Transferred DNA of Hfr (high frequency of recombination) is called exogenote and homologous part of F (fertility factor) genophore is called endogenote. An exogenote is genetic material that is released into the environment by prokaryotic cells, usually upon their  lysis . [ citation needed ]  This exogenous genetic material is then free to be taken up by other  competent  bacteria, and used as a template for protein synthesis or broken down for its molecules to be used elsewhere in the cell. Taking up genetic material into the cell from the surrounding environment is a form of  bacterial transformation . [ citation needed ]  Exogenotes can also be transferred directly from donor to recipient bacteria as an F'-plasmid in a process known as  bacterial conjugation . [ citation needed ]  F'-plasmids only form if the F+ factor is incorrectly translated, and results in a small amount of donor DNA erroneously transferring to the recipient with very high efficiency. [ citation needed ]


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In  genetics , an  expressed sequence tag  ( EST ) is a short sub-sequence of a  cDNA  sequence. [ 1 ]  ESTs may be used to identify gene  transcripts , and were instrumental in gene discovery and in gene-sequence determination. [ 2 ]  The identification of ESTs has proceeded rapidly, with approximately 74.2 million ESTs now available in public databases (e.g.  GenBank  1 January 2013, all species). EST approaches have largely been superseded by whole genome and transcriptome sequencing and metagenome sequencing.


An EST results from one-shot  sequencing  of a  cloned  cDNA. The cDNAs used for EST generation are typically individual clones from a  cDNA library . The resulting sequence is a relatively low-quality fragment whose length is limited by current technology to approximately 500 to 800  nucleotides . Because these clones consist of DNA that is complementary to mRNA, the ESTs represent portions of expressed genes. They may be represented in databases as either cDNA/mRNA sequence or as the reverse complement of the mRNA, the  template strand .


One can map ESTs to specific chromosome locations using  physical mapping  techniques, such as  radiation hybrid mapping ,  HAPPY mapping , or  FISH . Alternatively, if the genome of the organism that originated the EST has been sequenced, one can align the EST sequence to that genome using a computer.


The current understanding of the  human set of genes  (as of 2006 [update] ) includes the existence of thousands of genes based solely on EST evidence. In this respect, ESTs have become a tool to refine the predicted transcripts for those genes, which leads to the prediction of their protein products and ultimately of their function. Moreover, the situation in which those ESTs are obtained (tissue, organ, disease state - e.g.  cancer ) gives information on the conditions in which the corresponding gene is acting. ESTs contain enough information to permit the design of precise probes for  DNA microarrays  that then can be used to determine  gene expression  profiles.


Some authors use the term "EST" to describe genes for which little or no further information exists besides the tag. [ 3 ]


In 1979, teams at Harvard and Caltech extended the basic idea of making DNA copies of mRNAs in vitro to amplifying a library of such in bacterial plasmids. [ 4 ]


In 1982,  the idea of selecting random or semi-random clones from such a cDNA library for sequencing was explored by Greg Sutcliffe and coworkers. [ 5 ]


In 1983, Putney  et al. sequenced 178  clones from  a  rabbit muscle cDNA  library. [ 6 ]


In 1991, Adams and co-workers coined the term EST and initiated more systematic sequencing as a project (starting with 600 brain cDNAs). [ 2 ]


The dbEST is a division of Genbank established in 1992. As for  GenBank , data in dbEST is directly submitted by laboratories worldwide and is not curated.


Because of the way ESTs are sequenced, many distinct expressed sequence tags are often partial sequences that correspond to the same mRNA of an organism. In an effort to reduce the number of expressed sequence tags for downstream gene discovery analyses, several groups assembled expressed sequence tags into EST  contigs . Example of resources that provide EST contigs include: TIGR gene indices, [ 7 ]  Unigene, [ 8 ]  and STACK  [ 9 ]


Constructing EST contigs is not trivial and may yield artifacts (contigs that contain two distinct gene products). When the complete genome sequence of an organism is available and transcripts are annotated, it is possible to bypass contig assembly and directly match transcripts with ESTs. This approach is used in the TissueInfo system (see below) and makes it easy to link annotations in the genomic database to tissue information provided by EST data.


High-throughput analyses of ESTs often encounter similar data management challenges. A first challenge is that tissue provenance of EST libraries is described in plain English in dbEST. [ 10 ]  This makes it difficult to write programs that can  unambiguously determine that two EST libraries were sequenced from the same tissue. Similarly, disease conditions for the tissue are not annotated in a computationally friendly manner. For instance, cancer origin of a library is often mixed with the tissue name (e.g., the tissue name " glioblastoma " indicates that the EST library was sequenced from brain tissue and the disease condition is cancer). [ 11 ]  With the notable exception of cancer, the disease condition is often not recorded in dbEST entries. The TissueInfo project was started in 2000 to help with these challenges. The project provides curated data (updated daily) to disambiguate tissue origin and disease state (cancer/non cancer), offers a tissue ontology that links tissues and organs by "is part of" relationships (i.e., formalizes knowledge that hypothalamus is part of brain, and that brain is part of the central nervous system) and distributes open-source software for linking transcript annotations from sequenced genomes to tissue expression profiles calculated with data in dbEST. [ 12 ]


Fine structure genetics  encompasses a set of tools used to examine not just the mutations within an entire genome, but can be isolated to either specific pathways or regions of the genome. Ultimately, this more focused lens can lead to a more nuanced and interactive view of the function of a gene.


Similar to forward genetics, regional mutagenesis seeks to saturate with insertions or point mutations, but instead of for the entire  genome , it saturates only a small portion of the genome. By limiting the region in focus, researchers are then able to intensify the number of mutations within any genes or promoters within that regions, often illuminating more complicated functions than could be identified with a broader focus. Furthermore, such mutations can show how the specific structure of that region of a chromosome affect expression levels and function. [ 1 ]


Such mutations are introduced in the same means as  forward genetics , often through chemical induction or  transposable element  insertions. The creation of specific  balancer chromosomes  that are restrictive to only a small region of the genome can guarantee that mutations will only be isolated and reproduced only in that region. [ 1 ]


When a gene is identified as affecting a specific phenotype, a modifier screen can be used to assess which genes that either enhance or inhibit the phenotypic expression of the initial mutation. This is a powerful way of rapidly identifying many genes that are involved in the expression of a phenotype, but such screens can only say whether or not two genes interact, not what their exact function are, or how they relate. For instance, the product of the second gene may interact directly with that of the first gene, or it may be involved in distantly on the pathway. [ 1 ]


One of the major benefits of modifier screens is that screens do not necessarily have to take place in the organism of interest. For instance, a gene that corresponds to an important phenotype in an organism in which a set of screens involving mutagenesis (i.e. human beings), will often have a homologue in a model organism. In this case, that homologous gene can either be knocked out or the initial gene can be ectopically expressed in the model organism, at which point a screen for modifiers of the aberrant phenotype can take place. [ 1 ]


Enhancer trapping  involves the insertion of a reporter gene, such as  lac-Z  or  GFP , into the promoter region a desired gene, so that whenever the gene is expressed, it can be monitored by said reporter, giving a specific spatial and temporal map of when a gene expressed. This method again involves Transposable Element insertion, taking advantage of certain transposable elements that have a propensity to insert into promoter regions. This method is also advantageous as such insertions can be reversed. [ 1 ]


A similar method can be used to study novel phenotypes created by tissue specific gain-of-function or loss of function. In order to create gain-of-function, the TE is inserted with not just with a reporter gene, but also with the  GAL4  transcriptional activator. When this line is crossed with an organism with a gene fused with a GAL4 mediated promoter. This way anytime that particular promoter is turned on, it will not only express its original gene, it will also turn on expression of any gene the experimenter would like turned on. This is an easy way to ensure tissue or time specific expression of a gene where it is not usually expressed. Under a similar principle, the GAL4 transcriptional activator can be replaced with an RNAi construct for a specific gene. This can make any promoter into an inhibitor of a gene in a specific location. [ 1 ]


For a fuller explanation, see   Cre-Lox Recombination


With a similar effect as the insertion of TE with RNAi constructs, Cre-Lox recombinants can be used to have tissue specific loss-of-function. It is particularly useful in dissecting the specific functions of genes that are essential in development, and therefore knock-outs are lethal.


The  Fiocruz Genome Comparison Project  is a collaborative effort involving  Brazil 's  Oswaldo Cruz Institute  and  IBM 's  World Community Grid , designed to produce a  database  comparing the  genes  from many  genomes  with each other using SSEARCH. [ 1 ]  The program SSEARCH performs a rigorous  Smith–Waterman alignment  between a  protein sequence  and another protein sequence, a protein database, a  DNA  or a DNA library.


The nature of the computation in the project allows it to easily take advantage of  volunteer computing . This, along with the likely humanitarian benefits of the research, has led the  World Community Grid  (a volunteer computing grid that uses idle computer clock time) to run the Fiocruz project. All products are in the public domain by contract with WCG.


The problem is that a very large information body (structural, functional, cross-references, etc.) is attached to protein database entries. Once entered the information is rarely updated or corrected. This annotation of predicted protein function is often incomplete, uses non-standard nomenclature or can be incorrect when cross referenced from previous sometimes incorrectly annotated sequences. Additionally, many proteins composed of several structural and/or functional domains are overlooked by automated systems. The comparative information today is huge when compared to the early days of genomics. A single error is compounded and then made complex.


The Genome Comparison Project performs a complete pairwise comparison between all predicted  protein  sequences, obtaining indices used (together with standardized Gene Ontology [ 2 ] ) as a reference repository for the annotator community. The project provides invaluable data sources for biologists. The sequence similarity comparison program used in the Genome Comparison Project is called SSEARCH. This program mathematically finds best local alignment between sequence pairs, [ 3 ]  and is a freely available implementation of the Smith–Waterman algorithm. [ 4 ]


SSEARCH's use makes possible a precise annotation, inconsistencies correction, and possible functions assignment to hypothetical proteins of unknown function. Moreover, proteins with multiple domains and functional elements are correctly spotted. Even distant relationships are detected.


Functional genomics  is a field of  molecular biology  that attempts to describe  gene  (and  protein ) functions and interactions. Functional genomics make use of the vast data generated by  genomic  and  transcriptomic  projects (such as  genome sequencing projects  and  RNA sequencing ).  Functional genomics focuses on the dynamic aspects such as gene  transcription ,  translation ,  regulation of gene expression  and  protein–protein interactions , as opposed to the static aspects of the genomic information such as  DNA sequence  or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.


