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Human genome

For a non-technical introduction to the topic, see Introduction to genetics.

Complete set of nucleic acid sequences for humans


Graphical representation of the idealized human diploid karyotype, showing the organization of the genome into chromosomes. This drawing shows both the female (XX) and male (XY) versions of the 23rd chromosome pair. Chromosomes are shown aligned at their centromeres. The mitochondrial DNA is not shown.

NCBI genome ID51
Genome size3, Mbp[1] (mega-basepairs) per haploid genome
6, Mbp total (diploid).
Number of chromosomes23 pairs

The human genome is a complete set of nucleic acid sequences for humans, encoded as DNA within the 23 chromosome pairs in cell nuclei and in a small DNA molecule found within individual mitochondria. These are usually treated separately as the nuclear genome and the mitochondrial genome.[2] Human genomes include both protein-coding DNA genes and noncoding DNA. Haploid human genomes, which are contained in germ cells (the egg and spermgamete cells created in the meiosis phase of sexual reproduction before fertilization creates a zygote) consist of three billion DNAbase pairs, while diploid genomes (found in somatic cells) have twice the DNA content. While there are significant differences among the genomes of human individuals (on the order of % due to single-nucleotide variants[3] and % when considering indels),[4] these are considerably smaller than the differences between humans and their closest living relatives, the bonobos and chimpanzees (~% fixed single-nucleotide variants [5] and 4% when including indels).[6]

Although the sequence of the human genome has been (almost) completely determined by DNA sequencing, it is not yet fully understood. Most (though probably not all) genes have been identified by a combination of high throughput experimental and bioinformatics approaches, yet much work still needs to be done to further elucidate the biological functions of their protein and RNA products. Recent results suggest that most of the vast quantities of noncoding DNA within the genome have associated biochemical activities, including regulation of gene expression, organization of chromosome architecture, and signals controlling epigenetic inheritance.

Prior to the acquisition of the full genome sequence, estimates of the number of human genes ranged from 50, to , (with occasional vagueness about whether these estimates included non-protein coding genes).[7] As genome sequence quality and the methods for identifying protein-coding genes improved,[8] the count of recognized protein-coding genes dropped to 19,,[9] However, a fuller understanding of the role played by sequences that do not encode proteins, but instead express regulatory RNA, has raised the total number of genes to at least 46,,[10] plus another micro-RNA genes.[11] By , functional DNA elements that encode neither RNA nor proteins have been noted.[12] A population survey found another million bases of human genome that was not in the reference sequence.[13]

Protein-coding sequences account for only a very small fraction of the genome (approximately %), and the rest is associated with non-coding RNA genes, regulatory DNA sequences, LINEs, SINEs, introns, and sequences for which as yet no function has been determined.[14]


The first human genome sequences were published in nearly complete draft form in February by the Human Genome Project[15] and Celera Corporation.[16] Completion of the Human Genome Project's sequencing effort was announced in with the publication of a draft genome sequence, leaving just gaps in the sequence, representing highly-repetitive and other DNA that could not be sequenced with the technology available at the time.[8] The human genome was the first of all vertebrates to be sequenced to such near-completion, and as of , the diploid genomes of over a million individual humans had been determined using next-generation sequencing.[17] In it was reported that the T2T consortium had filled in all of the gaps. Thus there came into existence a complete human genome with no gaps.[18]

These data are used worldwide in biomedical science, anthropology, forensics and other branches of science. Such genomic studies have led to advances in the diagnosis and treatment of diseases, and to new insights in many fields of biology, including human evolution.

In June , scientists formally announced HGP-Write, a plan to synthesize the human genome.[19][20]


Main article: Human Genome Project §&#;State of completion

Although the 'completion' of the human genome project was announced in ,[14] there remained hundreds of gaps, with about 5–10% of the total sequence remaining undetermined. The missing genetic information was mostly in repetitive heterochromatic regions and near the centromeres and telomeres, but also some gene-encoding euchromatic regions.[21] There remained euchromatic gaps in when the sequences spanning another 50 formerly-unsequenced regions were determined.[22] Only in was the first truly complete telomere-to-telomere sequence of a human chromosome determined, namely of the X chromosome.[23] Level "complete genome" (without Y chromosome) was achieved in May [24][25]

Molecular organization and gene content[edit]

See also: Lists of human genes

The total length of the human reference genome, that does not represent the sequence of any specific individual, is over 3 billion base pairs. The genome is organized into 22 paired chromosomes, termed autosomes, plus the 23rd pair of sex chromosomes (XX) in the female, and (XY) in the male. These are all large linear DNA molecules contained within the cell nucleus. The genome also includes the mitochondrial DNA, a comparatively small circular molecule present in multiple copies in each the mitochondrion.


Original analysis published in the Ensembl database at the European Bioinformatics Institute (EBI) and Wellcome Trust Sanger Institute. Chromosome lengths estimated by multiplying the number of base pairs by nanometers (distance between base pairs in the most common structure of the DNA double helix; a recent estimate of human chromosome lengths based on updated data reports &#;cm for the diploid male genome and &#;cm for female, corresponding to weights of and picograms (pg), respectively[27]). Number of proteins is based on the number of initial precursor mRNA transcripts, and does not include products of alternative pre-mRNA splicing, or modifications to protein structure that occur after translation.

Variations are unique DNA sequence differences that have been identified in the individual human genome sequences analyzed by Ensembl as of December The number of identified variations is expected to increase as further personal genomes are sequenced and analyzed. In addition to the gene content shown in this table, a large number of non-expressed functional sequences have been identified throughout the human genome (see below). Links open windows to the reference chromosome sequences in the EBI genome browser.

Small non-coding RNAs are RNAs of as many as bases that do not have protein-coding potential. These include: microRNAs, or miRNAs (post-transcriptional regulators of gene expression), small nuclear RNAs, or snRNAs (the RNA components of spliceosomes), and small nucleolar RNAs, or snoRNA (involved in guiding chemical modifications to other RNA molecules). Long non-coding RNAs are RNA molecules longer than bases that do not have protein-coding potential. These include: ribosomal RNAs, or rRNAs (the RNA components of ribosomes), and a variety of other long RNAs that are involved in regulation of gene expression, epigenetic modifications of DNA nucleotides and histone proteins, and regulation of the activity of protein-coding genes. Small discrepancies between total-small-ncRNA numbers and the numbers of specific types of small ncNRAs result from the former values being sourced from Ensembl release 87 and the latter from Ensembl release

The number of genes in the human genome is not entirely clear because the function of numerous transcripts remains unclear. This is especially true for non-coding RNA. The number of protein-coding genes is better known but there are still on the order of 1, questionable genes which may or may not encode functional proteins, usually encoded by short open reading frames.

protein-coding genes 19, 20, 20, 21,
lncRNA genes 15, 14, 17, 18,
antisense RNA 28
miscellaneous RNA 13,
Pseudogenes 14, 15,
total transcripts , , , ,
Number of genes (orange) and base pairs (green, in millions) on each chromosome.

Information content[edit]

The haploid human genome (23 chromosomes) is about 3 billion base pairs long and contains around 30, genes.[33] Since every base pair can be coded by 2 bits, this is about megabytes of data. An individual somatic (diploid) cell contains twice this amount, that is, about 6 billion base pairs. Men have fewer than women because the Y chromosome is about 57 million base pairs whereas the X is about million. Since individual genomes vary in sequence by less than 1% from each other, the variations of a given human's genome from a common reference can be losslessly compressed to roughly 4 megabytes.[34]

The entropy rate of the genome differs significantly between coding and non-coding sequences. It is close to the maximum of 2 bits per base pair for the coding sequences (about 45 million base pairs), but less for the non-coding parts. It ranges between and bits per base pair for the individual chromosome, except for the Y-chromosome, which has an entropy rate below bits per base pair.[35]

Coding vs. noncoding DNA[edit]

The content of the human genome is commonly divided into coding and noncoding DNA sequences. Coding DNA is defined as those sequences that can be transcribed into mRNA and translated into proteins during the human life cycle; these sequences occupy only a small fraction of the genome (<2%). Noncoding DNA is made up of all of those sequences (ca. 98% of the genome) that are not used to encode proteins.

Some noncoding DNA contains genes for RNA molecules with important biological functions (noncoding RNA, for example ribosomal RNA and transfer RNA). The exploration of the function and evolutionary origin of noncoding DNA is an important goal of contemporary genome research, including the ENCODE (Encyclopedia of DNA Elements) project, which aims to survey the entire human genome, using a variety of experimental tools whose results are indicative of molecular activity.

Because non-coding DNA greatly outnumbers coding DNA, the concept of the sequenced genome has become a more focused analytical concept than the classical concept of the DNA-coding gene.[36][37]

Coding sequences (protein-coding genes) [edit]

For a comprehensive list, see List of human protein-coding genes 1, List of human protein-coding genes 2, List of human protein-coding genes 3, and List of human protein-coding genes 4.

Human genes categorized by function of the transcribed proteins, given both as number of encoding genes and percentage of all genes.[38]

Protein-coding sequences represent the most widely studied and best understood component of the human genome. These sequences ultimately lead to the production of all human proteins, although several biological processes (e.g. DNA rearrangements and alternative pre-mRNA splicing) can lead to the production of many more unique proteins than the number of protein-coding genes. The complete modular protein-coding capacity of the genome is contained within the exome, and consists of DNA sequences encoded by exons that can be translated into proteins. Because of its biological importance, and the fact that it constitutes less than 2% of the genome, sequencing of the exome was the first major milepost of the Human Genome Project.

Number of protein-coding genes. About 20, human proteins have been annotated in databases such as Uniprot.[39] Historically, estimates for the number of protein genes have varied widely, ranging up to 2,, in the late s,[40] but several researchers pointed out in the early s that the estimated mutational load from deleterious mutations placed an upper limit of approximately 40, for the total number of functional loci (this includes protein-coding and functional non-coding genes).[41] The number of human protein-coding genes is not significantly larger than that of many less complex organisms, such as the roundworm and the fruit fly. This difference may result from the extensive use of alternative pre-mRNA splicing in humans, which provides the ability to build a very large number of modular proteins through the selective incorporation of exons.

