Although it is unclear when the  AMY1  gene copy number began to increase, it is known and confirmed that the  AMY1  gene existed in early primates.  Chimpanzees , the closest evolutionary relatives to humans, were found to have two  diploid  copies of the  AMY1  gene that is identical in length to the human AMY1 gene, [ 9 ]  which is significantly less than that of humans. On the other hand,  bonobos , also a close relative of modern humans, were found to have more than two diploid copies of the  AMY1  gene. [ 9 ]  Nonetheless, the bonobo  AMY1  genes were sequenced and analyzed, and it was found that the coding sequences of the  AMY1  genes were disrupted, which may lead to the production of dysfunctional salivary amylase. [ 9 ]  It can be inferred from the results that the increase in bonobo  AMY1  copy number is likely not correlated to the amount of starch in their diet. It was further hypothesized that the increase in copy number began recently during early  hominin  evolution as none of the  great apes  had more than two copies of the  AMY1  gene that produced functional protein. [ 9 ]  In addition, it was speculated that the increase in the  AMY1  copy number began around 20,000 years ago when humans shifted from a  hunter-gatherer  lifestyle to  agricultural  societies, which was also when humans relied heavily on  root vegetables  high in starch. [ 9 ]  This hypothesis, although logical, lacks experimental evidence due to the difficulties in gathering information on the shift of human diets, especially on root vegetables that are high in starch as they cannot be directly observed or tested. Recent breakthroughs in DNA sequencing has allowed researchers to sequence older DNA such as that of  Neanderthals  to a certain degree of accuracy. Perhaps sequencing Neanderthal DNA can provide a time marker as to when the  AMY1  gene copy number increased and offer insight into human diet and gene evolution.


Currently it is unknown which mechanism gave rise to the initial duplication of the amylase gene, and it can imply that the insertion of the  retroviral  sequences was due to non-homologous end joining, which caused the duplication of the  AMY1  gene. [ 27 ]  However, there is currently no evidence to support this theory and therefore this hypothesis remains conjecture. The recent origin of the multi-copy  AMY1  gene implies that depending on the environment, the  AMY1  gene copy number can increase and decrease very rapidly relative to genes that do not interact as directly with the environment. [ 26 ]  The  AMY1  gene is an excellent example of how  gene dosage  affects the survival of an organism in a given environment. The multiple copies of the  AMY1  gene give those who rely more heavily on high starch diets an evolutionary advantage, therefore the high gene copy number persists in the population. [ 26 ]


Among the  neurons  in the  human brain , somatically derived copy number variations are frequent. [ 28 ]  Copy number variations show wide variability (9 to 100% of brain neurons in different studies). Most alterations are between 2 and 10 Mb in size with deletions far outnumbering amplifications. [ 28 ]


Genomic duplication and triplication of the gene appear to be a rare cause of  Parkinson's disease , although more common than point mutations. [ 29 ]


Copy number variants in  RCL1  gene are associated with a range of  neuropsychiatric  phenotypes in children. [ 30 ]


Recently, there had been discussion connecting copy number variations to  gene families . Gene families are defined as a set of related genes that serve similar functions but have minor temporal or spatial differences and these genes likely derived from one  ancestral  gene. [ 26 ]  The main reason copy number variations are connected to gene families is that there is a possibility that genes in a family may have derived from one ancestral gene which got duplicated into different copies. [ 26 ]  Mutations accumulate through time in the genes and with  natural selection  acting on the genes, some mutations lead to environmental advantages allowing those genes to be inherited and eventually clear gene families are separated out. An example of a gene family that may have been created due to copy number variations is the  globin  gene family. The globin gene family is an elaborate network of genes consisting of  alpha  and  beta  globin genes including genes that are expressed in both embryos and adults as well as  pseudogenes . [ 31 ]  These globin genes in the globin family are all well conserved and only differ by a small portion of the gene, indicating that they were derived from a common ancestral gene, perhaps due to duplication of the initial globin gene. [ 31 ]


Research has shown that copy number variations are significantly more common in genes that encode proteins that directly interact with the environment than proteins that are involved in basic cellular activities. [ 32 ]  It was suggested that the gene dosage effect accompanying copy number variation may lead to detrimental effects if essential cellular functions are disrupted, therefore proteins involved in cellular pathways are subjected to strong  purifying selection . [ 32 ]  In addition, proteins function together and interact with proteins of other pathways, therefore it is important to view the effects of natural selection on bio-molecular pathways rather than on individual proteins. With that being said, it was found that proteins in the periphery of the pathway are enriched in copy number variations whereas proteins in the center of the pathways are depleted in copy number variations. [ 33 ]  It was explained that proteins in the periphery of the pathway interact with fewer proteins and so a change in protein dosage affected by a change in copy number may have a smaller effect on the overall outcome of the cellular pathway. [ 33 ]


The  CoRR hypothesis  states that the location of  genetic information  in  cytoplasmic   organelles  permits regulation of its expression by the reduction-oxidation (" redox ") state of its  gene products .


CoRR is short for "co-location for redox regulation", itself a shortened form of "co-location (of gene and gene product) for (evolutionary) continuity of redox regulation of  gene expression ". [ 1 ] [ 2 ]


CoRR was put forward explicitly in 1993 in a paper in the  Journal of Theoretical Biology  with the title "Control of gene expression by redox potential and the requirement for chloroplast and mitochondrial genomes". [ 3 ]   The central concept had been outlined in a review of 1992. [ 4 ]   The term CoRR was introduced in 2003 in a paper in  Philosophical Transactions of the Royal Society  entitled "The function of genomes in bioenergetic organelles". [ 5 ]


Chloroplasts  and  mitochondria  are energy-converting organelles in the  cytoplasm  of  eukaryotic cells .  Chloroplasts in  plant cells  perform  photosynthesis ; the capture and conversion of the energy of  sunlight .  Mitochondria in both plant and  animal cells  perform  respiration ; the release of this stored energy when work is done.  In addition to these key reactions of  bioenergetics , chloroplasts and mitochondria each contain specialized and discrete genetic systems.  These genetic systems enable chloroplasts and mitochondria to make some of their own  proteins .


Both the genetic and energy-converting systems of chloroplasts and mitochondria are descended, with little modification, from those of the free-living bacteria that these organelles once were.  The existence of these cytoplasmic  genomes  is consistent with, and counts as evidence for, the  endosymbiont hypothesis .  Most genes for proteins of chloroplasts and mitochondria are, however, now located on  chromosomes  in the nuclei of eukaryotic cells.  There they code for protein precursors that are made in the  cytosol  for subsequent import into the organelles.


Why do mitochondria and chloroplasts require their own separate genetic systems, when other organelles that share the same cytoplasm, such as peroxisomes and lysosomes, do not? The question is not trivial, because maintaining a separate genetic system is costly: more than 90 proteins ... must be encoded by nuclear genes specifically for this purpose. ... The reason for such a costly arrangement is not clear, and the hope that the nucleotide sequences of mitochondrial and chloroplast genomes would provide the answer has proved to be unfounded. We cannot think of compelling reasons why the proteins made in mitochondria and chloroplasts should be made there rather than in the cytosol.


CoRR seeks to explain why chloroplasts and mitochondria retain  DNA , and thus why some characters are inherited through the cytoplasm in the phenomenon of cytoplasmic, non- Mendelian , uniparental, or  maternal inheritance .  CoRR does so by offering an answer to this question: why, in  evolution , did some bacterial,  endosymbiont  genes move to the  cell nucleus , while others did not?


CoRR states that chloroplasts and mitochondria contain those  genes  whose expression is required to be under the direct, regulatory control of the redox state of their gene products, or of  electron  carriers with which those gene products interact.  Such genes comprise a core, or primary subset, of organellar genes.  The requirement for redox control of each gene in the primary subset then confers an advantage upon location of that gene within the organelle.   Natural selection  therefore anchors some genes in organelles, while favouring location of others in the cell nucleus.


Chloroplast and mitochondrial genomes also contain genes for components of the chloroplast and mitochondrial genetic systems themselves.  These genes comprise a secondary subset of organellar genes: genetic system genes.  There is generally no requirement for redox control of expression of genetic system genes, though their being subject to redox control may, in some cases, allow  amplification  of redox signals acting upon genes in the primary subset (bioenergetic genes).


Retention of genes of the secondary subset (genetic system genes) is necessary for the operation of redox control of expression of genes in the primary subset.  If all genes disappear from the primary subset, CoRR predicts that there is no function for genes in the secondary subset, and such organelles will then, eventually, lose their genomes completely.  However, if even only one gene remains under redox control, then an organelle genetic system is required for the synthesis of its gene product.


dcGO  is a comprehensive ontology database for protein domains. [ 1 ]  As an ontology resource, dcGO integrates  Open Biomedical Ontologies  from a variety of contexts, ranging from functional information like Gene Ontology to others on enzymes and pathways, from phenotype information across major model organisms to information about human diseases and drugs. As a  protein domain  resource, dcGO includes annotations to both the individual domains and supra-domains (i.e., combinations of two or more successive domains).


There are two key concepts behind dcGO. The first concept is to label  protein domains  with ontology, for example, with Gene Ontology. That is why it is called dcGO, domain-centric Gene Ontology. The second concept is to use ontology-labeled protein domains for, for example, protein function prediction. Put it in a simple way, the first concept is about how to create dcGO resource, and the second concept is about how to use dcGO resource.


Recent use of dcGO is to build a domain network from a functional perspective for cross-ontology comparisons, [ 5 ]  and to combine with species tree of life (sTOL) to provide a phylogenetic context to function and phenotype. [ 6 ]


Open-source software  dcGOR  is developed using  R programming language  to analyse domain-centric ontologies and annotations. [ 7 ]
Supported analyses include:


Functionalities under active development are:


Delphibacteria  is a  candidate   bacterial phylum  in the  FCB group . The phylum was first proposed after analysis of two genomes from the mouths of two  bottlenose dolphins . "Dephibacteria" was proposed in recognition of the first genomic representatives having been recovered from the dolphin mouth. Members of the Delphibacteria phylum have been retroactively detected in a variety of environments.