In order to understand functional genomics it is important to first define function. In their paper [ 1 ]  Graur et al. define function in two possible ways. These are "selected effect" and "causal role". The "selected effect" function refers to the function for which a trait (DNA, RNA, protein etc.) is selected for. The "causal role" function refers to the function that a trait is sufficient and necessary for. Functional genomics usually tests the "causal role" definition of function.


The goal of functional genomics is to understand the function of genes or proteins, eventually all components of a genome. The term functional genomics is often used to refer to the many technical approaches to study an organism's genes and proteins, including the "biochemical, cellular, and/or physiological properties of each and every gene product" [ 2 ]  while some authors include the study of nongenic elements in their definition. [ 3 ]  Functional genomics may also include studies of natural genetic variation over time (such as an organism's development) or space (such as its body regions), as well as functional disruptions such as mutations.


The promise of functional genomics is to generate and synthesize genomic and proteomic knowledge into an understanding of the dynamic properties of an organism. This could potentially provide a more complete picture of how the genome specifies function compared to studies of single genes. Integration of functional genomics data is often a part of  systems biology  approaches.


Functional genomics includes function-related aspects of the genome itself such as  mutation  and  polymorphism  (such as  single nucleotide polymorphism  (SNP) analysis), as well as the measurement of molecular activities. The latter comprise a number of "- omics " such as  transcriptomics  ( gene expression ),  proteomics  ( protein production ), and  metabolomics . Functional genomics uses mostly  multiplex  techniques to measure the abundance of many or all gene products such as  mRNAs  or  proteins  within a  biological sample . A more focused functional genomics approach might test the function of all variants of one gene and quantify the effects of mutants by using sequencing as a readout of activity. Together these measurement modalities endeavor to quantitate the various biological processes and improve our understanding of gene and protein functions and interactions.


Systematic pairwise deletion of genes or inhibition of gene expression can be used to identify genes with related function, even if they do not interact physically.  Epistasis  refers to the fact that effects for two different gene knockouts may not be additive; that is, the phenotype that results when two genes are inhibited may be different from the sum of the effects of single knockouts.


Proteins formed by the translation of the mRNA (messenger RNA, a coded information from DNA for protein synthesis) play a major role in regulating gene expression. To understand how they regulate gene expression it is necessary to identify DNA sequences that they interact with. Techniques have been developed to identify sites of DNA-protein interactions. These include  ChIP-sequencing ,  CUT&RUN sequencing  and Calling Cards. [ 4 ]


Assays have been developed to identify regions of the genome that are accessible. These regions of accessible chromatin are candidate regulatory regions. These assays include  ATAC-seq ,  DNase-Seq  and  FAIRE-Seq .


Microarrays measure the amount of mRNA in a sample that corresponds to a given gene or probe DNA sequence. Probe sequences are immobilized on a solid surface and allowed to  hybridize  with fluorescently labeled "target" mRNA. The intensity of fluorescence of a spot is proportional to the amount of target sequence that has hybridized to that spot and therefore to the abundance of that mRNA sequence in the sample. Microarrays allow for the identification of candidate genes involved in a given process based on variation between transcript levels for different conditions and shared expression patterns with genes of known function.


Serial analysis of gene expression  (SAGE) is an alternate method of analysis based on RNA sequencing rather than hybridization. SAGE relies on the sequencing of 10–17 base pair tags which are unique to each gene. These tags are produced from  poly-A mRNA  and ligated end-to-end before sequencing. SAGE gives an unbiased measurement of the number of transcripts per cell, since it does not depend on prior knowledge of what transcripts to study (as microarrays do).


RNA sequencing has taken over microarray and SAGE technology in recent years, as noted in 2016, and has become the most efficient way to study  transcription and gene expression. This is typically done by  next-generation sequencing . [ 5 ]


A subset of sequenced RNAs are small RNAs, a class of non-coding RNA molecules that are key regulators of transcriptional and post-transcriptional gene silencing, or  RNA silencing . Next-generation sequencing is the gold standard tool for  non-coding RNA  discovery, profiling and expression analysis.


Massively parallel reporter assays is a technology to test the cis-regulatory activity of DNA sequences. [ 6 ] [ 7 ]  MPRAs use a  plasmid  with a synthetic cis-regulatory element upstream of a promoter driving a synthetic gene such as Green Fluorescent Protein. A library of cis-regulatory elements is usually tested using MPRAs, a library can contain from hundreds to thousands of cis-regulatory elements. The cis-regulatory activity of the elements is assayed by using the downstream reporter activity. The activity of all the library members is assayed in parallel using barcodes for each cis-regulatory element. One limitation of MPRAs is that the activity is assayed on a plasmid and may not capture all aspects of gene regulation observed in the genome.


STARR-seq is a technique similar to MPRAs to assay enhancer activity of randomly sheared genomic fragments. In the original publication, [ 8 ]  randomly sheared fragments of the  Drosophila  genome were placed downstream of a minimal promoter. Candidate enhancers amongst the randomly sheared fragments will transcribe themselves using the minimal promoter. By using sequencing as a readout and controlling for input amounts of each sequence the strength of putative enhancers are assayed by this method.


Perturb-seq couples CRISPR mediated gene knockdowns with single-cell gene expression. Linear models are used to calculate the effect of the knockdown of a single gene on the expression of multiple genes.


A yeast  two-hybrid screening  (Y2H) tests a "bait" protein against many potential interacting proteins ("prey") to identify physical protein–protein interactions. This system is based on a transcription factor, originally GAL4, [ 9 ]  whose separate DNA-binding and transcription activation domains are both required in order for the protein to cause transcription of a reporter gene. In a Y2H screen, the "bait" protein is fused to the binding domain of GAL4, and a library of potential "prey" (interacting) proteins is recombinantly expressed in a vector with the activation domain. In vivo interaction of bait and prey proteins in a yeast cell brings the activation and binding domains of GAL4 close enough together to result in expression of a  reporter gene . It is also possible to systematically test a library of bait proteins against a library of prey proteins to identify all possible interactions in a cell.


Mass spectrometry  (MS) can identify proteins and their relative levels, hence it can be used to study protein expression. When used in combination with  affinity purification ,  mass spectrometry  (AP/MS) can be used to study protein complexes, that is, which proteins interact with one another in complexes and in which ratios. In order to purify protein complexes, usually a "bait" protein is tagged with a specific protein or peptide that can be used to pull out the complex from a complex mix. The purification is usually done using an antibody or a compound that binds to the fusion part. The proteins are then digested into short  peptide  fragments and mass spectrometry is used to identify the proteins based on the mass-to-charge ratios of those fragments.


In deep mutational scanning, every possible amino acid change in a given protein is first synthesized. [ 10 ]  The activity of each of these protein variants is assayed in parallel using barcodes for each variant. [ 11 ]  By comparing the activity to the wild-type protein, the effect of each mutation is identified. While it is possible to assay every possible single amino-acid change due to combinatorics two or more concurrent mutations are hard to test. Deep mutational scanning experiments have also been used to infer protein structure and protein-protein interactions. [ 12 ]  Deep Mutational Scanning is an example of a multiplexed assays of variant effect (MAVEs), a family of methods that involve mutagenesis of a DNA-encoded protein or regulatory element followed by a multiplexed assay for some aspect of function. MAVEs enable the generation of ‘variant effect maps’ characterizing aspects of the function of every possible single nucleotide change in a gene or functional element of interest.  [ 13 ]


An important functional feature of genes is the phenotype caused by mutations. Mutants can be produced by random mutations or by directed mutagenesis, including site-directed mutagenesis, deleting complete genes, or other techniques.


Gene function can be investigated by systematically "knocking out" genes one by one. This is done by either  deletion  or disruption of function (such as by  insertional mutagenesis ) and the resulting organisms are screened for phenotypes that provide clues to the function of the disrupted gene. Knock-outs have been produced for whole genomes, i.e. by deleting all genes in a genome. For  essential genes , this is not possible, so other techniques are used, e.g. deleting a gene while expressing the gene from a  plasmid , using an inducible promoter, so that the level of gene product can be changed at will (and thus a "functional" deletion achieved).


Site-directed mutagenesis  is used to mutate specific bases (and thus  amino acids ). This is critical to investigate the function of specific amino acids in a protein, e.g. in the active site of an  enzyme .


RNA interference  (RNAi) methods can be used to transiently silence or knockdown gene expression using ~20 base-pair double-stranded RNA typically delivered by transfection of synthetic ~20-mer short-interfering RNA molecules (siRNAs) or by virally encoded short-hairpin RNAs (shRNAs). RNAi screens, typically performed in cell culture-based assays or experimental organisms (such as  C. elegans ) can be used to systematically disrupt nearly every gene in a genome or subsets of genes (sub-genomes); possible functions of disrupted genes can be assigned based on observed  phenotypes .


CRISPR-Cas9 has been used to delete genes in a multiplexed manner in cell-lines. Quantifying the amount of guide-RNAs for each gene before and after the experiment can point towards essential genes. If a guide-RNA disrupts an essential gene it will lead to the loss of that cell and hence there will be a depletion of that particular guide-RNA after the screen. In a recent CRISPR-cas9 experiment in mammalian cell-lines, around 2000 genes were found to be essential in multiple cell-lines. [ 15 ] [ 16 ]  Some of these genes were essential in only one cell-line. Most of genes are part of multi-protein complexes. This approach can be used to identify synthetic lethality by using the appropriate genetic background. CRISPRi and CRISPRa enable loss-of-function and gain-of-function screens in a similar manner. CRISPRi identified ~2100 essential genes in the K562 cell-line. [ 17 ] [ 18 ]  CRISPR deletion screens have also been used to identify potential regulatory elements of a gene. For example, a technique called ScanDel was published which attempted this approach. The authors deleted regions outside a gene of interest(HPRT1 involved in a Mendelian disorder) in an attempt to identify regulatory elements of this gene. [ 19 ]  Gassperini et al. did not identify any distal regulatory elements for HPRT1 using this approach, however such approaches can be extended to other genes of interest.


Putative genes can be identified by scanning a genome for regions likely to encode proteins, based on characteristics such as long  open reading frames , transcriptional initiation sequences, and  polyadenylation  sites. A sequence identified as a putative gene must be confirmed by further evidence, such as similarity to cDNA or EST sequences from the same organism, similarity of the predicted protein sequence to known proteins, association with promoter sequences, or evidence that mutating the sequence produces an observable phenotype.