Protein-coding capacity per chromosome. Protein-coding genes are distributed unevenly across the chromosomes, ranging from a few dozen to more than , with an especially high gene density within chromosomes 1, 11, and Each chromosome contains various gene-rich and gene-poor regions, which may be correlated with chromosome bands and GC-content.[42] The significance of these nonrandom patterns of gene density is not well understood.[43]

Size of protein-coding genes. The size of protein-coding genes within the human genome shows enormous variability. For example, the gene for histone H1a (HIST1HIA) is relatively small and simple, lacking introns and encoding an nucleotide-long mRNA that produces a amino acid protein from its nucleotide open reading frame. Dystrophin (DMD) was the largest protein-coding gene in the human reference genome, spanning a total of million nucleotides,[44] while more recent systematic meta-analysis of updated human genome data identified an even larger protein-coding gene, RBFOX1 (RNA binding protein, fox-1 homolog 1), spanning a total of million nucleotides.[45]Titin (TTN) has the longest coding sequence (, nucleotides), the largest number of exons (),[44] and the longest single exon (17, nucleotides). As estimated based on a curated set of protein-coding genes over the whole genome, the median size is 26, nucleotides (mean = 66,), the median exon size, nucleotides (mean = ), the median number of exons, 8 (mean = 11), and the median encoded protein is amino acids (mean = ) in length.[45]

ProteinChromGeneLengthExonsExon lengthIntron lengthAlt splicing
Breast cancer type 2 susceptibility protein13BRCA283,2711,72,yes
Cystic fibrosis transmembrane conductance regulator7CFTR,274,,yes
Cytochrome bMTMTCYB1,11,0no
Glyceraldehydephosphate dehydrogenase12GAPDH4,91,3,yes
Hemoglobin beta subunit11HBB1,3no
Histone H1A6HIST1H1A10no

Noncoding DNA (ncDNA)[edit]

Main article: Noncoding DNA

Noncoding DNA is defined as all of the DNA sequences within a genome that are not found within protein-coding exons, and so are never represented within the amino acid sequence of expressed proteins. By this definition, more than 98% of the human genomes is composed of ncDNA.

Numerous classes of noncoding DNA have been identified, including genes for noncoding RNA (e.g. tRNA and rRNA), pseudogenes, introns, untranslated regions of mRNA, regulatory DNA sequences, repetitive DNA sequences, and sequences related to mobile genetic elements.

Numerous sequences that are included within genes are also defined as noncoding DNA. These include genes for noncoding RNA (e.g. tRNA, rRNA), and untranslated components of protein-coding genes (e.g. introns, and 5' and 3' untranslated regions of mRNA).

Protein-coding sequences (specifically, coding exons) constitute less than % of the human genome.[14] In addition, about 26% of the human genome is introns.[47] Aside from genes (exons and introns) and known regulatory sequences (8–20%), the human genome contains regions of noncoding DNA. The exact amount of noncoding DNA that plays a role in cell physiology has been hotly debated. Recent analysis by the ENCODE project indicates that 80% of the entire human genome is either transcribed, binds to regulatory proteins, or is associated with some other biochemical activity.[12]

It however remains controversial whether all of this biochemical activity contributes to cell physiology, or whether a substantial portion of this is the result transcriptional and biochemical noise, which must be actively filtered out by the organism.[48] Excluding protein-coding sequences, introns, and regulatory regions, much of the non-coding DNA is composed of: Many DNA sequences that do not play a role in gene expression have important biological functions. Comparative genomics studies indicate that about 5% of the genome contains sequences of noncoding DNA that are highly conserved, sometimes on time-scales representing hundreds of millions of years, implying that these noncoding regions are under strong evolutionary pressure and positive selection.[49]

Many of these sequences regulate the structure of chromosomes by limiting the regions of heterochromatin formation and regulating structural features of the chromosomes, such as the telomeres and centromeres. Other noncoding regions serve as origins of DNA replication. Finally several regions are transcribed into functional noncoding RNA that regulate the expression of protein-coding genes (for example[50] ), mRNA translation and stability (see miRNA), chromatin structure (including histone modifications, for example[51] ), DNA methylation (for example[52] ), DNA recombination (for example[53] ), and cross-regulate other noncoding RNAs (for example[54] ). It is also likely that many transcribed noncoding regions do not serve any role and that this transcription is the product of non-specific RNA Polymerase activity.[48]


Main article: Pseudogene

Pseudogenes are inactive copies of protein-coding genes, often generated by gene duplication, that have become nonfunctional through the accumulation of inactivating mutations. The number of pseudogenes in the human genome is on the order of 13,,[55] and in some chromosomes is nearly the same as the number of functional protein-coding genes. Gene duplication is a major mechanism through which new genetic material is generated during molecular evolution.

For example, the olfactory receptor gene family is one of the best-documented examples of pseudogenes in the human genome. More than 60 percent of the genes in this family are non-functional pseudogenes in humans. By comparison, only 20 percent of genes in the mouse olfactory receptor gene family are pseudogenes. Research suggests that this is a species-specific characteristic, as the most closely related primates all have proportionally fewer pseudogenes. This genetic discovery helps to explain the less acute sense of smell in humans relative to other mammals.[56]

Genes for noncoding RNA (ncRNA)[edit]

Main article: Noncoding RNA

Noncoding RNA molecules play many essential roles in cells, especially in the many reactions of protein synthesis and RNA processing. Noncoding RNA include tRNA, ribosomal RNA, microRNA, snRNA and other non-coding RNA genes including about 60, long non-coding RNAs (lncRNAs).[12][57][58][59] Although the number of reported lncRNA genes continues to rise and the exact number in the human genome is yet to be defined, many of them are argued to be non-functional.[60]

Many ncRNAs are critical elements in gene regulation and expression. Noncoding RNA also contributes to epigenetics, transcription, RNA splicing, and the translational machinery. The role of RNA in genetic regulation and disease offers a new potential level of unexplored genomic complexity.[61]

Introns and untranslated regions of mRNA[edit]

In addition to the ncRNA molecules that are encoded by discrete genes, the initial transcripts of protein coding genes usually contain extensive noncoding sequences, in the form of introns, 5'-untranslated regions (5'-UTR), and 3'-untranslated regions (3'-UTR). Within most protein-coding genes of the human genome, the length of intron sequences is to times the length of exon sequences.

Regulatory DNA sequences[edit]

The human genome has many different regulatory sequences which are crucial to controlling gene expression. Conservative estimates indicate that these sequences make up 8% of the genome,[62] however extrapolations from the ENCODE project give that 20[63]%[64] of the genome is gene regulatory sequence. Some types of non-coding DNA are genetic "switches" that do not encode proteins, but do regulate when and where genes are expressed (called enhancers).[65]

Regulatory sequences have been known since the late s.[66] The first identification of regulatory sequences in the human genome relied on recombinant DNA technology.[67] Later with the advent of genomic sequencing, the identification of these sequences could be inferred by evolutionary conservation. The evolutionary branch between the primates and mouse, for example, occurred 70–90 million years ago.[68] So computer comparisons of gene sequences that identify conserved non-coding sequences will be an indication of their importance in duties such as gene regulation.[69]

Other genomes have been sequenced with the same intention of aiding conservation-guided methods, for exampled the pufferfish genome.[70] However, regulatory sequences disappear and re-evolve during evolution at a high rate.[71][72][73]

As of , the efforts have shifted toward finding interactions between DNA and regulatory proteins by the technique ChIP-Seq, or gaps where the DNA is not packaged by histones (DNase hypersensitive sites), both of which tell where there are active regulatory sequences in the investigated cell type.[62]

Repetitive DNA sequences[edit]

Repetitive DNA sequences comprise approximately 50% of the human genome.[74]

About 8% of the human genome consists of tandem DNA arrays or tandem repeats, low complexity repeat sequences that have multiple adjacent copies (e.g. "CAGCAGCAG").[75] The tandem sequences may be of variable lengths, from two nucleotides to tens of nucleotides. These sequences are highly variable, even among closely related individuals, and so are used for genealogical DNA testing and forensic DNA analysis.[76]

Repeated sequences of fewer than ten nucleotides (e.g. the dinucleotide repeat (AC)n) are termed microsatellite sequences. Among the microsatellite sequences, trinucleotide repeats are of particular importance, as sometimes occur within coding regions of genes for proteins and may lead to genetic disorders. For example, Huntington's disease results from an expansion of the trinucleotide repeat (CAG)n within the Huntingtin gene on human chromosome 4. Telomeres (the ends of linear chromosomes) end with a microsatellite hexanucleotide repeat of the sequence (TTAGGG)n.

Tandem repeats of longer sequences (arrays of repeated sequences 10–60 nucleotides long) are termed minisatellites.

Mobile genetic elements (transposons) and their relics[edit]

Transposable genetic elements, DNA sequences that can replicate and insert copies of themselves at other locations within a host genome, are an abundant component in the human genome. The most abundant transposon lineage, Alu, has about 50, active copies,[77] and can be inserted into intragenic and intergenic regions.[78] One other lineage, LINE-1, has about active copies per genome (the number varies between people).[79] Together with non-functional relics of old transposons, they account for over half of total human DNA.[80] Sometimes called "jumping genes", transposons have played a major role in sculpting the human genome. Some of these sequences represent endogenous retroviruses, DNA copies of viral sequences that have become permanently integrated into the genome and are now passed on to succeeding generations.

Mobile elements within the human genome can be classified into LTR retrotransposons (% of total genome), SINEs (% of total genome) including Alu elements, LINEs (% of total genome), SVAs and Class II DNA transposons (% of total genome).

Genomic variation in humans[edit]

Main article: Human genetic variation

Human reference genome[edit]

With the exception of identical twins, all humans show significant variation in genomic DNA sequences. The human reference genome (HRG) is used as a standard sequence reference.

There are several important points concerning the human reference genome:

  • The HRG is a haploid sequence. Each chromosome is represented once.
  • The HRG is a composite sequence, and does not correspond to any actual human individual.
  • The HRG is periodically updated to correct errors, ambiguities, and unknown "gaps".
  • The HRG in no way represents an "ideal" or "perfect" human individual. It is simply a standardized representation or model that is used for comparative purposes.

