Typically, tumor tissues are fixated in formalin to preserve tissue and cellular morphology in the formalin-fixed paraffin-embedded (FFPE) protocols. [ 24 ]  While FFPE offers a cost-effective method to store tissues for long durations of time, limitations must be considered as to how it will affect TMB calculations. [ 24 ]  One limitation of this method is that it induces the formation of various crosslinks, whereby strands of DNA become covalently bound to each other, which may consequently lead to  deamination  of cytosine bases. [ 22 ]  Cytosine deamination is the major cause of baseline noise in  Next Generation Sequencing , leading to the most prevalent sequence artifacts in FFPE (C:G > T:A). [ 22 ]  This may generate artefacts that must be removed in the downstream pipeline.


Different sequencing strategies enable different number of genes to be included in the calculation of TMB (with WGS and WES approaches allowing a greater quantity  of genes to be analyzed). While panel based approaches analyze comparatively fewer genes than other strategies, one advantage of panel based sequencing is that genes of interest can be covered in much greater sequencing depths, and rare variants can possibly be identified. [ 22 ]  The panel sizes vary across panels with 468 genes in the MSK-IMPACT panel, 315 genes in the Foundation Medicine panel, and 409 genes in the Life Technologies panel. [ 22 ]  As panel sizes are smaller, uncertainty associated with TMB estimation becomes greater, with coefficient of variance increases rapidly when the size of the targeted panels is less than 1 Mb. [ 24 ]


In most calculations of TMB,  synonymous variants  and germline variants are filtered out as they are unlikely to be directly involved in creating neoantigens. [ 22 ]  However, some pipelines maintain  synonymous variants . [ 24 ]  To account for germline variants, ideally sequencing would have been performed on a matched non-tumor sample from each patient. [ 24 ]  However, in a clinical practice, the availability of this matched sample may vary across different institutions and diverse organizational factors, and data unavailability may inhibit germline variants to be filtered. [ 24 ]  The choice of variant callers and other software in the downstream analyses may also affect how TMB is ultimately calculated. [ 24 ]  TMB can be calculated directly from histopathology images using a multiscale deep learning pipeline, avoiding the need for sequencing and variant calling. [ 21 ]


Different studies have assigned different cut-offs to delineate between high and low TMB status. [ 22 ]  In the lung, the median TMB across more than 18,000 lung cancer cases was 7.2 mutations/Mb, with approximately 12% of the patients showing more than 20 mutations/Mb. [ 24 ]  The authors identified a tumor mutational burden greater than or equal to 10 mutations/Mb as the optimal cut-off to benefit from combination  immunotherapy . [ 24 ]  However, in other cancer types, high TMB status has been classified as >20 mutations/Mb. [ 7 ]


One approved  biomarker  of ICI therapy is  PD-L1  expression, but the predictive power of this biomarker is affected by factors such as assay interpretation and lack of standard methods. [ 10 ]  TMB is also affected by these factors in addition to accessibility issues. [ 10 ]  Biological factors like specimen type and  cancer  type as well as technical factors like  sequencing technology  can affect evaluation of TMB. [ 1 ]  Thus, it is necessary to harmonize evaluation methods and there are still so many factors that can complicate this task. [ 1 ] [ 10 ]  For example,  gene fusions  and  post-translational changes  in proteins contribute to tumor behaviour and consequently response to therapy while these factors are not considered in TMB estimation. [ 10 ]  In addition, currently all mutations have the same weight in TMB calculation, while they can have very different effects on  proteins  and  pathways  activity. [ 10 ]  Furthermore, there is still no good answer to the question of how mutations in  genes  that are known to influence ICI therapy should be treated in TMB evaluation. [ 10 ]  It is also important to note that TMB is highly variable across cancer types and subtypes and different studies are being conducted to find distinct TMB thresholds. [ 10 ]


Some studies argue that to have better prediction of response to ICI therapy, TMB should be used as a complementary marker with other biomarkers such as  PD-L1 . [ 10 ]  Other studies have shown that a combination of TMB and neoantigen load can be used as a biomarker to predict survival in patients with  melanoma  who received adaptive T cell transfer therapy. [ 10 ]  Since TMB is a relatively new biomarker, there is still a need to perform more studies and many labs are being focused on different aspects of this biomarker. [ 10 ] [ 11 ]


V600E  is a   mutation  of the  BRAF   gene  in which  valine  (V) is substituted by  glutamic acid  (E) at  amino acid  600. [ 1 ] [ 2 ]  It is a driver mutation in a proportion of certain diagnoses, including  melanoma , [ 3 ] [ 4 ]   hairy cell leukemia , [ 5 ] [ 6 ]   papillary thyroid carcinoma , [ 7 ] [ 8 ]   colorectal cancer , [ 9 ]   non-small-cell lung cancer , [ 10 ] [ 11 ]   Langerhans cell histiocytosis , [ 12 ]   Erdheim–Chester disease  (a non-Langerhans-cell histiocytosis) and  ameloblastoma . [ 13 ]


The mechanism of the mutation is that the negative charge of the acidic glutamic acid residue causes it to be  phosphomimetic . This mimics the phosphorylation of the nearby T599  threonine  and S602  serine  residues in the activation segment of BRAF, which are used to activate the wild type form of the protein. The glutamate residue of the mutant therefore functions to activate BRAF by inhibiting the interaction of the BRAF's glycine rich loop and activation segment, which would ordinarily be inhibitory. The loss of inhibition of BRAF leads to an increase in its basal activity and hence is oncogenic.


Vemurafenib ,  encorafenib , and  dabrafenib  are approved by the  FDA  for treatment of metastatic melanomas that express V600E.


This  EC 2.7   enzyme -related article is a  stub . You can help Wikipedia by  expanding it .


Worm bagging  (also referred to as  facultative vivipary  or  endotokia matricida ) is a form of  vivipary  observed in  nematodes , namely  Caenorhabditis elegans . The process is characterized by eggs hatching within the parent and the larvae proceeding to consume and emerge from the parent. [ 1 ]


While the phenomenon was mentioned as a result of fluorodeoxyuridine treatment as early as 1979 [ 2 ]  and egg-laying mutants were identified in 1984, [ 1 ]  the natural circumstances and mechanisms resulting in this behavior were not fully explored until 2003. [ 3 ] [ 4 ]  From this point, modest explorations of the mechanisms underlying this behavior have been observed.


Bagging will occur in  vulvaless [ 5 ]  or egg-laying mutants [ 6 ]  of C. elegans but can also be induced in wild-type strains. [ 4 ]  Identified stressors that can induce bagging are starvation, high salt concentration, and antagonistic bacteria. [ 4 ]


It has been observed in larval development, that the WRT-5 protein is secreted into the  pharyngeal  lumen and the pharyngeal expression changes in a cycle that is connected to the molting cycle.  Deletion mutations  in wrt-5 cause embryonic lethality, which are temperature sensitive and more severe at 15 degrees C than at 25 degrees C. Additionally, animals that hatch exhibit variable abnormal morphology, for example, bagging worms, blistering, molting defects, or Roller phenotypes. [ 7 ]


Internal hatching is initiated by genes and is not restricted to the widely used laboratory  strain N2 . Internal hatching is rare when worms are maintained under standard laboratory conditions. However,  axenic  condition which is a transfer from solid to liquid medium along with adverse environmental conditions, such as starvation, exposure to harsh compounds, and bacteria can increase the frequency of worm bags. [ 8 ]


In a study C. elegans were starved and in stressful conditions such as a high salt environment. As a result there was a connection drawn between the pathway leading to the  dauer stage  and the pathway leading to bagging. Bagging was seen to be induced under stress, and was reversible if worms were relieved of the stress before internal larvae caused too much damage to the adult. Also, there was evidence of larvae developing in the adult and consuming parent body contents prior to emerging from the parent body. [ 9 ]


Larvae of internal hatching could have gonad damage, which reduces fitness of those individuals. Internally hatched larvae also survived equally as well or even better than externally hatched worms. This means that worm bagging does not negatively influence the size or survival of the offspring. [ 8 ]  The destruction of the mother provides nutrients to the offspring, which is an efficient transfer of nutrients. [ 9 ] [ 10 ]  In environments that lead to the starvation of individuals, worm bagging is beneficial because it improves the chances of offspring survival and the passing of their genes. If worm bagging does not occur in these environments, offspring will have an increased chance of starvation and removing their genes from the gene pool.


Aside from  C. elegans , Bagging is known to occur in other nematode species including:  Haemonchus contortus , Mehdinema allii, and Metacrobeles amblyurus . [ 2 ]  This phenomenon may be conserved among these species because it is a “life-history trait”. [ 4 ]  Offspring survivability is enhanced through bagging in a stressful environment as the young are physically protected through development to the larval stage and nourished through consumption of the parent body. [ 4 ]


While egg-laying mutants have been characterized, [ 6 ]  the natural processes that result in facultative vivipary have not been fully explored. Aging-related degeneration of the egg-laying system has been implicated in egg retention [ 8 ]  but the mechanism by which starvation signals result in this process has not been described. Experiments demonstrating time-dependent up/downregulation of genes (in nervous and vulval cells) throughout the span of the process would provide insight into the proximate causes. This research may provide insight into the mechanisms involved in the induction of egg-laying and may also improve understanding of birthing signals in higher organisms.