The Rosetta stone approach is a computational method for de-novo protein function prediction. It is based on the hypothesis that some proteins involved in a given physiological process may exist as two separate genes in one organism and as a single gene in another. Genomes are scanned for sequences that are independent in one organism and in a single open reading frame in another. If two genes have fused, it is predicted that they have similar biological functions that make such co-regulation advantageous.


Because of the large quantity of data produced by these techniques and the desire to find biologically meaningful patterns,  bioinformatics  is crucial to analysis of functional genomics data. Examples of techniques in this class are  data clustering  or  principal component analysis  for unsupervised  machine learning  (class detection) as well as  artificial neural networks  or  support vector machines  for supervised machine learning (class prediction,  classification ). Functional enrichment analysis is used to determine the extent of over- or under-expression (positive- or negative- regulators in case of RNAi screens) of functional categories relative to a background sets.  Gene ontology  based enrichment analysis are provided by  DAVID  and  gene set enrichment analysis  (GSEA), [ 20 ]  pathway based analysis by Ingenuity [ 21 ]  and Pathway studio [ 22 ]  and protein complex based analysis by COMPLEAT. [ 23 ]


New computational methods have been developed for understanding the results of a deep mutational scanning experiment. 'phydms' compares the result of a deep mutational scanning experiment to a phylogenetic tree. [ 24 ]  This allows the user to infer if the selection process in nature applies similar constraints on a protein as the results of the deep mutational scan indicate. This may allow an experimenter to choose between different experimental conditions based on how well they reflect nature. Deep mutational scanning has also been used to infer protein-protein interactions. [ 25 ]  The authors used a thermodynamic model to predict the effects of mutations in different parts of a dimer. Deep mutational structure can also be used to infer protein structure. Strong positive epistasis between two mutations in a deep mutational scan can be indicative of two parts of the protein that are close to each other in 3-D space. This information can then be used to infer protein structure. A proof of principle of this approach was shown by two groups using the protein GB1. [ 26 ] [ 27 ]


Results from MPRA experiments have required machine learning approaches to interpret the data. A gapped k-mer SVM model has been used to infer the kmers that are enriched within cis-regulatory sequences with high activity compared to sequences with lower activity. [ 28 ]  These models provide high predictive power. Deep learning and random forest approaches have also been used to interpret the results of these high-dimensional experiments. [ 29 ]  These models are beginning to help develop a better understanding of  non-coding DNA  function towards gene-regulation.


The  ENCODE  (Encyclopedia of DNA elements) project is an in-depth analysis of the human genome whose goal is to identify all the functional elements of genomic DNA, in both coding and non-coding regions. Important results include evidence from genomic tiling arrays that most nucleotides are transcribed as coding transcripts, non-coding RNAs, or random transcripts, the discovery of additional transcriptional regulatory sites, further elucidation of chromatin-modifying mechanisms.


The GTEx project is a human genetics project aimed at understanding the role of genetic variation in shaping variation in the transcriptome across tissues. The project has collected a variety of tissue samples (> 50 different tissues) from more than 700 post-mortem donors. This has resulted in the collection of >11,000 samples. GTEx has helped understand the tissue-sharing and tissue-specificity of  eQTLs . [ 30 ]  The genomic resource was developed to "enrich our understanding of how differences in our DNA sequence contribute to health and disease." [ 31 ]


The Atlas of Variant Effects Alliance  (AVE), [ 32 ]  founded in 2020, is an international consortium aiming to catalog the impact of all possible genetic variants for disease-related functional genomics by creating variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. AVE is funded in part through the Brotman Baty Institute at the University of Washington and the National Human Genome Research Institute, via funding from the Center of Excellence in Genome Science grant (NHGRI RM1HG010461). [ 33 ]


Fungal genomes  are among the smallest genomes of  eukaryotes . The sizes of fungal genomes range from less than 10 Mbp to hundreds of Mbp. [ 1 ] [ 2 ]  The average genome size is approximately 37 Mbp in  Ascomycota , 47 Mbp in  Basidiomycota  and 75 Mbp in  Oomycota . [ 1 ]  The sizes and gene numbers of the smallest genomes of free-living fungi such as those of  Wallemia ichthyophaga ,  Wallemia mellicola  or  Malassezia restricta  are comparable to  bacterial genomes . [ 3 ] [ 4 ] [ 5 ]  The genome of the extensively researched yeast  Saccharomyces cerevisiae  contains approximately 12 Mbp and was the first completely sequenced eukaryotic genome. [ 6 ]  Due to their compact size fungal genomes can be sequenced with less resources than most other eukaryotic genomes and are thus important models for research. [ 7 ]  Some fungi exist as stable haploid, diploid, or polyploid cells, others change  ploidy  in response to environmental conditions and  aneuploidy  is also observed in novel environments or during periods of stress. [ 8 ]


The comparison of fungal genomes has been used to study the evolution of fungi, to improve the resolution of the  phylogeny  of fungal species, and to determine the time of the emergence and changes in species traits and lifestyles, such as the evolution symbiotic or pathogenic interactions, and the evolution of different morphologies. [ 2 ]  Major  chromosomal rearrangements  in fungi were found to be more frequent than in other eukaryotes, thus  macrosynteny  in fungi is rare. [ 9 ]  However, in filamentous  ascomycetes  genes were found to be conserved within homologous chromosomes, but with randomized orders and orientations, a phenomenon named  mesosynteny . [ 9 ]  Mesosynteny was also observed in the basidiomycetous genus  Rhodotorula . [ 10 ]  A comparison of more than 1000  Saccharomyces cerevisiae  genomes was used to identify the geographical origin and several domestication events of the species as well as map genomic variants to the species-wide  phenotypic  landscape of the yeast. [ 11 ]  Comparisons of several genomes of the same species led to discovery of high levels of recombination in species that were previously considered  asexual . [ 12 ] [ 13 ] [ 14 ]  In the extremely  halotolerant  black yeast  Hortaea werneckii  it was discovered that while the species is clonal, both  haploid  and  diploid  strains can be found in nature and the diploid strains are highly heterozygous hybrids, which appear to be stable over large time scales and geographical distances. [ 15 ]


While  genomic distance  measures such as the  average nucleotide identity  (ANI) are used routinely to distinguish bacterial species, the use of fungal genomes in taxonomy is currently rare. Genome sequences can be used to expand the number of genes used in phylogenetic analyses, but many publicly available genomes lack gene annotations and popular rDNA markers are typically missing from genomic sequences or are incorrectly assembled. [ 16 ]  Suggested measures of overall genome related indices in yeast include ANI,  digital DNA–DNA hybridization  (dDDH) and K r  distance. [ 17 ]  Genomic collinearity was suggested as a possible source of markers to resolve species complexes. [ 18 ]  Pairwise Kr genomic distances and average nucleotide identity were used in the description of new species within the genera  Aureobasidium  and  Tilletia . [ 19 ] [ 20 ]


Alternatively, quick and simple to calculate similarity measures based on  MinHash  also appear to produce usefully accurate estimates of distance between genomes. For example, a fixed threshold genomic distance calculated tools such as  Mash  and Dashing was able to determine whether two genomes belong to the same or to different species with over 90% accuracy, indicating that simple measures of genomic distance might be useful to delineate fungal species and still largely support the existing fungal taxonomy. [ 21 ]


The  G-value paradox  arises from the lack of correlation between the number of  protein -coding  genes  among  eukaryotes  and their relative biological complexity. The microscopic  nematode   Caenorhabditis elegans , for example, is composed of only a thousand  cells  but has about the same number of genes as a human. [ 1 ] [ 2 ]  Researchers suggest resolution of the paradox may lie in mechanisms such as  alternative splicing  and complex  gene regulation  that make the genes of humans and other complex eukaryotes relatively more productive. [ 3 ]


The lack of correlation between the  morphological  complexity of eukaryotes and the amount of genetic information they carry has long puzzled researchers. [ 4 ]  The sheer amount of  DNA  in an organism, measured by the mass of DNA present in the  nucleus  or the number of constituent  nucleotide  pairs, varies by several orders of magnitude among eukaryotes and often is unrelated to an organism's size or developmental complexity. [ 5 ]  One  amoeba  has 200 times more DNA per cell than humans, [ 6 ]  and even insects and plants within the same  genus  can vary dramatically in their quantity of DNA. [ 7 ]  This  C-value  paradox troubled genome scientists for many years.


Eventually, researchers recognized that not all DNA contributes directly to the production of proteins and other biological functions. [ 8 ]   Susumu Ohno  coined the phrase " junk DNA " to describe these nonfunctional swaths of DNA. [ 9 ]  They include  introns , genetic sequences that are removed after  transcription  into  mRNA  and thus are not  translated  into proteins; [ 4 ] [ 10 ]   transposable elements  that are mobile fragments of DNA, most of which are nonfunctional in humans; [ 8 ] [ 11 ]  and  pseudogenes , nonfunctional DNA sequences that originated from functional genes. [ 12 ]  The share of the human genome that may be considered "junk" remains controversial. Estimates reach as low as 8% [ 13 ]  and as high as 80%, [ 14 ]  with one researcher arguing that there is a fixed ceiling of 15% imposed by the genome's  genetic load . [ 15 ]  ( Prokaryotes , which have little "junk" DNA by comparison, exhibit a fairly close relationship between genome size and biological functionality). [ 16 ]


In any case, the assumption was that once the C-paradox was swept away and the focus shifted to the number of protein-coding genes, the anticipated correlation between genetic information and biological complexity in eukaryotes would emerge. [ 3 ]  Unfortunately, the G-value paradox simply picked up where the C-value paradox left off, because the discrepancy persisted when comparisons were narrowed to just protein-coding genes. [ 3 ] [ 17 ]


Estimates of the number of coding genes in the human  genome  reached upwards of 100,000 prior to the  human genome project , [ 18 ]  but since have dwindled to as low as 19,000 following completion of that massive  sequencing  effort and subsequent refinements. [ 1 ]  By comparison, the microscopic water flea  Daphnia pulex  has about 31,000 genes; [ 19 ]  the nematode  C. elegans  about 19,700; [ 2 ]  the fruit fly  ( Drosophila melanogaster )  about 14,000; [ 20 ]   the zebrafish ( Danio rerio ),  26,000; [ 21 ]  and the small flowering plant  Arabidopsis   thaliana ,  27,000. [ 22 ]  Plants in general tend to have more genes than other eukaryotes. [ 23 ]  One explanation is their higher incidence of gene and whole genome duplication and retention of those additional genes, due in part to their development of a large collection of defensive  secondary metabolites . [ 23 ]


The apparent disconnect between the number of genes in a species and its biological complexity was dubbed the G-value paradox. [ 3 ]   While the C-value paradox unraveled with the discovery of massive sequences of noncoding DNA, resolution of the G-value paradox appears to rest on differences in genome productivity. Humans and other complex eukaryotes simply may be able to do more with what they have, genetically speaking.


