cell. Throughput varies based on the type of sample being sequenced. [ 25 ]  With the introduction of P6-C4 chemistry typical throughput per SMRT Cell increased to 500 million bases to 1 billion bases.


In September 2015, the company announced the launch of a new sequencing instrument, the Sequel System, that increased capacity to 1 million ZMW holes. [ 26 ] [ 27 ]


With the Sequel instrument initial read lengths were comparable to the RS, then later chemistry releases increased read length.


On January 23, 2017, the V2 chemistry was released. It increased average read lengths to between 10,000 and 18,000 bases. [ 28 ]


On March 8, 2018, the 2.1 chemistry was released. It increased average read length to 20,000 bases and half of all reads above 30,000 bases in length. Yield per SMRT Cell increased to 10 or 20 billion bases, for either large-insert libraries or shorter-insert (e.g.  amplicon ) libraries respectively. [ 29 ]


On 19 September 2018, the company announced the Sequel 6.0 chemistry with average read lengths increased to 100,000 bases for shorter-insert libraries and 30,000 for longer-insert libraries. SMRT Cell yield increased up to 50 billion bases for shorter-insert libraries. [ 10 ]


In April 2019 the company released a new SMRT Cell with eight million ZMWs, [ 30 ]  increasing the expected throughput per SMRT Cell by a factor of eight. [ 31 ]  Early access customers in March 2019 reported throughput over 58 customer run cells of 250 GB of raw yield per cell with templates about 15 kb in length, and 67.4 GB yield per cell with templates in higher weight molecules. [ 32 ]  System performance is now reported in either high-molecular-weight continuous long reads or in pre-corrected HiFi (also known as Circular Consensus Sequence (CCS)) reads. For high-molecular-weight reads roughly half of all reads are longer than 50 kb in length.


The HiFi performance includes corrected bases with quality above Phred score Q20, using repeated amplicon passes for correction. These take amplicons up to 20kb in length.


Single-molecule real-time sequencing may be applicable for a broad range of genomics research.


For  de novo  genome sequencing, read lengths from the single-molecule real-time sequencing are comparable to or greater than that from the Sanger sequencing method based on  dideoxynucleotide  chain termination. The longer read length allows  de novo  genome sequencing and easier genome assemblies. [ 2 ] [ 33 ] [ 34 ]  Scientists are also using single-molecule real-time sequencing in hybrid assemblies for de novo genomes to combine short-read sequence data with long-read sequence data. [ 35 ] [ 36 ]  In 2012, several peer-reviewed publications were released demonstrating the automated finishing of bacterial genomes, [ 37 ] [ 38 ]  including one paper that updated the Celera Assembler with a pipeline for genome finishing using long SMRT sequencing reads. [ 39 ]  In 2013, scientists estimated that long-read sequencing could be used to fully assemble and finish the majority of bacterial and archaeal genomes. [ 40 ]


The same DNA molecule can be resequenced independently by creating the circular DNA template and utilizing a strand displacing enzyme that separates the newly synthesized DNA strand from the template. [ 41 ]  In August 2012, scientists from the Broad Institute published an evaluation of SMRT sequencing for SNP calling. [ 42 ]


The dynamics of polymerase can indicate whether a base is  methylated . [ 43 ]  Scientists demonstrated the use of single-molecule real-time sequencing for detecting methylation and other base modifications. [ 44 ] [ 45 ] [ 46 ]  In 2012 a team of scientists used SMRT sequencing to generate the full methylomes of six bacteria. [ 47 ]  In November 2012, scientists published a report on genome-wide methylation of an outbreak strain of E. coli. [ 48 ]


Long reads make it possible to sequence full gene isoforms, including the 5' and 3' ends. This type of sequencing is useful to capture isoforms and splice variants. [ 49 ] [ 50 ]


SMRT sequencing has several applications in reproductive medical genetics research when investigating families with suspected parental gonadal mosaicism. Long reads enable haplotype phasing in patients to investigate parent-of-origin of mutations. Deep sequencing enables determination of allele frequencies in sperm cells, of relevance for estimation of recurrence risk for future affected offspring. [ 51 ] [ 52 ]


Within  genetics ,  post-genomic  research has rendered bacterial small  non-coding RNAs  (sRNAs) as major players in post-transcriptional regulation of gene expression in response to environmental stimuli. [ 1 ]  The  Alphaproteobacteria  includes  Gram-negative   microorganisms  with diverse life styles; frequently involving long-term interactions with higher  eukaryotes . [ 2 ]


Sinorhizobium meliloti  is an  agronomically  relevant α-proteobacterium able to induce the formation of new specialized organs, the so-called  nodules , in the roots of its cognate  legume  hosts (i.e. some  Medicago  species). [ 3 ]  Within the nodule cells bacteria undergo a  morphology  differentiation to  bacteroid , their  endosymbiotic  nitrogen-fixing competent form. [ 4 ]   Rhizobial  adaptations to soil and plant cell environments require the coordinate expression of complex gene networks in which sRNAs are expected to participate.