Delphibacteria is a  bacterial phylum  with  candidate  status, meaning it has no  cultured  representatives as of yet. It is part of the  FCB group . [ 1 ]


The phylum was first proposed following the recovery and analysis of two genomes, each from the mouth of a different  bottlenose dolphin . [ 1 ]  These dolphins were part of the  US Navy's Marine Mammal Program , although Delphibacteria  16S rRNA genes  have also been detected in the mouths of wild dolphins living off the coast of Florida, U.S. [ 1 ] [ 2 ]  The first characterized member of the Delphibacteria phylum was inferred to be a  heterotrophic  organism with the genomic potential for oxygen and most likely nitrate reduction. [ 1 ]  It was hypothesized that the ability to perform denitrification may have an impact on dolphin physiology and health, [ 1 ]  given that in humans denitrification by oral bacteria can affect oral and gastric blood flow, signalling in bacteria-bacteria and bacteria-host interactions, and mucus thickness in the stomach. [ 3 ] [ 4 ]


The name "Delphibacteria" was proposed in recognition of the first genomic representatives having been recovered from the dolphin mouth (Family  Delphinidae ) and due to its ubiquity in dolphin mouths. [ 1 ] [ 2 ]  Members of the Delphibacteria phylum have been detected (retroactively) in a variety of environments, including  bottom water  from the Northern Bering Sea ( EU734960.1 ),  marine sediment  from the Logatchec hydrothermal vent ( FN554086.1 ), and deep sea sediment from the  Okinawa Trough  ( KX097792.1 ). [ 1 ]


The  DNA Data Bank of Japan  ( DDBJ ) is a  biological database  that collects DNA sequences. [ 1 ] [ 2 ]  It is located at the  National Institute of Genetics  (NIG) in the  Shizuoka prefecture  of Japan. It is also a member of the  International Nucleotide Sequence Database Collaboration  or  INSDC . It exchanges its data with  European Molecular Biology Laboratory  at the  European Bioinformatics Institute  and with  GenBank  at the  National Center for Biotechnology Information  on a daily basis. Thus these three databanks contain the same data at any given time.


DDBJ began data bank activities in 1987 [ 3 ]  at NIG and remains the only nucleotide sequence data bank in Asia. [ 4 ]


Although DDBJ mainly receives its data from Japanese researchers, it can accept data from contributors from any other country. DDBJ is primarily funded by the Japanese  Ministry of Education, Culture, Sports, Science and Technology  (MEXT). DDBJ has an international advisory committee which consists of nine members, 3 members each from Europe, US, and Japan. This committee advises DDBJ about its maintenance, management and future plans once a year. Apart from this, DDBJ also has an international collaborative committee which advises on various technical issues related to international collaboration and consists of working-level participants. [ citation needed ]


The  double cut and join (DCJ) model  is a model for genome rearrangement used to define an  edit distance  between genomes based on gene order and orientation, rather than nucleotide sequence. It takes the fundamental units of a genome to be  synteny blocks , maximal sections of DNA conserved between genomes. It focuses on changes due to genome rearrangement operations such as  inversions ,  translocations  as well as the creation and absorption of circular intermediates. [ 1 ]
 [ 2 ]


A genome is described as a  directed , edge labeled  graph  with each vertex having degree 1 or 2.  Edges are labeled as synteny blocks,  vertices of degree 1 represent  telomeres , and vertices of degree 2 representing adjacencies between blocks.  This requires that the genome consist of  cycles  and  paths . Each component is called a chromosome. The beginning of each edge is called the tail, the end of each edge is called the head; together heads and tails are known as extremities. Vertices are described by their roles as heads and tails of blocks, for instance, in the figure, the adjacency which forms the head of marker 1 and the tail of marker 2 is labelled (h1, t2), the telomere formed by the head of 2 is (h2). A double cut and join (DCJ) operation consists of one of the following four transformations:


An edit distance, the double cut and join distance, is defined between genomes with the same number of edges

 G

 1

 {\displaystyle G_{1}}

  and

 G

 2

 {\displaystyle G_{2}}

 ,

 d

 D
 C
 J

 (
 A
 ,
 B
 )

 {\displaystyle d_{DCJ}(A,B)}

  as the minimum number of DCJ operations needed to transform

 A

 {\displaystyle A}

  into

 B

 {\displaystyle B}

 .


The DCJ distance defines a  metric space . To verify this, we first note that

 d

 D
 C
 J

 (
 G
 ,
 G
 )
 =
 0

 {\displaystyle d_{DCJ}(G,G)=0}

 , since no operations are needed to change G into itself, and if

 G

 1

 ≠

 G

 2

 {\displaystyle G_{1}\neq G_{2}}

 ,

 d

 D
 C
 J

 (

 G

 1

 ,

 G

 2

 )
 >
 0

 {\displaystyle d_{DCJ}(G_{1},G_{2})>0}

 , since at least one operation is needed to transform

 G

 1

 {\displaystyle G_{1}}

  into any other genome. (A proof that the

 d

 D
 C
 J

 (

 G

 1

 ,

 G

 2

 )

 {\displaystyle d_{DCJ}(G_{1},G_{2})}

  is always defined whenever

 G

 1

 {\displaystyle G_{1}}

  and

 G

 2

 {\displaystyle G_{2}}

  are genomes with the same edges will follow.) Note that each operation has an inverse: (i) and (ii) are their own inverses, and (iii) is the inverse of (iv). Thus

 d

 D
 C
 J

 (

 G

 1

 ,

 G

 2

 )
 =

 d

 D
 C
 J

 (

 G

 2

 ,

 G

 1

 )

 {\displaystyle d_{DCJ}(G_{1},G_{2})=d_{DCJ}(G_{2},G_{1})}

 . The  triangle inequality

 d

 D
 C
 J

 (

 G

 1

 ,

 G

 3

 )
 ≤

 d

 D
 C
 J

 (

 G

 1

 ,

 G

 2

 )
 +

 d

 D
 C
 J

 (

 G

 2

 ,

 G

 3

 )

 {\displaystyle d_{DCJ}(G_{1},G_{3})\leq d_{DCJ}(G_{1},G_{2})+d_{DCJ}(G_{2},G_{3})}

  holds because a series of DCJ operations transforming

 G

 1

 {\displaystyle G_{1}}

  to

 G

 2

 {\displaystyle G_{2}}

followed by a series of transformations from

 G

 2

 {\displaystyle G_{2}}

  to

 G

 3

 {\displaystyle G_{3}}

  will transform

 G

 1

 {\displaystyle G_{1}}

  to

 G

 3

 {\displaystyle G_{3}}

 , so the minimal number of operations needed to transform

 G

 1

 {\displaystyle G_{1}}

  to

 G

 3

 {\displaystyle G_{3}}

  must be no longer than this.


To compute the DCJ distance between two genomes

 G

 1

 {\displaystyle G_{1}}

  and

 G

 2

 {\displaystyle G_{2}}

  with the same set of synteny blocks, we construct a  bipartite   multigraph  known as the adjacency graph

 A
 =
 (
 V
 (

 G

 1

 )
 ,
 V
 (

 G

 2

 )
 ,
 E
 )

 {\displaystyle A=(V(G_{1}),V(G_{2}),E)}

  of the genomes. The vertex set of the adjacency graph is

 (
 V
 (

 G

 1

 )
 ,
 V
 (

 G

 2

 )
 )

 {\displaystyle (V(G_{1}),V(G_{2}))}

 , where

 V
 (

 G

 1

 )

 {\displaystyle V(G_{1})}

  is the vertex set of

 G

 1

 {\displaystyle G_{1}}

  and

 V
 (

 G

 2

 )

 {\displaystyle V(G_{2})}

  is the vertex set of

 G

 2

 {\displaystyle G_{2}}

 . For

 a
 ∈
 V
 (

 G

 1

 )

 {\displaystyle a\in V(G_{1})}

  and

 b
 ∈
 V
 (

 G

 2

 )

 {\displaystyle b\in V(G_{2})}

 , we have

 (
 a
 ,
 b
 )
 ∈
 E

 {\displaystyle (a,b)\in E}

  if

 a

 {\displaystyle a}

  and

 b

 {\displaystyle b}

  are an extremity of the same synteny block. If

 a

 {\displaystyle a}

  and

 b

 {\displaystyle b}

  share two extremities, we add two edges between

 a

 {\displaystyle a}

  and

 b

 {\displaystyle b}

  to

 G

 {\displaystyle G}

 .


If

 A
 =
 B

 {\displaystyle A=B}

 , we see that the adjacency graph is composed entirely of paths of length 1, connecting two telomeres, and cycles of length 2, connecting two adjacencies. We can use this fact to calculate

 d

 D
 C
 J

 {\displaystyle d_{DCJ}}

 {\displaystyle }

 . Let

 N

 {\displaystyle N}

  be the number of synteny blocks in genomes

 G

 1

 {\displaystyle G_{1}}

  and

 G

 2

 {\displaystyle G_{2}}

 ,

 C

 {\displaystyle C}

  be the number of cycles in their adjacency graph, and

 I

 {\displaystyle I}

  be the number of paths in their adjacency graph. Then

 d

 D
 C
 J

 (

 G

 1

 ,

 G

 2

 )
 =
 N
 −
 (
 C
 +
 1

 /

 2
 )
 .

 {\displaystyle d_{DCJ}(G_{1},G_{2})=N-(C+1/2).}

  The proof shows that each DCJ operation can decrease

 N
 −
 (
 C
 +
 1

 /

 2
 )

 {\displaystyle N-(C+1/2)}

  by no more than 1, and that if

 G

 1

 ≠

 G

 2

 {\displaystyle G_{1}\neq G_{2}}

 , there exists an operation decreasing

 N
 −
 (
 C
 +
 I

 /

 2
 )
 .

 {\displaystyle N-(C+I/2).}

 . This proves that

 d

 D
 C
 J

 {\displaystyle d_{DCJ}}

  is always defined, and gives a method for its calculation. Since it is easy to count cycles and paths,

 d

 D
 C
 J

 {\displaystyle d_{DCJ}}

  can be found in linear time. [ 3 ]


Duke Institute for Genome Sciences and Policy (IGSP)  is an institution established at  Duke University  to address the many issues in science and policy that the  Genome Revolution  and recent advances in  Genome Science  are expected to create. It is located in the  CIEMAS  building at Duke University and houses some well known researchers in the genomics field including  Huntington F. Willard , who is the director of the IGSP. In June 20145, IGSP was closed and transitioned to a new model of interrelated units. [ 1 ]


This article about a scientific organization is a  stub . You can help Wikipedia by  expanding it .


Duplex sequencing  is a  library  preparation and analysis method for  next-generation sequencing  (NGS) platforms that employs random tagging of double-stranded  DNA  to detect mutations with higher accuracy and lower error rates.