Biochemistry  or  biological chemistry  is the study of  chemical processes  within and relating to living  organisms . [ 1 ]   A sub-discipline of both  chemistry  and  biology , biochemistry may be divided into three fields:  structural biology ,  enzymology , and  metabolism . Over the last decades of the 20th century, biochemistry has become successful at explaining living processes through these three disciplines.  Almost all  areas of the life sciences  are being uncovered and developed through biochemical methodology and research. [ 2 ]   Biochemistry focuses on understanding the chemical basis which allows  biological molecules  to give rise to the processes that occur within living  cells  and between cells, [ 3 ]  in turn relating greatly to the understanding of  tissues  and  organs  as well as organism structure and function. [ 4 ]  Biochemistry is closely related to  molecular biology , the study of the  molecular  mechanisms of biological phenomena. [ 5 ]


Much of biochemistry deals with the structures, functions, and interactions of biological  macromolecules  such as  proteins ,  nucleic acids ,  carbohydrates , and  lipids . They provide the structure of cells and perform many of the functions associated with life. [ 6 ]  The chemistry of the cell also depends upon the reactions of small  molecules  and  ions .  These can be  inorganic  (for example,  water  and  metal  ions) or  organic  (for example, the  amino acids , which are used to  synthesize proteins ). [ 7 ]  The mechanisms used by  cells to harness energy  from their environment via  chemical reactions  are known as  metabolism . The findings of biochemistry are applied primarily in  medicine ,  nutrition  and  agriculture . In medicine,  biochemists  investigate the causes and  cures  of  diseases . [ 8 ]  Nutrition studies how to maintain health and wellness and also the effects of  nutritional deficiencies . [ 9 ]  In agriculture, biochemists investigate  soil  and  fertilizers  with the goal of improving crop cultivation, crop storage, and  pest control . In recent decades, biochemical principles and methods have been combined with problem-solving approaches from  engineering  to manipulate living systems in order to produce useful tools for research, industrial processes, and diagnosis and control of disease—the discipline of  biotechnology .


At its most comprehensive definition, biochemistry can be seen as a study of the components and composition of living things and how they come together to become life. In this sense, the history of biochemistry may therefore go back as far as the  ancient Greeks . [ 10 ]  However, biochemistry as a specific  scientific discipline  began sometime in the 19th century, or a little earlier, depending on which aspect of biochemistry is being focused on. Some argued that the beginning of biochemistry may have been the discovery of the first  enzyme ,  diastase  (now called  amylase ), in 1833 by  Anselme Payen , [ 11 ]  while others considered  Eduard Buchner 's first demonstration of a complex biochemical process  alcoholic fermentation  in cell-free extracts in 1897 to be the birth of biochemistry. [ 12 ] [ 13 ]   Some might also point as its beginning to the influential 1842 work by  Justus von Liebig ,  Animal chemistry, or,  Organic chemistry  in its applications to  physiology  and  pathology , which presented a chemical theory of metabolism, [ 10 ]  or even earlier to the 18th century studies on  fermentation  and  respiration  by  Antoine Lavoisier . [ 14 ] [ 15 ]  Many other pioneers in the field who helped to uncover the layers of complexity of biochemistry have been proclaimed founders of modern biochemistry.  Emil Fischer , who studied the chemistry of  proteins , [ 16 ]  and  F. Gowland Hopkins , who studied  enzymes  and the dynamic nature of biochemistry, represent two examples of early biochemists. [ 17 ]


The term "biochemistry" was first used when Vinzenz Kletzinsky (1826–1882) had his "Compendium der Biochemie" printed in Vienna in 1858; it derived from a combination of  biology  and  chemistry . In 1877,  Felix Hoppe-Seyler  used the term ( biochemie  in German) as a synonym for  physiological chemistry  in the foreword to the first issue of  Zeitschrift für Physiologische Chemie  (Journal of Physiological Chemistry) where he argued for the setting up of institutes dedicated to this field of study. [ 18 ] [ 19 ]  The German  chemist   Carl Neuberg  however is often cited to have coined the word in 1903, [ 20 ] [ 21 ] [ 22 ]  while some credited it to  Franz Hofmeister . [ 23 ]


It was once generally believed that life and its materials had some essential property or substance (often referred to as the " vital principle ") distinct from any found in non-living matter, and it was thought that only living beings could produce the molecules of life. [ 25 ]  In 1828,  Friedrich Wöhler  published a paper on his  serendipitous   urea   synthesis  from  potassium cyanate  and  ammonium sulfate ; some regarded that as a direct overthrow of vitalism and the establishment of  organic chemistry . [ 26 ] [ 27 ]   However, the Wöhler synthesis has sparked controversy as some reject the death of vitalism at his hands. [ 28 ]   Since then, biochemistry has advanced, especially since the mid-20th century, with the development of new techniques such as  chromatography ,  X-ray diffraction ,  dual polarisation interferometry ,  NMR spectroscopy ,  radioisotopic labeling ,  electron microscopy  and  molecular dynamics  simulations. These techniques allowed for the discovery and detailed analysis of many molecules and  metabolic pathways  of the  cell , such as  glycolysis  and the  Krebs cycle  (citric acid cycle), and led to an understanding of biochemistry on a molecular level. [ citation needed ]


Another significant historic event in biochemistry is the discovery of the  gene , and its role in the transfer of information in the cell. In the 1950s,  James D. Watson ,  Francis Crick ,  Rosalind Franklin  and  Maurice Wilkins  were instrumental in solving  DNA structure  and suggesting its relationship with the genetic transfer of information. [ 29 ]  In 1958,  George Beadle  and  Edward Tatum  received the  Nobel Prize  for work in fungi showing that  one gene produces one enzyme . [ 30 ]  In 1988,  Colin Pitchfork  was the first person convicted of murder with  DNA  evidence, which led to the growth of  forensic science . [ 31 ]  More recently,  Andrew Z. Fire  and  Craig C. Mello  received the  2006 Nobel Prize  for discovering the role of  RNA interference  (RNAi) in the silencing of  gene expression . [ 32 ]


Around two dozen  chemical elements  are essential to various kinds of  biological life . Most rare elements on Earth are not needed by life (exceptions being   selenium  and  iodine ), [ 33 ]  while a few common ones ( aluminum  and  titanium ) are not used. Most organisms share element needs, but there are a few differences between  plants  and  animals . For example, ocean algae use  bromine , but land plants and animals do not seem to need any. All animals require  sodium , but is not an essential element for plants. Plants need  boron  and  silicon , but animals may not (or may need ultra-small amounts). [ citation needed ]


Just six elements— carbon ,  hydrogen ,  nitrogen ,  oxygen ,  calcium  and  phosphorus —make up almost 99% of the mass of living cells, including those in the human body (see  composition of the human body  for a complete list). In addition to the six major elements that compose most of the human body, humans require smaller amounts of possibly 18 more. [ 34 ]


The 4 main classes of molecules in biochemistry (often called  biomolecules ) are  carbohydrates ,  lipids ,  proteins , and  nucleic acids . [ 35 ]  Many biological molecules are  polymers : in this terminology,  monomers  are relatively small macromolecules that are linked together to create large  macromolecules  known as polymers. When monomers are linked together to synthesize a  biological polymer , they undergo a process called  dehydration synthesis . Different macromolecules can assemble in larger complexes, often needed for  biological activity .


Two of the main functions of carbohydrates are energy storage and providing structure. One of the common  sugars  known as  glucose  is a carbohydrate, but not all carbohydrates are sugars. There are more carbohydrates on Earth than any other known type of biomolecule; they are used to store energy and  genetic information , as well as play important roles in cell to  cell interactions  and  communications . [ citation needed ]


The simplest type of carbohydrate is a  monosaccharide , which among other properties contains  carbon ,  hydrogen , and  oxygen , mostly in a ratio of 1:2:1 (generalized formula C n H 2 n O n , where  n  is at least 3).  Glucose  (C 6 H 12 O 6 ) is one of the most important carbohydrates; others include  fructose  (C 6 H 12 O 6 ), the sugar commonly associated with the  sweet taste  of  fruits , [ 36 ] [ a ]  and  deoxyribose  (C 5 H 10 O 4 ), a component of  DNA .  A monosaccharide can switch between  acyclic (open-chain) form  and a  cyclic  form. The open-chain form can be turned into a ring of carbon atoms bridged by an  oxygen  atom created from the  carbonyl group  of one end and the  hydroxyl  group of another. The cyclic molecule has a  hemiacetal  or  hemiketal  group, depending on whether the linear form was an  aldose  or a  ketose . [ 38 ]


In these cyclic forms, the ring usually has  5  or  6  atoms. These forms are called  furanoses  and  pyranoses , respectively—by analogy with  furan  and  pyran , the simplest compounds with the same carbon-oxygen ring (although they lack the carbon-carbon  double bonds  of these two molecules).  For example, the aldohexose  glucose  may form a hemiacetal linkage between the hydroxyl on carbon 1 and the oxygen on carbon 4, yielding a molecule with a 5-membered ring, called  glucofuranose .  The same reaction can take place between carbons 1 and 5 to form a molecule with a 6-membered ring, called  glucopyranose . Cyclic forms with a 7-atom ring called  heptoses  are rare. [ citation needed ]


Two monosaccharides can be joined by a  glycosidic  or  ester bond  into a  disaccharide  through a  dehydration reaction  during which a molecule of water is released. The reverse reaction in which the glycosidic bond of a disaccharide is broken into two monosaccharides is termed  hydrolysis .  The best-known disaccharide is  sucrose  or ordinary  sugar , which consists of a  glucose  molecule and a  fructose  molecule joined. Another important disaccharide is  lactose  found in milk, consisting of a glucose molecule and a  galactose  molecule. Lactose may be hydrolysed by  lactase , and deficiency in this enzyme results in  lactose intolerance .