Two complementary computational screens,  eQRNA and RNAz , were used to search for novel  sRNA -encoding genes in the intergenic regions   IGRs  of  S. meliloti . Verification of eQRNA/RNAz predictions by  Northern hybridization  and  RACE mapping  led to the identification of eight previously unknown genes, with recognizable promoter and termination signatures, expressing small transcripts. These new genomic  loci  were referred to as  smr , for  S. meliloti  RNA. Seven of the Smr transcripts, which conservation is restricted to phylogenetically related Alphaproteobacteria, accumulated differentially in free-living and endosymbiotic bacteria. These findings anticipate a function for these sRNAs as  trans -acting antisense riboregulators of Alphaproteobacteria-eukaryote interactions. [ 5 ]


Single nucleotide polymorphism annotation  ( SNP  annotation) is the process of predicting the effect or function of an individual SNP using SNP annotation tools. In SNP  annotation  the biological  information  is extracted, collected and displayed in a clear form amenable to query. SNP functional annotation is typically performed based on the available information on  nucleic acid  and  protein sequences . [ 1 ]


Single nucleotide polymorphisms (SNPs) play an important role in  genome wide association  studies because they act as primary  biomarkers . SNPs are currently the marker of choice due to their large numbers in virtually all  populations  of individuals. The location of these biomarkers can be tremendously important in terms of predicting functional significance,  genetic mapping  and  population genetics . [ 3 ]  Each SNP represents a nucleotide change between two individuals at a defined location. SNPs are the most common genetic variant found in all individual with one SNP every 100–300  bp  in some  species . [ 4 ]  Since there is a massive number of SNPs on the  genome , there is a clear need to prioritize SNPs according to their potential effect in order to expedite genotyping and analysis.
 [ 5 ]


Annotating large numbers of SNPs is a difficult and complex process, which need computational methods to handle such a large dataset. Many tools available have been developed for SNP annotation in different organisms: some of them are optimized for use with  organisms  densely sampled for SNPs (such as  humans ), but there are currently few tools available that are species non-specific or support non-model organism data. The majority of SNP annotation tools provide computationally predicted putative deleterious effects of SNPs. These tools examine whether a SNP resides in functional genomic regions such as exons, splice sites, or transcription regulatory sites, and predict the potential corresponding functional effects that the SNP may have using a variety of machine-learning approaches.  But the tools and systems that prioritize functionally significant SNPs, suffer from few limitations: First, they examine the putative deleterious effects of SNPs with respect to a single biological function that provide only partial information about the functional significance of SNPs. Second, current systems classify SNPs into deleterious or neutral group. [ 6 ]


Many annotation algorithms focus on single nucleotide variants (SNVs), considered more rare than SNPs as defined by their minor allele frequency (MAF). [ 7 ] [ 8 ]   As a consequence, training data for the corresponding prediction methods may be different and hence one should be careful to select the appropriate tool for a specific purpose.  For the purposes of this article, "SNP" will be used to mean both SNP and SNV, but readers should bear in mind the differences.


For SNP annotation, many kinds of genetic and genomic information are used. Based on the different features used by each annotation tool, SNP annotation methods may be split roughly into the following categories:


Genomic information from surrounding genomic elements is among the most useful information for interpreting the biological function of an observed variant. Information from a known  gene  is used as a reference to indicate whether the observed variant resides in or near a gene and if it has the potential to disrupt the  protein sequence  and its function. Gene based annotation is based on the fact that non-synonymous  mutations  can alter the protein sequence and that   splice site mutation  may disrupt the transcript splicing pattern. [ 9 ]


Knowledge base annotation is done based on the information of gene attribute, protein function and its  metabolism . In this type of annotation more emphasis is given to  genetic variation  that disrupts the protein function domain,  protein-protein interaction  and  biological pathway . The non-coding region of genome contain many important regulatory elements including  promoter ,  enhancer  and insulator, any kind of change in this  regulatory region  can change the functionality of that protein. [ 10 ]  The mutation in DNA can change the  RNA sequence  and then influence the  RNA secondary structure , RNA binding protein recognition and miRNA binding activity. [ 11 ] [ 12 ]


This method mainly identifies variant function based on the information whether the variant loci are in the known functional region that harbor genomic or epigenomic signals. The function of non-coding variants are extensive in terms of the affected genomic region and they involve in almost all processes of gene regulation from transcriptional to post translational level  [ 13 ]


Transcriptional gene regulation process depends on many spatial and temporal factors in the nucleus such as global or local chromatin states, nucleosome positioning, TF binding, enhancer/promoter activities. Variant that alter the function of any of these biological processes may alter the gene regulation and cause phenotypic abnormality. [ 14 ]  Genetic variants that located in distal regulatory region can affect the binding motif of TFs, chromatin regulators and other distal transcriptional factors, which disturb the interaction between enhancer/silencer and its target gene. [ 15 ]


Alternative splicing  is one of the most important components that show functional complexity of genome. Modified splicing has significant effect on the phenotype that is relevance to disease or drug metabolism. A change in splicing can be caused by modifying any of the components of the splicing machinery such as splice sites or splice enhancers or silencers. [ 16 ]  Modification in the alternative splicing site can lead to a different protein form which will show a different function. Humans use an estimated 100,000 different proteins or more, so some genes must be capable of coding for a lot more than just one protein. Alternative splicing occurs more frequently than was previously thought and can be hard to control; genes may produce tens of thousands of different transcripts, necessitating a new gene model for each alternative splice.


Mutations in the untranslated region (UTR) affect many  post-transcriptional regulation . Distinctive structural features are required for many RNA molecules and cis-acting regulatory elements to execute effective functions during gene regulation. SNVs can alter the secondary structure of RNA molecules and then disrupt the proper folding of RNAs, such as tRNA/mRNA/lncRNA folding and miRNA binding recognition regions. [ 17 ]


Single nucleotide variant can also affect the cis-acting regulatory elements in mRNA’s to inhibit/promote the translation initiation. Change in the synonymous codons region due to mutation may affect the translation efficiency because of codon usage biases. The translation elongation can also be retarded by mutations along the ramp of ribosomal movement. In the post-translational level, genetic variants can contribute to proteostasis and amino acid modifications. However, mechanisms of variant effect in this field are complicated and there are only a few tools available to predict variant’s effect on translation related modifications. [ 18 ]