Among the mechanisms cited for this greater productivity are more sophisticated  transcriptional controls , [ 24 ]  multifunctional proteins, more interaction between protein products, alternative splicing [ 25 ]  and  post-translational modifications  that may produce several protein products from the same genetic raw material. [ 3 ] [ 24 ]  In addition, thousands of  non-coding RNAs  that are transcribed from DNA but not translated into protein have emerged as important regulators of gene expression and development in humans and other eukaryotes. [ 26 ]  They include short RNA sequences, such as  microRNAs  (miRNAs),  small interfering RNAs  (siRNAs) and  Piwi-interacting RNAs  (piRNAs), [ 26 ]   and  long, non-coding RNAs  (lncRNA) that may regulate gene expression at different stages of development. [ 27 ]  Some researchers suggest that instead of the number of genes the focus now should shift to gene interactions and the network of genetic  regulatory mechanisms  that allow them to support a variety of biological activities. [ 28 ] [ 24 ]  These transitions have taken analysis of genetic complexity from the C-value to the G-value to what some refer to as the I-value, a measure of the total information contained in a genome. [ 3 ]


One of the challenges in the long debate over the mismatch between genome size and biological complexity has been ambiguity in defining complexity. Is it the number of  cell types  in an organism, the sophistication of its  nervous system  or the number of different proteins it produces? [ 17 ]  By some definitions, the greater complexity of humans compared to other organisms may be illusory. [ 29 ]  Even once complexity is defined, some researchers argue complexity in function does not necessarily require the same complexity in process. Evolution is not a paragon of efficiency but travels a crooked path that leads to a more cumbersome genome than is necessary in some species. [ 30 ]


G&T-seq  (short for  single cell genome and transcriptome sequencing ) is a novel form of  single cell sequencing  technique allowing one to simultaneously obtain both  transcriptomic  and  genomic  data from single cells, allowing for direct comparison of gene expression data to its corresponding genomic data in the same cell... [ 1 ]


The advent of single-cell sequencing has provided researchers with the tools to resolve  genotypically  and  phenotypically  distinct cells within a mixed population. [ 2 ]   In cases where such  heterogeneity  is relevant, such as in tumours, this technique enables the study of  clonal relationships  and  tumour evolution . [ 3 ]  As well, rare cell types and samples otherwise containing low cell numbers, such as in the case of circulating tumour cells, can also be studied in greater detail. [ 4 ]  However, previous methods of  library preparation  typically involve the capture of either mRNA or genomic DNA (gDNA), but not both. [ 5 ]  By simultaneously capturing and sequencing both DNA and RNA through a method called G&T sequencing, researchers are able to obtain sequence information for both genome and transcriptome analysis from single cell libraries, thereby allowing integrated studies involving both networks. As a proof of concept, the authors of G&T-seq demonstrated its ability to acquire both the messenger RNA (mRNA) and genomic DNA (gDNA) by using  paramagnetic beads  with  biotinylated   oligo-deoxy-Thymine(dT) primer  to separate the polyadenylated (Poly-A) RNA from its gDNA prior to amplification and library preparation. Validation experiments on G&T-seq performed using cell lines with previous sequencing data available show that  sequencing coverage ,  gene expression profile , and  DNA copy number profiles  were reliably reproduced by G&T sequencing, and that this method was able to call a majority (87%) of all previously annotated single nucleotide variants (SNVs) in these cell lines. The authors have argued on this basis that the process of physically separating mRNA from gDNA did not negatively affect the yield or quality of sequencing data. [ 1 ]


Similar to conventional single-cell sequencing, G&T-seq involves the harvesting and lysis of desired cells. However, both gDNA and polyA-mRNA are captured and physically separated prior to amplification and library construction for analysis using  sequencing platforms .


G&T sequencing separates the mRNA from the gDNA using an unbiased global amplification procedure described previously. [ 6 ]  First, mRNA is isolated on specialized oligo-dT (5’-biotin-triethyleneglycol-AAGCAGTGGTATCAACGCAGAGTAC(T)30VN-3’) conjugated to streptavidin-coupled paramagnetic beads. [ 7 ]  The oligo-dT binds to the poly-A tails of processed mRNA, fishing them out from the pool of genomic material. Next, the paramagnetic beads are spatially isolated by magnetization.  The genomic material remaining in the  supernatant  is extracted and physically separated from the mRNA. [ 1 ]


The authors that developed G&T-seq utilized and validated two methods for whole-genome amplification: Multiple displacement amplification and PicoPlex. Other methods, such as  MALBAC , may be applicable but have yet to be validated. [ 1 ] [ 8 ]


MDA amplification technique can be used to generate long, high quality reads that produce sequencing data of comparable quality to bulk sequencing using  PCR amplification . [ 9 ]  This method involves the use of hexamer primers that bind randomly to the template, followed by DNA elongation using  phi29 DNA polymerase . Upon reaching the 5’ end of a downstream primer, the polymerase displaces that elongating strand to continue synthesis. The displaced strand becomes open for pairing with more primers, allowing for amplification of the displaced strand. The process continues and produces a branched DNA library that can be cut and sequenced. The authors of the G&T technique found that, though MDA used in G&T-seq yielded genomic coverage of similar breadth as MDA performed in conventional single cell sequencing, the distribution of read coverage was less even across the genome. [ 1 ]


Though MDA produces higher quality reads suitable for SNP analysis, DNA copy number profiles generated by such a technique are not highly accurate and reproducible due to its non-uniform amplification. [ 5 ] [ 10 ]  An alternate technique called PicoPlex, developed by Rubicon Genomics, has been shown to produce better results. [ 1 ]  Here, elongation of random primers ligated to an  adapter  creates a  complementary  strand with an adapter that, when denatured and randomly reprimed, produces a double stranded fragment with complementary adapters. Denaturation into single strands allows for the formation of  hairpin loops  due to the complementary nature of their adapters, creating a hairpin loop library that cannot be used for subsequent amplification, thereby preventing exponential amplification of initial bias. [ 11 ] [ 12 ]


Messenger RNA bound to oligo-dT is reverse transcribed into  cDNA  using the oligo-dT primers with the addition of Template-Switching Oligo (TSO, 5"-AAGCAGTGGTATCAACGCAGAGTACrGrG+G-3’) and Superscript II reverse transcriptase. [ 13 ] [ 14 ]  Superscript II reverse transcriptase has additional terminal transferase activity which adds a variable number of cytosine residues to the end the 3’ terminal cDNA molecule. The overhang of 3’ cytosine residues bind to the TSO, creating an extended template. The Superscript II reverse transcriptase switches templates and continues transcribing to complete the 3’ end of the cDNA. This results in a full length cDNA containing the 5’ oligo-dT primer, cDNA transcribed from mRNA, and the 3’ universal priming site for second-strand synthesis.
The cDNA undergoes amplification using the universal primer (5’- AAGCAGTGGTATCAACGCAGAGT-3’) for 18 cycles of PCR before it undergoes library preparation using the Nextera XT Kit from Illumina and sequencing by the Illumina HiSeq platform. [ 1 ] [ 15 ]


A similar method to G&T-seq, developed months earlier, is DR-seq (DNA and RNA sequencing). The primary difference between the two techniques is the amplification step, where DNA and polyA-RNA amplification occurs without their prior separation. [ 16 ]  DR-seq uses random priming, where primers containing a common 27-nucleotide sequence along with a variable 8-nucleotide (ad2 primers) bind to different locations on the cDNA. [ 12 ]  Despite there being multiple (50-250) primer binding sites on most cDNA, each original (i.e. not the product of amplification/in vitro  transcription ) cDNA molecule is usually primed only once during the initial amplification step, thus creating a single  amplicon  of a unique length, containing the ad2 primer on the 5' end. The 3' end contains the ad1 primer, which is the original poly-dT primer used for reverse transcription. This unique amplicon is termed the length-based identifier. Importantly, the length-based identifier is created, but not amplified by this quasilinear PCR step. The number of unique length-based identifiers for each gene can then be used to infer the number of original cDNA (and thus mRNA) molecules present for the gene, providing a method of estimating gene expression that avoids the effect of amplification bias. To further amplify the cDNA for RNA-seq, the cDNA amplicons generated by the original PCR step undergoes in vitro transcription using the T7 promoter incorporated in the ad1 primer to ensure RNA transcripts come from cDNA, not gDNA.


Advantages of the DR-seq technique include the reduction of the possibility for contamination and RNA loss, since the extra step of DNA/RNA separation is skipped. As well, amplification bias is reduced due to the use of the aforementioned length-based identifiers. However, since DNA and polyA-RNA is not separated prior to amplification and subsequent sequencing, the exonic regions must be  computationally masked , leaving only reads that originate from gDNA, in order to determine copy number. This creates issues for accurately determining copy number counts from gDNA. The authors note, though, that copy number count over large genomic regions is apparently not impacted by masking as a result because  coding regions  compose a relatively small portion of the genome. [ 16 ]


Dual genome and transcriptome sequencing allows researchers to establish high resolution correlations of genomic aberrations with alterations to levels of transcription. For example, the authors of this technique were able to detect single cells with chromosomal  aneuploidies , and establish that these aneuploidies corresponded with increased or decreased overall chromosomal gene expression when there was a respective chromosomal gain (e.g.  Trisomy ) or loss. Subchromosomal changes could also be correlated with changes in expression of genes at affected loci. As well, the authors were able to find a fusion transcript and locate the chromosomal breakpoint in the same cell resulting in the fusion. [ 1 ]


G&T-seq also provides a strategy for establishing causative links between genotype and phenotype associations in single cells (e.g. Non-coding SNVs). While bulk sequencing of genome and transcriptome may allow one to associate a collection of genotypic features with mean expression patterns in a population of cells, it overlooks subtle or temporal differences between individual cells that may arise due to cell ecology. [ 17 ]  This presents an obstacle for researchers trying to pinpoint the genomic causes underlying transcript alterations, especially when compounded with tumour samples where heterogeneity is widespread and background genetic variation could confound relevant mutations. [ 3 ] [ 18 ] [ 19 ]  Conventional single cell sequencing, on the other hand, prevents one from making direct associations between mutations and changes in the transcriptome because either the DNA or the RNA is lost in the process. Traditionally, researchers would have to use other methods, such as classification based on cell markers. However, such methods of discrimination rely on the availability of specific antibodies, and provide relatively coarse discrimination compared to sequencing since expression of cell surface markers constitute only a fraction of its overall phenotype [ 20 ] [ 21 ]


Finally, separation of DNA from RNA paves the way for dual sequencing of the epigenome and transcriptome, two components of the cell that are intricately linked to each other. However, this would require validation with conventional single cell  bisulphite sequencing  to ensure separation of DNA and RNA doesn’t affect the  DNA methylation  status.