When a few (around three to six) monosaccharides are joined, it is called an  oligosaccharide  ( oligo-  meaning "few"). These molecules tend to be used as markers and  signals , as well as having some other uses. [ 39 ]  Many monosaccharides joined form a  polysaccharide . They can be joined in one long linear chain, or they may be  branched . Two of the most common polysaccharides are  cellulose  and  glycogen , both consisting of repeating glucose  monomers .   Cellulose  is an important structural component of plant's  cell walls  and  glycogen  is used as a form of energy storage in animals.


Sugar  can be characterized by having  reducing  or non-reducing ends. A  reducing end  of a carbohydrate is a carbon atom that can be in equilibrium with the open-chain  aldehyde  ( aldose ) or keto form ( ketose ). If the joining of monomers takes place at such a carbon atom, the free  hydroxy group  of the  pyranose  or  furanose  form is exchanged with an OH-side-chain of another sugar, yielding a full  acetal . This prevents opening of the chain to the aldehyde or keto form and renders the modified residue non-reducing. Lactose contains a reducing end at its glucose moiety, whereas the galactose moiety forms a full acetal with the C4-OH group of glucose.  Saccharose  does not have a reducing end because of full acetal formation between the aldehyde carbon of glucose (C1) and the keto carbon of fructose (C2).


Lipids  comprise a diverse range of  molecules  and to some extent is a catchall for relatively water-insoluble or  nonpolar  compounds of biological origin, including  waxes ,  fatty acids , fatty-acid derived  phospholipids ,  sphingolipids ,  glycolipids , and  terpenoids  (e.g.,  retinoids  and  steroids ). Some lipids are linear, open-chain  aliphatic  molecules, while others have ring structures. Some are  aromatic  (with a cyclic [ring] and planar [flat] structure) while others are not. Some are flexible, while others are rigid. [ 42 ]


Lipids are usually made from one molecule of  glycerol  combined with other molecules. In  triglycerides , the main group of bulk lipids, there is one molecule of glycerol and three  fatty acids . Fatty acids are considered the monomer in that case, and may be  saturated  (no  double bonds  in the carbon chain) or unsaturated (one or more double bonds in the carbon chain). [ citation needed ]


Most lipids have some  polar  character and are largely nonpolar. In general, the bulk of their structure is nonpolar or  hydrophobic  ("water-fearing"), meaning that it does not interact well with polar solvents like  water . Another part of their structure is polar or  hydrophilic  ("water-loving") and will tend to associate with polar solvents like water. This makes them  amphiphilic  molecules (having both hydrophobic and hydrophilic portions). In the case of  cholesterol , the polar group is a mere –OH (hydroxyl or alcohol).  [ citation needed ]


In the case of phospholipids, the polar groups are considerably larger and more polar, as described below.


Lipids are an integral part of our daily diet. Most  oils  and  milk products  that we use for cooking and eating like  butter ,  cheese ,  ghee  etc. are composed of  fats .  Vegetable oils  are rich in various  polyunsaturated fatty acids  (PUFA). Lipid-containing foods undergo digestion within the body and are broken into fatty acids and glycerol, the final degradation products of fats and lipids. Lipids, especially  phospholipids , are also used in various  pharmaceutical products , either as co-solubilizers (e.g. in parenteral infusions) or else as  drug carrier  components (e.g. in a  liposome  or  transfersome ).


Proteins  are very large molecules—macro-biopolymers—made from monomers called  amino acids . An amino acid consists of an alpha carbon atom attached to an  amino  group, –NH 2 , a  carboxylic acid  group, –COOH (although these exist as –NH 3 +  and –COO −  under physiologic conditions), a simple  hydrogen atom , and a side chain commonly denoted as "–R". The side chain  "R" is different for each amino acid of which there are 20  standard ones .  It is this "R" group that makes each amino acid different, and the properties of the side chains greatly influence the overall  three-dimensional conformation  of a protein. Some amino acids have functions by themselves or in a modified form; for instance,  glutamate  functions as an important  neurotransmitter . Amino acids can be joined via a  peptide bond . In this  dehydration  synthesis, a  water molecule  is removed and the peptide bond connects the  nitrogen  of one amino acid's amino group to the carbon of the other's carboxylic acid group. The resulting molecule is called a  dipeptide , and short stretches of amino acids (usually, fewer than thirty) are called  peptides  or  polypeptides . Longer stretches merit the title  proteins . As an example, the important blood  serum  protein  albumin  contains 585  amino acid residues . [ 43 ]


Proteins can have structural and/or functional roles. For instance, movements of the proteins  actin  and  myosin  ultimately are responsible for the contraction of skeletal muscle. One property many proteins have is that they specifically bind to a certain molecule or class of molecules—they may be  extremely  selective in what they bind.  Antibodies  are an example of proteins that attach to one specific type of molecule. Antibodies are composed of heavy and light chains. Two heavy chains would be linked to two light chains through  disulfide linkages  between their amino acids. Antibodies are specific through variation based on differences in the N-terminal domain. [ 44 ]


The  enzyme-linked immunosorbent assay  (ELISA), which uses antibodies, is one of the most sensitive tests modern medicine uses to detect various biomolecules. Probably the most important proteins, however, are the  enzymes . Virtually every reaction in a living cell requires an enzyme to lower the  activation energy  of the reaction. These molecules recognize specific reactant molecules called  substrates ; they then  catalyze  the reaction between them. By lowering the  activation energy , the enzyme speeds up that reaction by a rate of 10 11  or more; a reaction that would normally take over 3,000 years to complete spontaneously might take less than a second with an enzyme. The enzyme itself is not used up in the process and is free to catalyze the same reaction with a new set of substrates. Using various modifiers, the activity of the enzyme can be regulated, enabling control of the biochemistry of the cell as a whole.


The structure of proteins is traditionally described in a hierarchy of four levels. The  primary structure  of a protein consists of its linear sequence of amino acids; for instance, "alanine-glycine-tryptophan-serine-glutamate-asparagine-glycine-lysine-...".  Secondary structure  is concerned with local morphology (morphology being the study of structure). Some combinations of amino acids will tend to curl up in a coil called an  α-helix  or into a sheet called a  β-sheet ; some α-helixes can be seen in the hemoglobin schematic above.  Tertiary structure  is the entire three-dimensional shape of the protein. This shape is determined by the sequence of amino acids. In fact, a single change can change the entire structure. The alpha chain of hemoglobin contains 146 amino acid residues; substitution of the  glutamate  residue at position 6 with a  valine  residue changes the behavior of hemoglobin so much that it results in  sickle-cell disease . Finally,  quaternary structure  is concerned with the structure of a protein with multiple peptide subunits, like hemoglobin with its four subunits. Not all proteins have more than one subunit. [ 45 ]


Ingested proteins are usually broken up into single amino acids or dipeptides in the  small intestine  and then absorbed. They can then be joined to form new proteins. Intermediate products of glycolysis, the citric acid cycle, and the  pentose phosphate pathway  can be used to form all twenty amino acids, and most bacteria and plants possess all the necessary enzymes to synthesize them. Humans and other mammals, however, can synthesize only half of them. They cannot synthesize  isoleucine ,  leucine ,  lysine ,  methionine ,  phenylalanine ,  threonine ,  tryptophan , and  valine . Because they must be ingested, these are the  essential amino acids . Mammals do possess the enzymes to synthesize  alanine ,  asparagine ,  aspartate ,  cysteine ,  glutamate ,  glutamine ,  glycine ,  proline ,  serine , and  tyrosine , the nonessential amino acids. While they can synthesize  arginine  and  histidine , they cannot produce it in sufficient amounts for young, growing animals, and so these are often considered essential amino acids.


If the amino group is removed from an amino acid, it leaves behind a carbon skeleton called an α- keto acid . Enzymes called  transaminases  can easily transfer the amino group from one amino acid (making it an α-keto acid) to another α-keto acid (making it an amino acid). This is important in the biosynthesis of amino acids, as for many of the pathways, intermediates from other biochemical pathways are converted to the α-keto acid skeleton, and then an amino group is added, often via  transamination . The amino acids may then be linked together to form a protein.


A similar process is used to break down proteins. It is first hydrolyzed into its component amino acids. Free  ammonia  (NH3), existing as the  ammonium  ion (NH4+) in blood, is toxic to life forms. A suitable method for excreting it must therefore exist. Different tactics have evolved in different animals, depending on the animals' needs.  Unicellular  organisms release the ammonia into the environment. Likewise,  bony fish  can release ammonia into the water where it is quickly diluted. In general, mammals convert ammonia into urea, via the  urea cycle .


In order to determine whether two proteins are related, or in other words to decide whether they are homologous or not, scientists use sequence-comparison methods. Methods like  sequence alignments  and  structural alignments  are powerful tools that help scientists identify  homologies  between related molecules. The relevance of finding homologies among proteins goes beyond forming an evolutionary pattern of  protein families . By finding how similar two protein sequences are, we acquire knowledge about their structure and therefore their function.