The Genome Reference Consortium is responsible for updating the HRG. Version 38 was released in December [81]

Measuring human genetic variation[edit]

Most studies of human genetic variation have focused on single-nucleotide polymorphisms (SNPs), which are substitutions in individual bases along a chromosome. Most analyses estimate that SNPs occur 1 in base pairs, on average, in the euchromatic human genome, although they do not occur at a uniform density. Thus follows the popular statement that "we are all, regardless of race, genetically % the same",[82] although this would be somewhat qualified by most geneticists. For example, a much larger fraction of the genome is now thought to be involved in copy number variation.[83] A large-scale collaborative effort to catalog SNP variations in the human genome is being undertaken by the International HapMap Project.

The genomic loci and length of certain types of small repetitive sequences are highly variable from person to person, which is the basis of DNA fingerprinting and DNA paternity testing technologies. The heterochromatic portions of the human genome, which total several hundred million base pairs, are also thought to be quite variable within the human population (they are so repetitive and so long that they cannot be accurately sequenced with current technology). These regions contain few genes, and it is unclear whether any significant phenotypic effect results from typical variation in repeats or heterochromatin.

Most gross genomic mutations in gamete germ cells probably result in inviable embryos; however, a number of human diseases are related to large-scale genomic abnormalities. Down syndrome, Turner Syndrome, and a number of other diseases result from nondisjunction of entire chromosomes. Cancer cells frequently have aneuploidy of chromosomes and chromosome arms, although a cause and effect relationship between aneuploidy and cancer has not been established.

Mapping human genomic variation[edit]

Whereas a genome sequence lists the order of every DNA base in a genome, a genome map identifies the landmarks. A genome map is less detailed than a genome sequence and aids in navigating around the genome.[84][85]

An example of a variation map is the HapMap being developed by the International HapMap Project. The HapMap is a haplotype map of the human genome, "which will describe the common patterns of human DNA sequence variation."[86] It catalogs the patterns of small-scale variations in the genome that involve single DNA letters, or bases.

Researchers published the first sequence-based map of large-scale structural variation across the human genome in the journal Nature in May [87][88] Large-scale structural variations are differences in the genome among people that range from a few thousand to a few million DNA bases; some are gains or losses of stretches of genome sequence and others appear as re-arrangements of stretches of sequence. These variations include differences in the number of copies individuals have of a particular gene, deletions, translocations and inversions.

Structural variation[edit]

Structural variation refers to genetic variants that affect larger segments of the human genome, as opposed to point mutations. Often, structural variants (SVs) are defined as variants of 50 base pairs (bp) or greater, such as deletions, duplications, insertions, inversions and other rearrangements. About 90% of structural variants are noncoding deletions but most individuals have more than a thousand such deletions; the size of deletions ranges from dozens of base pairs to tens of thousands of bp.[89] On average, individuals carry ~3 rare structural variants that alter coding regions, e.g. delete exons. About 2% of individuals carry ultra-rare megabase-scale structural variants, especially rearrangements. That is, millions of base pairs may be inverted within a chromosome; ultra-rare means that they are only found in individuals or their family members and thus have arisen very recently.[89]

SNP frequency across the human genome[edit]

Single-nucleotide polymorphisms (SNPs) do not occur homogeneously across the human genome. In fact, there is enormous diversity in SNP frequency between genes, reflecting different selective pressures on each gene as well as different mutation and recombination rates across the genome. However, studies on SNPs are biased towards coding regions, the data generated from them are unlikely to reflect the overall distribution of SNPs throughout the genome. Therefore, the SNP Consortium protocol was designed to identify SNPs with no bias towards coding regions and the Consortium's , SNPs generally reflect sequence diversity across the human chromosomes. The SNP Consortium aims to expand the number of SNPs identified across the genome to by the end of the first quarter of [90]

TSC SNP distribution along the long arm of chromosome 22 (from Each column represents a 1 Mb interval; the approximate cytogenetic position is given on the x-axis. Clear peaks and troughs of SNP density can be seen, possibly reflecting different rates of mutation, recombination and selection.

Changes in non-coding sequence and synonymous changes in coding sequence are generally more common than non-synonymous changes, reflecting greater selective pressure reducing diversity at positions dictating amino acid identity. Transitional changes are more common than transversions, with CpG dinucleotides showing the highest mutation rate, presumably due to deamination.

Personal genomes[edit]

See also: Personal genomics

A personal genome sequence is a (nearly) complete sequence of the chemical base pairs that make up the DNA of a single person. Because medical treatments have different effects on different people due to genetic variations such as single-nucleotide polymorphisms (SNPs), the analysis of personal genomes may lead to personalized medical treatment based on individual genotypes.[91]

The first personal genome sequence to be determined was that of Craig Venter in Personal genomes had not been sequenced in the public Human Genome Project to protect the identity of volunteers who provided DNA samples. That sequence was derived from the DNA of several volunteers from a diverse population.[92] However, early in the Venter-led Celera Genomics genome sequencing effort the decision was made to switch from sequencing a composite sample to using DNA from a single individual, later revealed to have been Venter himself. Thus the Celera human genome sequence released in was largely that of one man. Subsequent replacement of the early composite-derived data and determination of the diploid sequence, representing both sets of chromosomes, rather than a haploid sequence originally reported, allowed the release of the first personal genome.[93] In April , that of James Watson was also completed. In , Stephen Quake published his own genome sequence derived from a sequencer of his own design, the Heliscope.[94] A Stanford team led by Euan Ashley published a framework for the medical interpretation of human genomes implemented on Quake’s genome and made whole genome-informed medical decisions for the first time.[95] That team further extended the approach to the West family, the first family sequenced as part of Illumina’s Personal Genome Sequencing program.[96] Since then hundreds of personal genome sequences have been released,[97] including those of Desmond Tutu,[98][99] and of a Paleo-Eskimo.[] In , the whole genome sequences of two family trios among genomes was made public.[3] In November , a Spanish family made four personal exome datasets (about 1% of the genome) publicly available under a Creative Commons public domain license.[][] The Personal Genome Project (started in ) is among the few to make both genome sequences and corresponding medical phenotypes publicly available.[][]

The sequencing of individual genomes further unveiled levels of genetic complexity that had not been appreciated before. Personal genomics helped reveal the significant level of diversity in the human genome attributed not only to SNPs but structural variations as well. However, the application of such knowledge to the treatment of disease and in the medical field is only in its very beginnings.[]Exome sequencing has become increasingly popular as a tool to aid in diagnosis of genetic disease because the exome contributes only 1% of the genomic sequence but accounts for roughly 85% of mutations that contribute significantly to disease.[]

Human knockouts[edit]

In humans, gene knockouts naturally occur as heterozygous or homozygousloss-of-function gene knockouts. These knockouts are often difficult to distinguish, especially within heterogeneous genetic backgrounds. They are also difficult to find as they occur in low frequencies.

Populations with a high level of parental-relatedness result in a larger number of homozygous gene knockouts as compared to outbred populations.[]

Populations with high rates of consanguinity, such as countries with high rates of first-cousin marriages, display the highest frequencies of homozygous gene knockouts. Such populations include Pakistan, Iceland, and Amish populations. These populations with a high level of parental-relatedness have been subjects of human knock out research which has helped to determine the function of specific genes in humans. By distinguishing specific knockouts, researchers are able to use phenotypic analyses of these individuals to help characterize the gene that has been knocked out.

A pedigree displaying a first-cousin mating (carriers both carrying heterozygous knockouts mating as marked by double line) leading to offspring possessing a homozygous gene knockout.

Knockouts in specific genes can cause genetic diseases, potentially have beneficial effects, or even result in no phenotypic effect at all. However, determining a knockout's phenotypic effect and in humans can be challenging. Challenges to characterizing and clinically interpreting knockouts include difficulty calling of DNA variants, determining disruption of protein function (annotation), and considering the amount of influence mosaicism has on the phenotype.[]

One major study that investigated human knockouts is the Pakistan Risk of Myocardial Infarction study. It was found that individuals possessing a heterozygous loss-of-function gene knockout for the APOC3 gene had lower triglycerides in the blood after consuming a high fat meal as compared to individuals without the mutation. However, individuals possessing homozygous loss-of-function gene knockouts of the APOC3 gene displayed the lowest level of triglycerides in the blood after the fat load test, as they produce no functional APOC3 protein.[]

Human genetic disorders[edit]

Further information: Genetic disorder

Most aspects of human biology involve both genetic (inherited) and non-genetic (environmental) factors. Some inherited variation influences aspects of our biology that are not medical in nature (height, eye color, ability to taste or smell certain compounds, etc.). Moreover, some genetic disorders only cause disease in combination with the appropriate environmental factors (such as diet). With these caveats, genetic disorders may be described as clinically defined diseases caused by genomic DNA sequence variation. In the most straightforward cases, the disorder can be associated with variation in a single gene. For example, cystic fibrosis is caused by mutations in the CFTR gene and is the most common recessive disorder in caucasian populations with over 1, different mutations known.[]

Disease-causing mutations in specific genes are usually severe in terms of gene function and are fortunately rare, thus genetic disorders are similarly individually rare. However, since there are many genes that can vary to cause genetic disorders, in aggregate they constitute a significant component of known medical conditions, especially in pediatric medicine. Molecularly characterized genetic disorders are those for which the underlying causal gene has been identified. Currently there are approximately 2, such disorders annotated in the OMIM database.[]

Studies of genetic disorders are often performed by means of family-based studies. In some instances, population based approaches are employed, particularly in the case of so-called founder populations such as those in Finland, French-Canada, Utah, Sardinia, etc. Diagnosis and treatment of genetic disorders are usually performed by a geneticist-physician trained in clinical/medical genetics. The results of the Human Genome Project are likely to provide increased availability of genetic testing for gene-related disorders, and eventually improved treatment. Parents can be screened for hereditary conditions and counselled on the consequences, the probability of inheritance, and how to avoid or ameliorate it in their offspring.

There are many different kinds of DNA sequence variation, ranging from complete extra or missing chromosomes down to single nucleotide changes. It is generally presumed that much naturally occurring genetic variation in human populations is phenotypically neutral, i.e., has little or no detectable effect on the physiology of the individual (although there may be fractional differences in fitness defined over evolutionary time frames). Genetic disorders can be caused by any or all known types of sequence variation. To molecularly characterize a new genetic disorder, it is necessary to establish a causal link between a particular genomic sequence variant and the clinical disease under investigation. Such studies constitute the realm of human molecular genetics.