Non-synonymous is the variant in exons that change the amino acid sequence encoded by the gene, including single base changes and non frameshift indels. It has been extremely investigated the function of non-synonymous variants on protein and many algorithms have been developed to predict the deleteriousness and pathogenesis of single nucleotide variants (SNVs). Classical bioinformatics tools, such as SIFT, Polyphen and  MutationTaster , successfully predict the functional consequence of non-synonymous substitution. [ 19 ] [ 20 ] [ 21 ] [ 22 ]  PopViz webserver provides a gene-centric approach to visualize the mutation damage prediction scores (CADD, SIFT, PolyPhen-2) or the population genetics (minor allele frequency) versus the amino acid positions of all coding variants of a certain human gene. [ 23 ]  PopViz is also cross-linked with UniProt database, where the protein domain information can be found, and to then identify the predicted deleterious variants fall into these protein domains on the PopViz plot. [ 23 ]


Comparative genomics  approaches were used to predict the function-relevant variants under the assumption that the functional genetic locus should be conserved across different species at an extensive phylogenetic distance. On the other hand, some adaptive traits and the population differences are driven by positive selections of advantageous variants, and these genetic mutations are functionally relevant to population specific phenotypes. Functional prediction of variants’ effect in different biological processes is pivotal to pinpoint the molecular mechanism of diseases/traits and direct the experimental validation. [ 24 ]


To annotate the vast amounts of available NGS data, currently a large number of SNPs annotation tools are available. Some of them are specific to specific SNPs while others are more general. Some of the available SNPs annotation tools are as follows SNPeff, Ensembl Variant Effect Predictor (VEP), ANNOVAR, FATHMM, PhD-SNP, PolyPhen-2, SuSPect, F-SNP, AnnTools, SeattleSeq, SNPit, SCAN, Snap, SNPs&GO, LS-SNP, Snat, TREAT, TRAMS, Maviant,  MutationTaster , SNPdat, Snpranker, NGS – SNP, SVA, VARIANT, SIFT, LIST-S2, PhD-SNP and FAST-SNP. The functions and approaches used in SNPs annotation tools are listed below.


Variant annotation tools use machine learning algorithms to predict variant annotations. Different annotation tools use different algorithms. Common algorithms include:


A large number of variant annotation tools are available for variant annotation. The annotation by different tools does not alway agree amongst each other, as the defined rules for data handling differ between applications. It is frankly impossible to perform a perfect comparison of the available tools. Not all tools have the same input and output nor the same functionality. Below is a table of major annotation tools and their functional area.


CompleteGenomics,
GFF3-SOLiD,
SOAPsnp, MAQ,
CASAVA


GATK BED


Different annotations capture diverse aspects of variant function. [ 60 ]  Simultaneous use of multiple, varied functional annotations could improve  rare variants  association analysis power of  whole exome  and  whole genome sequencing  studies. [ 61 ]  Some tools have been developed to enable functionally-informed phenotype-genotype association analysis for common and rare variants by incorporating functional annotations in biobank-scale cohorts.  [ 62 ] [ 63 ] [ 64 ] [ 65 ]


The next generation of SNP annotation webservers can take advantage of the growing amount of data in core bioinformatics resources and use intelligent agents to fetch data from different sources as needed. From a user’s point of view, it is more efficient to submit a set of SNPs and receive results in a single step, which makes meta-servers the most attractive choice. However, if SNP annotation tools deliver heterogeneous data covering sequence, structure, regulation, pathways, etc., they must also provide frameworks for integrating data into a decision algorithms, and quantitative confidence measures so users can assess which data are relevant and which are not.


Sociogenomics , also known as  social genomics , is the field of research that examines why and how different social factors and processes (e.g., social stress, conflict, isolation, attachment, etc.) affect the activity of the  genome . [ 1 ] [ 2 ]  Social genomics as a field is very young (< 20 years old) and was spurred by the scientific understanding that the expression of genes to their gene products, though not the  DNA  sequence itself, is affected by the external environment. [ 3 ]  Social genomics researchers have thus examined the role of social factors (e.g. isolation, rejection) on the expression of individual genes, or more commonly, clusters of many genes (i.e. gene profiles, or gene programs).


In the early 2000s, initial work on this topic was conducted in animal model systems, such as  zebra finch ,  honeybee , and  cichlid , by Gene E. Robinson [ 1 ] [ 4 ]  at the  University of Illinois  among others. In 2007,  Steve Cole  at  UCLA  published the first study of social factors, in this case social connection, on the immune cell  gene expression  among healthy older adults. [ 5 ]   Shortly thereafter, a series of papers were published by  Youssef Idaghdour  and his colleagues looking at the role of environmental factors on gene expression throughout the genome where they found that only 5% of the variation in genomic expression was attributable to genetic factors (i.e. sequence variation in the genome) whereas, as much as half was due to the living environment of the individual, either urban or rural. [ 6 ]  These studies set the stage for looking at environmental modulation of gene expression including social influences.


The 23 pairs of  DNA  molecules called  chromosomes  contain the approximately 21,000  genes  comprising the “human blueprint.” For this blueprint to have any biological affect however, it must be  transcribed  to  RNA  and then into  proteins . This process of translation, or “turning on” of a gene to its final gene products is termed  gene expression . Genetic expression is far from random, allowing the differentiation and specialization of different cell types with identical genomes. Transcription factors are the proteins which control gene expression, and they can either increase (i.e. an activator) or decrease (i.e. a repressor) expression. Multiple  transcription factors  exist that are responsive to the internal environment of the cell (e.g. to maintain cell differentiation), but several also appear to be responsive to external factors including several  hormones ,  neurotransmitters , and  growth factors . The sum total of genes expressed into RNA in a particular population of cells is referred to as the  transcriptome .