Nucleic acids , so-called because of their prevalence in cellular  nuclei , is the generic name of the family of  biopolymers . They are complex, high-molecular-weight biochemical macromolecules that can convey  genetic information  in all living cells and viruses. [ 2 ]  The monomers are called  nucleotides , and each consists of three components: a nitrogenous heterocyclic  base  (either a  purine  or a  pyrimidine ), a pentose sugar, and a  phosphate  group. [ 46 ]


The most common nucleic acids are  deoxyribonucleic acid  (DNA) and  ribonucleic acid  (RNA). The  phosphate group  and the sugar of each nucleotide bond with each other to form the backbone of the nucleic acid, while the sequence of nitrogenous bases stores the information. The most common nitrogenous bases are  adenine ,  cytosine ,  guanine ,  thymine , and  uracil . The  nitrogenous bases  of each strand of a nucleic acid will form  hydrogen bonds  with certain other nitrogenous bases in a complementary strand of nucleic acid. Adenine binds with thymine and uracil, thymine binds only with adenine, and cytosine and guanine can bind only with one another. Adenine, thymine, and uracil contain two hydrogen bonds, while hydrogen bonds formed between cytosine and guanine are three.


Aside from the genetic material of the cell, nucleic acids often play a role as  second messengers , as well as forming the base molecule for  adenosine triphosphate  (ATP), the primary energy-carrier molecule found in all living organisms. Also, the nitrogenous bases possible in the two nucleic acids are different: adenine, cytosine, and guanine occur in both RNA and DNA, while thymine occurs only in DNA and uracil occurs in RNA.


Glucose is an energy source in most life forms. For instance, polysaccharides are broken down into their monomers by  enzymes  ( glycogen phosphorylase  removes glucose residues from glycogen, a polysaccharide). Disaccharides like lactose or sucrose are cleaved into their two component monosaccharides. [ 47 ]


Glucose is mainly metabolized by a very important ten-step  pathway  called  glycolysis , the net result of which is to break down one molecule of glucose into two molecules of  pyruvate . This also produces a net two molecules of  ATP , the energy currency of cells, along with two reducing equivalents of converting  NAD +  (nicotinamide adenine dinucleotide: oxidized form) to NADH (nicotinamide adenine dinucleotide: reduced form). This does not require oxygen; if no oxygen is available (or the cell cannot use oxygen), the NAD is restored by converting the pyruvate to  lactate (lactic acid)  (e.g. in humans) or to  ethanol  plus carbon dioxide (e.g. in  yeast ). Other monosaccharides like galactose and fructose can be converted into intermediates of the glycolytic pathway. [ 48 ]


In  aerobic  cells with sufficient  oxygen , as in most human cells, the pyruvate is further metabolized. It is irreversibly converted to  acetyl-CoA , giving off one carbon atom as the waste product  carbon dioxide , generating another reducing equivalent as  NADH . The two molecules acetyl-CoA (from one molecule of glucose) then enter the  citric acid cycle , producing two molecules of ATP, six more NADH molecules and two reduced (ubi)quinones (via  FADH 2  as enzyme-bound cofactor), and releasing the remaining carbon atoms as carbon dioxide. The produced NADH and quinol molecules then feed into the enzyme complexes of the respiratory chain, an  electron transport system  transferring the electrons ultimately to oxygen and conserving the released energy in the form of a proton gradient over a membrane ( inner mitochondrial membrane  in eukaryotes). Thus, oxygen is reduced to water and the original electron acceptors NAD +  and  quinone  are regenerated. This is why humans breathe in oxygen and breathe out carbon dioxide. The energy released from transferring the electrons from high-energy states in NADH and quinol is conserved first as proton gradient and converted to ATP via ATP synthase. This generates an additional  28  molecules of ATP (24 from the 8 NADH + 4 from the 2 quinols), totaling to 32 molecules of ATP conserved per degraded glucose (two from glycolysis + two from the citrate cycle). [ 49 ]  It is clear that using oxygen to completely oxidize glucose provides an organism with far more energy than any oxygen-independent metabolic feature, and this is thought to be the reason why complex life appeared only after Earth's atmosphere accumulated large amounts of oxygen.


In  vertebrates , vigorously contracting  skeletal muscles  (during weightlifting or sprinting, for example) do not receive enough oxygen to meet the energy demand, and so they shift to  anaerobic metabolism , converting glucose to lactate.
The combination of glucose from noncarbohydrates origin, such as fat and proteins. This only happens when  glycogen  supplies in the liver are worn out. The pathway is a crucial reversal of  glycolysis  from pyruvate to glucose and can use many sources like amino acids, glycerol and  Krebs Cycle . Large scale protein and fat  catabolism  usually occur when those suffer from starvation or certain endocrine disorders. [ 50 ]  The  liver  regenerates the glucose, using a process called  gluconeogenesis . This process is not quite the opposite of glycolysis, and actually requires three times the amount of energy gained from glycolysis (six molecules of ATP are used, compared to the two gained in glycolysis). Analogous to the above reactions, the glucose produced can then undergo glycolysis in tissues that need energy, be stored as glycogen (or  starch  in plants), or be converted to other monosaccharides or joined into di- or oligosaccharides. The combined pathways of glycolysis during exercise, lactate's crossing via the bloodstream to the liver, subsequent gluconeogenesis and release of glucose into the bloodstream is called the  Cori cycle . [ 51 ]


Researchers in biochemistry use specific techniques native to biochemistry, but increasingly combine these with techniques and ideas developed in the fields of  genetics ,  molecular biology , and  biophysics . There is not a defined line between these disciplines. Biochemistry studies the  chemistry  required for biological activity of molecules, molecular biology studies their biological activity,  genetics  studies their heredity, which happens to be carried by their  genome . This is shown in the following schematic that depicts one possible view of the relationships between the fields:


Genomics  is an interdisciplinary field of  molecular biology  focusing on the structure, function, evolution, mapping, and editing of  genomes . A genome is an organism's complete set of  DNA , including all of its genes as well as its hierarchical, three-dimensional structural configuration. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ excessive citations ]  In contrast to  genetics , which refers to the study of  individual  genes and their roles in inheritance, genomics aims at the collective characterization and quantification of  all  of an organism's genes, their interrelations and influence on the organism. [ 5 ]  Genes may direct the production of  proteins  with the assistance of enzymes and messenger molecules. In turn, proteins make up body structures such as organs and tissues as well as control chemical reactions and carry signals between cells. Genomics also involves the sequencing and analysis of genomes through uses of high throughput  DNA sequencing  and  bioinformatics  to assemble and analyze the function and structure of entire genomes. [ 6 ] [ 7 ]  Advances in genomics have triggered a revolution in discovery-based research and  systems biology  to facilitate understanding of even the most complex biological systems such as the brain. [ 8 ]


The field also includes studies of intragenomic (within the genome) phenomena such as  epistasis  (effect of one gene on another),  pleiotropy  (one gene affecting more than one trait),  heterosis  (hybrid vigour), and other interactions between  loci  and  alleles  within the genome. [ 9 ]


From the Greek ΓΕΝ [ 10 ]   gen , "gene" (gamma, epsilon, nu, epsilon) meaning "become, create, creation, birth", and subsequent variants: genealogy, genesis, genetics, genic, genomere, genotype, genus etc. While the word  genome  (from the  German   Genom , attributed to  Hans Winkler ) was in use in  English  as early as 1926, [ 11 ]  the term  genomics  was coined by Tom Roderick, a  geneticist  at the  Jackson Laboratory  ( Bar Harbor, Maine ), over beers with Jim Womack, Tom Shows and  Stephen O’Brien  at a meeting held in  Maryland  on the mapping of the human genome in 1986. [ 12 ]  First as the name for a  new journal  and then as a whole new science discipline. [ 13 ]


Following  Rosalind Franklin 's confirmation of the helical structure of DNA,  James D. Watson  and  Francis Crick 's publication of the structure of DNA in 1953 and  Fred Sanger 's publication of the  Amino acid  sequence of insulin in 1955, nucleic acid sequencing became a major target of early  molecular biologists . [ 14 ]  In 1964,  Robert W. Holley  and colleagues published the first nucleic acid sequence ever determined, the  ribonucleotide  sequence of  alanine   transfer RNA . [ 15 ] [ 16 ]  Extending this work,  Marshall Nirenberg  and  Philip Leder  revealed the triplet nature of the  genetic code  and were able to determine the sequences of 54 out of 64  codons  in their experiments. [ 17 ]  In 1972,  Walter Fiers  and his team at the Laboratory of Molecular Biology of the  University of Ghent  ( Ghent ,  Belgium ) were the first to determine the sequence of a gene: the gene for  Bacteriophage MS2  coat protein. [ 18 ]  Fiers' group expanded on their MS2 coat protein work, determining the complete nucleotide-sequence of bacteriophage MS2-RNA (whose genome encodes just four genes in 3569  base pairs  [bp]) and  Simian virus 40  in 1976 and 1978, respectively. [ 19 ] [ 20 ]


In addition to his seminal work on the amino acid sequence of insulin,  Frederick Sanger  and his colleagues played a key role in the development of DNA sequencing techniques that enabled the establishment of comprehensive genome sequencing projects. [ 9 ]  In 1975, he and Alan Coulson published a sequencing procedure using DNA polymerase with radiolabelled nucleotides that he called the  Plus and Minus technique . [ 21 ] [ 22 ]  This involved two closely related methods that generated short oligonucleotides with defined 3' termini. These could be fractionated by  electrophoresis  on a  polyacrylamide  gel (called polyacrylamide gel electrophoresis) and visualised using autoradiography. The procedure could sequence up to 80 nucleotides in one go and was a big improvement, but was still very laborious.  Nevertheless, in 1977 his group was able to sequence most of the 5,386 nucleotides of the single-stranded  bacteriophage   φX174 , completing the first fully sequenced DNA-based genome. [ 23 ]  The refinement of the  Plus and Minus  method resulted in the chain-termination, or  Sanger method  (see  below ), which formed the basis of the techniques of DNA sequencing, genome mapping, data storage, and bioinformatic analysis most widely used in the following quarter-century of research. [ 24 ] [ 25 ]  In the same year  Walter Gilbert  and  Allan Maxam  of  Harvard University  independently developed the  Maxam-Gilbert  method (also known as the  chemical method ) of DNA sequencing, involving the preferential cleavage of DNA at known bases, a less efficient method. [ 26 ] [ 27 ]  For their groundbreaking work in the sequencing of nucleic acids, Gilbert and Sanger shared half the 1980  Nobel Prize  in chemistry with  Paul Berg  ( recombinant DNA ).