With the advent of the Human Genome and International HapMap Project, it has become feasible to explore subtle genetic influences on many common disease conditions such as diabetes, asthma, migraine, schizophrenia, etc. Although some causal links have been made between genomic sequence variants in particular genes and some of these diseases, often with much publicity in the general media, these are usually not considered to be genetic disorders per se as their causes are complex, involving many different genetic and environmental factors. Thus there may be disagreement in particular cases whether a specific medical condition should be termed a genetic disorder.

Additional genetic disorders of mention are Kallman syndrome and Pfeiffer syndrome (gene FGFR1), Fuchs corneal dystrophy (gene TCF4), Hirschsprung's disease (genes RET and FECH), Bardet-Biedl syndrome 1 (genes CCDC28B and BBS1), Bardet-Biedl syndrome 10 (gene BBS10), and facioscapulohumeral muscular dystrophy type 2 (genes D4Z4 and SMCHD1).[]

Genome sequencing is now able to narrow the genome down to specific locations to more accurately find mutations that will result in a genetic disorder. Copy number variants (CNVs) and single nucleotide variants (SNVs) are also able to be detected at the same time as genome sequencing with newer sequencing procedures available, called Next Generation Sequencing (NGS). This only analyzes a small portion of the genome, around %. The results of this sequencing can be used for clinical diagnosis of a genetic condition, including Usher syndrome, retinal disease, hearing impairments, diabetes, epilepsy, Leigh disease, hereditary cancers, neuromuscular diseases, primary immunodeficiencies, severe combined immunodeficiency (SCID), and diseases of the mitochondria.[] NGS can also be used to identify carriers of diseases before conception. The diseases that can be detected in this sequencing include Tay-Sachs disease, Bloom syndrome, Gaucher disease, Canavan disease, familial dysautonomia, cystic fibrosis, spinal muscular atrophy, and fragile-X syndrome. The Next Genome Sequencing can be narrowed down to specifically look for diseases more prevalent in certain ethnic populations.[]

Disorder Prevalence Chromosome or gene involved
Chromosomal conditions
Down syndrome Chromosome 21
Klinefelter syndrome– males Additional X chromosome
Turner syndrome females Loss of X chromosome
Sickle cell anemia1 in 50 births in parts of Africa; rarer elsewhere β-globin (on chromosome 11)
Bloom syndrome Ashkenazi Jews BLM
Breast/Ovarian cancer (susceptibility) ~5% of cases of these cancer types BRCA1, BRCA2
FAP (hereditary nonpolyposis coli) APC
Lynch syndrome5–10% of all cases of bowel cancer MLH1, MSH2, MSH6, PMS2
Fanconi anemia births FANCC
Neurological conditions
Huntington disease Huntingtin
Alzheimer disease ‐ early onset PS1, PS2, APP
Tay-Sachs births in Ashkenazi Jews HEXA gene (on chromosome 15)
Canavan disease% Eastern European Jewish ancestry ASPA gene (on chromosome 17)
Familial dysautonomia known cases worldwide since discovery IKBKAP gene (on chromosome 9)
Fragile X syndrome in males, in females FMR1 gene (on X chromosome)
Mucolipidosis type IV to in Ashkenazi Jews MCOLN1
Other conditions
Cystic fibrosis CFTR
Duchenne muscular dystrophy boys Dystrophin
Becker muscular dystrophy males DMD
Beta thalassemia HBB
Congenital adrenal hyperplasia in Native Americans and Yupik Eskimos

in American Caucasians

Glycogen storage disease type I births in America G6PC
Maple syrup urine disease in the U.S.

in Mennonite/Amish communities

in Austria

Niemann–Pick disease, SMPD1-associated1, cases worldwide SMPD1
Usher syndrome in the U.S.

in Norway

in Germany



See also: Human evolution and Chimpanzee Genome Project

Comparative genomics studies of mammalian genomes suggest that approximately 5% of the human genome has been conserved by evolution since the divergence of extant lineages approximately million years ago, containing the vast majority of genes.[][] The published chimpanzee genome differs from that of the human genome by % in direct sequence comparisons.[] Around 20% of this figure is accounted for by variation within each species, leaving only ~% consistent sequence divergence between humans and chimps at shared genes.[] This nucleotide by nucleotide difference is dwarfed, however, by the portion of each genome that is not shared, including around 6% of functional genes that are unique to either humans or chimps.[]

In other words, the considerable observable differences between humans and chimps may be due as much or more to genome level variation in the number, function and expression of genes rather than DNA sequence changes in shared genes. Indeed, even within humans, there has been found to be a previously unappreciated amount of copy number variation (CNV) which can make up as much as 5 – 15% of the human genome. In other words, between humans, there could be +/- ,, base pairs of DNA, some being active genes, others inactivated, or active at different levels. The full significance of this finding remains to be seen. On average, a typical human protein-coding gene differs from its chimpanzee ortholog by only two amino acid substitutions; nearly one third of human genes have exactly the same protein translation as their chimpanzee orthologs. A major difference between the two genomes is human chromosome 2, which is equivalent to a fusion product of chimpanzee chromosomes 12 and [] (later renamed to chromosomes 2A and 2B, respectively).

Humans have undergone an extraordinary loss of olfactory receptor genes during our recent evolution, which explains our relatively crude sense of smell compared to most other mammals. Evolutionary evidence suggests that the emergence of color vision in humans and several other primate species has diminished the need for the sense of smell.[]

In September , scientists reported that, based on human DNA genetic studies, all non-Africans in the world today can be traced to a single population that exited Africa between 50, and 80, years ago.[]

Mitochondrial DNA[edit]

The human mitochondrial DNA is of tremendous interest to geneticists, since it undoubtedly plays a role in mitochondrial disease. It also sheds light on human evolution; for example, analysis of variation in the human mitochondrial genome has led to the postulation of a recent common ancestor for all humans on the maternal line of descent (see Mitochondrial Eve).

Due to the lack of a system for checking for copying errors,[] mitochondrial DNA (mtDNA) has a more rapid rate of variation than nuclear DNA. This fold higher mutation rate allows mtDNA to be used for more accurate tracing of maternal ancestry.[citation needed] Studies of mtDNA in populations have allowed ancient migration paths to be traced, such as the migration of Native Americans from Siberia[] or Polynesians from southeastern Asia.[citation needed] It has also been used to show that there is no trace of Neanderthal DNA in the European gene mixture inherited through purely maternal lineage.[] Due to the restrictive all or none manner of mtDNA inheritance, this result (no trace of Neanderthal mtDNA) would be likely unless there were a large percentage of Neanderthal ancestry, or there was strong positive selection for that mtDNA. For example, going back 5 generations, only 1 of a person's 32 ancestors contributed to that person's mtDNA, so if one of these 32 was pure Neanderthal an expected ~3% of that person's autosomal DNA would be of Neanderthal origin, yet they would have a ~97% chance of having no trace of Neanderthal mtDNA.[citation needed]


See also: Epigenetics

Epigenetics describes a variety of features of the human genome that transcend its primary DNA sequence, such as chromatin packaging, histone modifications and DNA methylation, and which are important in regulating gene expression, genome replication and other cellular processes. Epigenetic markers strengthen and weaken transcription of certain genes but do not affect the actual sequence of DNA nucleotides. DNA methylation is a major form of epigenetic control over gene expression and one of the most highly studied topics in epigenetics. During development, the human DNA methylation profile experiences dramatic changes. In early germ line cells, the genome has very low methylation levels. These low levels generally describe active genes. As development progresses, parental imprinting tags lead to increased methylation activity.[][]

Epigenetic patterns can be identified between tissues within an individual as well as between individuals themselves. Identical genes that have differences only in their epigenetic state are called epialleles. Epialleles can be placed into three categories: those directly determined by an individual's genotype, those influenced by genotype, and those entirely independent of genotype. The epigenome is also influenced significantly by environmental factors. Diet, toxins, and hormones impact the epigenetic state. Studies in dietary manipulation have demonstrated that methyl-deficient diets are associated with hypomethylation of the epigenome. Such studies establish epigenetics as an important interface between the environment and the genome.[]

See also[edit]


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Overlapping genes in natural and engineered genomes


Modern genome-scale methods that identify new genes, such as proteogenomics and ribosome profiling, have revealed, to the surprise of many, that overlap in genes, open reading frames and even coding sequences is widespread and functionally integrated into prokaryotic, eukaryotic and viral genomes. In parallel, the constraints that overlapping regions place on genome sequences and their evolution can be harnessed in bioengineering to build more robust synthetic strains and constructs. With a focus on overlapping protein-coding and RNA-coding genes, this Review examines their discovery, topology and biogenesis in the context of their genome biology. We highlight exciting new uses for sequence overlap to control translation, compress synthetic genetic constructs, and protect against mutation.


When the first DNA genome was sequenced by Frederick Sanger in , the results solved a perplexing mystery that had bothered scientists for some time. Previous analysis of the proteins produced by bacteriophage φX during infection seemed to require coding sequences (CDSs) longer than the measured length of the phage genome1. The mystery was solved when analysis of the genome sequence revealed extensive overlap between coding regions, with the internal scaffolding gene overlapping the genome replication gene and the lysis gene embedded entirely within the external scaffolding gene1,2. The compressed nature of these viral genes led to the conclusion that hidden within the genome could be other undiscovered sites of polypeptide synthesis2. Further refinement of the φX gene model showed an alternative start site within the genome replication gene A that produced a truncated protein with an identical CDS to the C-terminus of the A protein but holding a distinct function3,4. Thus, overlapping genes have been observed from the very beginning of sequencing and genomics. Since then, overlapping genes, and more specifically open reading frames (ORFs) and CDSs, have become a common genetic feature described during viral genome annotation5, including within the SARS-CoV-2 genome6. However, until recently, their true abundance and importance was overlooked outside of the realm of viral genomics7 and their discovery and annotation within cellular genomes have generally been treated as unique and idiosyncratic.

Today, we are seeing a renaissance of the field owing to the rapid advancement of genome-scale protein and RNA measurement tools and increasingly advanced prediction algorithms (Box 1), which have collectively revealed an abundance of overlapping genes and ORFs within cellular genomes. Recent work on the human genome has placed estimates of overlapping features much higher than previously thought8,9, encompassing 26% of all protein-coding genes10. This estimate will likely increase in the future as small ORFs (sORFs) encoding microproteins are increasingly being found in the human genome within previously annotated genes11,12,13.