The MDA amplification has an inherent bias against repeat sequences which were underrepresented in MDA products. In the context of G&T sequencing, this results in a reduced read count as the % of  GC content  increases for a particular region.


Comparing the amplification of single cell residual genomic DNA after mRNA isolation by MDA to amplification of single cell genomic DNA without mRNA isolation by MDA, showed a less evenly distributed coverage across the genome after mRNA isolation. Although there was a reduction in coverage distribution, it was not by a large proportion.


Isolation of mRNA by the G&T-seq technique described is only capable of capturing mRNAs which have a sufficient length poly-A tail which can be captured by the oligo-dT bait. [ 6 ]  This is not a complete representation of the mRNA present in the cell.  Some mRNAs have crucial roles in phenotypic expression but do not present the standard polyA tail length due to alternative polyadenylation. [ 22 ]  Therefore, G&Ts comparison of genotype–phenotype correlation does not necessarily represent the best causal link between the two.


The mRNA isolation is not the only hurdle in establishing genotype–phenotype relation. It is not sufficient to use mRNA as a surrogate to total protein expression, because other RNA species exist which also play important roles in phenotypic expression. Another auxiliary technique which can bolster the claims made by G&T sequencing is a total proteome analysis by mass spectrometry, giving a better presentation of the relation between genomic changes and phenotypic presentation [ 15 ]


Gene set enrichment analysis (GSEA)  (also called  functional enrichment analysis  or  pathway enrichment analysis ) is a method to identify classes of  genes  or  proteins  that are over-represented in a large set of genes or proteins, and may have an association with different  phenotypes  (e.g. different organism growth patterns or diseases). The method uses statistical approaches to identify significantly enriched or depleted groups of genes.  Transcriptomics technologies  and  proteomics  results often identify thousands of genes, which are used for the analysis. [ 1 ]


Researchers performing  high-throughput experiments  that yield sets of genes (for example, genes that are differentially  expressed  under different conditions) often want to retrieve a functional profile of that gene set, in order to better understand the underlying biological processes. This can be done by comparing the input gene set to each of the bins (terms) in the  gene ontology  – a  statistical test  can be performed for each bin to see if it is enriched for the input genes.


After the completion of the  Human Genome Project , the problem of how to interpret and analyze it remained. In order to seek out genes associated with diseases,  DNA microarrays  were used to measure the amount of gene expression in different cells. Microarrays on thousands of different genes were carried out, and comparisons the results of two different cell categories, e.g. normal cells versus cancerous cells.  However, this method of comparison is not sensitive enough to detect the subtle differences between the expression of individual genes, because diseases typically involve entire groups of genes. [ 2 ]  Multiple genes are linked to a single biological pathway, and so it is the additive change in expression within gene sets that leads to the difference in phenotypic expression. Gene Set Enrichment Analysis was developed  [ 2 ]  to focus on the changes of expression in groups of a priori defined gene sets.  By doing so, this method resolves the problem of the undetectable, small changes in the expression of single genes. [ 3 ]


Gene set enrichment analysis uses  a priori  gene sets that have been grouped together by their involvement in the same biological pathway, or by proximal location on a chromosome. [ 1 ]  A database of these predefined sets can be found at the  Molecular signatures database  (MSigDB). [ 4 ] [ 5 ]  In GSEA, DNA microarrays, or now  RNA-Seq , are still performed and compared between two cell categories, but instead of focusing on individual genes in a long list, the focus is put on a gene set. [ 1 ]  Researchers analyze whether the majority of genes in the set fall in the extremes of this list: the top and bottom of the list correspond to the largest differences in expression between the two cell types. If the gene set falls at either the top (over-expressed) or bottom (under-expressed), it is thought to be related to the phenotypic differences.


In the method that is typically referred to as standard GSEA, there are three steps involved in the analytical process. [ 1 ] [ 2 ]  The general steps are summarized below:


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 {\displaystyle {\begin{alignedat}{1}&P_{hit}(S,i)=\sum _{g_{j}\in S,j\leq i}{\dfrac {|r_{j}|^{p}}{N_{R}}};\\[0.6ex]&P_{miss}(S,i)=\sum _{g_{j}\not \in S,j\leq i}{\dfrac {1}{N-N_{H}}};\\[0.6ex]&N_{R}=\sum _{g_{j}\in S}|r_{j}|^{p};\\[0.6ex]&ES=P(S,i)=P_{hit}(S,i)-P_{miss}(S,i)=max(|P_{hit}(S,i)-P_{miss}(S,i)|)\\[0.6ex]\end{alignedat}}}

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When GSEA was first proposed in 2003 some immediate concerns were raised regarding its methodology. These criticisms led to the use of the correlation-weighted  Kolmogorov–Smirnov test , the normalized ES, and the false discovery rate calculation, all of which are the factors that currently define standard GSEA. [ 6 ]  However, GSEA has now also been criticized for the fact that its null distribution is superfluous, and too difficult to be worth calculating, as well as the fact that its Kolmogorov–Smirnov-like statistic is not as sensitive as the original. [ 6 ]  As an alternative, the method known as Simpler Enrichment Analysis (SEA), was proposed. This method assumes gene independence and uses a simpler approach to calculate t-test. However, it is thought that these assumptions are in fact too simplifying, and gene correlation cannot be disregarded. [ 6 ]


One other limitation to Gene Set Enrichment Analysis is that the results are very dependent on the algorithm that clusters the genes, and the number of clusters being tested. [ 7 ]  Spectral Gene Set Enrichment (SGSE) is a proposed, unsupervised test. The method's founders claim that it is a better way to find associations between MSigDB gene sets and microarray data. The general steps include:


1.    Calculating the association between principal components and gene sets. [ 7 ]


2.    Using the weighted Z-method to calculate the association between the gene sets and the spectral structure of the data. [ 7 ]


GSEA uses complicated statistics, so it requires a computer program to run the calculations. GSEA has become standard practice, and there are many websites and downloadable programs that will provide the data sets and run the analysis.


Multi-Ontology Enrichment Tool (MOET) is a web-based ontology analysis tool that provides functionality for multiple ontologies, including Disease, GO, Pathway, Phenotype, and Chemical entities (ChEBI) for multiple species, including rat, mouse, human, bonobo, squirrel, dog, pig, chinchilla, naked mole-rat and vervet (green monkey). [ 8 ]  It outputs a downloadable graph and a list of statistically overrepresented terms in the user's list of genes using hypergeometric distribution. MOET also displays the corresponding  Bonferroni correction  and  odds ratio  on the results page. It is simple to use, and results are provided with a few clicks in seconds; no software installations or programming skills are required. In addition, MOET is updated weekly, providing the user with the most recent data for analyses.


NASQAR (Nucleic Acid SeQuence Analysis Resource) is an open source, web-based platform for high-throughput sequencing data analysis and visualization. [ 9 ] [ 10 ]  GSEA can be run using the R-based clusterProfiler package. [ 11 ]   NASQAR currently supports GO Term and  KEGG Pathway  enrichment with all organisms supported by an Org.Db database. [ 12 ]


The  gene ontology  (GO) annotation for 165 plant species and GO enrichment analysis is available. [ 13 ]


The Molecular Signatures Database hosts an extensive collection of annotated gene sets that can be used with most GSEA Software. [ 14 ]


The  Broad Institute  website is in cooperation with MSigDB and has a downloadable GSEA software, as well a general tutorial. [ 15 ]


WebGestalt  [ 16 ]  is a web based gene set analysis toolkit. It supports three well-established and complementary methods for enrichment analysis, including Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA), and Network Topology-based Analysis (NTA). Analysis can be performed against 12 organisms and 321,251 functional categories using 354 gene identifiers from various databases and technology platforms.


Enrichr [ 17 ] [ 18 ] [ 19 ]  is a gene set enrichment analysis tool for mammalian gene sets. It contains background libraries for transcription regulation, pathways and protein interactions, ontologies including GO and the human and mouse phenotype ontologies, signatures from cells treated with drugs, gene sets associated with human diseases, and expression of genes in different cells and tissues. The background libraries are from over 200 resources and contain over 450,000 annotated gene sets. The tool can be accessed through API and provides different ways to visualize the results. [ 20 ]


GeneSCF is a real-time based functional enrichment tool with support for multiple organisms [ 21 ]  and is designed to overcome the problems associated with using outdated resources and databases. [ 22 ]  Advantages of using GeneSCF: real-time analysis, users do not have to depend on enrichment tools to get updated, easy for computational biologists to integrate GeneSCF with their NGS pipeline, it supports multiple organisms, enrichment analysis for multiple gene list using multiple source database in single run, retrieve or download complete GO terms/Pathways/Functions with associated genes as simple table format in a plain text file. [ 23 ] [ 24 ]


DAVID  is the database for annotation, visualization and integrated discovery, a  bioinformatics  tool that pools together information from most major bioinformatics sources, with the aim of analyzing large gene lists in a  high-throughput  manner. [ 25 ]  DAVID goes beyond standard GSEA with additional functions like switching between gene and protein identifiers on the genome-wide scale, [ 25 ]  however, the annotations used by DAVID was not updated since October 2016 to Dec 2021, [ 26 ]  which can have a considerable impact on practical interpretation of results. [ 27 ]  However, A most recent update was performed in 2021 [ 26 ]