Research has shown that the activity of gene profiles or gene programs can be affected by the physical and social environments that humans inhabit. The pattern of social stress-related changes in gene expression has been termed by  Steve Cole  and  George Slavich  at UCLA as a  conserved transcriptional response to adversity  (CTRA). [ 7 ]  In healthy situations, the human immune system is biased towards anti-viral readiness. However under conditions of  social stress  there appears to be a shift towards  pro-inflammatory  immunological processes including the production of various pro-inflammatory  cytokines  including  IL-1β  and  IL-6 . Simultaneously, social stress is associated with the down-regulation of anti-viral gene products including  interferon type 1  and specific antibody isotypes (e.g.  immunoglobulin G ). This pattern of up-regulated pro-inflammatory transcription coupled with down-regulated anti-viral transcription challenged the previously held belief that social stress was generally immunosuppressive.


An evolutionary explanation for the origin of the CTRA, characterized by increased pro-inflammatory gene expression and a suppression of anti-viral gene expression, has been proposed. From an evolutionary perspective, the frequent social contact of homo-sapiens increases the probability of viral infection. Thus a bias towards anti-viral readiness would be adaptive. In conditions of social stress however, the up-regulation of pro-inflammatory gene expression prepares the body to better deal with bodily injury and bacterial infection which is more likely under conditions of social stress either through hostile human contact, or increased predatory vulnerability due to separation from the social group. In the modern age however, the chronic elevation of pro-inflammatory gene expression produced by social stress is more likely to result in inflammation-related diseases including various  cancers ,  cardiovascular disease  and  rheumatoid arthritis . Concurrently, the down-regulation of anti-viral gene expression leaves the individual more vulnerable to viral infection such as the  flu  and the  common cold .


Social signal transduction  is the process through which social factors influence the  transcriptome . This process is mediated by the  central nervous system  via changes in  hormonal  and  neurotransmitter  signals. For example  catecholamines , the class of neurotransmitters that includes  dopamine  and  norepinephrine , have been linked with responses to acute stressors including the  fight-or-flight response , and also appear to modulate the transcription of multiple  transcription factors  that impact inflammatory and anti-viral genes.  Norepinephrine  release, for example, results in the activation of the transcription factor  CREB , via the activity of  β-adrenergic receptors . CREB then is able to up-regulate the transcription of many different genes. Thus, through effects of canonical neurotransmitter systems such as  catecholamines , social stressors are able to penetrate the  nucleus  of various cell types and alter the gene transcription profiles within these cells.


Other transcription factors that have been known to respond to social factors include some factors broadly related to the neurobiology of threat including  NF-κB   (which, in addition to CREB, is a widely implicated transcription factor affecting pro-inflammatory gene expression),  cyclic adenosine monophosphate ( cAMP ),  glucocorticoids  (in particular glucocorticoid insensitivity, where inflammatory pathways are unusually insensitive to negative regulation by glucocorticoids), and interferon transcription factors (which mediate the depression of anti-viral immunity).


Epigenetic  factors including  DNA methylation  and  histone modification  have also been proposed as possible biological mechanisms. For example, childhood maltreatment in rodent models and humans has been shown to alter the epigenetics of the glucocorticoid receptor gene. [ 8 ] [ 9 ]   The epigenetic influences on social genomic outcomes are still largely unknown at present and require additional research.


While the majority of experimental social genomics research has elucidated the role of acute social stress on the CTRA, it has been proposed that social factors can, under some circumstances, promote more persistent modulation of the human transcriptome. Several pro-inflammatory gene products, including multiple cytokines, exist in a recursive system wherein their presence promotes their own transcription. From a psychological standpoint, the experience of social stressors can, in certain individuals, promote the experience of future social stressors, as in the stress-generation theory of depression, wherein depressive symptoms increase the likelihood of future stressful events. [ 10 ] [ 11 ]  Future studies are needed to test whether individual differences in the magnitude of the CTRA are biologically related to stress-generation.


Epidemiological research  has demonstrated that social factors including  social isolation  can have large effects on various diseases and all-cause mortality. [ 12 ]  Social genomics represents a plausible mechanism subserving this link between the social environment and disease risk. For example, individuals who have chronic social isolation have different transcriptome profiles for genes related to immune system factors including elevated expression of pro-inflammatory cytokine genes and depressed expression of anti-viral genes. [ 5 ]  Chronically isolated individuals are also more likely to develop inflammation-related diseases thus providing a plausible biological connection between social variables (e.g. isolation, rejection, social stress, and socioeconomic status) and disease risk and mortality, namely heightened inflammation mediated by differential gene expression. [ 13 ] [ 14 ]  Though this line of research is relatively young, acute and chronic social stressors have been linked with altered gene expression in various tissues in addition to immune cells including  breast tissue ,  lymph nodes , and  brain cells , and in diseased tissues including  ovarian ,  prostate , and  breast cancer .


Simultaneously, chronic social stressors results in the individual being more susceptible to viral infection as a consequence of the down-regulation of anti-viral gene expression. The enhanced susceptibility to various viral infections maintained the hypothesis that social stress was generally immunosuppressive and only recently, through social genomic research, has the immunosuppressive hypothesis been challenged.


One consistent observation in social genomics research is that the perception of social stressors is a stronger and more reliable predictor of the CTRA than the objective presence of social stressor. [ 15 ]  For example, the subjective perception of isolation is a stronger predictor of pro-inflammatory gene expression than is the objective size of one's social network. This neurocognitive control of the CTRA suggests that altering one's perception of their social situation, for example by utilizing skills honed in  cognitive therapy  may be able to alleviate the negative consequences of the social stress and the CTRA. [ 16 ]


The  genome  of most cells of  eukaryotes  remains mainly constant during life. However, there are cases of genome being altered in specific cells or in different life cycle stages during development. For example, not every human cell has the same genetic content as red blood cells which are devoid of nucleus. One of the best known groups in respect of changes in somatic genome are  ciliates . The process resulting in a variation of somatic genome that differs from  germline  genome is called  somatic genome processing .