The advent of these technologies resulted in a rapid intensification in the scope and speed of completion of  genome sequencing projects . The first complete genome sequence of a  eukaryotic organelle , the human  mitochondrion  (16,568 bp, about 16.6 kb [kilobase]), was reported in 1981, [ 28 ]  and the first  chloroplast  genomes followed in 1986. [ 29 ] [ 30 ]  In 1992, the first eukaryotic  chromosome , chromosome III of brewer's yeast  Saccharomyces cerevisiae  (315 kb) was sequenced. [ 31 ]  The first free-living organism to be sequenced was that of  Haemophilus influenzae  (1.8 Mb [megabase]) in 1995. [ 32 ]  The following year a consortium of researchers from laboratories across  North America ,  Europe , and  Japan  announced the completion of the first complete genome sequence of a eukaryote,  S. cerevisiae  (12.1 Mb), and since then genomes have continued being sequenced at an exponentially growing pace. [ 33 ]  As of October 2011 [update] , the complete sequences are available for: 2,719  viruses , 1,115  archaea  and  bacteria , and 36  eukaryotes , of which about half are  fungi . [ 34 ] [ 35 ]


Most of the microorganisms whose genomes have been completely sequenced are problematic  pathogens , such as  Haemophilus influenzae , which has resulted in a pronounced bias in their phylogenetic distribution compared to the breadth of microbial diversity. [ 36 ] [ 37 ]  Of the other sequenced species, most were chosen because they were well-studied model organisms or promised to become good models. Yeast ( Saccharomyces cerevisiae ) has long been an important  model organism  for the  eukaryotic cell , while the fruit fly  Drosophila melanogaster  has been a very important tool (notably in early pre-molecular  genetics ). The worm  Caenorhabditis elegans  is an often used simple model for  multicellular organisms . The zebrafish  Brachydanio rerio  is used for many developmental studies on the molecular level, and the plant  Arabidopsis thaliana  is a model organism for flowering plants.  The  Japanese pufferfish  ( Takifugu rubripes ) and the  spotted green pufferfish  ( Tetraodon nigroviridis ) are interesting because of their small and compact genomes, which contain very little  noncoding DNA  compared to most species. [ 38 ] [ 39 ]  The mammals dog ( Canis familiaris ), [ 40 ]  brown rat ( Rattus norvegicus ), mouse ( Mus musculus ), and chimpanzee ( Pan troglodytes ) are all important model animals in medical research. [ 27 ]


A rough draft of the  human genome  was completed by the  Human Genome Project  in early 2001, creating much fanfare. [ 41 ]  This project, completed in 2003, sequenced the entire genome for one specific person, and by 2007 this sequence was declared "finished" (less than one error in 20,000 bases and all chromosomes assembled). [ 41 ]  In the years since then, the genomes of many other individuals have been sequenced, partly under the auspices of the  1000 Genomes Project , which announced the sequencing of 1,092 genomes in October 2012. [ 42 ]  Completion of this project was made possible by the development of dramatically more efficient sequencing technologies and required the commitment of significant  bioinformatics  resources from a large international collaboration. [ 43 ]  The continued analysis of human genomic data has profound political and social repercussions for human societies. [ 44 ]


The English-language  neologism   omics  informally refers to a field of study in biology ending in  -omics , such as genomics,  proteomics  or  metabolomics . The related suffix  -ome  is used to address the objects of study of such fields, such as the  genome ,  proteome , or  metabolome  ( lipidome ) respectively. The suffix  -ome  as used in molecular biology refers to a  totality  of some sort; similarly  omics  has come to refer generally to the study of large, comprehensive biological data sets. While the growth in the use of the term has led some scientists ( Jonathan Eisen , among others [ 45 ] ) to claim that it has been oversold, [ 46 ]  it reflects the change in orientation towards the quantitative analysis of complete or near-complete assortment of all the constituents of a system. [ 47 ]  In the study of  symbioses , for example, researchers which were once limited to the study of a single gene product can now simultaneously compare the total complement of several types of biological molecules. [ 48 ] [ 49 ]


After an organism has been selected, genome projects involve three components: the sequencing of DNA, the assembly of that sequence to create a representation of the original chromosome, and the annotation and analysis of that representation. [ 9 ]


Historically, sequencing was done in  sequencing centers , centralized facilities (ranging from large independent institutions such as  Joint Genome Institute  which sequence dozens of terabases a year, to local molecular biology core facilities)  which contain research laboratories with the costly instrumentation and technical support necessary. As sequencing technology continues to improve, however, a new generation of effective fast turnaround benchtop sequencers has come within reach of the average academic laboratory. [ 50 ] [ 51 ]  On the whole, genome sequencing approaches fall into two broad categories,  shotgun  and  high-throughput  (or  next-generation ) sequencing. [ 9 ]


Shotgun sequencing is a sequencing method designed for analysis of DNA sequences longer than 1000 base pairs, up to and including entire chromosomes. [ 52 ]  It is named by analogy with the rapidly expanding, quasi-random firing pattern of a  shotgun . Since gel electrophoresis sequencing can only be used for fairly short sequences (100 to 1000 base pairs), longer DNA sequences must be broken into random small segments which are then sequenced to obtain  reads . Multiple overlapping reads for the target DNA are obtained by performing several rounds of this fragmentation and sequencing. Computer programs then use the overlapping ends of different reads to assemble them into a continuous sequence. [ 52 ] [ 53 ]   Shotgun sequencing is a random sampling process, requiring over-sampling to ensure a given  nucleotide  is represented in the reconstructed sequence; the average number of reads by which a genome is over-sampled is referred to as  coverage . [ 54 ]


For much of its history, the technology underlying shotgun sequencing was the classical chain-termination method or ' Sanger method ', which is based on the selective incorporation of chain-terminating  dideoxynucleotides  by  DNA polymerase  during  in vitro   DNA replication . [ 23 ] [ 55 ]  Recently, shotgun sequencing has been supplanted by  high-throughput sequencing  methods, especially for large-scale, automated  genome  analyses. However, the Sanger method remains in wide use, primarily for smaller-scale projects and for obtaining especially long contiguous DNA sequence reads (>500 nucleotides). [ 56 ]  Chain-termination methods require a single-stranded DNA template, a DNA  primer , a  DNA polymerase , normal deoxynucleosidetriphosphates (dNTPs), and modified nucleotides (dideoxyNTPs) that terminate DNA strand elongation. These chain-terminating nucleotides lack a 3'- OH  group required for the formation of a  phosphodiester bond  between two nucleotides, causing DNA polymerase to cease extension of DNA when a ddNTP is incorporated. The ddNTPs may be radioactively or  fluorescently  labelled for detection in  DNA sequencers . [ 9 ]  Typically, these machines can sequence up to 96 DNA samples in a single batch (run) in up to 48 runs a day. [ 57 ]


The high demand for low-cost sequencing has driven the development of high-throughput sequencing technologies that  parallelize  the sequencing process, producing thousands or millions of sequences at once. [ 58 ] [ 59 ]  High-throughput sequencing is intended to lower the cost of DNA sequencing beyond what is possible with standard dye-terminator methods. In ultra-high-throughput sequencing, as many as 500,000 sequencing-by-synthesis operations may be run in parallel. [ 60 ] [ 61 ]


The Illumina dye sequencing method is based on reversible dye-terminators and was developed in 1996 at the Geneva Biomedical Research Institute, by  Pascal Mayer  and Laurent Farinelli. [ 62 ]  In this method, DNA molecules and primers are first attached on a slide and amplified with  polymerase  so that local clonal colonies, initially coined "DNA colonies", are formed. To determine the sequence, four types of reversible terminator bases (RT-bases) are added and non-incorporated nucleotides are washed away. Unlike pyrosequencing, the DNA chains are extended one nucleotide at a time and image acquisition can be performed at a delayed moment, allowing for very large arrays of DNA colonies to be captured by sequential images taken from a single camera. Decoupling the enzymatic reaction and the image capture allows for optimal throughput and theoretically unlimited sequencing capacity; with an optimal configuration, the ultimate throughput of the instrument depends only on the  A/D conversion  rate of the camera. The camera takes images of the  fluorescently labeled  nucleotides, then the dye along with the terminal 3' blocker is chemically removed from the DNA, allowing the next cycle. [ 63 ]