Metascape  is a biologist-oriented gene-list analysis portal. [ 28 ]   Metascape  integrates pathway enrichment analysis, protein complex analysis, and multi-list meta-analysis into one seamless workflow accessible through a significantly simplified user interface.   Metascape  maintains analysis accuracy by updating its 40 underlying knowledgebases monthly.  Metascape  presents results using easy-to-interpret graphics, spreadsheets, and publication quality presentations, and is freely available. [ 29 ]


The  Gene Ontology  (GO) consortium has also developed their own online GO term enrichment tool, [ 30 ]
allowing species-specific enrichment analysis versus the complete database, coarser-grained GO slims, or custom references. [ 31 ]


Genomic region enrichment of annotations tool (GREAT) is a software which takes advantage of  regulatory domains  to better associate gene ontology terms to genes. [ 32 ] [ 33 ]  Its primary purpose is to identify pathways and processes that are significantly associated with factor regulating activity. This method maps genes with regulatory regions through a  hypergeometric test  over genes, inferring proximal gene regulatory domains. It does this by using the total fraction of the genome associated with a given ontology term as the expected fraction of input regions associated with the term by chance. Enrichment is calculated by all regulatory regions, and several experiments were performed to validate GREAT, one of which being enrichment analyses done on 8  ChIP-seq  datasets. [ 32 ]


The Functional Enrichment Analysis (FunRich) tool [ 34 ]  is mainly used for the functional enrichment and network analysis of  Omics  data. [ 35 ]


The FuncAssociate tool enables Gene Ontology and custom enrichment analyses. [ 36 ]  It allows inputting ordered sets as well as weighted gene space files for background. [ 37 ]


Instances of  InterMine  automatically provide enrichment analysis  [ 38 ]  for uploaded sets of genes and other biological entities.


ToppGene is a one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network. [ 39 ]   Developed and maintained by the Division of Biomedical Informatics at  Cincinnati Children's Hospital Medical Center .


Quantitative Set Analysis for Gene Expression (QuSAGE)] is a computational method for gene set enrichment analysis. [ 40 ]  QuSAGE improves power by accounting for inter-gene correlations and quantifies gene set activity with a complete  probability density function  (PDF). From this PDF,  P values  and  confidence intervals  can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. The applicability of QuSAGE has been extended to  longitudinal studies  by adding functionality for general linear mixed models. [ 41 ]  QuSAGE was used by the NIH/NIAID to identify baseline transcriptional signatures that were associated with human  influenza vaccination  responses. [ 42 ]  QuSAGE is available as a R/ Bioconductor  package.
 [ 43 ]


Blast2GO is a bioinformatics platform for functional annotation and analysis of genomic datasets. [ 44 ]   This tool allows to perform gene set enrichment analysis, [ 45 ]  among other functions.


g:Profiler is a toolset for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. [ 46 ]  g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. g:Profiler supports close to 500 species and strains, including vertebrates, plants, fungi, insects and parasites.


Single-nucleotide polymorphisms , or SNPs, are single base mutations that may be associated with diseases. One base change has the potential to affect the protein that results from that gene being expressed; however, it also has the potential to have no effect at all.  Genome-wide association studies  (GWAS) are comparisons between healthy and disease genotypes to try to find SNPs that are overrepresented in the disease genomes, and might be associated with that condition. Before GSEA, the accuracy of genome-wide SNP association studies was severely limited by a high number of false positives. [ 47 ]  The theory that the SNPs contributing to a disease tend to be grouped in a set of genes that are all involved in the same biological pathway, is what the GSEA-SNP method is based on. This application of GSEA does not only aid in the discovery of disease-associated SNPs, but helps illuminate the corresponding pathways and mechanisms of the diseases. [ 47 ]


Gene set enrichment methods led to the discovery of new suspect genes and biological pathways related to  spontaneous preterm births . [ 48 ]   Exome  sequences from women who had experienced SPTB were compared to those from females from the 1000 Genome Project, using a tool that scored possible disease-causing variants. Genes with higher scores were then run through different programs to group them into gene sets based on pathways and ontology groups. This study found that the variants were significantly clustered in sets related to several pathways, all suspects in SPTB. [ 48 ]


Gene set enrichment analysis can be used to understand the changes that cells undergo during  carcinogenesis  and  metastasis . In a study, microarrays were performed on  renal cell carcinoma  metastases, primary renal tumors, and normal kidney tissue, and the data was analyzed using GSEA. [ 49 ]  This analysis showed significant changes of expression in genes involved in pathways that have not been previously associated with the progression of renal cancer. From this study, GSEA has provided potential new targets for renal cell carcinoma therapy.


GSEA can be used to help understand the molecular mechanisms of complex disorders. Schizophrenia is a largely heritable disorder, but is also very complex, and the onset of the disease involves many genes interacting within multiple pathways, as well the interaction of those genes with environmental factors. For instance, epigenetic changes, like  DNA methylation , are affected by the environment, but are also inherently dependent on the DNA itself. DNA methylation is the most well-studied epigenetic change, and was recently analyzed using GSEA in relation to schizophrenia-related intermediate phenotypes. [ 50 ]  Researchers ranked genes for their correlation between methylation patterns and each of the phenotypes. They then used GSEA to look for an enrichment of genes that are predicted to be targeted by microRNAs in the progression of the disease. [ 50 ]


GSEA can help provide molecular evidence for the association of biological pathways with diseases. Previous studies have shown that long-term depression symptoms are correlated with changes in immune response and inflammatory pathways. [ 51 ]  Genetic and molecular evidence was sought to support this. Researchers took blood samples from sufferers of depression, and used genome-wide expression data, along with GSEA to find expression differences in gene sets related to inflammatory pathways. This study found that those people who rated with the most severe depression symptoms also had significant expression differences in those gene sets, and this result supports the association hypothesis. [ 51 ]


GeneDx  is a  genetic testing  company that was founded in 2000 by two scientists from the  National Institutes of Health  (NIH), Sherri Bale and John Compton. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ]  They started the company to provide clinical diagnostic services for patients and families with rare and ultra-rare disorders, for which no such commercial testing was available at the time. The company started in the Technology Development Center, a biotech incubator supported by the state of Maryland and Montgomery County, MD. In 2006, BioReference Laboratories acquired GeneDx. [ 8 ]  Since then, GeneDx has operated as a subsidiary of this parent company under the leadership of Bale (retired in 2016) and Compton (retired in 2013). [ 9 ]  In October 2016, Benjamin D. Solomon was appointed as managing director. [ 10 ]


GeneDx works with the medical, scientific, and  patient advocacy  communities to continuously develop new genetic tests not currently available at other clinical laboratories. GeneDx currently offers tests for hundreds of rare diseases, as well as panels of genes and whole exome sequencing (20,000 gene)  Mendelian  disorders using massively-parallel  DNA sequencing  and deletion/duplication analysis of the associated gene(s). GeneDx also performs  oligonucleotide -based microarray testing for the detection of  chromosomal abnormalities  (genomic losses or gains) in individuals with chromosomal anomalies. GeneDx provides testing for  autism  spectrum disorders, various forms of  cardiomyopathy , inherited eye, skin, muscle, hearing, metabolic, neurologic, and  mitochondrial disease . [ citation needed ]


GeneDx settled a patent dispute with  Myriad Genetics  in February 2015 after GeneDx launched a  BRCA mutation  breast cancer genetic screening test following the  US Supreme Court  decision in  Association for Molecular Pathology v. Myriad Genetics, Inc.  that concluded isolated gene sequences were patent ineligible. [ 11 ]


GeneMark  is a generic name for a family of  ab initio  gene prediction algorithms and software programs developed at the  Georgia Institute of Technology  in  Atlanta . Developed in 1993, original GeneMark was used in 1995 as a primary gene prediction tool for annotation of the first completely sequenced bacterial genome of  Haemophilus influenzae , and in 1996 for the first archaeal genome of  Methanococcus jannaschii . The algorithm introduced  inhomogeneous  three-periodic  Markov chain  models of protein-coding  DNA sequence  that became standard in gene prediction as well as Bayesian approach to gene prediction in two DNA strands simultaneously. Species specific parameters of the models were estimated from training sets of sequences of known type (protein-coding and non-coding). The major step of the algorithm computes for a given DNA fragment posterior probabilities of either being "protein-coding" (carrying  genetic code )  in each of six possible reading frames (including three frames in the  complementary DNA  strand) or being "non-coding". The original GeneMark  (developed before the advent of the HMM applications in Bioinformatics) was an HMM-like algorithm; it could be viewed as approximation to known in the HMM theory posterior decoding algorithm for appropriately defined HMM model of DNA sequence.


The GeneMark.hmm algorithm (1998) was designed to improve  accuracy of prediction of short genes and gene starts. The idea was to use the inhomogeneous Markov chain models introduced in GeneMark for computing likelihoods of the sequences emitted by the states of  a   hidden Markov model , or rather semi-Markov HMM, or generalized HMM describing the genomic sequence. The borders between coding and non-coding regions were formally interpreted as transitions between hidden states. Additionally, the  ribosome   binding site  model was added to the GHMM model to improve accuracy of gene start prediction. The next important step in the algorithm development was introduction of self-training or unsupervised training of the model parameters in the new gene prediction tool GeneMarkS (2001). Rapid accumulation of prokaryotic genomes in the following years has shown that the structure of sequence patterns related to gene expression regulation signals near gene starts may vary. Also, it was observed that prokaryotic genome may exhibit GC content variability due to the lateral gene transfer. The new algorithm, GeneMarkS-2 was designed to make automatic adjustments to the types of gene expression patterns and the GC content changes along the genomic sequence. GeneMarkS and, then GeneMarkS-2 have been used in the NCBI pipeline for prokaryotic genomes annotation (PGAP).
( www.ncbi.nlm.nih.gov/genome/annotation_prok/process ).


Accurate identification of species specific parameters of a gene finding algorithm is a necessary condition for making accurate gene predictions. However, in the studies of viral genomes one needs to estimate parameters from a rather short sequence that has no large genomic context. Importantly, starting 2004, the same question had to be addressed for gene prediction in short metagenomic sequences. A surprisingly accurate answer was found by introduction of parameter generating functions depending on a single variable, the sequence G+C content ("heurisic method" 1999). Subsequently, analysis of several hundred prokaryotic genomes led to developing more advanced heuristic method in 2010 (implemented in MetaGeneMark). Further on, the need to predict genes in RNA transcripts led to development of GeneMarkS-T (2015), a tool that identifies intron-less genes in long transcript sequences assembled from RNA-Seq reads.