The result of this process is the removal of a whole genome from a cell. The most known example is the  enucleation  process of  erythrocytes . A processed stem cell goes through changes causing it to lose a nucleus. In the beginning phase,  pro-erythroblast  goes through another  mitotic divisions , in which an  erythroblast  with a smaller nucleus is created and moved to the side of the cell. The nucleus becomes isolated from cytoplasm and then erythroblast is divided into  reticulocyte  with  cytoplasm  and  pyrenocyte  with condensed nucleus. Pyrenocyte with all genetic material from cell is degraded then by a  macrophage . Loss of genome is in this case advantageous, since pyrenocyte can accumulate more  hemoglobin . Mature red blood cell without a nucleus, can properly deliver oxygen. [ 1 ]


Chromatin diminution  is a process of partial elimination of  chromatin  genetic material from genome of prospective  somatic cells . This process was found to occur during the early developmental stage in three groups:  nematodes ,  copepods , and  hagfish [ 2 ]  One of the first studies regarding somatic genome processing was observed by Boveri large-scale chromatin elimination in parasitic nematode  Parascaris univalens . [ 3 ]  During chromatin diminution, somatic chromosomes becomes fragmented with new  telomeres  added in many different places and devoid of  heterochromatin  so it differs from germline cell in respect of structure and genetic content. Germline cells of  P. univalens  contain only two chromosomes, but in early embryogenesis central euchromatic regions of the chromosomes fragment into diploid somatic set of 2×29  autosomes  and  2×6 X chromosomes in females or 2×29 autosomes and 6 X chromosomes in males, which segregate to the two daughter nuclei. After all heterochromatin becomes degraded in cytoplasm. As a result of chromatin diminution  P. univalens loses about  80–90% of the total nuclear germ line DNA. [ 4 ] [ 5 ] [ 6 ]


Chromatin diminution occurs also in unicellular eukaryotes, such as ciliates. Ciliates have two nuclei: micronucleus (germ-line cell nucleus) that does not express genes and macronucleus, where most genes are expressed, and is subject to chromatin elimination. During this process chromosomes are fragmented, chromatin eliminated and new DNA molecules with added telomeres are created. The final macronucleus has greater genetic content than micronucleus. In ciliates there are two types of diminution: the first is fragmentation of genome and loss of repetitive sequences and the second is deletion of internally eliminated sequences in chromosomes and the rejoining of remaining DNA fragments. [ 6 ]


Gene unscrambling is type of genome-wide processing found particularly in ciliates. The germline genes in the micronucleus of ciliates are composed of protein-coding DNA fragments (MDSs) interrupted by many  non-coding DNA  sequences, also called internal eliminated (IESs).


In the  Spirotrichea  class, to which  Oxytricha  belongs, protein-coding DNA fragments in a  micronucleus  are located in permuted order. During sexual development the genetic content of somatic macronucleus is derived from micronucleus. First some parts, including IESs, of micronuclear DNA are removed to give transcriptionally active genome in macronucleus. Also micronuclear-encoded MDSs which are nonsequential must undergo gene unscrambling to be ligated in correct order to give functional genes [ 7 ] [ 8 ]


Local rearrangements affect specific loci only. Such rearrangements, for instance, help create genes that produce a great variation of immunoglobulins in vertebrates. During life, organisms have contact with a large number of  antigens . Which means that the  immune system  needs to synthesize a wide range of  antibodies . Each  immunoglobulin  is a tetramer consisting of four polypeptides connected by  disulfide bridges . They form two long heavy chains and  two short light chains. But vertebrate genome does not code entire genes of heavy and light immunoglobulins, only gene segments. Segments of heavy chain are located on chromosome 14, they include 11 constant gene segments (C H ), that are preceded by 123-129 variable segments (V H ), 27 diversity gene segments (D H ) and 9 joining segments (J H ), coding different versions of components V, D, J. Loci of light chains on  chromosome 2  (locus κ) and  chromosome 22  (locus λ) have similar structure, but they do not contain D segments. At the early stage of  lymphocyte B  development, loci of immunoglobulins are rearranged. During rearrangement, segment V H  on heavy chain locus is connected with one D H  segment, then V-D group is combined with J H  segment. Eventually, exon with  open reading frame  coding segments: V H , D H , J H  of immunoglobulin. Through  RNA splicing  during transcription, this  exon  becomes connected to exon for C H  segment. Complementary mRNA of heavy chain can be translated into immunoglobulin specific only for one lymphocyte. [ 9 ]


In  bioinformatics , a  spaced seed  is a pattern of relevant and irrelevant positions in a  biosequence  and a method of  approximate string matching  that allows for  substitutions . They are a straightforward modification to the earliest  heuristic -based alignment efforts that allow for minor differences between the sequences of interest. Spaced seeds have been used in  homology search ., [ 1 ]   alignment , [ 2 ]   assembly , [ 3 ]  and  metagenomics . [ 4 ]  They are usually represented as a sequence of zeroes and ones, where a one indicates relevance and a zero indicates irrelevance at the given position. Some visual representations use  pound signs  for relevant and  dashes  or  asterisks  for irrelevant positions.


Due to a number of functional and  evolutionary  constraints, nucleic acid sequences between individuals tend to be highly conserved, with the typical difference between two human genomes estimated on the order of 0.6% (or around 20 million base pairs). [ 5 ]  Identification of highly similar regions in the  genome  may indicate functional importance, as mutations in these areas that would result in cessation of function or loss of regulatory ability would be evolutionary unfavorable. More observed differences between two sequences may arise as a result of stochastic  sequencing errors . Similarly, when performing assembly of a previously characterized genome, an attempt is made to align the newly sequenced DNA fragments to the existing genome sequence.