An alternative approach, ion semiconductor sequencing, is based on standard DNA replication chemistry. This technology measures the release of a hydrogen ion each time a base is incorporated. A microwell containing template DNA is flooded with a single  nucleotide , if the nucleotide is complementary to the template strand it will be incorporated and a hydrogen ion will be released. This release triggers an  ISFET  ion sensor. If a  homopolymer  is present in the template sequence multiple nucleotides will be incorporated in a single flood cycle, and the detected electrical signal will be proportionally higher. [ 64 ]


Sequence assembly refers to  aligning  and merging fragments of a much longer  DNA  sequence in order to reconstruct the original sequence. [ 9 ]  This is needed as current  DNA sequencing  technology cannot read whole genomes as a continuous sequence, but rather reads small pieces of between 20 and 1000 bases, depending on the technology used. Third generation sequencing technologies such as PacBio or Oxford Nanopore routinely generate sequencing reads 10-100 kb in length; however, they have a high error rate at approximately 1 percent. [ 65 ] [ 66 ]  Typically the short fragments, called reads, result from  shotgun sequencing   genomic  DNA, or  gene transcripts  ( ESTs ). [ 9 ]


Assembly can be broadly categorized into two approaches:  de novo  assembly, for genomes which are not similar to any sequenced in the past, and comparative assembly, which uses the existing sequence of a closely related organism as a reference during assembly. [ 54 ]  Relative to comparative assembly,  de novo  assembly is computationally difficult ( NP-hard ), making it less favourable for short-read NGS technologies. Within the  de novo  assembly paradigm there are two primary strategies for assembly, Eulerian path strategies, and overlap-layout-consensus (OLC) strategies. OLC strategies ultimately try to create a Hamiltonian path through an overlap graph which is an NP-hard problem. Eulerian path strategies are computationally more tractable because they try to find a Eulerian path through a deBruijn graph. [ 54 ]


Finished genomes are defined as having a single contiguous sequence with no ambiguities representing each  replicon . [ 67 ]


The DNA sequence assembly alone is of little value without additional analysis. [ 9 ]  Genome annotation is the process of attaching biological information to  sequences , and consists of three main steps: [ 68 ]


Automatic annotation tools try to perform these steps  in silico , as opposed to manual annotation (a.k.a. curation) which involves human expertise and potential experimental verification. [ 69 ]  Ideally, these approaches co-exist and complement each other in the same annotation  pipeline  (also see  below ).


Traditionally, the basic level of annotation is using  BLAST  for finding similarities, and then annotating genomes based on homologues. [ 9 ]  More recently, additional information is added to the annotation platform. The additional information allows manual annotators to deconvolute discrepancies between genes that are given the same annotation. Some databases use genome context information, similarity scores, experimental data, and integrations of other resources to provide genome annotations through their Subsystems approach. Other databases (e.g.  Ensembl ) rely on both curated data sources as well as a range of software tools in their automated genome annotation pipeline. [ 70 ]   Structural annotation  consists of the identification of genomic elements, primarily  ORFs  and their localisation, or gene structure.  Functional annotation  consists of attaching biological information to genomic elements.


The need for reproducibility and efficient management of the large amount of data associated with genome projects mean that  computational pipelines  have important applications in genomics. [ 71 ]


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


A major branch of genomics is still concerned with  sequencing  the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of  functional genomics , mainly concerned with patterns of  gene expression  during various conditions. The most important tools here are  microarrays  and  bioinformatics .


Structural genomics seeks to describe the  3-dimensional structure  of every protein encoded by a given  genome . [ 72 ] [ 73 ]  This genome-based approach allows for a high-throughput method of structure determination by a combination of  experimental and modeling approaches . The principal difference between structural genomics and  traditional structural prediction  is that structural genomics attempts to determine the structure of every protein encoded by the genome, rather than focusing on one particular protein. With full-genome sequences available, structure prediction can be done more quickly through a combination of experimental and modeling approaches, especially because the availability of large numbers of sequenced genomes and previously solved protein structures allow scientists to model protein structure on the structures of previously solved homologs. Structural genomics involves taking a large number of approaches to structure determination, including experimental methods using genomic sequences or modeling-based approaches based on sequence or  structural homology  to a protein of known structure or based on chemical and physical principles for a protein with no homology to any known structure. As opposed to traditional  structural biology , the determination of a  protein structure  through a structural genomics effort often (but not always) comes before anything is known regarding the protein function. This raises new challenges in  structural bioinformatics , i.e. determining protein function from its  3D  structure. [ 74 ]


Epigenomics is the study of the complete set of  epigenetic  modifications on the genetic material of a cell, known as the  epigenome . [ 75 ]    Epigenetic modifications are reversible modifications on a cell's DNA or histones that affect gene expression without altering the DNA sequence (Russell 2010 p. 475).  Two of the most characterized epigenetic modifications are  DNA methylation  and  histone modification . [ 76 ]   Epigenetic modifications play an important role in gene expression and regulation, and are involved in numerous cellular processes such as in  differentiation/development [ 77 ]  and  tumorigenesis . [ 75 ]   The study of epigenetics on a global level has been made possible only recently through the adaptation of genomic high-throughput assays. [ 78 ]


Metagenomics is the study of  metagenomes ,  genetic  material recovered directly from  environmental  samples.  The broad field may also be referred to as environmental genomics, ecogenomics or community genomics. While traditional  microbiology  and microbial  genome sequencing  rely upon cultivated  clonal   cultures , early environmental gene sequencing cloned specific genes (often the  16S rRNA  gene) to produce a  profile of diversity  in a natural sample.  Such work revealed that the vast majority of  microbial biodiversity  had been missed by  cultivation-based  methods. [ 79 ]  Recent studies use "shotgun"  Sanger sequencing  or massively parallel  pyrosequencing  to get largely unbiased samples of all genes from all the members of the sampled communities. [ 80 ]  Because of its power to reveal the previously hidden diversity of microscopic life, metagenomics offers a powerful lens for viewing the microbial world that has the potential to revolutionize understanding of the entire living world. [ 81 ] [ 82 ]


Bacteriophages  have played and continue to play a key role in bacterial  genetics  and  molecular biology . Historically, they were used to define  gene  structure and gene regulation. Also the first  genome  to be sequenced was a  bacteriophage . However, bacteriophage research did not lead the genomics revolution, which is clearly dominated by bacterial genomics. Only very recently has the study of bacteriophage genomes become prominent, thereby enabling researchers to understand the mechanisms underlying  phage  evolution.  Bacteriophage genome sequences can be obtained through direct sequencing of isolated bacteriophages, but can also be derived as part of microbial genomes. Analysis of bacterial genomes has shown that a substantial amount of microbial DNA consists of  prophage  sequences and prophage-like elements. [ 83 ]  A detailed database mining of these sequences offers insights into the role of prophages in shaping the bacterial genome: Overall, this method verified many known bacteriophage groups, making this a useful tool for predicting the relationships of prophages from bacterial genomes. [ 84 ] [ 85 ]


At present there are 24  cyanobacteria  for which a total genome sequence is available. 15 of these cyanobacteria come from the marine environment. These are six  Prochlorococcus  strains, seven marine  Synechococcus  strains,  Trichodesmium erythraeum  IMS101 and  Crocosphaera watsonii   WH8501 . Several studies have demonstrated how these sequences could be used very successfully to infer important ecological and physiological characteristics of marine cyanobacteria. However, there are many more genome projects currently in progress, amongst those there are further  Prochlorococcus  and marine  Synechococcus  isolates,  Acaryochloris  and  Prochloron , the N 2 -fixing filamentous cyanobacteria  Nodularia  spumigena ,  Lyngbya aestuarii  and  Lyngbya majuscula , as well as  bacteriophages  infecting marine cyanobaceria. Thus, the growing body of genome information can also be tapped in a more general way to address global problems by applying a comparative approach. Some new and exciting examples of progress in this field are the identification of genes for regulatory RNAs, insights into the evolutionary origin of  photosynthesis , or estimation of the contribution of  horizontal gene transfer  to the genomes that have been analyzed. [ 86 ]


Genomics has provided applications in many fields, including  medicine ,  biotechnology ,  anthropology  and other  social sciences . [ 44 ]


Next-generation genomic technologies allow clinicians and biomedical researchers to drastically increase the amount of genomic data collected on large study populations. [ 87 ]  When combined with new informatics approaches that integrate many kinds of data with genomic data in disease research, this allows researchers to better understand the genetic bases of drug response and disease. [ 88 ] [ 89 ]


Early efforts to apply the genome to medicine included those by a Stanford team led by  Euan Ashley  who developed the first tools for the medical interpretation of a human genome. [ 90 ] [ 91 ] [ 92 ]  The Genomes2People research program at  Brigham and Women’s Hospital ,  Broad Institute  and Harvard Medical School was established in 2012 to conduct empirical research in translating genomics into health.  Brigham and Women's Hospital  opened a Preventive Genomics Clinic in August 2019, with  Massachusetts General Hospital  following a month later. [ 93 ] [ 94 ]  The  All of Us  research program aims to collect genome sequence data from 1 million participants to become a critical component of the precision medicine research platform [ 95 ]  and the  UK Biobank  initiative has studied more than 500.000 individuals with deep genomic and phenotypic data. [ 96 ]


The growth of genomic knowledge has enabled increasingly sophisticated applications of  synthetic biology . [ 97 ]  In 2010 researchers at the  J. Craig Venter Institute  announced the creation of a partially synthetic species of  bacterium ,  Mycoplasma laboratorium , derived from the  genome  of  Mycoplasma genitalium . [ 98 ]


Population genomics  has developed as a popular field of research, where genomic sequencing methods are used to conduct large-scale comparisons of DNA sequences among populations - beyond the limits of genetic markers such as short-range  PCR  products or  microsatellites  traditionally used in  population genetics . Population genomics studies  genome -wide effects to improve our understanding of  microevolution  so that we may learn the  phylogenetic  history and  demography  of a population. [ 99 ]  Population genomic methods are used for many different fields including  evolutionary biology ,  ecology ,  biogeography ,  conservation biology  and  fisheries management . Similarly,  landscape genomics  has developed from  landscape genetics  to use genomic methods to identify relationships between patterns of environmental and genetic variation.