In this Review, we define a gene overlap in eukaryotes when at least one nucleotide is shared between the outermost boundaries of the primary transcripts of two or more genes, such that a DNA base mutation at the point of overlap would affect transcripts of all genes involved in the overlap (Fig. 1a, top). Thus, overlapping genes as defined here include 5′ and 3′ untranslated regions (UTRs) as well as introns. Overlapping ORFs and CDSs, which are components of genes, are distinctly defined here as when the overlap occurs in a sequence region of two or more genes that encode protein in the mature transcript such that a DNA base mutation at the point of overlap would alter a codon and potentially the protein sequence of one or more members of the overlap. We define a gene overlap in prokaryotes and viruses as when the CDSs of two genes share a nucleotide either on the same or opposite strands (Fig. 1a, bottom). These definitions are compatible with a recently updated, community-driven effort to create consensus classifications of non-canonical ORFs, of which overlaps are one example14.

a | Gene overlap definitions differ between prokaryotes and eukaryotes. (Top) Eukaryote overlaps are most frequently defined as overlaps between the boundaries of the primary transcript, shown here in the shaded region. Often, the overlap is only between the 5′ untranslated region (UTR) or 3′ UTR of both transcripts (5′ UTR overlap shown)10,. (Bottom) In contrast, prokaryote and virus genes are only considered to overlap if their coding sequences overlap5,27. Thin boxes denote 5′ and 3′ UTRs while thick boxes are coding sequences. Arrowheads indicate the extent of the consensus definition of gene boundaries within studies referenced in this review. b | Genes and open reading frames (ORFs) can be overlapped in one of three topologies. Unidirectional (also called tandem) overlaps occur between genes and ORFs on the same strand. Divergent (also called head-to-head) overlaps occur between genes and ORFs on opposite strands that overlap at their 5′-ends. Convergent (also called tail-to-tail) overlaps occur between genes and ORFs on opposite strands that overlap at the 3′-ends27. c | Gene and ORF interactions can be either overlapped, where only limited portions of each gene or ORF are overlapping, or nested, where the entire sequence of one partner falls within the boundaries of the other.

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Here, we review overlapping genes as fundamental features of both cellular and viral genomes. We first discuss the diverse topologies and functions of overlapping genes in natural genomes across prokaryotes, eukaryotes and viruses. We then highlight their importance for synthetic biology approaches, as bioengineers are both faced with disentangling CDSs to refactor gene clusters and whole genomes and inspired to implement these features in synthetic genetic constructs to control protein expression and slow evolution. We limit our discussion to protein-coding and RNA-coding regions within genomes that partially or completely overlap at least one other gene. For information on ORFs localized entirely within 5′ or 3′ UTRs, which itself is a rapidly evolving field, we direct readers to other works15,16.

Box 1 Identifying overlapping genes and ORFs

Genome annotation is the bedrock against which genome-scale measurements are compared, with most bioinformatics pipelines today annotating genomes through a combination of sequence alignments and hidden Markov modelling. However, many of these standardized methods may be inappropriate for the discovery of overlapping genes because they are reliant on already curated genes, where overlapping genes are poorly represented and contain atypical sequence composition40,41,. For example, the RAST pipeline uses both ab initio (GLIMMER) and sequence homology steps (SEED genome database) to annotate genomes but markedly penalizes overlaps between predicted open reading frames (ORFs), which potentially misses vital features. Furthermore, genome annotation standards are biased against feature overlaps, especially genes “completely contained in another gene”. The solution may be custom algorithms tailored for overlap mapping that have been created specifically for viral genome annotations (for example, OLGenie) and annotation pipelines based on hidden Markov models trained on databases of experimentally confirmed overlapping genes. Some tools, such as Glimmer3 and BG7, are more tolerant of overlapping ORFs by retaining candidate ORFs even if they overlap other predicted ORFs,. New annotation databases, such as OpenProt, are being created in response to the growing realization that eukaryotic gene models need to include polycistronic transcripts with non-AUG initiation sites.

Proteogenomic methods, including bottom-up proteomics and ribosome profiling, in combination with DNA sequencing and perturbation, have been critical for the identification of overlapping genes. Mass spectrometry-based proteomic techniques are used mainly to confirm the expression of gene products based on genomic sequence annotation and are notionally limited by the quality of annotations. Most commonly, proteomics is performed using shotgun tandem mass spectrometry, whereby proteolytic peptide digests are ionized and sequenced based on peptide fragment ion mass-to-charge ratios, thus providing primary evidence of translated gene products. However, for large-scale studies, MS data must be computationally matched to in silico digests of the theoretical proteome. Unbiased six-frame genome translations can be used to maximize the proteome ‘search space’ but are rarely implemented due to expanded computational analysis time and high false-discovery rates. In addition, recent studies have shown unexpectedly strong non-AUG translation initiation,, which are not accounted for in standard six-frame AUG translations. N-terminal peptide enrichment strategies can be used to identify sites of translation initiation, regardless of start codon used,, but the database needs to already include these candidates. Despite these considerations, proteomic measurements can be powerful, with one study identifying 1, alternative proteins produced from previously annotated human transcripts.

Complementary to mass spectrometry proteomics, ribosome profiling (Ribo-Seq) is a method that involves capturing ribosomes as they decode mRNA and sequencing the section of the transcript bound by the ribosome. In particular, the translation initiation site Ribo-Seq variant, which uses inhibitors to pause ribosomes on the start codon, has revealed an abundance of new translation initiation sites within transcripts in prokaryotic29, eukaryotic11 and viral genomes,,.

RNA sequencing alone can also identify genomic regions with overlapping transcripts. For example, , alternate ORFs within previously annotated coding regions were found in humans66, and a transcription start site profiling study in Helicobacter pylori identified pervasive transcription on the opposite strand of canonical genes (that is, antisense transcription).

Overlapping ORFs discovered using the above methods have been verified using a variety of reverse genetics approaches, including CRISPR–Cas9 and catalytically dead Cas9 (dCas9) disruption11,12,65, as well as an attempt at proof-by-synthesis to establish the absence of any undiscovered overlapping genes.

Overlapping gene topology and function

Studying overlapping genes across cellular and viral genomes reveals different patterns of overlap topologies that vary in frequency between prokaryotes and eukaryotes8,17. The reasons for these observed patterns are either more frequent biogenesis of certain types, evolutionary selection for retention of certain topologies or a combination of the two. At the moment, no consensus exists for the relative importance of these two factors, that is, creation versus retention. Overlap is thought to arise from at least six mechanisms that result in one gene becoming entangled with another, either through sequence extension9,18,19, re-arrangement of existing genes20,21, or de novo gene and ORF creation within an existing gene22.

Three directional overlap topologies are possible (Fig. 1b). Unidirectional overlaps (→→) occur between genes encoded on the same strand and may be further categorized according to the reading frame for overlapping ORFs. The remaining two topologies occur between genes on opposite strands and are called convergent (→←) and divergent (←→) (Fig. 1b). Unidirectional overlaps are more frequent in genomes of viruses and bacteria5,17, whereas the divergent and convergent overlaps are more frequent in eukaryote genomes10,23. The way the two genes interact can be described as either overlapped, with only part of each gene sequence occupying the same genomic region, or nested (Fig. 1c), whereby the entire extent of one gene is enclosed within the borders of a larger gene. The relationship between overlapping and nested genes has been described in other ways, including ‘internal–external’20 or ‘mother–daughter’ genes24.

The different ways that genes are defined in prokaryotes and eukaryotes in the literature has possibly biased estimates of the prevalent types of overlaps between these groups. For example, in prokaryotic and virus literature, gene overlaps are only considered when the CDSs of the genes overlap5,17, whereas in eukaryotic literature overlaps are more often considered between the primary transcript boundaries10,25 (Fig. 1a). The effect of these different definitions is that certain types of overlap seem to be more prevalent in eukaryotes versus prokaryotes but, if the same definitions were used for both, these apparent differences could in fact disappear. For instance, overlapping CDSs have certain constraints on relative reading frame and sequence composition26,27 that overlaps between 5′ and 3′ UTR do not. Within the limitations posed by the way overlapping genes are described in the literature, we compare and discuss prokaryotic and eukaryotic gene overlap from both their idiosyncratic aspects as well as their similarities, where present.


Overlapping CDSs within prokaryotic genomes have been reported in both bacteria28,29,30 and archaea31 and, on average, 27% of CDSs in these groups are involved in at least one instance of overlap19. Across prokaryotes, the frequency of CDS overlap within a genome seems to be constant regardless of genome size17,32, although certain groups can deviate sharply from this pattern. For example, intracellular microbial parasites show a weak correlation between genome size and the number of overlapping CDSs33.

In prokaryotic genomes, 84% of CDS overlaps are unidirectional17 (→→) and produced through start codon or stop codon loss, resulting in one member of a pair of adjacent non-overlapped CDSs expanding their coding sequence into their adjacent partner (Fig. 2a,b). Sequence analysis shows that stop codon loss of the upstream partner is the most frequent mechanism for unidirectional overlap creation32,34. Start codon loss of the downstream partner and de novo start codon creation within an existing CDS (Fig. 2c) also generate unidirectional overlaps18,32. Over 98% of currently identified unidirectional overlaps are less than bp long, with the vast majority of these short overlaps either 1 bp or 4 bp overlapping start and stop codons (TA[A]TG, TG[A]TG, or [ATGA])17,35. This overlap motif may be intimately tied to prokaryotic operons, where clusters of related genes are under the regulatory control of a single promoter, and overlapping start and stop codons of their respective CDSs may facilitate enhanced regulatory control through translational coupling between adjacent partners36.

New overlaps can be created through a range of mechanisms and likely require numerous complementary developments to produce the appropriate sequence context for retention of gene or open reading frame (ORF) functionality. a | Mutations removing the start codon of a downstream ORF may result in the next available upstream start codon being utilized, which could be within an upstream ORF18. b | Mutational loss of a stop codon may result in the extension of an ORF. Similar to start codon loss, the next available stop codon may be utilized, which could be within a downstream ORF19. c | De novo generation of an ORF may begin with the creation of a start codon within an existing coding region through mutation and, in conjunction with a downstream stop codon, produces an overlapping ORF18. d | Non-coding intron sequences may acquire a start codon through mutation and, in conjunction with a downstream stop codon, produce a nested ORF20. e | Mutations that result in the de novo development of a sequence capable of recruiting transcriptional machinery (such as a promoter or enhancer) may result in a new overlapping gene. f | Genome rearrangements, such as inversions and translocations, may result in distant non-overlapping genes becoming overlapped. This mechanism has been seen within human cancers. g | Mobile genetic elements carrying genes (such as transposons or proviral genes) may localize to within a gene, generating a new gene overlap,.