In both cases, it is useful to be able to directly compare nucleic acid sequences. Since the sequences are not expected to be exactly identical, however, it is beneficial to focus on smaller subsequences that are more likely to be locally identical. Spaced seeds allow for even more permissive local matching by allowing certain base pairs (defined by the pattern of the specific spaced seed) to mismatch without penalty, thus allowing algorithms that use the general "hit-extend" strategy of alignment to explore additional potential matches that would be otherwise ignored.


As a simple example, consider the following  DNA sequences :


CTAA GT CACG
CTAA CA CACG
1111001111


Upon visual inspection, it's easy to see that there is a mismatch between the two sequences at the fifth and six  base  positions (in bold, above). However, the sequences still share 80% sequence similarity. The mismatches may be due to a real (biological) change or a sequencing error. In a non-spaced model, this putative match would be ignored if a seed size greater than 4 is specified. But a spaced seed of

 1111001111

 {\displaystyle 1111001111}

  could be used to effectively zero-weighting the mismatch sites, treating the sequences as same for the purposes of hit identification. In reality, of course, we don't know the relative positioning of the "true" mismatches, so there can be different spaced seed patterns depending on where the mismatches are anticipated.


The concept of spaced seeds has been explored in literature under different names. One of the early uses was in sequence homology where the FLASH algorithm from 1993 referred to it as "non-contiguous sub-sequences of tokens" that were generated from all combinations of positions within a sequence window. [ 6 ]  In 1995, a similar concept was used in approximate string matching where "gapped  tuples " of positions in a sequence were explored to identify common substrings between a large text and a query. [ 7 ]  The term "shape" was used in a 2001 paper to describe gapped  q-grams  where it refers to a set of relevant positions in a substring [ 8 ]  and soon after in 2002,  PatternHunter  introduced "spaced model" which was proposed as an improvement upon the consecutive seeds used in  BLAST , [ 1 ]  which was ultimately adopted by newer versions of it. Finally, in 2003, PatternHunter II settled on the term "spaced seed" to refer to the approach used in PatternHunter [ 9 ]


Popular alignment algorithms such as BLAST and MegaBLAST use a non-spaced model, where the entire length of the seed is made of exact matches. [ 10 ]  Thus, any mismatching base pair along the length of the seed will result in the program ignoring the potential hit. In a spaced model (such as PatternHunter), the matches are not necessarily consecutive.


More formally, the difference in a spaced seed model as compared to a non-spaced model is the relative positioning (otherwise known as weight,

 k

 {\displaystyle k}

 ) of the matched bases. In a non-spaced model, the length of the seed model,

 M

 {\displaystyle M}

  and the weight,

 k

 {\displaystyle k}

  of the seeds are the same, as they must be consecutive while in a spaced model, the weight is not necessarily equal to the length of the seed model, since match positions may be non-consecutive. [ 1 ]  Therefore, a spaced seed model may be longer than a non-spaced seed model but have the same weight. For example, a non-spaced seed

 11111

 {\displaystyle 11111}

  has the same weight as a spaced model

 1101101

 {\displaystyle 1101101}

 , but their lengths differ.


The predicted number of hits can be calculated from PatternHunter [ 1 ]  using the following  lemma :


(
 L
 −
 M
 +
 1
 )
 ∗

 p

 k

 {\displaystyle (L-M+1)*p^{k}}


Where

 L

 {\displaystyle L}

  is the length of the sequence the model is compared to,

 M

 {\displaystyle M}

  is the length of the seed model,

 p

 {\displaystyle p}

  is the probability of a match and

 k

 {\displaystyle k}

  is the weight of the seed used.


The type of seed model used for sequence alignment can affect the processing time and memory usage when doing large-scale homology searches – two considerations that have been central in the development of modern homology search algorithms. It may also affect the sensitivity. Using spaced seed models has been demonstrated to allow for faster homology searches as seen with PatternHunter wherein homology searches were twenty times faster and used less memory than BLASTn (using a non-spaced model). [ 1 ]


Most aligners first find candidate locations (or seeds) in the target sequence and then inspect those more closely to verify the alignment. [ 11 ] [ 2 ] [ 12 ] [ 13 ]  Ideally, this first step would find all relevant locations in the target so  sensitivity  is prioritized but due to computational intensity, many popular algorithms (such as the earlier implementations of BLAST and  FASTA ) use heuristics to "short-cut" exploring all locations, ultimately missing many but running relatively quickly. One possible way to increase sensitivity, as done in the SHRiMP2 [ 2 ]  algorithm, is to use spaced seeds to allow for small differences between the query and the candidate locations so that somewhat more locations are identified as candidates. SHRiMP2 specifically uses multiple spaced seeds for this and requires multiple matches, increasing sensitivity as it allows different possible combinations of differences while maintaining speed comparable to original methods.


A variation of spaced seeds with a single contiguous gap has been used in  de novo  sequence assembly . [ 3 ]  In this instance, the design has an equal number of ones at either end of the sequence with a run of zeroes in between. The reasoning behind this design is that in assemblers that utilize  De Bruijn graphs , increasing  k-mer  size inflates memory usage, as k-mers are more likely to be unique. However, the most important parts of a k-mer are its ends, as they are what are used to extend sequences in a graph. Thus, to circumvent the problem with memory usage, the less-important middle part (covered by the gap) is ignored. This approach has the additional advantage, as in other uses of spaced seeds, of taking into account any sequencing errors that may have occurred in the gap area. It has been noted [ 3 ]  that increasing the length of the gap also increases the uniqueness of k-mers in both  E. coli  and  H. sapiens  genomes.