This method uses  degenerate molecular tags  in addition to sequencing adapters to recognize reads originating from each strand of DNA. As the two strands are complementary, true mutations are found at the same position in both strands. In contrast, PCR or sequencing errors result in mutations in only one strand and can thus be discounted as technical error. Duplex sequencing theoretically can detect mutations with frequencies as low as 5 x 10 −8  --that is more than 10,000 times higher in accuracy compared to the conventional next-generation sequencing methods. [ 1 ] [ 2 ]


The estimated error rate of standard next-generation sequencing platforms is 10 −2  to 10 −3  per base call. With this error rate, billions of base calls that are produced by NGS will result in millions of errors. The errors are introduced during sample preparation and sequencing such as  polymerase chain reaction , sequencing, and image analysis errors. While the NGS platforms' error rate is acceptable in some applications such as detection of  clonal variants , it is a major limitation for applications that require higher accuracy for detection of low-frequency variants such as detection of intra-organismal  mosaicism ,  subclonal variants  in genetically  heterogeneous cancers , or circulating tumor DNA. [ 3 ] [ 4 ] [ 5 ]


Several library preparation strategies have been developed that increase accuracy of NGS platforms such as molecular barcoding and circular consensus sequencing method. [ 6 ] [ 7 ] [ 8 ] [ 9 ]  Like NGS platforms, the data generated by these methods originates from a single strand of DNA, and therefore the errors that are introduced during  PCR amplification ,  tissue processing ,  DNA extraction , hybridization capture (where used) or  DNA sequencing  itself can still be distinguished as a true variant. The duplex sequencing method addresses this problem by taking advantage of the complementary nature of two strands of DNA and confirming only variants that are present in both strands of DNA. Because the probability of two complementary errors arising at the same location in both strands is exceedingly low, duplex sequencing increases the accuracy of sequencing significantly. [ 1 ] [ 6 ] [ 8 ] [ 10 ]


Duplex sequencing tagged adapters can be used in combination with the majority of NGS adapters. In the figures and workflow section of this article, Illumina sequencing adapters are used as an example following the original published protocol. [ 1 ] [ 2 ]


Two oligonucleotides are used for this step (Figure 1: Adapter oligos). One of the oligonucleotides contains a 12-nucleotide single-stranded random tag sequence followed by a fixed 5' nucleotide sequence (black sequence in Figure 1). In this step,  oligonucleotides  are  annealed  in a complementary region by incubation at the required temporal condition. [ 1 ] [ 2 ]


The adapters that  annealed  successfully are extended and synthesized by a  DNA polymerase  to complete a double-stranded adapter containing complementary tags (Figure 1). [ 1 ] [ 2 ]


The extended double-stranded adapters are cleaved by  HpyCH4III  at a specific  restriction site  located at 3’ side of the tag sequence and will result in a 3’-dT overhang that will be ligated to the 3’-dA overhang on DNA libraries in the adapter  ligation  step (Figure 1). [ 1 ] [ 2 ]


Double-stranded DNA is  sheared  using one of these methods:  sonication , enzymatic digestion, or nebulization. Fragments are size selected using Ampure XP beads.  Gel -based size selection is not recommended since it can cause melting of DNA double strands and DNA damage due to  UV exposure . The size of selected fragments of DNA are subjected to 3’-end-dA-tailing. [ 1 ] [ 2 ]


In this step, two tagged adapters are ligated from 3’-dT-tails to 3’-dA-tails on both sides of double-stranded DNA library fragments. This process results in double-stranded library fragments that contain two random tags (α and β) on each side that are the reverse complement of each other (Figure 1 and 2). The "DNA: adapter" ratio is crucial in determining the success of ligation. [ 1 ] [ 2 ]


In the last step of duplex sequencing library preparation, Illumina sequencing adapters are added to the tagged double stranded libraries by PCR amplification using primers containing sequencing adapters. During PCR amplification, both complementary strands of DNA are amplified and generate two types of PCR products. Product 1 derives from strand 1's which have a unique tag sequence (called α in Figure 2) next to the Illumina adapter 1 and product 2 has a unique tag (called β in Figure 2) next to the Illumina adapter 1. (In each strand, tag α is the reverse complement of tag β and vice versa). The libraries containing duplex tags and Illumina adapters are sequenced using the Illumina TruSeq system. Reads that are originating from every single strand of DNA form a group of reads (tag families) that share the same tag. The detected families of reads will be used in the next step for analyzing sequencing data. [ 1 ] [ 2 ]


Adapter ligation efficiency is very important in successful duplex sequencing. An extra amount of libraries or adapters can affect the DNA to adapter balance, resulting in inefficient ligation and an excess amount of primer dimers, respectively. Therefore, it is important to keep the molar concentration of DNA to adapter at the optimal ratio (0.05). [ 2 ]


The efficiency of duplex sequencing depends on the final number of DCSs which is directly related to the number of reads in each family (family size). If the family size is too small then the DCS can not be assembled and if too many reads are sharing the same tag, the data yield will be low. Family size is determined by the amount of DNA template needed for PCR amplification and the dedicated sequencing lane fraction. The optimal tag family size is between 6 and 12 members. To obtain the optimal family size, the amounts of DNA template and the dedicated sequencing lane fraction need to be adjusted. The following formula takes into account the most important variables that can affect depth of coverage (N=40DG÷R) where "N" is the number of reads, "D" is the desired depth of coverage, "G" is the size of DNA target in base pair, and "R" is final read length.


Each duplex sequencing read contains a fixed 5-nucleotide sequence (shown in figures in black) located  upstream  of the 12-nucleotide tag sequence. The reads are filtered if they do not have the expected 5-nucleotide sequence or have more than nine identical or ambiguous bases within each tag. The two 12-nucleotide tags at each end of the reads are combined and moved to the read header. Two families of reads are formed that originate from the two strands of DNA. One family contains reads with αβ header originating from strand 1 and the second contains reads with βα header originating from strand 2 (Figure 2). The reads are then trimmed by removing the fixed 5-base pair sequence and 4 error-prone nucleotides located at the sites of ligation and end repair. [ 1 ] [ 2 ]  The remaining reads are assembled to  consensus sequences  using SSCS and DCS assemblies.


Trimmed sequences from the previous step are  aligned  to the  reference genome  using a  Burrows–Wheeler aligner  (BWA) and the unmapped reads are removed. The aligned reads that have the same 24-base pair tag sequence and genomic region are detected and grouped (family αβ and βα in Figure 2). Each group represents a “tag family.” Tag families with fewer than three members are not analyzed. To remove errors that arise during PCR amplification or sequencing, mutations that are supported by less than 70% of the members (reads) are filtered out from the analysis. A consensus sequence is then generated for each family using the identical sequences in each position of the remaining reads. The consensus sequence is called the SSCS. It increases the NGS accuracy to about 20 fold higher; however, this method relies on the sequencing information from single strands of DNA and therefore is sensitive to the errors induced at the first round or before PCR amplification. [ 1 ] [ 2 ]


The reads from the last step are realigned to the reference genome. In this method, SSCS family pairs that have complementary tags will be grouped (family αβ and βα in Figure 2). These reads originate from two complementary strands of DNA. High confidence sequences are selected based on the perfectly matched base calls of each family. The final sequence is called the DCS. True mutations are those that match perfectly between complementary SSCSs. This step filters out remaining errors raised during the first round of PCR amplification or during sample preparation. [ 1 ] [ 2 ]


The high error rate (0.01-0.001) of standard NGS platforms introduced during sample preparation or sequencing is a major limitation for the detection of variants present in a small fraction of cells. Due to the duplex tagging system and use of information in both strands of DNA, duplex sequencing has significantly decreased the error rate of sequencing about 10 million fold using both SSCS and DCS method. [ 1 ] [ 2 ] [ 10 ]


It is challenging to identify rare variants accurately using standard NGS methods with a mutation rate of (10 −2  to 10 −3 ). Errors that happen early during sample preparation can be detected as rare variants. An example of such errors is C>A/G>T  transversion , detected in low frequencies using deep sequencing or targeted capture data and arising due to DNA oxidation during sample preparation. [ 11 ]  These types of false-positive variants are filtered out by the duplex sequencing method since mutations need to be accurately matched in both strands of DNA to be validated as true mutations. Duplex sequencing can theoretically detect mutations with frequencies as low as 10 −8  compared to the 10 −2  rate of standard NGS methods. [ 1 ] [ 2 ] [ 10 ]


Another advantage of duplex sequencing is that it can be used in combination with the majority of NGS platforms without making significant changes to the standard protocols.


Because duplex sequencing provides a significantly higher sequencing accuracy and uses information in both strands of DNA, this method needs a much higher sequencing depth and therefore is a costly approach. The expense limits its application to targeted and amplicon sequencing at present time and will not be applicable for whole genome sequencing approaches. However, the application of duplex sequencing for larger DNA targets will be more feasible when the cost of NGS decreases.


Duplex sequencing is a new method and its efficiency was studied in limited applications such as detecting  point mutations  using targeted capture sequencing. [ 12 ]  More studies need to be performed to expand the application and feasibility of duplex sequencing to more complex samples with larger numbers of mutations, indels, and  copy number variations .


Duplex sequencing and the significant increase of sequencing accuracy has had an important impact on applications such as detection of rare human genetic variants, detection of subclonal mutations involved in mechanisms of resistance to therapy in genetically heterogeneous cancers, screening variants in circulating tumor DNA as a non-invasive biomarker, and  prenatal screening  for genetic abnormalities in a fetus.


Another application for duplex sequencing is in the detection of DNA/RNA copy numbers by estimating the relative frequency of variants. A method for counting PCR template molecules with application to next-generation sequencing is an example. [ 1 ]


A list of required tools and packages for SSCS and DCS analysis can be found  online.