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Convergent (→←) and divergent (←→) overlaps (Fig. 1b) are observed at lower frequencies in prokaryotes compared with eukaryotes, and similar to unidirectional overlaps, are biased towards short overlap lengths35. Short convergent overlaps are strongly biased towards 4-bp stop codon overlaps owing to the incompatibility of forward-strand stop codons (TAA, TAG, TGA) with reverse-strand stop codons (TTA, CTA, TCA) in any other configuration37. Divergent overlaps (Fig. 1b) do not have strong phase biases but are substantially rarer than convergent overlaps38, which is likely due to the presence of critical sequence structures in the 5′-end of CDSs that impose additional evolutionary constraints on the successful retention of these overlap topologies.

It is currently unclear whether the commonness of short tandem start–stop overlaps compared to long nested overlaps (Fig. 1b) is a result of biology or merely reflects our ease to detect them. Despite increasing numbers of fully nested CDSs within prokaryotes being discovered due to a convergence of proteomic and ribosome profiling methods (Box 1), the idea that many more long nested overlaps within prokaryotes remain to be discovered is contentious19,35 and genome annotation pipelines are biased against their existance39. The unusual sequence characteristics of long overlapping CDSs may have also contributed to the difficulty of their discovery, resulting in undercounting40,41. One reason put forward to explain why long nested overlaps should be rare includes the evolutionary burden of maintaining larger overlaps, although evidence to the contrary showing positive selection at overlaps27,42,43 shows that this explanation may be too simplistic. Selection for long convergent overlaps has been shown to have a strong reading frame bias and it has been suggested that retention involves positive selection at the birth of the overlap, followed by purifying selection afterwards27. Recently, an overlapping protein-encoding CDS with extensive  bp overlap has been discovered embedded in the highly conserved ompA gene in enterohaemorrhagic Escherichiacoli44, showing that, with improved measurement tools, more of these long nested overlaps may be discovered42.

While the precise selective forces governing the retention of long unidirectional CDS overlaps in prokaryotes are unknown, the selective forces governing the retention of some short stop–start overlaps likely act through their enhancing effect on gene expression36 (Fig. 3a,b). Furthermore, overlapping CDS frequency is higher in fast-growing thermophilic organisms, which suggests that genome streamlining is an adaptive strategy for fast growth at high temperatures45,46. Mechanistically, overlaps between start and stop codons of adjacent unidirectional CDSs provide additional benefits for translational coupling47,48,49,50 and ribosome re-initiation48,50 (Fig. 3a,b) in addition to benefits already provided by operons51,52. The menaquinone biosynthesis pathway in E. coli is an example of multiple gene members connected via overlapped stop–start sites within a single operon across all three reading frames (Fig. 4a).

a,b | Overlapping start and stop codons cause translation coupling between unidirectional overlapping open reading frames (ORFs) through unwinding of mRNA secondary structure around the ribosome binding site and start codon and by enhancing ribosome re-initiation48. c | Overlapping sequence regions cause mutations to affect more than one ORF, increasing fitness cost and preserving overlapped sequences under mutational pressure71,75. d | Encoding more ORFs in the same sequence region allows genetic novelty with reduced genome changes, which is particularly advantageous for viruses that have spatial constraints on genome size76,77. e | Sense–antisense gene and ORF overlap is frequently involved with gene expression regulation, including non-coding RNA and long non-coding RNA96. f | Transcriptional tuning from convergent overlapping genes and ORFs as a result of interactions between RNA polymerase collisions (transcriptional interference,).

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a | Escherichia coli menaquinone biosynthesis operon contains three short stop–start coding sequence (CDS) overlaps. b | The large human gene NF1 and internal nested protein-coding ORFs OMG, EVI2B and EVI2A are located within NF1 introns. c | Recently described alt-RPL36 (bottom) overlaps the human ribosomal protein gene RPL36 (ref.65) through an out-of-frame GTG start codon within a 5′-extended RPL36 exon present on RPL36 transcript variant 2. The alt-RPL36 CDS generates a longer protein with an entirely different sequence from RPL36 (ref.65). d | The virus φX contains overlaps in all three reading frames: three short unidirectional stop–start CDS overlaps, two nested CDSs, and one in-frame start generating an N-terminally trunkated protein.

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Functional entanglement of overlapped CDSs can act on their retention over evolutionary selection beyond gene expression levels. For example, the overlapping drrA/drrB genes encode an efflux pump for the anticancer agent doxorubicin in the production strain Streptomyces peucetius. When the overlap was disrupted, the expression levels of DrrA and DrrB proteins remained unchanged and membrane trafficking was unaffected but functional assembly of the protein complex was lost47. Correct protein complex assemply has been revealed to be spatially regulated at the translation level for genes linked in operons, which may explain the DrrA/DrrB finding47,53. Overlap functions such as this are likely to be prevalent for overlapped CDSs given the functional assortment of genes involved in overlap54.


In eukaryotic genomes, the prevalence of overlapping genes is difficult to assess because of the inconsistent nomenclature that is used to describe the relationship between the genes, their 5′ or 3′ UTRs, and CDSs. Unlike prokaryotes, classifications and studies of overlapping genes in eukaryotes are as varied as their genome size and complexity. The predominant type of overlap is convergent8,10,23 (Fig. 1b), although generalization within eukaryotes is less useful given their genome diversity, which ranges from unicellular eukaryotes with compact, intron-poor genomes to complex, multicellular eukaryotes with expanded genomes and high intron densities55,56.

Most overlapping genes in eukaryotes are classified as such because their 5′ or 3′ UTRs overlap57. Of those with overlaps between the start and stop codon boundaries of either member (Fig. 1a), introns provide an additional non-coding location for gene transcripts to overlap. When an entire ORF is contained within an overlapping gene’s intron it is referred to as intron nesting20,58. True exon–exon overlaps make up the minority of transcript overlap in eukaryotes8,23 but new technologies (Box 1) suggest that they may be more common than currently appreciated11,12.

Nested gene overlaps in eukaryotes occur most frequently within an intron of the larger partner as is the case for three antisense nested genes, EVI2A, EVI2B and OMG, within intron 27b of the human NF1 gene (Fig. 4b). Nested overlaps are thought to be created through four processes: (1) mobilization of a distal gene into the intron of another gene (for example, through retrotransposition), (2) de novo creation of an ORF within an intron of an existing gene, (3) one ORF is internalized after an adjacent gene acquires additional exons and (4) two external genes flanking another gene fuse, thus internalizing the other gene20 (Fig. 2). The introns that harbour nested genes are considerably longer than other introns, suggesting acquisition of an existing gene through retrotransposition, among other mechanisms, is a dominant process rather than de novo evolution21,59. However, evidence from metazoans shows that several de novo genes have emerged from introns in that lineage20,60. The extent of the nesting can vary from an internal gene with a single exon residing within the intron of an external gene (for example, H2BFS within HSF2BP in humans21) to multiple layered ‘Russian doll-like’ nestings in Drosophila melanogaster20.

Eukaryotic overlapping protein-coding genes are implicated in lineage-specific groups. For example, the majority of vertebrate genes with overlapping transcripts are not conserved across species9,57 likely because overlapping genes tend to be young and frequently lost during evolutionary time57. A broad study of five well-described metazoan genomes (Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Mus musculus and human) found that, for protein-coding genes, transcript overlap is selected against and mainly species specific and the majority of new overlaps are in terminal non-coding exons25. Overlap between opposite strand exons containing coding sequence is also lineage specific, with the mammalian genes THRA (which encodes thyroid hormone receptor alpha) and NR1D1 (which encodes nuclear receptor subfamily 1 group D member 1) displaying convergent overlap in the coding sequence portion of their 3′ exons, whereas marsupials seem to have lost this feature since their divergent evolution over 90 million years ago. This change results in an absence, during marsupial development, of the TRα2 protein, a variant of the receptor unable to bind the hormone61.

Although rare, eukaryotes contain genes with CDS overlaps8,9,62,63 as well as overlaps that span exon–intron boundaries57,64. A community-driven roadmap on translated ORFs has proposed that these overlapping CDSs be annotated as novel genes despite the shared locus14. The recently described alt-RPL36 ORF65 (Fig. 4c) is one such example of a gene possessing two distinct and functional CDSs overlapping the same genomic sequence. These alternative ORFs66 are often functionally related and implicated in a range of human diseases12,67. For example, the cyclin-dependent kinase inhibitor 2A (p16INK4a) and tumour suppressor ARF, which regulate the tumour suppressors retinoblastoma protein (RB) and p53 transcription factor68, are produced as alternatively spliced transcripts from what is now considered the same gene (CDKN2A), even though the proteins do not share sequence or structural similarity, and the E1b exon that produces the ARF protein is ~20 kb upstream of the other CDKN2A exons68. Similarly, a recently discovered nested overlapping ORF within the FUS ORF (alt-FUS) is associated with neurodegeneration69 and alt-Ataxin is mutated in spinocerebellar ataxia type 1 (ref.64).


The topology of overlapping genes in viruses is determined both by the host cell type as well as by constraints unique to viruses. Despite viruses having diverse genomes (RNA or DNA in single-stranded or double-stranded form) and lifestyles, overlapping CDSs are found across all known virus groups5,70. The proportion of viruses with overlapping CDSs within their genomes varies from double-stranded RNA viruses having fewer than a quarter to almost three-quarters of retroviridae (single-stranded RNA using reverse transcriptase) and single-stranded DNA genomes containing overlapping CDSs5. Segmented viruses, those with the genome split into separate pieces and packaged either all in the same capsid or in separate capsids, are more likely to contain an overlap than non-segmented viruses5. The retention of overlapping CDSs in viruses has been attributed to enabling evolutionary rate reduction and increasing mutational robustness71,72 as well as being a result of capsid size limitations73.