A metagenomics study will commonly start with the high-throughput sequencing of a mixture of distinct species (e.g. from the  human gut ), yielding a set of sequences but with unknown origins. As such, one common goal is to identify which genome each sequence is  phylogenetically  most similar to. One approach could be to take  k-mers  from each sequence and see which, in a set of genomes, it has most sequence similarity with. Spaced seeds have been successfully utilized for this purpose [ 4 ]  by finding how many k-mers are found in each genome (hit number) and the total number of positions these k-mers cover (coverage).


An improvement to (single) spaced seeds was first demonstrated by PatternHunter in 2002 [ 1 ]  where a set of spaced seeds were used, in which a "hit" was called whenever one of the set matched. PatternHunter II, in 2003, demonstrated that this approach could offer higher sensitivity than BLAST while maintaining similar speed. [ 9 ]  Identifying an optimal set of spaced seeds is an  NP-hard  problem, however, even finding a "good" set of spaced seeds remains difficult, although several attempts have been made to computationally identify them. [ 14 ] [ 15 ] [ 16 ]  Since the speed of the algorithm must decrease with an increasing number of spaced seeds, it makes sense to only consider multiple seeds when all offer some useful contribution. There is ongoing research on how to quickly calculate good multiple spaced seeds, as previous homology search software calculated and  hard-coded  their seeds [ 16 ]  – it would be advantageous to be able to calculate purpose-driven multiple spaced seeds instead.


The  split gene theory  is a theory of the origin of  introns , long non-coding sequences in eukaryotic genes between the  exons . [ 1 ] [ 2 ] [ 3 ]  The theory holds that the randomness of primordial  DNA  sequences would only permit small (< 600bp)  open reading frames  (ORFs), and that important intron structures and regulatory sequences are derived from  stop codons . In this introns-first framework, the spliceosomal machinery and the nucleus evolved due to the necessity to join these ORFs (now "exons") into larger proteins, and that intronless bacterial genes are less ancestral than the split eukaryotic genes. The theory originated with  Periannan Senapathy .


The theory provides solutions to key questions concerning the split gene architecture, including split eukaryotic genes, exons, introns, splice junctions, and branch points, based on the origin of split genes from random genetic sequences. It also provides possible solutions to the origin of the spliceosomal machinery, the nuclear boundary and the eukaryotic cell.


This theory led to the  Shapiro–Senapathy algorithm , which provides the methodology for detecting the splice sites, exons and split genes in eukaryotic DNA, and which is the main method for detecting splice site mutations in genes that cause hundreds of diseases.


Split gene theory requires a separate origin of all eukaryotic species. It also requires that the simpler prokaryotes evolved from eukaryotes. This completely contradicts the scientific consensus about the formation of eukaryotic cells by endosymbiosis of bacteria. In 1994, Senapathy wrote a book about this aspect of his theory -  The Independent Birth of Organisms.  It proposed that all eukaryotic genomes were formed separately in a primordial pool. Dutch biologist Gert Korthoff criticized the theory by posing various problems that cannot be explained by a theory of independent origins. He pointed out that various eukaryotes need nurturing and called this the 'boot problem', in that even the initial eukaryote needed parental care. Korthoff notes that a large fraction of eukaryotes are parasites. Senapathy's theory would require a coincidence to explain their existence. [ 4 ] [ 5 ]  Senapathy's theory cannot explain the strong  evidence for common descent  ( homology , universal genetic code,  embryology ,  fossil record .) [ 6 ]


Genes of all organisms, except bacteria, consist of short protein-coding regions ( exons ) interrupted by long sequences ( introns ). [ 1 ] [ 2 ]  When a gene is expressed, its DNA sequence is copied into a “primary RNA” sequence by the enzyme  RNA polymerase . Then the “spliceosome” machinery physically removes the introns from the RNA copy of the gene by the process of splicing, leaving only a contiguously connected series of exons, which becomes  messenger RNA  (mRNA). This mRNA is now read by the  ribosome , which produces the encoded protein. Thus, although introns are not physically removed from a gene, a gene's sequence is read as if introns were not present.


Exons are usually short, with an average length of about 120 bases (e.g. in human genes). Intron lengths vary widely from 10 to 500,000, but exon lengths have an upper bound of about 600 bases in most eukaryotes. Because exons code for protein sequences, they are important for the cell, yet constitute only ~2% of the sequences. Introns, in contrast, constitute 98% of the sequences but seem to have few crucial functions, except for enhancer sequences and developmental regulators in rare instances. [ 7 ] [ 8 ]


Until  Philip Sharp [ 9 ] [ 10 ]  and  Richard Roberts [ 11 ]  discovered introns [ 12 ]  within eukaryotic genes in 1977, it was believed that the coding sequence of all genes was always in one single stretch, bounded by a single long ORF. The discovery of introns was a profound surprise, which instantly brought up the questions of how, why and when the introns came into the eukaryotic genes.


It soon became apparent that a typical eukaryotic gene was interrupted at many locations by introns, dividing the coding sequence into many short exons. Also surprising was that the introns were long, as long as hundreds of thousands of bases. These findings prompted the questions of why many introns occur within a gene (for example, ~312 introns occur in the human gene TTN), why they are long, and why exons are short.


introns in the gene


It was also discovered that the spliceosome machinery was large and complex with ~300 proteins and several  SnRNA  molecules. The questions extended to the origin of the spliceosome. Soon after the discovery of introns, it became apparent that the junctions between exons and introns on either side exhibited specific sequences that directed the spliceosome machinery to the exact base position for splicing. How and why these splice junction signals came into being was another important question.


The discovery of introns and the split gene architecture of the eukaryotic genes started a new era of eukaryotic biology. The question of why eukaryotic genes had fragmented genes prompted speculation and discussion almost immediately.