Personalized medicine  involves  medical treatments  based on the characteristics of individual patients, including their  medical history ,  family history , and  genetics . Although personal genetic information is becoming increasingly important in healthcare, there is a lack of sufficient education in  medical genetics  among physicians and the general public. [ 1 ] [ 2 ] [ 3 ]  For example,  pharmacogenomics  (genetic factors influencing drug response) is practiced worldwide by only a limited number of pharmacists, although most pharmacy colleges in the United States now include it in their curriculum. [ 3 ]  It is also increasingly common for  genetic testing  to be offered directly to consumers, who subsequently seek out educational materials and bring their results to their doctors. [ 1 ]  Issues involving genetic testing also invariably lead to ethical and legal concerns, such as the potential for inadvertent effects on family members, increased insurance rates, or increased psychological stress. [ 4 ] [ 5 ]


As of 2009, the  primary of the care physicians did not have adequate training in genetics or  genomics . [ 2 ]  Although medical school curricula typically include medical genetics, fewer than half offer a standalone course, and the emphasis on practical applications is weak. [ 1 ] [ 2 ] [ 6 ]


In the United States,  Stanford University  was the first medical school in the United States to offer a course teaching the interpretation of genetic data. [ 7 ]  Students were able to study their own  genotypes , determined using commercially available  genotyping platforms  ( 23andMe  or  Navigenics ). [ 8 ]  Although there was skepticism that this would improve educational outcomes, [ 8 ]  a survey later showed that this had increased students’ enthusiasm for the subject. [ 9 ]  A similar class is offered at  Mount Sinai School of Medicine , launched in 2012, in which students have the option of analyzing their entire genome sequence instead of only their genotype. [ 10 ] [ 11 ]


In 2010, the  University of California, Berkeley  offered entering students a genetic test for  SNPs  affecting alcohol, lactose, and folate metabolism. [ 12 ] [ 13 ]  The goal was “to spark discussion during orientation on how genetic testing works, the results of the students' tests and their decisions on whether or not to participate.” [ 8 ]  However, criticism of the program led to an informational hearing by the California State Committee on Higher Education, and a bill was introduced by  Chris Norby  to prevent California state universities from genetically testing their students. [ 14 ] [ 15 ]  The California Department of Public Health concluded that the program constituted clinical testing, and the university released only aggregate information instead of personal results. [ 16 ] [ 17 ]


As described in the preceding section, in some courses on personalized medicine, students have been able to study their personal genetic information. For the Stanford course, which was designed for graduate and medical students, a review was conducted in 2009-2010 by a “joint genotyping task force” including research and clinical faculty, biomedical ethicists, genetic counselors, and legal counsel. [ 4 ] [ 12 ]  The recommendations adopted included: making genetic testing optional (with instructors blinded to the choice of the students), strict data confidentiality (only aggregate data was made available during discussions), incorporation of lectures discussing issues related to personal genotyping, and availability of genetic counseling to students if necessary. [ 4 ]


Epigenomics  is the study of the complete set of  epigenetic  modifications on the genetic material of a cell, known as the  epigenome . The field is analogous to  genomics  and  proteomics , which are the study of the genome and proteome of a cell. [ 1 ] [ 2 ]  Epigenetic modifications are reversible modifications on a cell's DNA or histones that affect gene expression without altering the DNA sequence. [ 3 ]  Epigenomic maintenance is a continuous process and plays an important role in stability of eukaryotic genomes by taking part in crucial biological mechanisms like DNA repair. [ 4 ] [ 5 ]  Plant flavones are said to be inhibiting epigenomic marks that cause cancers. [ 6 ]  Two of the most characterized epigenetic modifications are  DNA methylation  and  histone modification . Epigenetic modifications play an important role in gene expression and regulation, and are involved in numerous cellular processes such as in  differentiation/development [ 7 ]  and  tumorigenesis . [ 8 ]  The study of epigenetics on a global level has been made possible only recently through the adaptation of genomic high-throughput assays. [ 9 ] [ 7 ]


Genomic modifications that alter gene expression that cannot be attributed to modification of the primary DNA sequence and that are heritable  mitotically  and  meiotically  are classified as epigenetic modifications.  DNA methylation and histone modification are among the best characterized epigenetic processes. [ 3 ]


The first epigenetic modification to be characterized in depth was DNA methylation.   As its name implies, DNA methylation is the process by which a  methyl group  is added to DNA.  The enzymes responsible for catalyzing this reaction are the  DNA methyltransferases (DNMTs) .  While DNA methylation is stable and heritable, it can be reversed by an antagonistic group of enzymes known as DNA de-methylases.   In eukaryotes, methylation is most commonly found on the carbon 5 position of  cytosine residues  (5mC) adjacent to  guanine , termed  CpG dinucleotides . [ 9 ] [ 10 ]


DNA methylation patterns vary greatly between species and even within the same organism.  The usage of methylation among animals is quite different; with  vertebrates  exhibiting the highest levels of 5mC and  invertebrates  more moderate levels of 5mC.   Some organisms like  Caenorhabditis elegans  have not been demonstrated to have 5mC nor a conventional DNA methyltransferase; this would suggest that other mechanisms other than DNA methylation are also involved. [ 11 ]


Within an organism, DNA methylation levels can also vary throughout development and by region.  For example, in mouse primordial  germ cells , a genome wide de-methylation even occurs; by implantation stage, methylation levels return to their previous somatic values. [ 11 ]   When DNA methylation occurs at  promoter regions , the sites of transcription initiation, it has the effect of repressing gene expression. This is in contrast to unmethylated promoter regions which are associated with actively expressed genes. [ 9 ]


The mechanism by which DNA methylation represses gene expression is a multi-step process.  The distinction between methylated and unmethylated cytosine residues is carried out by specific DNA-binding proteins.   Binding of these proteins recruit  histone deacetylases (HDACs)  enzyme which initiate  chromatin remodeling  such that the DNA becoming less accessible to transcriptional machinery, such as  RNA polymerase , effectively repressing gene expression. [ 12 ]


In  eukaryotes , genomic DNA is coiled into protein-DNA complexes called  chromatin .    Histones , which are the most prevalent type of protein found in chromatin, function to condense the DNA; the net positive charge on histones facilitates their bonding with DNA, which is negatively charged.  The basic and repeating units of chromatin,  nucleosomes , consist of an  octamer of histone proteins  (H2A, H2B, H3 and H4) and a 146 bp length of DNA wrapped around it.  Nucleosomes and the DNA connecting form a 10 nm diameter chromatin fiber, which can be further condensed. [ 13 ] [ 14 ]


Chromatin packaging of DNA varies depending on the cell cycle stage and by local DNA region. [ 15 ]  The degree to which chromatin is condensed is associated with a certain transcriptional state. Unpackaged or loose chromatin is more transcriptionally active than tightly packaged chromatin because it is more accessible to transcriptional machinery. By remodeling chromatin structure and changing the density of DNA packaging, gene expression can thus be modulated. [ 14 ]


Chromatin remodeling occurs via  post-translational modifications  of the  N-terminal tails of core histone proteins . [ 16 ]  The collective set of histone modifications in a given cell is known as the  histone code . Many different types of histone modification are known, including:  acetylation ,  methylation ,  phosphorylation ,  ubiquitination ,  SUMOylation ,  ADP-ribosylation ,  deamination  and  proline isomerization ; acetylation, methylation, phosphorylation and ubiquitination have been implicated in gene activation whereas methylation, ubiquitination, SUMOylation, deimination and proline isomerization have been implicated in gene repression.  Note that several modification types including methylation, phosphorylation and ubiquitination can be associated with different transcriptional states depending on the specific amino acid on the histone being modified.  Furthermore, the DNA region where histone modification occurs can also elicit different effects; an example being methylation of the 3rd core histone at lysine residue 36 (H3K36).  When H3K36 occurs in the coding sections of a gene, it is associated with gene activation but the opposite is found when it is within the promoter region. [ 14 ]


Histone modifications regulate gene expression by two mechanisms: by  disruption of the contact between nucleosomes  and by  recruiting chromatin remodeling ATPases .   An example of the first mechanism occurs during the acetylation of  lysine  terminal tail amino acids, which is catalyzed by  histone acetyltransferases (HATs) .  HATs are part of a multiprotein complex that is recruited to chromatin when activators bind to DNA binding sites.  Acetylation effectively neutralizes the basic charge on lysine, which was involved in stabilizing chromatin through its affinity for negatively charged DNA.   Acetylated histones therefore favor the dissociation of nucleosomes and thus unwinding of chromatin can occur.  Under a loose chromatin state, DNA is more accessible to transcriptional machinery and thus expression is activated.  The process can be reversed through removal of histone acetyl groups by deacetylases. [ 14 ] [ 16 ]


The second process involves the recruitment of chromatin remodeling complexes by the binding of activator molecules to corresponding enhancer regions.  The nucleosome remodeling complexes reposition nucleosomes by several mechanisms, enabling or disabling accessibility of transcriptional machinery to DNA.  The  SWI/SNF protein complex  in yeast is one example of a chromatin remodeling complex that regulates the expression of many genes through chromatin remodeling. [ 14 ] [ 17 ]


Epigenomics shares many commonalities with other genomics fields, in both methodology and in its abstract purpose.  Epigenomics seeks to identify and characterize epigenetic modifications on a global level, similar to the study of the complete set of DNA in genomics or the complete set of proteins in a cell in proteomics. [ 1 ] [ 2 ]  The logic behind performing epigenetic analysis on a global level is that inferences can be made about epigenetic modifications, which might not otherwise be possible through analysis of specific loci. [ 13 ] [ 1 ]  As in the other genomics fields, epigenomics relies heavily on  bioinformatics , which combines the disciplines of biology, mathematics and computer science. [ 18 ]  However while epigenetic modifications had been known and studied for decades, it is through these advancements in bioinformatics technology that have allowed analyses on a global scale.  Many current techniques still draw on older methods, often adapting them to genomic assays as is described in the next section.


The cellular processes of  transcription ,  DNA replication  and  DNA repair  involve the interaction between genomic DNA and nuclear proteins.   It had been known that certain regions within chromatin were extremely susceptible to  DNAse I  digestion, which cleaves DNA in a low sequence specificity manner.  Such  hypersensitive sites  were thought to be transcriptionally active regions, as evidenced by their association with  RNA polymerase  and  topoisomerases I and II . [ 19 ]


It is now known that sensitivity to DNAse I regions correspond to regions of chromatin with loose DNA-histone association.   Hypersensitive sites most often represent promoters regions, which require for DNA to be accessible for DNA binding transcriptional machinery to function. [ 20 ]


Histone modification was first detected on a genome wide level through the coupling of  chromatin immunoprecipitation (ChIP)  technology with  DNA microarrays , termed  ChIP-Chip . [ 13 ]  However instead of isolating a DNA-binding transcription factor or enhancer protein through chromatin immunoprecipitation, the proteins of interest are the modified histones themselves.   First, histones are  cross-linked  to DNA in vivo through light chemical treatment (e.g.,  formaldehyde ).  The cells are next lysed, allowing for the chromatin to be extracted and fragmented, either by  sonication  or treatment with a non-specific restriction enzyme (e.g.,  micrococcal nuclease ).  Modification-specific antibodies in turn, are used to immunoprecipitate the DNA-histone complexes. [ 14 ]  Following immunoprecipitation, the DNA is purified from the histones, amplified via PCR and labeled with a  fluorescent tag  (e.g.,  Cy5, Cy3 ).  The final step involves hybridization of labeled DNA, both immunoprecipitated DNA and non-immunoprecipitated onto a microarray containing immobilized gDNA.  Analysis of the relative signal intensity allows the sites of histone modification to be determined. [ 21 ] [ 22 ]