The role of overlapping genes in reducing the rate of viral evolution has been most intensively examined in RNA viruses, which have higher mutation rates, smaller genomes and less CDS overlap than DNA viruses of comparable length5,73,74. Studies have supported the notion that CDS overlap increases hypersensitivity to mutation (as a mutation on average would affect more than one CDS)26 but that genome (or population) mutational robustness is increased overall71 (Fig. 3c). This has been eloquently demonstrated with the overlapping rev and tat genes of the RNA virus HIV1 (ref.75). Functional segregation is observed between the overlapped regions, facilitating the purging of possible deleterious mutations; that is, important nucleotide or amino acid regions of one gene overlap regions subject to fewer constraints in the other75.

Thus, given that gene overlap regions are likely protective and increase fitness, why then do RNA viruses have fewer overlapping genes than DNA viruses with lower mutation rates and less restrictive genome sizes?5,73 The answer may lie in the balancing of different selection pressures. For instance, the lower mutation rate of DNA viruses facilitates greater genomic novelty and evolutionary exploration within a structurally constrained genome and may therefore be the primary driver of gene overlaps76,77 (Fig. 3d). By contrast, in RNA viruses, overlaps may primarily be a means for maintaining mutational robustness in the face of higher mutational rates (Fig. 3c)71,75 as exemplified with the population fitness advantage conferred by the rev and tat overlap of HIV1 (ref.75).

Virus capsid size restrictions driving the evolution of gene overlaps has been a focal point of investigation due to early observations of dramatic viability loss in viruses with genomes engineered to be longer than wild type78. For instance, increasing the single-stranded DNA genome length of ΦX by >1% results in almost complete loss of infectivity79. This is thought to be the result of the strict physical constraints imposed by the finite capsid volume and, as such, any evolutionary innovation must be facilitated in the existing sequence space (Fig. 3d) rather than by increasing genome length. This idea is supported by work with adeno-associated viruses as gene delivery vectors, where viral packaging is constrained by genetic cargo size limits80, necessitating the use of multiple vectors to deliver large human genes such as CFTR81. Studies have shown a strong prevalence of overlapping CDS births in the +2 frame over the +3 frame40,77, which is likely due to two factors: mutational bias, whereby start codons are more prevalent in the +2 reading frame relative to known CDSs40,74, and recent evidence suggesting that the sequence of known CDSs in the +2/–2 reading frames preserves key physicochemical properties of the original sequence82.

The seemingly simple relationship between genome and capsid has also been questioned. Combined structural and genomic data have shown that most viruses do not fully utilize the available internal space of the capsid76. Furthermore, viruses are highly biased towards short overlaps, with the vast majority less than 50 nt (ref.5) in length, overall negatively correlated with genome length70, with absolute nucleotide overlap summed across the genome rarely exceeding 1, nt (ref.76). This distribution of overlap length within viruses points towards overlaps being favoured for several different reasons, with short CDS overlaps enabling translational coupling, whereas long overlaps being retained mainly when they generate genetic novelty that increases fitness. For example, a 4-nt (ATGA) stop–start overlap within a Totivirus directs coupled translation of the CDSs83, whereas a nt overlap in phage ΦX between its recently evolved lysis gene E and scaffolding gene D (Fig. 4d) enables the phage to lyse its host and release virions more efficiently1,2.

Overlap of ncRNA with protein-coding genes

Another important and highly abundant type of overlap within genomes is between non-coding RNA (ncRNA) genes and those of protein-coding genes. Shared sequence overlap may be between the mature ncRNA transcript region and the CDS region of mature protein-coding mRNA or it may only occur between 5′ and/or 3′ UTR regions of the transcripts.

In prokaryotic genomes, ncRNAs are an increasingly identified feature84, with cis-encoded antisense RNA regulation being a major player in physiological responses84,85. Examples of these pairings have demonstrated tight-knit regulation of expression of the protein-coding gene such as in type I toxin/antitoxin systems86 and in Mg2+ tolerance and virulence87. Interestingly, examples of unusually long antisense RNA have also been found, which likely hold greater regulatory control functions (such as regulation of entire operons) and have acquired their own designation as ‘excludons’84,88. Overlapping regulatory RNAs embedded within the coding sequence of bacterial genes can act in diverse regulatory roles89,90,91. Evidence is also emerging that ncRNAs in prokaryotes can contain protein-coding ORFs92,93. For more information on prokaryotic overlapping ncRNAs, we refer readers to another review84.

In eukaryotes, the sense–antisense overlapping transcripts are called cis-natural antisense transcripts (cis-NATs) and this type of overlap topology is frequently found in eukaryotic genomes in convergent or divergent relationships (Fig. 1b). Cis-NATs have regulatory functions at the RNA level25,94 and the most frequent combination is one protein-coding transcript paired with an antisense non-protein-coding transcript95 enabling enhanced transcriptional and post-transcriptional gene regulation96 (Fig. 3e,f). The regulatory roles of cis-NATs span major biological functions97 but can be generalized into protein expression regulation98, splice site masking99,, double-stranded RNA-dependent mechanisms, and chromatin remodelling,. Furthermore, due to the cis-acting mechanism and shared genetic loci, the evolutionary trajectories of both genes are closely entwined,. As such, interesting questions surround their evolution and acquisition, such as whether one member of the pair arose de novo through the acquisition of a promoter or by other mechanisms (Fig. 2). Recently, some overlapping ncRNA antisense transcripts have been found to also encode proteins11,, further increasing the complexity and constraints of these overlapping interactions.

Many cis-NATs have been associated with human disease, including cancer progression,,,. For example, the convergently overlapping WDR83 and DHPS genes both encode proteins; together, RNA duplexing of their 3′ UTRs results in the concordant increase in their transcript stability and protein expression, ultimately resulting in increased cell proliferation in gastric cancer cells. In a subpopulation of patients with α-thalassaemia, the disorder is caused by a chromosomal deletion that creates a new gene overlap between HBA2 and LUC7L, resulting in antisense transcripts from LUC7L silencing the otherwise intact copy of HBA2 through CpG island methylation.

An emerging feature of many ncRNAs is the presence of internal translationally active sequences termed sORFs. These sORFs are commonly defined as an ORF that spans no more than  nt that, owing to these small lengths, have lain hidden within previously described ncRNA transcripts. In humans, 30% of sORF-derived proteins (also called microproteins) identified by mass spectrometry were mapped internally to annotated genes. Subsequent studies have expanded this number using a variety of methods,, including recent work that systematically uncovered hundreds of sORFs. The sORFs were found overlapping both internal sequences as well as the start codons of annotated ORFs11. Investigations into the functionality of the overlapping sORFs have implicated many in human disease pathology,. Furthermore, it is likely that many of the ncRNAs found to possess sORFs are in fact misannotated and should be re-defined as mRNA; however, there are examples of RNA that possess dual functionality (non-coding and coding), thereby complicating classifications,. More information on this developing area can be found in recent reviews,, including the in-motion and recent community-driven initiative to comprehensively define and catalogue these classes of non-canonical ORFs in major databases14.

Overlapping genes in bioengineering

As we have outlined, gene overlaps in natural genomes are complex and their true number is only beginning to emerge. However, in synthetic biology, the re-engineering of natural genomes is well under way. Synthetic biology uses raw genetic material from diverse sources within heterologous systems to create new metabolic pathways, enzymatic activities, orthogonal transcription, and translation initiation systems,, and complex genetic devices,. As such, the functional characteristics of overlapped genetic elements are becoming increasingly important to understand. Furthermore, the field of synthetic genomics is rapidly rebuilding entire genomes from the ground up (for example, E. coli or the yeast Saccharomyces cerevisiae), with important choices to be made during the design stage for how to deal with overlapping sequences.

Refactoring overlapping genes

Genome refactoring is a process of reorganizing gene architecture by reformatting the underlying sequences while maintaining functionality. With the aim of increasing modularity, refactoring is often used to remove overlaps between genes so each is encoded on a separate piece of DNA. The effects of removing overlaps by encoding CDSs into their own distinct sequence regions may disrupt regulatory elements, such as promoters, or important RNA secondary structure elements as well as translational coupling from stop–start overlaps. Genome refactoring was pioneered with the bacteriophage T7 (ref.) but is now commonly applied to biosynthetic gene clusters, where the aim is to exert transcriptional and translational control over the cluster in a heterologous host.

Over the past 15 years, a number of genome engineering projects that modified overlapping CDSs and gene 5′ or 3′ UTRs have resulted in losses in viability and efficiency in the final bioengineered product,,,,. For example, removing CDS overlaps in the bacteriophage T7 resulted in infectious virus yet significantly reduced fitness. Subsequent work using serial passaging and selection for high growth rate over generations was able to show substantial fitness increases similar to pre-adapted wild-type levels. Similarly, a project to ‘decompress’ bacteriophage φX had the explicit aim to test the essentiality of CDS overlaps. While coding potential was retained (Fig. 5a), this refactoring led to numerous phenotypic defects, including a substantial reduction in burst size and lower attachment efficiency, along with large changes in levels of several essential assembly and replication proteins produced during the infection cycle.

a | Creation of φXf, also known as decompressed φX, disrupted four unidirectional stop–start coding sequence (CDS) overlaps and two fully nested overlapping CDSs. b | Refactoring the nitrogen fixation cluster from Klebsiella oxytoca disrupted four stop–start CDS overlaps and CDS overlaps varying from 1–14 bp (ref.).

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The first complete refactoring of a complex biosynthetic cluster involving overlapping CDSs involved moving the nitrogen fixation cluster of Klebsiella oxytoca into E. coli. This process involved rebuilding the entire gene cluster from the bottom up, with the removal of non-essential CDSs, codon optimization and disruption of six CDS overlaps (Fig. 5b). In a subsequent, larger project, the group refactored the Salmonella pathogenicity island 1 to isolate and control production of the type III secretion system. The refactoring disrupted eight CDS overlaps potentially involved in translational coupling and totalling 90 bp in length. Interestingly, the team discovered that the spaO gene contained an in-frame alternative start site at a GTG codon, essentially an in-frame overlapped CDS. In both the nitrogen fixation cluster and the type III secretion system, potential functional deficiencies caused by the removal of CDS overlaps and translational coupling were compensated through careful empirical tuning of the individual ribosome binding sites (RBSs) and transcriptional regulation,.