Conservationists can use the information gathered by genomic sequencing in order to better evaluate genetic factors key to species conservation, such as the  genetic diversity  of a population or whether an individual is heterozygous for a recessive inherited genetic disorder. [ 100 ]  By using genomic data to evaluate the effects of  evolutionary processes  and to detect patterns in variation throughout a given population, conservationists can formulate plans to aid a given species without as many variables left unknown as those unaddressed by standard  genetic approaches . [ 101 ]


The  three-base periodicity property  in the field of  Genomics  is a property that is characteristic of protein-coding  DNA sequences . The existence of this property can be shown by performing  Fourier analysis  on signals derived from segments of DNA sequences. Because of its predictive power, it has been used as a preliminary indicator in gene prediction.


DNA sequences are inherently signals as they are functions of an independent variable, position on the sequence. [ 1 ]  Thus, signal processing methods can be applied to them after the symbolic string is properly mapped to one (or more) numerical sequences. The reason for this periodicity is due to the biased distribution towards codon triplets, which is a consequence of genetic code degeneracy; while non-coding segments are uniformly randomly distributed and produce no significant signal in the frequency space.


This property has been dissected, tested and derived in a chronology of papers from different universities. The initial discovery was made in 1980 by  Trifonov  and  Sussman  who observed periodicity in DNA sequences by applying the autocorrelation function to  chromatin  DNA. [ 2 ]  Silverman and Linsker defined the Fourier transform of a sequence of bases, described how to "fourier analyze" it and proposed sample applications of this technique. [ 3 ]  Tsonis, Elsner and Tsonis did Fourier analysis of coding, non-coding and random sequences and proposed a reason for the 3-periodicity property found in coding sequences. [ 4 ]  Dodin proposed a method for analyzing the periodicity of DNA sequences based on the correlation function of the symbolic sequence. [ 5 ]  Tiwari, Ramachandran, Bhattacharya and Ramaswamy examined the signal-to-noise ratio of the period-3 peak within a sliding window over a sequence to identify likely coding regions. [ 6 ]


DNA stores the information required to assemble, maintain and reproduce every living organism. A  protein  is a large molecule ("macromolecule") made up of smaller subunits, amino acids. DNA sequences are made up of  codons , three-long nucleotide stretches, that correspond to specific amino acids. DNA creates RNA which then helps synthesize proteins. Thus, coding DNA is defined as the sections of the genome that are actually transcribed into amino acids in proteins. Noncoding DNA is sections of a DNA sequence that don't necessarily code for proteins. Identification of  coding regions  is important, as this information can be used in gene identification and then more generally  full-genome annotation .


The 3-periodicity property states that the spectral energy


derived from the DFT's of the four binary signals representing a DNA protein coding region of length

 N

 {\displaystyle N}

 , exhibits a peak at discrete frequency

 k
 =
 N

 /

 3

 {\displaystyle k=N/3}

 . [ 7 ]


Formally, a DNA sequence

 s
 [
 i
 ]

 {\displaystyle s[i]}

  is an ordered list of symbols from a dictionary of nucleotides

 (
 A
 ,
 C
 ,
 T
 ,
 G
 )

 {\displaystyle (A,C,T,G)}

 . There are multiple ways to view this as a numerical sequence, and they will all be applied to the same example:


Define a binary signal for each nucleotide,

 U

 α

 [
 i
 ]

 {\displaystyle U_{\alpha }[i]}

 , which is 1 when the i-th position in the sequence is the nucleotide

 α

 {\displaystyle \alpha }

  and 0 otherwise. Formally,


This creates four signals which encode the position of the four nucleotides in the sequence. For the above example, the projected signals would be


ATGCAGC,

 u

 a

 {\displaystyle u_{a}}

  = 1000100,

 u

 c

 {\displaystyle u_{c}}

  = 0001001,

 u

 t

 {\displaystyle u_{t}}

  = 0100000,

 u

 g

 {\displaystyle u_{g}}

  = 0010010


Let

 E
 (
 i
 ,
 j
 )

 {\displaystyle E(i,j)}

  be a function that is 1 if

 s
 [
 i
 ]
 =
 s
 [
 j
 ]

 {\displaystyle s[i]=s[j]}

  and

 0

 {\displaystyle 0}

  otherwise. Let

 c
 [
 t
 ]
 =

 ∑

 i
 =
 t

 N

 E
 (
 i
 ,
 i
 −
 t
 )

 {\displaystyle c[t]=\sum _{i=t}^{N}E(i,i-t)}

 . In other words, it counts the number of times that the nucleotide at a position is equal to the nucleotide t-away on the sequence. It is invariant to changing the labeling of the bases.

 c
 [
 0
 ]

 {\displaystyle c[0]}

  will always be equal to the length of the sequence. For the above example, the projected signals would be


c
 (
 0
 )
 =
 7

 {\displaystyle c(0)=7}

 ,

 c
 (
 1
 )
 =
 0

 {\displaystyle c(1)=0}

 ,

 c
 (
 2
 )
 =
 0

 {\displaystyle c(2)=0}

 ,

 c
 (
 3
 )
 =
 2

 {\displaystyle c(3)=2}

 ,

 c
 (
 4
 )
 =
 1

 {\displaystyle c(4)=1}

 ,

 c
 (
 5
 )
 =
 0

 {\displaystyle c(5)=0}

 ,

 c
 (
 6
 )
 =
 0

 {\displaystyle c(6)=0}


For mathematical purposes, a gene sequence could be viewed as a signal by mapping each nucleotide into the range [1,4]. For the example above and map

 {
 A
 −
 >
 1
 ,
 T
 −
 >
 2
 ,
 C
 −
 >
 3
 ,
 G
 −
 >
 4
 }

 {\displaystyle \{A->1,T->2,C->3,G->4\}}

 , the sequence would manifest itself as

 1243143

 {\displaystyle 1243143}

 .


This process generates a single signal for the sequence, but raises questions such as which out of 24 (4!) maps should be used and what effect does this map have on our analysis. Unlike the other two mapping methods, this one is not invariant to changing the labeling of the bases but having the same structure (i.e. AACA -> TTGT), which could be detrimental for some applications.


Once the DNA sequence has been converted into a numerical sequence, spectral analysis can be performed on that sequence. Recall the  DFT  is defined by the analysis equation


X
 [
 k
 ]
 =

 ∑

 n
 =
 0

 N
 −
 1

 x
 [
 n
 ]
 ⋅

 e

 −
 2
 π
 i
 k
 n

 /

 N

 ,

 {\displaystyle X[k]=\sum _{n=0}^{N-1}x[n]\cdot e^{-2\pi ikn/N},}


and produces a N-long complex signal

 X
 [
 k
 ]

 {\displaystyle X[k]}

  that represents the spectrum of the signal. The index

 k

 {\displaystyle k}

  of the frequency response corresponds to an angular frequency

 ω

 {\displaystyle \omega }

  of

 2
 π
 k

 /

 N

 {\displaystyle 2\pi k/N}

 . For the 3-base periodicity property, the frequency content at

 k
 =
 N

 /

 3

 {\displaystyle k=N/3}

  should be analyzed as it corresponds to a period of 3.


Recall that the power spectra of a sequence is equal to the magnitude of the frequency vector squared,


P
 [
 k
 ]
 =

 |

 X
 [
 k
 ]

 |

 2

 {\displaystyle P[k]=|X[k]|^{2}}


Tiwari applied the DFT to analysis of DNA sequences using the binary projection operator. [ 6 ]  They calculated the spectra of the four projected nucleotide sequences using the DFT. Call them

 C

 A

 [
 k
 ]
 ,

 C

 C

 [
 k
 ]
 ,

 C

 T

 [
 k
 ]
 ,

 C

 G

 [
 k
 ]

 {\displaystyle C_{A}[k],C_{C}[k],C_{T}[k],C_{G}[k]}

 . Then, the power spectra of all the signals were combined,


C
 [
 k
 ]
 =

 |

 C

 A

 [
 k
 ]

 |

 2

 +

 |

 C

 C

 [
 k
 ]

 |

 2

 +

 |

 C

 T

 [
 k
 ]

 |

 2

 +

 |

 C

 G

 [
 k
 ]

 |

 2

 .

 {\displaystyle C[k]=|C_{A}[k]|^{2}+|C_{C}[k]|^{2}+|C_{T}[k]|^{2}+|C_{G}[k]|^{2}.}


Then, they calculated the  Signal-to-Noise ratio  of this signal and performed a threshold test on that value to determine whether or not that stretch of DNA is coding.