ChIP-chip was used extensively to characterize the global histone modification patterns of  yeast .   From these studies, inferences on the function of histone modifications were made; that transcriptional activation or repression was associated with certain histone modifications and by region.  While this method was effective providing near full coverage of the yeast epigenome, its use in larger genomes such as humans is limited. [ 13 ] [ 14 ]


In order to study histone modifications on a truly genome level, other high-throughput methods were coupled with the chromatin immunoprecipitation, namely:  SAGE: serial analysis of gene expression  (ChIP-SAGE),  PET: paired end ditag sequencing   ( ChIP-PET ) and more recently, next-generation sequencing  (ChIP-Seq) .  ChIP-seq follows the same protocol for chromatin immunoprecipitation but instead of amplification of purified DNA and hybridization to a microarray, the DNA fragments are directly sequenced using next generation parallel re-sequencing.  It has proven to be an effective method for analyzing the global histone modification patterns and protein target sites, providing higher resolution than previous methods. [ 13 ] [ 21 ]


Techniques for characterizing primary DNA sequences could not be directly applied to methylation assays.  For example, when DNA was amplified in  PCR  or bacterial cloning techniques, the methylation pattern was not copied and thus the information lost.  The  DNA hybridization technique  used in DNA assays, in which radioactive probes were used to map and identify DNA sequences, could not be used to distinguish between methylated and non-methylated DNA. [ 23 ] [ 9 ]


The earliest methylation detection assays used methylation modification sensitive  restriction endonucleases .   Genomic DNA was digested with both methylation-sensitive and insensitive restriction enzymes recognizing the same restriction site.  The idea being that whenever the site was methylated, only the methylation insensitive enzyme could cleave at that position.   By comparing  restriction fragment  sizes generated from the methylation-sensitive enzyme to those of the methylation-insensitive enzyme, it was possible to determine the methylation pattern of the region.  This analysis step was done by amplifying the restriction fragments via PCR, separating them through  gel electrophoresis  and analyzing them via  southern blot  with probes for the region of interest. [ 23 ] [ 9 ]


This technique was used to compare the DNA methylation modification patterns in the human adult and  hemoglobin gene loci .  Different regions of the gene (gamma delta beta globin) were known to be expressed at different stages of development. [ 24 ]  Consistent with a role of DNA methylation in gene repression, regions that were associated with high levels of DNA methylation were not actively expressed. [ 25 ]


This method was limited not suitable for studies on the global methylation pattern, or ‘methylome’. Even within specific loci it was not fully representative of the true methylation pattern as only those restriction sites with corresponding methylation sensitive and insensitive restriction assays could provide useful information. Further complications could arise when incomplete digestion of DNA by restriction enzymes generated false negative results. [ 9 ]


DNA methylation profiling on a large scale was first made possible through the  Restriction Landmark Genome Scanning (RLGS)  technique.  Like the locus-specific DNA methylation assay, the technique identified methylated DNA via its digestion methylation sensitive enzymes. However it was the use of  two-dimensional gel electrophoresis  that allowed be characterized on a broader scale. [ 9 ]


However it was not until the advent of microarray and next generation sequencing technology when truly high resolution and genome-wide DNA methylation became possible. [ 26 ]  As with RLGS, the endonuclease component is retained in the method but it is coupled to new technologies.  One such approach is the differential methylation hybridization (DMH), in which one set of genomic DNA is digested with methylation-sensitive restriction enzymes and a parallel set of DNA is not digested.  Both sets of DNA are subsequently amplified and each labelled with fluorescent dyes and used in two-colour array hybridization. The level of DNA methylation at a given loci is determined by the relative intensity ratios of the two dyes.   Adaptation of next generation sequencing to DNA methylation assay provides several advantages over array hybridization.  Sequence-based technology provides higher resolution to allele specific DNA methylation, can be performed on larger genomes, and does not require creation of DNA microarrays which require adjustments based on CpG density to properly function. [ 9 ]


Bisulfite sequencing  relies on chemical conversion of unmethylated cytosines exclusively, such that they can be identified through standard DNA sequencing techniques.   Sodium bisulfate  and alkaline treatment does this by converting unmethylated cytosine residues into  uracil  while leaving methylated cytosine unaltered.  Subsequent amplification and sequencing of untreated DNA and sodium bisulphite treated DNA allows for methylated sites to be identified.  Bisulfite sequencing, like the traditional restriction based methods, was historically limited to methylation patterns of specific gene loci, until whole genome sequencing technologies became available.  However, unlike traditional restriction based methods, bisulfite sequencing provided resolution on a nucleotide level. [ 23 ] [ 9 ]


Limitations of the bisulfite technique  include the incomplete conversion of cytosine to uracil, which is a source of false positives. Further, bisulfite treatment also causes DNA degradation and requires an additional purification step to remove the sodium bisulfite. [ 9 ]


Next-generation sequencing is well suited in complementing bisulfite sequencing in  genome-wide methylation analysis .  While this now allows for methylation pattern to be determined on the highest resolution possible, on the single nucleotide level, challenges still remain in the assembly step because of reduced sequence complexity in bisulphite treated DNA.   Increases in read length seek to address this challenge, allowing for whole genome shotgun bisulphite sequencing (WGBS) to be performed.  The WGBS approach using an Illumina Genome Analyzer platform and has already been implemented in  Arabidopsis thaliana . [ 9 ]  Reduced representation genomic methods based on bisulfite sequencing exist as well, [ 27 ] [ 28 ]  and they are particularly suitable for species with large genome sizes. [ 29 ]


Chromatin accessibility is the measure of how "accessible" or "open" a region of genome is to transcription or binding of transcription factors. The regions which are inaccessible (i.e. because they're bound by  nucleosomes ) are not actively transcribed by the cell while open and accessible regions are actively transcribed. [ 30 ]  Changes in chromatin accessibility are important epigenetic regulatory processes that govern cell- or context-specific expression of genes. [ 31 ]  Assays such as MNase-seq, DNase-seq, ATAC-seq or FAIRE-seq are routinely used to understand the accessible chromatin landscape of cells. The main feature of all these methods is that they're able to selectively isolate either the DNA sequences that are bounded to the  histones , or those that are not. These sequences are then compared to a reference genome that allows to identify their relative position. [ 32 ]


MNase-seq and DNase-seq both follow the same principles, as they employ lytic enzymes that target nucleic acids to cut the DNA strands unbounded by nucleosomes or other proteic factors, while the bounded pieces are sheltered, and can be retrieved and analysed. Since active, unbound regions are destroyed, their detection can only be indirect, by sequencing with a  Next Generation Sequencing  technique and comparison with a reference. MNase-seq utilises a micrococcal nuclease that produces a single strand cleavage on the opposite strand of the target sequence. [ 33 ]  DNase-seq employs  DNase I , a non-specific double strand-cleaving endonuclease. This technique has been used to such an extent that nucleosome-free regions have been labelled as DHSs, DNase I hypersensitive sites, [ 34 ]   and has been ENCODE consortium's election method for genome wide chromatin accessibility analyses. [ 35 ]  The main issue of this technique is that the cleavage distribution can be biased, [ 36 ]  lowering the quality of the results.


FAIRE-seq (Formaldehyde-Assisted Isolation of Regulatory Elements) requires as its first step crosslinking of the DNA with nucleosomes, then DNA shearing by  sonication . The free and linked fragments are separated with a traditional phenol-chloroform extraction, since the proteic fraction is stuck in the interphase while the unlinked DNA shifts to the aqueous phase and can be analysed with various methods. [ 37 ]  Sonication produces random breaks, and therefore is not subject to any kind of bias, and is also the bigger length of the fragments (200-700 nt) makes this technique suitable for wider regions, while it's unable to resolve the single nucleosome. [ 32 ]  Unlike the nuclease-based methods, FAIRE-seq allows the direct identification of the transcriptionally active sites, and a less laborious sample preparation. [ 38 ]


ATAC-seq is based on the activity of Tn5 transposase. The transposase is used to insert tags in the genome, with higher frequency on regions not covered by proteic factors. The tags are then used as adapters for PRC or other analytical tools. [ 39 ]


Polymerase sensitivity in  single-molecule real-time sequencing  made it possible for scientists to directly detect epigenetic marks such as methylation as the polymerase moves along the DNA molecule being sequenced. [ 40 ]  Several projects have demonstrated the ability to collect genome-wide epigenetic data in bacteria. [ 41 ] [ 42 ] [ 43 ] [ 44 ]


Nanopore sequencing  is based on changes of electrolytic current signals according to base modifications (e.g. Methylation). A  polymerase  mediates the entrance of  ssDNA  in the pore: the ion-current variation is modulated by a section of the pore and the consequently generated difference is recorded revealing the position of  CpG . Discrimination between hydroxymethylation and  methylation  is possible thanks to solid-state  nanopores  even if the current while passing through the high-field region of the pore may be slightly influenced in it. [ 45 ]  As a reference amplified DNA is used which will not present copied methylationed sites after the  PCR  process. [ 46 ]  The Oxford Nanopore Technologies  MinION  sequencer is a technology where, according to a hidden  Markov  model, it is possible to distinguish unmethylated cytosine from the methylated one even without chemical treatment that acts to enhance the signal of that modification. The data are registered commonly in picoamperes during established time. Other devices are the Nanopolish and the SignaAlign: the former expresses the frequency of a methylation in a read while the latter gives a probability of it derived from the sum of all the reads. [ 47 ]


Single-molecule real-time sequencing (SMRT)   is a single-molecule DNA sequencing method. Single-molecule real-time sequencing utilizes a  zero-mode waveguide  (ZMW).
A single DNA polymerase enzyme is bound to the bottom of a ZMW with a single molecule of DNA as a template.
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 the detector detects the fluorescent signal of the nucleotide incorporation.
As the sequencing occurs, the polymerase enzyme kinetics shift when it encounters a region of methylation or any other base modification. When the enzyme encounters chemically modified bases, it will slow down or speed up in a uniquely identifiable way.
Fluorescence pulses in SMRT sequencing are characterized not only by their emission spectra but also by their duration and by the interval between successive pulses. These metrics, defined as  pulse width  and interpulse duration (IPD), add valuable information about DNA polymerase kinetics. Pulse width is a function of all kinetic steps after nucleotide binding and up to fluorophore release, and IPD is determined by the kinetics of nucleotide binding and polymerase translocation.