In eukaryotic genomes modeling of  exon  borders with introns and intergenic regions present a major challenge. The GHMM architecture of eukaryotic GeneMark.hmm includes hidden states for initial, internal, and terminal exons,  introns ,  intergenic regions  and single exon genes located in both DNA strands. Initial version of the eukaryotic GeneMark.hmm needed manual compilation of training sets of protein-coding sequences for estimation of the algorithm parameters. However, in 2005, the first self-training eukaryotic gene finder, GeneMark-ES, was developed. A fungal version of GeneMark-ES developed in 2008 features a more complex intron model and hierarchical strategy of self-training. In 2014, in GeneMark-ET the self-training of parameters was aided by extrinsic hints generated by mapping to the genome short RNA-Seq reads. Extrinsic evidence is not limited to the 'native' RNA sequences. The cross-species proteins collected in the vast protein databases could be a source for external hints, if the homologous relationships between the already known proteins and the proteins encoded by yet unknown genes in the novel genome are established. This task was solved upon developing the new algorithm, GeneMark-EP+ (2020). Integration of the RNA and protein sources of the intrinsic hints was done in GeneMark-ETP (2023). Versatility and accuracy of the eukaryotic gene finders of the GeneMark family have led to their incorporation into number of pipelines of genome annotation. Also, since 2016, the pipelines BRAKER1, BRAKER2, BRAKER3 were developed to combine the strongest features of GeneMark and AUGUSTUS.


Notably, gene prediction in eukaryotic transcripts can be done by the new algorithm GeneMarkS-T (2015)


The  Generic Model Organism Database  ( GMOD ) project provides biological research communities with a toolkit of  open-source software  components for visualizing, annotating, managing, and storing biological data. The GMOD project is funded by the United States  National Institutes of Health ,  National Science Foundation  and the  USDA Agricultural Research Service .


The GMOD project was started in the early 2000s as a collaboration between several  model organism databases  (MODs) who shared a need to create similar  software  tools for processing data from sequencing projects. MODs, or organism-specific  databases , describe  genome  and other information about important  experimental organisms  in the life sciences and capture the large volumes of data and information being generated by modern  biology . Rather than each group designing their own software, four major MODs-- FlyBase ,  Saccharomyces Genome Database ,  Mouse Genome Database , and at or run off a Chado schema database.


The Chado [ 1 ]  schema aims to cover many of the classes of data frequently used by modern biologists, from genetic data to phylogenetic trees to publications to organisms to microarray data to IDs to RNA/protein expression. Chado makes extensive use of controlled vocabularies to type all entities in the database; for example: genes, transcripts, exons, transposable elements, etc., are stored in a feature table, with the type provided by  Sequence Ontology . When a new type is added to the Sequence Ontology, the feature table requires no modification, only an update of the data in the database. The same is largely true of analysis data that can be stored in Chado as well.


The existing core modules of Chado are:


The full list of GMOD software components is found on the GMOD Components page. [ 2 ]   These components include:


The following organism databases are contributing to and/or adopting GMOD components for model organism databases.


A  GeneRIF  or  Gene Reference Into Function  is a short (255 characters or fewer) statement about the function of a gene.  GeneRIFs provide a simple  mechanism  for allowing  scientists  to add to the functional  annotation  of  genes  described in the Entrez Gene database. In practice,  function  is constructed quite broadly.  For example, there are GeneRIFs that discuss the role of a gene in a disease, GeneRIFs that point the viewer towards a review article about the gene, and GeneRIFs that discuss the structure of a gene.  However, the stated intent is for GeneRIFs to be about gene function. Currently over half a million geneRIFs have been created for genes from almost 1000 different species. [ 1 ]


GeneRIFs are always associated with specific entries in the Entrez Gene database.  Each GeneRIF has a pointer to the PubMed ID (a type of document identifier) of a scientific publication that provides evidence for the statement made by the GeneRIF.  GeneRIFs are often extracted directly from the document that is identified by the PubMed ID, very frequently from its title or from its final sentence.


GeneRIFs are usually produced by  NCBI  indexers, but anyone may submit a GeneRIF.
To be processed, a valid Gene ID must exist for the specific gene, or the Gene staff must have assigned an overall Gene ID to the  species . The latter case is implemented via records in Gene with the symbol NEWENTRY. Once the Gene ID is identified, only three types of information are required to complete a  submission :


Here are some GeneRIFs taken from  Entrez Gene  for GeneID 7157, the human gene  TP53 .
The  PubMed  document identifiers have been omitted from the examples.  Note the wide variability with respect to the presence or absence of punctuation and of sentence-initial capital letters.


GeneRIFs are an unusual type of textual genre, and they have recently been the subject of a number of articles from the natural language processing community.


A  genome  is all the genetic information of an organism. [ 1 ]  It consists of  nucleotide  sequences of  DNA  (or  RNA  in  RNA viruses ). The nuclear genome includes protein-coding genes and non-coding genes, other functional regions of the genome such as regulatory sequences (see  non-coding DNA ), and often a substantial fraction of  junk DNA  with no evident function. [ 2 ] [ 3 ]  Almost all  eukaryotes  have  mitochondria  and a small  mitochondrial genome . [ 2 ]  Algae and plants also contain  chloroplasts  with a chloroplast genome.


The study of the genome is called  genomics . The genomes of many organisms have been  sequenced  and various regions have been annotated. The first genome to be sequenced was that of the virus φX174 in 1977; [ 4 ]  the first genome sequence of a prokaryote ( Haemophilus influenzae ) was published in 1995; [ 5 ]  the yeast ( Saccharomyces cerevisiae ) genome was the first eukaryotic genome to be sequenced in 1996. [ 6 ]  The  Human Genome Project  was started in October 1990, and the first draft sequences of the  human genome  were reported in February 2001. [ 7 ]


The term  genome  was created in 1920 by  Hans Winkler , [ 8 ]  professor of  botany  at the  University of Hamburg , Germany. The website  Oxford Dictionaries  and the  Online Etymology Dictionary  suggest the name is a blend of the words  gene  and  chromosome . [ 9 ] [ 10 ] [ 11 ] [ 12 ]  However, see  omics  for a more thorough discussion. A few related  -ome  words already existed, such as  biome  and  rhizome , forming a vocabulary into which  genome  fits systematically. [ 13 ]


The term "genome" usually refers to the DNA (or sometimes RNA) molecules that carry the genetic information in an organism, but sometimes it is uncertain which molecules to include; for example, bacteria usually have one or two large DNA molecules ( chromosomes ) that contain all of the essential genetic material but they also contain smaller extrachromosomal  plasmid  molecules that carry important genetic information. In the scientific literature, the term 'genome' usually refers to the large chromosomal DNA molecules in bacteria. [ 14 ]


Eukaryotic genomes are even more difficult to define because almost all eukaryotic species contain nuclear chromosomes plus extra DNA molecules in the  mitochondria . In addition, algae and plants have  chloroplast  DNA. Most textbooks make a distinction between the nuclear genome and the organelle (mitochondria and chloroplast) genomes so when they speak of, say, the human genome, they are only referring to the genetic material in the nucleus. [ 2 ] [ 15 ]  This is the most common use of 'genome' in the scientific literature.


Most eukaryotes are  diploid , meaning that there are two of each chromosome in the nucleus but the 'genome' refers to only one copy of each chromosome. Some eukaryotes have distinctive sex chromosomes, such as the X and Y chromosomes of mammals, so the technical definition of the genome must include both copies of the sex chromosomes. For example, the standard reference genome of humans consists of one copy of each of the 22 autosomes plus one X chromosome and one Y chromosome. [ 16 ]


A  genome sequence  is the complete list of the  nucleotides  (A, C, G, and T for DNA genomes) that make up all the  chromosomes  of an individual or a species. Within a species, the vast majority of nucleotides are identical between individuals, but sequencing multiple individuals is necessary to understand the genetic diversity.


In 1976,  Walter Fiers  at the  University of Ghent  (Belgium) was the first to establish the complete nucleotide sequence of a viral RNA-genome ( Bacteriophage MS2 ). The next year,  Fred Sanger  completed the first DNA-genome sequence:  Phage X174 , of 5386 base pairs. [ 17 ]  The first bacterial genome to be sequenced was that of  Haemophilus influenzae , completed by a team at  The Institute for Genomic Research  in 1995. A few months later, the first eukaryotic genome was completed, with sequences of the 16 chromosomes of budding yeast  Saccharomyces cerevisiae  published as the result of a European-led effort begun in the mid-1980s. The first genome sequence for an  archaeon ,  Methanococcus jannaschii , was completed in 1996, again by The Institute for Genomic Research. [ citation needed ]


The development of new technologies has made genome sequencing dramatically cheaper and easier, and the number of complete genome sequences is growing rapidly. The  US National Institutes of Health  maintains one of several comprehensive databases of genomic information. [ 18 ]  Among the thousands of completed genome sequencing projects include those for  rice , a  mouse , the plant  Arabidopsis thaliana , the  puffer fish , and the bacteria  E. coli . In December 2013, scientists first sequenced the entire  genome  of a  Neanderthal , an extinct species of  humans . The genome was extracted from the  toe bone  of a 130,000-year-old Neanderthal found in a  Siberian cave . [ 19 ] [ 20 ]


Viral genomes  can be composed of either RNA or DNA. The genomes of  RNA viruses  can be either  single-stranded RNA  or  double-stranded RNA , and may contain one or more separate RNA molecules (segments: monopartit or multipartit genome). DNA viruses can have either single-stranded or double-stranded genomes. Most DNA virus genomes are composed of a single, linear molecule of DNA, but some are made up of a circular DNA molecule. [ 21 ]


Prokaryotes and eukaryotes have DNA genomes. Archaea and most bacteria have a single  circular chromosome , [ 22 ]  however, some bacterial species have linear or multiple chromosomes. [ 23 ] [ 24 ]  If the DNA is replicated faster than the bacterial cells divide, multiple copies of the chromosome can be present in a single cell, and if the cells divide faster than the DNA can be replicated, multiple replication of the chromosome is initiated before the division occurs, allowing daughter cells to inherit complete genomes and already partially replicated chromosomes. Most prokaryotes have very little repetitive DNA in their genomes. [ 25 ]  However, some  symbiotic bacteria  (e.g.  Serratia symbiotica ) have reduced genomes and a high fraction of pseudogenes: only ~40% of their DNA encodes proteins. [ 26 ] [ 27 ]


Some bacteria have auxiliary genetic material, also part of their genome, which is carried in  plasmids . For this, the word  genome  should not be used as a synonym of  chromosome .