Other smaller-scale refactoring projects have targeted overlapping CDSs specifically to remove engineering limitations. For example, the gene overlaps in the dbz operon in Rhodococcus erythropolis, which is used to remove sulfur and upgrade petroleum, were removed to relieve a bottleneck in the efficiency of the process. Through rational design targeting the rate-limiting enzyme of this operon (DszB), removal of the overlap of the start and stop codons of dszA and dszB CDSs resulted in a fold increase in desulfurization activity over the wild-type operon. Similarly, M13 phage CDSs VII and IX


Projects size 47 common

Governor Newsom Signs College Affordability and Accessibility Legislation, Highlights $ Billion Higher Education Package

AB AB and AB make it easier for students to transfer into four year universities 

AB and SB improve housing affordability for students, complementing the California Comeback Plan’s unprecedented $2 billion investment in student housing

NORTHRIDGE – At California State University, Northridge today, Governor Gavin Newsom signed legislation to improve college affordability and increase access to higher education, and highlighted the historic $ billion higher education package – the most ever invested in higher education in modern history.

“We’re turning commitments into reality by ensuring that our students have more access to high-quality educational opportunities, creating a change of course for generations to come and bolstering California’s innovation economy,” said Governor Newsom. “Californians have thrived at our world class universities for decades, but not everyone has had similar access – today that’s changing. Everyone deserves a shot at the ‘California Dream’ – we’re eliminating equity gaps and increasing opportunities at our universities to make those dreams a reality for more California students.”

Governor Newsom signed legislation at CSU Northridge to improve college affordability and increase access to higher education.

“Over the last five years I’ve held hearings across California to discuss higher education issues,” said Assemblymember Marc Berman, Chair of the Assembly Select Committee on the Master Plan for Higher Education in California. “When students discussed their experience with the transfer process from community college to four-year university their message was loud and clear: transfer is too complex, confusing, and difficult to navigate. Instead of being a clear path, it’s a maze, and it’s costing students time and money that they can’t afford. Together, Assembly Bills and will make it easier for students to achieve their educational goals. I am grateful that Governor Newsom signed these historic bills, and for the advocates and students who inspired these reforms.”

“From historic investments in financial aid and student housing that will benefit students to a radical revamping of transfer, is a landmark year for public higher education in California,” said California State University Chancellor Joseph I. Castro. “We appreciate the bold vision demonstrated by Governor Newsom and his commitment to further improving education access and outcomes throughout the Golden State.”

Increasing Transfer Rates for Underserved Students

Governor Newsom today signed legislation to help facilitate access to the University of California (UC) and California State University (CSU) systems for students to attain four-year degrees and help further prepare them for the economy of tomorrow:

  • AB by Assemblymember Marc Berman (D-Menlo Park) &#; Requires the CSU and UC to jointly establish a singular lower division general education pathway for transfer admission into both segments. Also requires California Community Colleges (CCC) to place students who declare a goal of transfer on an Associate Degree for Transfer (ADT) pathway for their intended major, and establishes the ADT intersegmental implementation committee as the primary oversight entity.
  • AB by Assemblymember Marc Berman (D-Menlo Park) &#; Requires, by July 1, , the CCCs adopt a common course numbering system (C-ID) at all community colleges and for each community college campus catalog. This common course numbering system is required to be student-facing and ensures that comparable courses across all community colleges have the same course number.

Finding Solutions to the Student Housing Crisis

On top of the unprecedented $2 billion investment to significantly increase affordable housing for students and help address the student housing crisis, Governor Newsom signed legislation to create long-overdue housing plans at the UC and CSU systems:

  • AB by Assemblymember Kevin McCarty (D-Sacramento) &#; Requires the CSU system, and requests the UC system, to conduct a student housing needs assessment for each campus, and create a student housing plan outlining how projected student housing needs will be met.
  • SB by Senator María Elena Durazo (D-Los Angeles) &#; Requires the Los Angeles Community College District (LACCD) to develop a pilot program to provide affordable housing to students or employees of LACCD. This bill also allows LACCD to enter into agreements with nonprofit or private entities to lease real property under certain conditions, in order to develop affordable housing.

Making Financial Aid More Accessible

Governor Newsom’s California Comeback Plan requires all students to submit a Free Application for Federal Aid (FAFSA) or California Dream Act application in order to significantly increase federal aid opportunities for California students, and today he signed legislation to further expand such supports:

  • AB by Assemblymember Chris Ward (D-San Diego)  &#; Conforms the state&#;s college savings plan statute to recent changes in federal tax law, expanding allowable withdrawals from plans to include expenses associated with participation in a registered apprenticeship program and student loan repayment.
  • AB by Assemblymember Eloise Gómez Reyes (D-San Bernardino) &#; Requires, on or before September 1, , and each year thereafter, the California Student Aid Commission and the California Department of Education to facilitate the completion of the Free Application for Student Aid and the California Dream Act Application, through the sharing of specified data.
  • SB by Senator Monique Limón (D- Santa Barbara) &#; Modifies and expands criteria for which the California Student Aid Commission may apportion funds to support projects under the California Student Opportunity and Access program, and additionally expands the duties and responsibilities of funded projects.

Overall $ Billion Higher Education Package 

The Budget’s unprecedented level of investment in higher education reflects a continued commitment to affordability, more accessible institutions, higher quality programs, equitable outcomes, and more efficient degree pathways—all of which are critical for driving upward mobility across the state.

The Budget includes total funding of $ billion ($ billion General Fund and local property tax and $ billion other funds) for all higher education entities in The state’s three public segments—the University of California (UC), the California State University (CSU), and the California Community Colleges (CCC)—receive substantial ongoing base augmentations, and the Budget includes significant investments to make postsecondary education more affordable, including expanding the state’s Cal Grant program to additional CCC students. Also included are investments to make college savings accounts widely available to low-income children; provide grants to advance training and education for workers impacted by the COVID Pandemic; promote learning-aligned, long-term career development opportunities; and support regional K education collaboratives focused on streamlining educational pathways leading to in-demand jobs.

A full list of the bills signed by the Governor is below:

  • AB by Assembymember David Chiu (D-San Francisco) &#; Educational equity: student records: name and gender changes.
  • AB by Assemblymember Jose Medina (D-Riverside) &#; Classified community college employees.
  • AB by Assemblymember Chris Ward (D-San Diego) &#; Golden State Scholarshare Trust: Personal Income Tax Law: gross income: deductions.
  • AB by Assemblymember Kevin McCarty (D-Sacramento) &#; Rising Scholars Network: justice-involved students.
  • AB by Assemblymember Mark Stone (D-Monterey Bay) &#; Private Student Loan Collections Reform Act: collection actions.
  • AB by Assemblymember Eloise Gómez Reyes (D-San Bernardino) &#; Pupil instruction: financial aid applications.
  • AB by Assemblymember Laurie Davies (R-Laguna Niguel) &#; Public postsecondary education: student orientation: CalFresh.
  • AB by Assemblymember Brian Maienschein (D-San Diego) &#; Community colleges: apportionments: waiver of open course provisions: military personnel.
  • AB by Assemblymember Freddie Rodriguez (D-Pomona) &#; Higher Education Employer-Employee Relations Act: procedures relating to employee termination or discipline.
  • AB by Assemblymember Akilah Weber (D-San Diego) &#; Public postsecondary education: California State University: proficiency level of entering students.
  • AB by Assemblymember Jose Medina (D-Riverside) &#; Public postsecondary education: community colleges: statewide baccalaureate degree program.
  • AB by Assemblymember Marc Berman (D-Menlo Park) &#; Student Transfer Achievement Reform Act of Associate Degree for Transfer Intersegmental Implementation Committee.
  • AB by Assemblymember Steven Choi (R-Irvine) &#; Postsecondary education: course credit for prior military education, training, and service.
  • AB by Assemblymember Marc Berman (D-Menlo Park) &#; Postsecondary education: common course numbering system.
  • AB by Assemblymember Jose Medina (D-Riverside) &#; Public postsecondary education: exemption from tuition and fees: qualifying survivors of persons providing medical or emergency services deceased during COVID California state of emergency.
  • AB by Assemblymember Joaquin Arambula (D-Fresno) &#; Public social services: county liaison for higher education.
  • AB by Assemblymember Kevin McCarty (D-Sacramento) &#; Student housing plans.
  • SB by Senator María Elena Durazo (D-Los Angeles) &#; Los Angeles Community College District Affordable Housing Pilot Program.
  • SB by Senator Brian Dahle (R-Bieber) &#; Community colleges: nonresident tuition.
  • SB by Senator Dave Min (D-Irvine) &#; Public postsecondary education: support services for foster youth: Cooperating Agencies Foster Youth Educational Support Program.
  • SB by Senator Monique Limón (D-Santa Barbara) &#; California Student Opportunity and Access Program.

For full text of the bills, visit:


Common Project Achilles Low Review and Sizing!!

So you've found your perfect pair of Common Projects sneakers – congratulations! But before you race to the checkout, you're going to want to ensure you're buying the correct size. Which is where we come in, with our comprehensive Common Projects sizing guide. First things first though – allow us to introduce the brand

The idea for Common Projects was dreamt up way back in , by New Yorkers Peter Poopat and Flavio Girolami. The pair saw a gap in the market for minimalist luxury sneakers that could bridge the gap between smart and casual. They kicked off their range with the Achilles, a sleek, understated sneaker that has garnered a cult following in stylish circles. Fast forward 15 years, and Common Projects' sneakers are as popular as ever. Their understated aesthetic lends them inherent versatility and timelessness, meaning that they can be worn with just about anything – from formal suiting to casual weekend attire.

Common Projects sizing notes

  • Common Projects shoes generally fit true to size, so take your normal size if you have wide feet.
  • Common Projects shoes can sometimes run large, so go down a size if you're between sizes or have narrow feet.
  • As with many artisanal footwear labels, Common Projects make their shoes in full sizes only, so go down to the nearest whole size if you usually take a half size.
  • Common Projects uses European sizing.

Common Projects size chart:

If you are the following UK size in most sneakers…Then buy this size in Common Projects sneakers
UK 5UK 39
UK 6UK 40
UK 7UK 41
UK 8UK 42
UK 9UK 43
UK 10UK 44
UK 11UK 45

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