In 2010 a team of scientists demonstrated the use of single-molecule real-time sequencing for direct detection of modified nucleotide in the DNA template including  N6-methyladenosine ,  5-methylcytosine  and  5-hydroxylcytosine . These various modifications affect polymerase kinetics differently, allowing discrimination between them. [ 48 ]


In 2017, another team proposed a combined bisulfite conversion with third-generation single-molecule real-time sequencing, it is called single-molecule real-time bisulfite sequencing (SMRT-BS), which is an accurate targeted CpG methylation analysis method capable of a high degree of multiplying and long read lengths (1.5 kb) without the need for PCR amplicon sub-cloning. [ 49 ]


First mathematical models for different nucleosome states affecting gene expression were introduced in 1980s [ref]. Later, this idea was almost forgotten, until the experimental evidence has indicated a possible role of covalent histone modifications as an  epigenetic code . [ 50 ]  In the next several years, high-throughput data have indeed uncovered the abundance of epigenetic modifications and their relation to chromatin functioning which has motivated new theoretical models for the appearance, maintaining and changing these patterns,. [ 51 ] [ 52 ]  These models are usually formulated in the frame of one-dimensional lattice approaches. [ 53 ]


Epistasis  refers to genetic interactions in which the  mutation  of one gene masks the  phenotypic  effects of a mutation at another  locus . [ 1 ]  Systematic analysis of these epistatic interactions can provide insight into the structure and function of genetic pathways. Examining the phenotypes resulting from pairs of mutations helps in understanding how the function of these genes intersects. Genetic interactions are generally classified as either Positive/Alleviating or Negative/Aggravating. Fitness epistasis (an interaction between non- allelic  genes) is positive (in other words, diminishing, antagonistic or buffering) when a loss of function mutation of two given genes results in exceeding the  fitness  predicted from individual effects of deleterious mutations, and it is negative (that is, reinforcing, synergistic or aggravating) when it decreases fitness. [ 2 ]  Ryszard Korona and Lukas Jasnos showed that the epistatic effect is usually positive in  Saccharomyces cerevisiae . Usually, even in case of positive interactions double mutant has smaller fitness than single mutants. [ 2 ]  The positive interactions occur often when both genes lie within the same  pathway [ 3 ]  Conversely, negative interactions are characterized by an even stronger defect than would be expected in the case of two single mutations, and in the most extreme cases (synthetic sick/lethal) the double mutation is lethal. This aggravated phenotype arises when genes in compensatory pathways are both  knocked out .


High-throughput methods of analyzing these types of interactions have been useful in expanding our knowledge of genetic interactions.  Synthetic genetic arrays  (SGA),  diploid based synthetic lethality analysis on microarrays  (dSLAM), and  epistatic miniarray profiles  (E-MAP) are three important methods which have been developed for the systematic analysis and mapping of genetic interactions. This systematic approach to studying epistasis on a genome wide scale has significant implications for  functional genomics . By identifying the negative and positive interactions between an unknown gene and a set genes within a known pathway, these methods can elucidate the function of previously uncharacterized genes within the context of a  metabolic  or developmental pathway.


In order to understand how information about epistatic interactions relates to gene pathways, consider a simple example of vulval cell differentiation in  C. elegans . Cells differentiate from Pn cells to Pn.p cells to VP cells to vulval cells. Mutation of lin-26 [ 4 ]  blocks differentiation of Pn cells to Pn.p cells. Mutants of lin-36 [ 5 ]  behave similarly, blocking differentiation at the transition to VP cells. In both cases, the resulting phenotype is marked by an absence of vulval cells as there is an upstream block in the differentiation pathway. A double mutant in which both of these genes have been disrupted exhibits an equivalent phenotype that is no worse than either single mutant. The upstream disruption at lin-26 masks the phenotypic effect of a mutation at lin-36  [ 1 ]  in a classic example of an alleviating epistatic interaction.


Aggravating mutations on the other hand give rise to a phenotype which is worse than the cumulative effect of each single mutation. This aggravated phenotype is indicative of two genes in compensatory pathways. In the case of the single mutant a parallel pathway is able to compensate for the loss of the disrupted pathway however, in the case of the double mutant the action of this compensatory pathway is lost as well, resulting in the more dramatic phenotype observed. This relationship has been significantly easier to detect than the more subtle alleviating phenotypes and has been extensively studied in S. cerevisiae through  synthetic sick/lethal  (SSL) screens which identify double mutants with significantly decreased growth rates.


It should be pointed out that these conclusions from double-mutant analysis, while they apply to many pathways and mutants, are not universal. For example, genes can act in opposite directions in pathways, so that knocking out both produces a near-normal phenotype, while each single mutant is severely affected (in opposite directions). A well-studied example occurs during early development in Drosophila, wherein gene products from the  hunchback  and  nanos  genes are present in the egg, and act in opposite directions to direct anterior-posterior pattern formation. Something similar often happens in signal transduction pathways, where knocking out a negative regulator of the pathway causes a hyper-activation phenotype, while knocking out a positively acting component produces an opposite phenotype. In linear pathways with a single "output", when knockout mutations in two oppositely-acting genes are combined in the same individual, the phenotype of the double mutant is typically the same as the phenotype of the single mutant whose normal gene product acts downstream in the pathway.


Synthetic genetic arrays  (SGA) and diploid based synthetic lethality analysis of microarrays (dSLAM) are two key methods which have been used to identify synthetic sick lethal mutants and characterize negative epistatic relationships. Sequencing of the entire yeast genome has made it possible to generate a library of knock-out mutants for nearly every gene in the genome. These  molecularly bar-coded  mutants greatly facilitate high-throughput epistasis studies, as they can be pooled and used to generate the necessary double mutants. Both SGA and dSLAM approaches rely on these yeast knockout strains which are transformed/mated to generate haploid double mutants. Microarray profiling is then used to compare the fitness of these single and double mutants. In the case of SGA, the double mutants examined are haploid and collected after mating with a mutant strain followed by several rounds of selection. dSLAM strains of both single and double mutants originate from the same diploid heterozygote strain (indicated by “diploid” of “dSLAM”). In the case of dSLAM analysis the fitness of single and double mutants is assessed by microarray analysis of a growth competition assay.


In order to develop a richer understanding of genetic interactions, experimental approaches are shifting away from this  binary classification  of phenotypes as wild type or synthetic lethal. The E-MAP approach is particularly compelling because of its ability to highlight both alleviating and aggravating effects and this capacity is what distinguishes this method from others such as SGA and dSLAM. Furthermore, not only does the E-MAP identify both types of interactions but also recognizes gradations in these interactions and the severity of the masked phenotype, represented by the interaction score applied to each pair of genes.


E-MAPs exploit an SGA approach in order to analyze genetic interactions in a  high-throughput  manner. While the method has been particularly developed for examining epistasis in S. cerevisiae, it could be applied to other  model organisms  as well. An E-MAP collates data generated from the systematic generation of double mutant strains for a large clearly defined group of genes. Each phenotypic response is quantified by imaging colony size to determine growth rate. This fitness score is compared to the predicted fitness for each single mutant, resulting in a genetic interaction score.
 Hierarchical clustering  of this data to group genes with similar interaction profiles allows for the identification of epistatic relationships between genes with and without known function. By sorting the data in this way, genes known to interact will cluster together alongside genes which exhibit a similar pattern of interactions but whose function has not yet been identified. The E-MAP data is therefore able to place genes into new functions within well characterized pathways. Consider for example E-MAP presented by Collins et al. which clusters the  transcriptional elongation  factor Dst1 [ 6 ]  alongside components of the mid region of the Mediator complex, which is involved in  transcriptional regulation . [ 7 ]  This suggests a new role for Dst1, functioning in concert with Mediator.


The choice of genes examined within a given E-MAP is critical to achieving fruitful results. It is particularly important that a significant subset of the genes examined have been well established in the literature. These genes are thus able to act as controls for the E-MAP allowing for greater certainty in analyzing the data from uncharacterized genes. Clusters organized by sub-cellular localization and general cellular processes (e.g.  cell cycle ) have yielded profitable results in S. cerevisiae. Data from  protein-protein interaction  studies can also provide a useful basis for selecting gene groups for E-MAP data. We would expect genes which exhibit physical interactions to also demonstrate interactions at the genetic level and thus these can serve as adequate controls for E-MAP data. Collins et al. (2007) carried out a comparison of E-MAP scores and physical interaction data from large-scale  affinity purification  methods (AP-MS) and their data demonstrate that an E-MAP approach identifies protein-protein interactions with a specificity equal to that of traditional methods such as AP-MS .


High throughput methods of examining epistatic relationships face difficulties, however as the number of possible gene pairs is extremely large (~20 million in S. cerevisiae) and the estimated density of genetic interactions is quite low. [ 8 ]  These difficulties can be countered by examining all possible interactions in a single cluster of genes rather than examining pairs across the whole genome. If well chosen, these functional clusters contain a significantly higher density of genetic interactions than other regions of the genome and thus allows for a higher rate of detection while dramatically decreasing the number of gene pairs to be examined. [ 8 ]


Generating data for the E-MAP depends upon the creation of thousands of double mutant strains; a study of 483 alleles, for example, resulted in an E-MAP with ~100,000 distinct double mutant pairs. The generation of libraries of essential gene mutants presents significant difficulties however, as these mutations have a lethal phenotype. Thus, E-MAP studies rely upon strains with intermediate expression levels of these genes. The decreased abundance by messenger RNA perturbation (DAmP) strategy is particularly common for the high-throughput generation of mutants necessary for this kind of analysis and allows for the partial disruption of essential genes without loss of viability. [ 9 ]  DAmP relies upon the destabilization of  mRNA  transcripts by integrating an  antibiotic  selectable  marker  into the 3’UTR, downstream of the  stop codon  (figure 2). mRNA’s with 3’ extended transcripts are rapidly targeted for degradation and the result is a downregulation of the gene of interest while it remains under the control of its native promoter. In the case of non-essential genes, deletion strains may be used. Tagging at the deletion sites with molecular barcodes, unique 20-bp sequences, allows for the identification and study of relative fitness levels in each mutant strain.


Essential genes  are indispensable  genes  for organisms to grow and reproduce offspring under certain environment. [ 1 ]  However, being  essential  is highly dependent on the circumstances in which an organism lives. For instance, a gene required to digest  starch  is only essential if starch is the only source of energy. Recently, systematic attempts have been made to identify those genes that are absolutely required to maintain life, provided that all nutrients are available. [ 2 ]   Such experiments have led to the conclusion that the absolutely required number of genes for bacteria is on the order of about 250–300. Essential genes of single-celled organisms encode proteins for three basic functions including genetic information processing, cell envelopes and energy production. [ 1 ]  Those gene functions are used to maintain a central  metabolism ,  replicate DNA ,  translate genes  into  proteins , maintain a basic cellular structure, and mediate transport processes into and out of the cell. Compared with single-celled organisms, multicellular organisms have more essential genes related to communication and development. Most of the essential genes in viruses are related to the processing and maintenance of genetic information. In contrast to most single-celled organisms, viruses lack many essential genes for metabolism, [ 1 ]  which forces them to hijack the host's metabolism.  Most genes are not essential but convey  selective advantages  and increased  fitness . Hence, the vast majority of genes are not essential and many can be deleted without consequences, at least under most circumstances.