Eukaryotic genomes are composed of one or more linear DNA chromosomes. The number of chromosomes varies widely from  Jack jumper ants  and an  asexual nemotode , [ 28 ]  which each have only one pair, to a  fern species  that has 720 pairs. [ 29 ]  It is surprising the amount of DNA that eukaryotic genomes contain compared to other genomes. The amount is even more than what is necessary for DNA protein-coding and noncoding genes due to the fact that eukaryotic genomes show as much as 64,000-fold variation in their sizes. [ 30 ]  However, this special characteristic is caused by the presence of repetitive DNA, and transposable elements (TEs).


A typical human cell has two copies of each of 22  autosomes , one inherited from each parent, plus two  sex chromosomes , making it diploid.  Gametes , such as ova, sperm, spores, and pollen, are haploid, meaning they carry only one copy of each chromosome. In addition to the chromosomes in the nucleus, organelles such as the  chloroplasts  and  mitochondria  have their own DNA. Mitochondria are sometimes said to have their own genome often referred to as the " mitochondrial genome ". The DNA found within the chloroplast may be referred to as the " plastome ". Like the bacteria they originated from, mitochondria and chloroplasts have a circular chromosome.


Unlike prokaryotes where exon-intron organization of protein coding genes exists but is rather exceptional, eukaryotes generally have these features in their genes and their genomes contain variable amounts of repetitive DNA. In mammals and plants, the majority of the genome is composed of repetitive DNA. [ 31 ]


High-throughput technology makes sequencing to assemble new genomes accessible to everyone. Sequence polymorphisms are typically discovered by comparing resequenced isolates to a reference, whereas analyses of coverage depth and mapping topology can provide details regarding structural variations such as chromosomal translocations and segmental duplications.


DNA sequences that carry the instructions to make proteins are referred to as coding sequences. The proportion of the genome occupied by coding sequences varies widely. A larger genome does not necessarily contain more genes, and the proportion of non-repetitive DNA decreases along with increasing genome size in complex eukaryotes. [ 31 ]


Noncoding sequences include  introns , sequences for non-coding RNAs, regulatory regions, and repetitive DNA. Noncoding sequences make up 98% of the human genome. There are two categories of repetitive DNA in the genome:  tandem repeats  and interspersed repeats. [ 32 ]


Short, non-coding sequences that are repeated head-to-tail are called  tandem repeats . Microsatellites consisting of 2–5 basepair repeats, while minisatellite repeats are 30–35 bp. Tandem repeats make up about 4% of the human genome and 9% of the fruit fly genome. [ 33 ]  Tandem repeats can be functional. For example,  telomeres  are composed of the tandem repeat TTAGGG in mammals, and they play an important role in protecting the ends of the chromosome.


In other cases, expansions in the number of tandem repeats in exons or introns can cause  disease . [ 34 ]  For example, the human gene huntingtin (Htt) typically contains 6–29 tandem repeats of the nucleotides CAG (encoding a polyglutamine tract). An expansion to over 36 repeats results in  Huntington's disease , a neurodegenerative disease. Twenty human disorders are known to result from similar tandem repeat expansions in various genes. The mechanism by which proteins with expanded polygulatamine tracts cause death of neurons is not fully understood. One possibility is that the proteins fail to fold properly and avoid degradation, instead accumulating in aggregates that also sequester important transcription factors, thereby altering gene expression. [ 34 ]


Tandem repeats are usually caused by slippage during replication, unequal crossing-over and gene conversion. [ 35 ]


Transposable elements (TEs) are sequences of DNA with a defined structure that are able to change their location in the genome. [ 33 ] [ 25 ] [ 36 ]  TEs are categorized as either as a mechanism that replicates by copy-and-paste or as a mechanism that can be excised from the genome and inserted at a new location. In the human genome, there are three important classes of TEs that make up more than 45% of the human DNA; these classes are The long interspersed nuclear elements (LINEs), The interspersed nuclear elements (SINEs), and endogenous retroviruses. These elements have a big potential to modify the genetic control in a host organism. [ 30 ]


The movement of TEs is a driving force of genome evolution in eukaryotes because their insertion can disrupt gene functions, homologous recombination between TEs can produce duplications, and TE can shuffle exons and regulatory sequences to new locations. [ 37 ]


Retrotransposons [ 38 ]  are found mostly in eukaryotes but not found in prokaryotes. Retrotransposons form a large portion of the genomes of many eukaryotes. A retrotransposon is a transposable element that transposes through an  RNA  intermediate. Retrotransposons [ 39 ]  are composed of  DNA , but are transcribed into RNA for transposition, then the RNA transcript is copied back to DNA formation with the help of a specific enzyme called reverse transcriptase. A retrotransposon that carries reverse transcriptase in its sequence can trigger its own transposition but retrotransposons that lack a reverse transcriptase must use reverse transcriptase synthesized by another retrotransposon.  Retrotransposons  can be transcribed into RNA, which are then duplicated at another site into the genome. [ 40 ]  Retrotransposons can be divided into  long terminal repeats  (LTRs) and non-long terminal repeats (Non-LTRs). [ 37 ]


Long terminal repeats (LTRs)  are derived from ancient retroviral infections, so they encode proteins related to retroviral proteins including gag (structural proteins of the virus), pol (reverse transcriptase and integrase), pro (protease), and in some cases env (envelope) genes. [ 36 ]  These genes are flanked by long repeats at both 5' and 3' ends. It has been reported that LTRs consist of the largest fraction in most plant genome and might account for the huge variation in genome size. [ 41 ]


Non-long terminal repeats (Non-LTRs)  are classified as  long interspersed nuclear elements  (LINEs),  short interspersed nuclear elements  (SINEs), and Penelope-like elements (PLEs). In  Dictyostelium discoideum , there is another DIRS-like elements belong to Non-LTRs. Non-LTRs are widely spread in eukaryotic genomes. [ 42 ]


Long interspersed elements (LINEs) encode genes for reverse transcriptase and endonuclease, making them autonomous transposable elements. The human genome has around 500,000 LINEs, taking around 17% of the genome. [ 43 ]


Short interspersed elements (SINEs) are usually less than 500 base pairs and are non-autonomous, so they rely on the proteins encoded by LINEs for transposition. [ 44 ]  The  Alu element  is the most common SINE found in primates. It is about 350 base pairs and occupies about 11% of the human genome with around 1,500,000 copies. [ 37 ]


DNA transposons  encode a transposase enzyme between inverted terminal repeats. When expressed, the transposase recognizes the terminal inverted repeats that flank the transposon and catalyzes its excision and reinsertion in a new site. [ 33 ]  This cut-and-paste mechanism typically reinserts transposons near their original location (within 100 kb). [ 37 ]  DNA transposons are found in bacteria and make up 3% of the human genome and 12% of the genome of the roundworm  C. elegans . [ 37 ]


Genome size  is the total number of the DNA base pairs in one copy of a  haploid  genome. Genome size varies widely across species. Invertebrates have small genomes, this is also correlated to a small number of transposable elements. Fish and Amphibians have intermediate-size genomes, and birds have relatively small genomes but it has been suggested that birds lost a substantial portion of their genomes during the phase of transition to flight.  Before this loss, DNA methylation allows the adequate expansion of the genome. [ 30 ]


In humans, the nuclear genome comprises approximately 3.1 billion nucleotides of DNA, divided into 24 linear molecules, the shortest 45 000 000 nucleotides in length and the longest 248 000 000 nucleotides, each contained in a different chromosome. [ 45 ]  There is no clear and consistent correlation between morphological complexity and genome size in either  prokaryotes  or lower  eukaryotes . [ 31 ] [ 46 ]  Genome size is largely a function of the expansion and contraction of repetitive DNA elements.


Since genomes are very complex, one research strategy is to reduce the number of genes in a genome to the bare minimum and still have the organism in question survive. There is experimental work being done on minimal genomes for single cell organisms as well as minimal genomes for multi-cellular organisms (see  developmental biology ). The work is both  in vivo  and  in silico . [ 47 ] [ 48 ]


There are many enormous differences in size in genomes, specially mentioned before in the multicellular eukaryotic genomes. Much of this is due to the differing abundances of transposable elements, which evolve by creating new copies of themselves in the chromosomes. [ 30 ]  Eukaryote genomes often contain many thousands of copies of these elements, most of which have acquired mutations that make them defective.


All the cells of an organism originate from a single cell, so they are expected to have identical genomes; however, in some cases, differences arise. Both the process of copying DNA during cell division and exposure to environmental mutagens can result in mutations in somatic cells. In some cases, such mutations lead to cancer because they cause cells to divide more quickly and invade surrounding tissues. [ 49 ]  In certain lymphocytes in the human immune system,  V(D)J recombination  generates different genomic sequences such that each cell produces a unique antibody or T cell receptors.


During  meiosis , diploid cells divide twice to produce haploid germ cells. During this process, recombination results in a reshuffling of the genetic material from homologous chromosomes so each gamete has a unique genome.


Genome-wide reprogramming in mouse  primordial germ cells  involves  epigenetic  imprint erasure leading to  totipotency . Reprogramming is facilitated by active  DNA demethylation , a process that entails the DNA  base excision repair  pathway. [ 50 ]  This pathway is employed in the erasure of  CpG methylation  (5mC) in primordial germ cells. The erasure of 5mC occurs via its conversion to  5-hydroxymethylcytosine  (5hmC) driven by high levels of the ten-eleven dioxygenase enzymes  TET1  and  TET2 . [ 51 ]


Genomes are more than the sum of an organism's  genes  and have traits that may be  measured  and studied without reference to the details of any particular genes and their products. Researchers compare traits such as  karyotype  (chromosome number),  genome size , gene order,  codon usage bias , and  GC-content  to determine what mechanisms could have produced the great variety of genomes that exist today (for recent overviews, see Brown 2002; Saccone and Pesole 2003; Benfey and Protopapas 2004; Gibson and Muse 2004; Reese 2004; Gregory 2005).