Ford Doolittle published a paper in 1978 in which he stated that most molecular biologists assumed that the eukaryotic genome arose from a ‘simpler’ and more ‘primitive’ prokaryotic genome rather like that of  Escherichia coli . [ 13 ]  However, this type of evolution would require that introns be introduced into the coding sequences of bacterial genes. Regarding this requirement, Doolittle said, “It is extraordinarily difficult to imagine how informationally irrelevant sequences could be introduced into pre-existing structural genes without deleterious effects.” He stated “I would like to argue that the eukaryotic genome, at least in that aspect of its structure manifested as ‘genes in pieces’ is in fact the primitive original form.”


James Darnell  expressed similar views in 1978. He stated, “The differences in the biochemistry of messenger RNA formation in eukaryotes compared to  prokaryotes  are so profound as to suggest that sequential prokaryotic to eukaryotic cell evolution seems unlikely. The recently discovered non-contiguous sequences in eukaryotic DNA that encode messenger RNA may reflect an ancient, rather than a new, distribution of information in DNA and that eukaryotes evolved independently of prokaryotes.” [ 14 ]


However, in an apparent attempt to reconcile with the idea that RNA preceded DNA in evolution, and with the concept of the three evolutionary lineages of  archea , bacteria and eukarya, both Doolittle and Darnell deviated from their original speculation in a joint paper in 1985. [ 15 ]  They suggested that the ancestor of all three groups of organisms, the ‘ progenote ,’ had a genes-in-pieces structure, from which all three lineages evolved. They speculated that the precellular stage had primitive RNA genes which had introns, which were reverse transcribed into DNA and formed the progenote. Bacteria and archea evolved from the progenote by losing introns, and ‘urkaryote’ evolved from it by retaining introns. Later, the eukaryote evolved from the urkaryote by evolving a nucleus and absorbing  mitochondria  from bacteria. Multicellular organisms then evolved from the eukaryote.


These authors predicted that the distinctions between the prokaryote and the eukaryote were so profound that the prokaryote to eukaryote evolution was not tenable, and had different origins. However, other than the speculations that the precellular RNA genes must have had introns, they did not address the key questions of intron origin. No explanations described why exons were short and introns were long, how the splice junctions originated, what the structure and sequence of the splice junctions meant, and why eukaryote genomes were large.


Around the same time that Doolittle and Darnell suggested that introns in eukaryotic genes could be ancient, Colin Blake [ 16 ]  and  Walter Gilbert [ 17 ] [ 18 ]  published their views on intron origins independently. In their view, introns originated as spacer sequences that enabled convenient recombination and shuffling of exons that encoded distinct functional domains in order to evolve new genes. Thus, new genes were assembled from exon modules that coded for functional domains, folding regions, or structural elements from preexisting genes in the genome of an ancestral organism, thereby evolving genes with new functions. They did not specify how exons or introns originated. In addition, even after many years, extensive analysis of thousands of proteins and genes showed that only extremely rarely do genes exhibit the supposed exon shuffling phenomenon. [ 19 ] [ 20 ]  Furthermore, molecular biologists questioned the exon shuffling proposal, from a purely evolutionary view for both methodological and conceptual reasons, and, in the long run, this theory did not survive.


Around the time introns were discovered, Senapathy was asking how genes themselves could have originated. He surmised that for any gene to come into being, genetic sequences (RNA or DNA) must have been present in the prebiotic environment. A basic question he asked was how protein-coding sequences could have originated from primordial DNA sequences at the origin of the first cells.


To answer this, he made two basic assumptions:


He also surmised that codons must have been established prior to the origin of the first genes. If primordial DNA did contain random nucleotide sequences, he asked: Was there an upper limit in coding-sequence lengths, and, if so, did this limit play a crucial role in the formation of the structural features of genes at the origin of genes?


His logic was the following. The average length of proteins in living organisms, including the eukaryotic and bacterial organisms, was ~400 amino acids. However, much longer proteins existed, even longer than 10,000-30,000 amino acids in both eukaryotes and bacteria. [ 21 ]  Thus, the coding sequence of thousands of bases existed in a single stretch in bacterial genes. In contrast, the coding sequence of eukaryotes existed only in short segments of exons of ~120 bases regardless of the length of the protein. If the coding sequence ORF lengths in random DNA sequences were as long as those in bacterial organisms, then long, contiguous coding genes were possible in random DNA. This was not known, as the distribution of ORF lengths in a random DNA sequence had never been studied.


As random DNA sequences could be generated in the computer, Senapathy thought that he could ask these questions and conduct his experiments  in silico . Furthermore, when he began studying this question, sufficient DNA and protein sequence information existed in the National Biomedical Research Foundation (NBRF) database in the early 1980s.


Senapathy analyzed the distribution of the ORF lengths in computer-generated random DNA sequences first. Surprisingly, this study revealed that about 200 codons (600 bases) was the upper limit in ORF lengths. The shortest ORF (zero base in length) was the most frequent. At increasing lengths of ORFs, their frequency decreased logarithmically, approaching zero at about 600 bases. When the probability of ORF lengths in a random sequence was plotted, it revealed that the probability of increasing lengths of ORFs decreased exponentially and tailed off at a maximum of about 600 bases. From this “negative exponential” distribution of ORF lengths, it was found that most of ORFs were far shorter than the maximum.


This finding was surprising because the coding sequence for the average protein length of 400 AAs (with ~1,200 bases of coding sequence) and longer proteins of thousands of AAs (requiring >10,000 bases of coding sequence) would not occur at a stretch in a random sequence. If this was true, a typical gene with a contiguous coding sequence could not originate in a random sequence. Thus, the only possible way that any gene could originate from a random sequence was to split the coding sequence into shorter segments and select these segments from short ORFs available in the random sequence, rather than to increase the ORF length by eliminating consecutive stop codons. This process of choosing short segments of coding sequences from the available ORFs to make a long ORF would lead to a split structure.