Two main strategies have been employed to identify essential genes on a genome-wide basis: directed deletion of genes and random  mutagenesis  using  transposons . In the first case, annotated individual genes (or  ORFs ) are completely deleted from the  genome  in a systematic way. In transposon-mediated mutagenesis, transposons are randomly inserted in as many positions in a genome as possible, aiming to disrupt the function of the targeted genes (see figure below). Insertion mutants that are still able to survive or grow suggest the transposon inserted in a gene that is not essential for survival. The location of the transposon insertions can be determined through hybridization to microarrays  [ 3 ]  or through  transposon sequencing  . With the development of  CRISPR , gene essentiality has also been determined through inhibition of gene expression through  CRISPR interference . A summary of such screens is shown in the table. [ 2 ] [ 4 ]


Table 1. Essential genes in bacteria .  Mutagenesis :  targeted  mutants are gene deletions;  random  mutants are  transposon  insertions.  Methods :  Clones  indicate single gene deletions,  population  indicates whole population mutagenesis, e.g. using transposons. Essential genes from population screens include genes essential for fitness (see text).  ORFs : number of all  open reading frames  in that genome.  Notes : (a) mutant collection available; (b) direct essentiality screening method (e.g. via antisense RNA) that does not provide information about nonessential genes. (c) Only partial dataset available. (d) Includes predicted gene essentiality and data compilation from published single-gene essentiality studies. (e) Project in progress. (f) Deduced by comparison of the two gene essentiality datasets obtained independently in the  P. aeruginosa  strains PA14 and PAO1. (g) The original result of 271 essential genes has been corrected to 261, with 31 genes that were thought to be essential being in fact non-essential whereas 20 novel essential genes have been described since then. [ 22 ]  (h) Counting genes with essential domains and those that lead to growth-defects when disrupted as essential, and those who lead to growth-advantage when disrupted as non-essential. (i) Involved a fully saturated mutant library of 14 replicates, with 84.3% of possible insertion sites with at least one transposon insertion. (j) Each essential gene has been independently confirmed at least five times.


On the basis of genome-wide experimental studies and systems biology analysis, an essential gene database has been developed by Kong et al. (2019) for predicting > 4000 bacterial species. [ 42 ]


In  Saccharomyces cerevisiae  (budding yeast) 15-20% of all genes are essential. In  Schizosaccharomyces pombe  (fission yeast) 4,836 heterozygous deletions covering 98.4% of the 4,914 protein coding open reading frames have been constructed. 1,260 of these deletions turned out to be essential. [ 43 ]


Similar screens are more difficult to carry out in other multicellular organisms, including  mammals  (as a model for humans), due to technical reasons, and  their results are less clear. However, various methods have been developed for the nematode worm  C. elegans , [ 44 ]  the fruit fly, [ 45 ]  and zebrafish [ 46 ]  (see table). A recent study of 900 mouse genes concluded that 42% of them were essential although the selected genes were not representative. [ 47 ]


Gene knockout experiments are not possible or at least not ethical in humans. However, natural mutations have led to the identification of mutations that lead to early embryonic or later death. [ 48 ]  Note that many genes in humans are not absolutely essential for survival but can cause severe disease when mutated. Such mutations are catalogued in the  Online Mendelian Inheritance in Man  (OMIM) database. In a computational analysis of genetic variation and mutations in  2,472  human   orthologs  of known essential genes in the mouse, Georgi et al. found strong, purifying selection and comparatively reduced levels of sequence variation, indicating that these human genes are essential too. [ 49 ]


While it may be difficult to prove that a gene is essential in humans, it can be demonstrated that a gene is  not  essential or not even causing disease. For instance, sequencing the genomes of 2,636 Icelandic citizens and the genotyping of 101,584 additional subjects found 8,041 individuals who had 1 gene completely knocked out (i.e. these people were homozygous for a non-functional gene). [ 50 ]   Of the 8,041 individuals with complete knock-outs, 6,885 were estimated to be  homozygotes , 1,249 were estimated to be compound  heterozygotes  (i.e. they had both  alleles  of a gene knocked out but the two alleles had different mutations). In these individuals, a total of 1,171 of the 19,135 human ( RefSeq ) genes (6.1%) were completely knocked out. It was concluded that these 1,171 genes are  non-essential  in humans — at least no associated diseases were reported. [ 50 ]  Similarly, the exome sequences of 3222 British Pakistani-heritage adults with high parental relatedness revealed 1111 rare-variant homozygous genotypes with predicted loss of gene function (LOF = knockouts) in 781 genes. [ 51 ]  This study found an average of 140 predicted LOF genotypes (per subject), including 16 rare (minor  allele frequency  <1%) heterozygotes, 0.34 rare homozygotes, 83.2 common heterozygotes and 40.6 common homozygotes. Nearly all rare homozygous LOF genotypes were found within  autozygous  segments (94.9%). [ 51 ]  Even though most of these individuals had no obvious health issue arising from their defective genes, it is possible that minor health issues may be found upon more detailed examination.


A summary of essentiality screens is shown in the table below (mostly based on the Database of Essential Genes. [ 52 ]


Viruses lack many genes necessary for metabolism, [ 1 ]  forcing them to hijack the host's metabolism. Screens for essential genes have been carried out in a few viruses. For instance,  human cytomegalovirus  (CMV) was found to have 41 essential, 88 nonessential, and 27 augmenting ORFs (150 total ORFs). Most essential and augmenting genes are located in the central region, and nonessential genes generally cluster near the ends of the viral genome. [ 60 ]


Tscharke and Dobson (2015) compiled a comprehensive survey of essential genes in  Vaccinia Virus  and assigned roles to each of the 223 ORFs of the Western Reserve (WR) strain and 207 ORFs of the Copenhagen strain, assessing their role in replication in cell culture. According to their definition, a gene is considered essential (i.e. has a role in cell culture) if its deletion results in a decrease in virus titre of greater than 10-fold in either a single or multiple step growth curve. All genes involved in wrapped virion production, actin tail formation, and extracellular virion release were also considered as essential. Genes that influence plaque size, but not replication were defined as non-essential. By this definition 93 genes are required for Vaccinia Virus replication in cell culture, while 108 and 94 ORFs, from WR and Copenhagen respectively, are non-essential. [ 61 ]  Vaccinia viruses with deletions at either end of the genome behaved as expected, exhibiting only mild or host range defects. In contrast, combining deletions at both ends of the genome for VACV strain WR caused a devastating growth defect on all cell lines tested. This demonstrates that single gene deletions are not sufficient to assess the essentiality of genes and that more genes are essential in Vaccinia virus than originally thought. [ 61 ]


One of the  bacteriophages  screened for essential genes includes  mycobacteriophage  Giles. At least 35 of the 78 predicted Giles genes (45%) are non-essential for lytic growth. 20 genes were found to be essential. [ 62 ]  A major problem with phage genes is that a majority of their genes remain functionally unknown, hence their role is difficult to assess. A screen of  Salmonella enterica  phage SPN3US revealed 13 essential genes although it remains a bit obscure how many genes were really tested. [ 63 ]


In theory, essential genes are qualitative. [ 1 ]  However, depending on the surrounding environment, certain essential gene mutants may show partial functions, which can be quantitatively determined in some studies. For instance, a particular gene deletion may reduce growth rate (or fertility rate or other characters) to 90% of the wild-type. If there are isozymes or alternative pathways for the essential genes, they can be deleted completely. [ 1 ]  Using  CRISPR interference , the expression of essential genes can be modulated or "tuned", leading to quantitative (or continuous) relationships between the level of gene-expression and the magnitude of fitness cost exhibited by a given mutant. [ 20 ]


Two genes are synthetic lethal if neither one is essential but when both are mutated the double-mutant is lethal. Some studies have estimated that the number of synthetic lethal genes may be on the order of 45% of all genes. [ 64 ] [ 65 ]


Many genes are essential only under certain circumstances. For instance, if the amino acid  lysine  is supplied to a cell any gene that is required to make lysine is non-essential. However, when there is no lysine supplied, genes encoding enzymes for lysine biosynthesis become essential, as no protein synthesis is possible without lysine. [ 4 ]


Streptococcus pneumoniae  appears to require 147 genes for growth and survival in  saliva , [ 66 ]   more than the 113-133 that have been found in previous studies.


The deletion of a gene may result in  death  or in a block of  cell division . While the latter case may implicate "survival" for some time, without cell division the cell may still die eventually. Similarly, instead of blocked cell division a cell may have reduced  growth  or  metabolism  ranging from nearly undetectable to almost normal. Thus, there is gradient from "essential" to completely non-essential, again depending on the condition. Some authors have thus distinguished between genes " essential for survival " and " essential for fitness ". [ 4 ]


The role of genetic background . Similar to environmental conditions, the genetic background can determine the essentiality of a gene: a gene may be essential in one individual but not another, given his or her genetic background. Gene duplications are one possible explanation (see below).


Metabolic dependency . Genes involved in certain biosynthetic pathways, such as  amino acid synthesis , can become non-essential if one or more amino acids are supplied by culture medium [ 1 ]  or by another organism. [ 67 ]  This is the main reason why many parasites (e.g.  Cryptosporidium hominis ) [ 68 ]  or  endosymbiontic bacteria  lost many genes (e.g.  Chlamydia ). Such genes may be essential but only present in the host organism. For instance,  Chlamydia trachomatis  cannot synthesize  purine  and  pyrimidine   nucleotides   de novo , so these bacteria are dependent on the nucleotide biosynthetic genes of the host. [ 69 ]


Another kind of metabolic dependency, unrelated to cross-species interactions, can be found when bacteria are grown under  specific nutrient conditions . For example, more than 100 genes become essential when  Escherichia coli  is grown on nutrient-limited media. Specifically,  isocitrate dehydrogenase  (icd) and  citrate synthase  (gltA) are two enzymes that are part of the  tricarboxylic acid (TCA) cycle . Both genes are essential in M9  minimal media  (which provides only the most basic nutrients). However, when the media is supplementing with  2-oxoglutarate  or  glutamate , these genes are not essential any more. [ 70 ]


Many genes are duplicated within a genome and many organisms have different metabolic pathways (alternative metabolic pathway [ 1 ] ) to synthesis same products. Such  duplications  ( paralogs ) and alternative metabolic pathways often render essential genes non-essential because the duplicate can replace the original copy. For instance, the gene encoding the enzyme  aspartokinase  is essential in  E. coli . By contrast, the  Bacillus subtilis  genome contains three copies of this gene, none of which is essential on its own. However, a triple-deletion of all three genes is lethal. In such cases, the essentiality of a gene or a group of paralogs can often be predicted based on the essentiality of an essential single gene in a different species. In yeast, few of the essential genes are duplicated within the genome: 8.5% of the non-essential genes, but only 1% of the essential genes have a homologue in the yeast genome. [ 59 ]


In the worm  C. elegans , non-essential genes are highly over-represented among duplicates, possibly because duplication of essential genes causes overexpression of these genes. Woods et al. found that non-essential genes are more often successfully duplicated (fixed) and lost compared to essential genes. By contrast, essential genes are less often duplicated but upon successful duplication are maintained over longer periods. [ 71 ]


In  bacteria , essential genes appear to be more conserved than nonessential genes  [ 73 ]   but the correlation is not very strong. For instance, only 34% of the  B. subtilis  essential genes have reliable  orthologs  in all  Bacillota  and 61% of the  E. coli  essential genes have reliable orthologs in all  Gamma-proteobacteria . [ 72 ]  Fang et al. (2005) defined  persistent genes  as the genes present in more than 85% of the genomes of the clade. [ 72 ]   They found 475 and 611 of such genes for  B. subtilis  and  E. coli , respectively. Furthermore, they classified genes into five classes according to persistence and essentiality: persistent genes, essential genes, persistent nonessential (PNE) genes (276 in  B. subtilis , 409 in  E. coli ), essential nonpersistent (ENP) genes (73 in  B. subtilis , 33 in  E. coli ), and nonpersistent nonessential (NPNE) genes (3,558 in  B. subtilis , 3,525 in  E. coli ). Fang et al. found 257 persistent genes, which exist both in  B. subtilis  (for the Bacillota) and  E. coli  (for the Gamma-proteobacteria). Among these, 144 (respectively 139) were previously identified as essential in  B. subtilis  (respectively  E. coli ) and 25 (respectively 18) of the 257 genes are not present in the 475  B. subtilis  (respectively 611  E. coli)  persistent genes. All the other members of the pool are PNE genes. [ 72 ]


In  eukaryotes , 83% of the one-to-one orthologs between  Schizosaccharomyces pombe  and  Saccharomyces cerevisiae  have conserved essentiality, that is, they are nonessential in both species or essential in both species. The remaining 17% of genes are nonessential in one species and essential in the other. [ 74 ]  This is quite remarkable, given that  S. pombe  is separated from  S. cerevisiae  by approximately 400 million years of evolution. [ 75 ]


In general, highly conserved and thus older genes (i.e. genes with earlier phylogenetic origin) are more likely to be essential than younger genes - even if they have been duplicated. [ 76 ]


The experimental study of essential genes is limited by the fact that, by definition, inactivation of an essential gene is lethal to the organism. Therefore, they cannot be simply deleted or mutated to analyze the resulting  phenotypes  (a common technique in  genetics ).


There are, however, some circumstances in which essential genes can be manipulated. In  diploid  organisms, only a single functional copy of some essential genes may be needed ( haplosufficiency ), with the heterozygote displaying an instructive phenotype. Some essential genes can tolerate mutations that are deleterious, but not wholly lethal, since they do not completely abolish the gene's function.


Computational analysis  can reveal many properties of proteins without analyzing them experimentally, e.g. by looking at  homologous proteins , function, structure etc. (see also below,  Predicting essential genes ). The products of essential genes can also be studied when  expressed in other organisms , or when purified and studied  in vitro .


Conditionally essential genes  are easier to study. Temperature-sensitive variants of essential genes have been identified which encode products that lose function at high temperatures, and so only show a phenotype at increased temperature. [ 77 ]


If screens for essential genes are repeated in independent laboratories, they often result in different gene lists. For instance, screens in  E. coli  have yielded from ~300 to ~600 essential genes (see  Table 1 ). Such differences are even more pronounced when different bacterial strains are used (see  Figure 2 ). A common explanation is that the experimental conditions are different or that the nature of the mutation may be different (e.g. a complete gene deletion vs. a transposon mutant). [ 4 ]  Transposon screens in particular are hard to reproduce, given that a transposon can insert at many positions within a gene. Insertions towards the 3' end of an essential gene may not have a lethal phenotype (or no phenotype at all) and thus may not be recognized as such. This can lead to erroneous annotations (here: false negatives). [ 78 ]


Comparison of  CRISPR /cas9 and  RNAi  screens . Screens to identify essential genes in the human  chronic myelogenous leukemia  cell line K562 with these two  methods showed only limited overlap. At a 10% false positive rate there were ~4,500 genes identified in the Cas9 screen versus ~3,100 in the  shRNA  screen, with only ~1,200 genes identified in both. [ 79 ]


Different organisms may have different essential genes. For instance,  Bacillus subtilis  has 271 essential genes. [ 21 ]  About one-half (150) of the  orthologous  genes in  E. coli  are also essential. Another 67 genes that are essential in  E. coli  are not essential in  B. subtilis , while 86  E. coli  essential genes have no  B. subtilis  ortholog. [ 25 ]  In  Mycoplasma genitalium  at least 18 genes are essential that are not essential in  M. bovis. [ 80 ]  Many of these different essential genes are caused by paralogs or alternative metabolic pathways. [ 1 ]


Such different essential genes in bacteria can be used to develop targeted antibacterial therapies against certain specific pathogens to reduce  antibiotic resistance  in the microbiome era. [ 81 ]  Stone et al (2015) have used the difference in essential genes in bacteria to develop selective drugs against the oral pathogen  Porphyromonas gingivalis , rather than the beneficial bacteria  Streptococcus sanguis . [ 82 ]


Essential genes can be predicted computationally. However, most methods use experimental data ("training sets") to some extent. Chen et al. [ 83 ]  determined four criteria to select training sets for such predictions: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. They also found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Some approaches for predicting essential genes are:


Comparative genomics . Shortly after the first genomes (of  Haemophilus influenzae  and  Mycoplasma genitalium ) became available, Mushegian et al. [ 84 ]  tried to predict the number of essential genes based on common genes in these two species. It was surmised that only essential genes should be conserved over the long evolutionary distance that separated the two bacteria. This study identified approximately 250 candidate essential genes. [ 84 ]  As more genomes became available the number of predicted essential genes kept shrinking because more genomes shared fewer and fewer genes. As a consequence, it was concluded that the universal conserved core consists of less than 40 genes. [ 85 ] [ 86 ]   However, this set of conserved genes is not identical to the set of essential genes as different species rely on different essential genes.


A similar approach has been used to infer essential genes from the  pan-genome  of  Brucella  species. 42 complete  Brucella  genomes and a total of 132,143 protein-coding genes were used to predict 1252 potential essential genes, derived from the core genome by comparison with a prokaryote database of essential genes. [ 87 ]


Network analysis . After the first protein interaction networks of  yeast  had been published, [ 88 ]  it was found that highly connected proteins (e.g. by  protein-protein interactions ) are more likely to be essential. [ 89 ]  However, highly connected proteins may be experimental artifacts and high connectivity may rather represent  pleiotropy  instead of essentiality. [ 90 ]  Nevertheless, network methods have been improved by adding other criteria and therefore do have some value in predicting essential genes. [ 91 ]


Machine Learning . Hua et al. used  Machine Learning  to predict essential genes in 25 bacterial species . [ 92 ]


Hurst index . Liu et al. (2015) [ 93 ]   used the  Hurst exponent , a characteristic parameter to describe long-range correlation in DNA to predict essential genes. In 31 out of 33 bacterial genomes the significance levels of the Hurst exponents of the essential genes were significantly higher than for the corresponding full-gene-set, whereas the significance levels of the Hurst exponents of the nonessential genes remained unchanged or increased only slightly.


Minimal genomes . It was also thought that essential genes could be inferred from  minimal genomes  which supposedly contain only essential genes. The problem here is that the smallest genomes belong to parasitic (or symbiontic) species which can survive with a reduced gene set as they obtain many nutrients from their hosts. For instance, one of the smallest genomes is that of  Hodgkinia cicadicola , a  symbiont  of cicadas, containing only 144 Kb of DNA encoding only 188 genes. [ 94 ]  Like other symbionts,  Hodgkinia  receives many of its nutrients from its host, so its genes do not need to be essential.


Metabolic modelling . Essential genes may be also predicted in completely sequenced genomes by  metabolic reconstruction , that is, by reconstructing the complete metabolism from the gene content and then identifying those genes and pathways that have been found to be essential in other species. However, this method can be compromised by proteins of unknown function. In addition, many organisms have backup or alternative pathways which have to be taken into account (see figure 1). Metabolic modeling was also used by Basler (2015) to develop a method to predict essential metabolic genes. [ 95 ]   Flux balance analysis , a method of metabolic modeling, has recently been used to predict essential genes in clear cell renal cell carcinoma metabolism. [ 96 ]


Genes of unknown function . Surprisingly, a significant number of essential genes has no known function. For instance, among the 385 essential candidates in  M. genitalium , no function could be ascribed to 95 genes [ 6 ]  even though this number had been reduced to 75 by 2011. [ 86 ]  Most of unknown functionally essential genes have potential biological functions related to one of the three fundamental functions. [ 1 ]


ZUPLS . Song et al. presented a novel method to predict essential genes that only uses the Z-curve and other sequence-based features. [ 97 ]   Such features can be calculated readily from the DNA/amino acid sequences. However, the reliability of this method remains a bit obscure.


Essential gene prediction servers . Guo et al. (2015) have developed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains. [ 98 ]  Kong et al. (2019) have developed the  ePath  database, which can be used to search > 4000 bacterial species for predicting essential genes. [ 42 ]


Although most essential genes encode proteins, many essential proteins consist of a single domain. This fact has been used to identify essential protein domains. Goodacre et al. have identified hundreds of essential  domains of unknown function  (eDUFs). [ 99 ]  Lu et al. [ 100 ]   presented a similar approach and identified 3,450 domains that are essential in at least one microbial species.


An  exogenote  is a piece of donor  DNA  that is involved in the mating of  prokaryotic organisms . [ 1 ]