Instead of computing a Discrete Fourier Transform on different segments of the signal, the analysis can be performed in the time-domain through the use of an anti-notch filter at frequency

 N

 /

 3

 {\displaystyle N/3}

 , which is much faster and performs a similar operation. An anti-notch filter applied to the signals essentially approximates the magnitude of the DFT at

 N

 /

 3

 {\displaystyle N/3}

 , and a similar method is used to threshold this value to determine whether or not the window is coding or non-coding. Vaidyanathan and Yoon implemented and evaluated this method. [ 8 ]  An ideal anti-notch filter at frequency

 N

 /

 3

 {\displaystyle N/3}

  has an  impulse response  of


and thus the frequency content of the output of an anti-notch filtered signal is

 X
 [
 k
 ]
 =
 A
 [
 N

 /

 3
 ]

 {\displaystyle X[k]=A[N/3]}

  if

 k
 =
 N

 /

 3

 {\displaystyle k=N/3}

  and 0 otherwise. By taking the magnitude of the time-domain signal, and invoking  Parseval's Theorem , we get the magnitude of the frequency response. By the above logic, this is just

 |

 A
 [
 N

 /

 3
 ]

 |

 2

 {\displaystyle |A[N/3]|^{2}}

  which is the quantity of interest.


Spectrograms  are a good way to view how the frequency content of a signal changes over time. The most common way to compute a spectrogram is to compute a Fourier transform over different segments of the signals, convert the frequency magnitude plot into an image, and concatenate those images. This is a useful way to visually identify coding and non-coding regions of DNA and to inspect other patterns that might exist. [ 9 ]


Identifying genes in a DNA sequence is harder than just finding what segments are coding and near impossible to identify by visually inspecting spectrograms.  Genes  are made up of both coding and non-coding regions, called  introns  and  exons . Thus, the transition between coding and non-coding regions must be examined and analyzed properly to identify genes. Computing the "level" of 3-periodicity over different (possibly overlapping) windows of the sequence generates a plot of 3-periodicity over time. [ 10 ]


These long stretches of coding vs. non-coding can then be classified as introns or exons and the entire segment heuristically labeled as gene or non-gene.


Binary signals can be parsed into something called a position count function (PCF), which counts the number of one's at phase

 s

 {\displaystyle s}

  at positions that are multiples of

 ω

 {\displaystyle \omega }

 . This is expressed mathematically for a binary signal

 A

 {\displaystyle A}

  as


For

 ω
 =
 3

 {\displaystyle \omega =3}

 , there are three PCFs

 (

 C

 3

 A

 (
 0
 )

 {\displaystyle (C_{3}^{A}(0)}

 ,

 C

 3

 A

 (
 1
 )

 {\displaystyle C_{3}^{A}(1)}

  and

 C

 3

 A

 (
 2
 )
 )

 {\displaystyle C_{3}^{A}(2))}

  which each count the number of one's at positions

 {
 0
 ,
 3
 ,
 6
 ,
 .
 .
 .
 }

 {\displaystyle \{0,3,6,...\}}

 ,

 {
 1
 ,
 4
 ,
 7
 ,
 .
 .
 .
 }

 {\displaystyle \{1,4,7,...\}}

 ,

 {
 2
 ,
 5
 ,
 8
 ,
 .
 .
 .
 }

 {\displaystyle \{2,5,8,...\}}

  respectively. It can be shown that the magnitude of the DFT at

 N

 /

 3

 {\displaystyle N/3}

  is in fact directly related to the PCF and is equal to


In other words, the spectral power at

 N

 /

 3

 {\displaystyle N/3}

  in the DFT depends only on the difference between the PCFs at different phases. This relation here is the fundamental to the explanation for why this method can identify coding regions. This is because the spectral power at

 N

 /

 3

 {\displaystyle N/3}

  will be determined by how the codons that make up the sequence are sampled.


If the codons are sampled uniformly at random, as they would be in a noncoding segment of DNA, there is a high chance that the PCFs would not differ by a significant amount and the power at that frequency will be low.


However, in a protein-coding sequence, the DNA sequence is made up of a string of codons which correspond to amino acids. Because the  genetic code  is degenerate (more than one codon map to a single amino acid) and samples from the amino acids rather than the codons, the codons are not sampled uniformly thus leading to differences in the PCFs.


There is also empirical evidence for why this method works. In other words, over multiple studies this method has been able to discriminate coding vs.  non-coding DNA  segments. These are discussed in the next section.


This method has been applied to sequence data from a number of organisms, details of which can be found in the references section. A few will be summarized here.


Tiwari, who wrote the paper to first apply DFT to analyzing periodicity of DNA sequences, applied this method to  S.cerevisiae  and  H.influenzuae . For  S.cerevisiae , they were able to locate 413 out of 483 probable genes (ORFs). For  H.influenzuae , they were able to locate 167 out of 194 identified genes. In both studies, they had a zero false-positive rate.


Datta and Asif analyzed the algorithm's ability to identify coding regions of different lengths in chromosome III of  C. elegans . Longer coding sequences are detected with higher probability. This seems to be a consequence of the  Uncertainty principle  (shorter-time signals spread out in frequency content) and the fact that fewer codons are provided in shorter sequences.


The method can be run on any DNA sequence, where as other methods such as  BLAST ,  FASTA  and  Smith-Waterman  require empirical data.


This is because the total spectral power is


and no one base contributes more than another.


This is due to the property that shifting a sequence does not change the magnitude of its Discrete Fourier Transform.


The  100,000 Genomes Project  is a now-completed  UK Government  project managed by  Genomics England  that is  sequencing  whole  genomes  from  National Health Service  patients. The project is focusing on  rare diseases , some common types of  cancer , and infectious diseases. [ 1 ] [ 2 ]  Participants give consent for their genome data to be linked to information about their medical condition and health records. The medical and genomic data is shared with researchers to improve knowledge of the causes, treatment, and care of diseases. [ 3 ]  The project has received over £300 million from public and private investment. [ 4 ]


The project was first announced by  UK Prime Minister   David Cameron  in December 2012. [ 5 ]  The Government set up a new company  Genomics England  to oversee the project [ 2 ]  with the plan to focus on rare diseases, cancer, and infectious diseases announced by  Health Secretary   Jeremy Hunt  in July 2013. The project was also made possible by the  National Institute for Health and Care Research  (NIHR),  NHS England ,  Public Health England , and  Health Education England .  In 2015,  Northern Ireland [ 6 ]  and  Scotland [ 7 ]  also joined the project with plans to start work the following year. In 2016,  the Welsh government  issued a statement of intent [ 8 ]  and is considering participating in the 100,000 Genomes Project. [ 7 ]


The initial participants were recruited from  Cambridge University Hospitals ,  University College London Partners , and  Newcastle upon Tyne NHS Foundation Trust . The following medical centres joined the project a short time later:  Central Manchester University Hospitals NHS Foundation Trust ,  Great Ormond Street Hospital ,  Guy's and St Thomas' NHS Foundation Trust ,  Moorfields Eye Hospital NHS Foundation Trust  and  Oxford University Hospitals NHS Trust . [ 9 ]  In total, the coordination of recruitment for the 100,000 Genome Project was overseen by 13 Genomic Medicine Centers that have been established across 85 NHS Trusts in England, Northern Ireland, and Scotland. [ 10 ]


In September 2015, Genomics England announced it had contracted with interpretation partners  Congenica  and  Omicia . This is in addition to ongoing work with sequencing partner  Illumina . [ 11 ]


As of 1 October 2018, the 100,000 Genomes Project had completed the sequencing of 87,231 whole genomes in England [ 12 ]  and results are in the process of being returned to  NHS Genomic Medicine Centres  and ultimately back to participants; the first diagnoses from the Project were returned to patients in spring 2015 and over 2,000 families' results have been returned to the NHS in the rare disease programme to date (July 2017). [ 13 ]


In December 2018, the full 100,000 genomes milestone was reached. [ 14 ]


In July 2019, Genomics England announced Data Release 7, which included the 100,000th whole genome made available to researchers. [ 15 ]


A 2019 review identified the initiative as an 'exemplar' in involving the public in genomic research. [ 16 ]


In June 2020, Lifebit, a UK-based biotechnology company, was announced as the provider of the trusted research environment that will link the genomic data gathered as part of the 100,000 Genome Project with academic research institutions. [ 17 ] [ 18 ]


The Genomics England Clinical Interpretation Partnership (GeCIP) includes 2,500 UK and international clinicians and scientists from approximately 300 institutions in 24 countries. There are plans to increase this number. Researchers are organised in "domains" formed around particular conditions, cancer types and research areas such as  Machine Learning  and  Health Economics . The partnership is integrated with the NHS and the aims include improving the use of  genotype  and  phenotype  data in healthcare, and providing a platform for genomic research collaborations to add to the knowledge base for  genetic disorders . [ 19 ] [ 20 ]


The 100,000 Genomes Project provided a pre-COVID reference set in the GenOMICC study on COVID-19.  Genomics England worked in partnership with the GenOMICC consortium, led by the University of Edinburgh, to analyse the whole genome sequences of approximately 20,000 people who have been severely affected by COVID-19. This data was compared with 15,000 other genomes from people who were only mildly affected.  It was combined with data set which now includes more than 120,000 genomes (from the "100,000 Genomes" Project).  The 100,000 Genomes Project was referred to as the "incredibly important" pre-COVID reference set. [ 21 ]


The  $1,000 genome  refers to an era of predictive and  personalized medicine  during which the cost of fully  sequencing  an individual's  genome  ( WGS ) is roughly one thousand USD. [ 1 ] [ 2 ]  It is also the title of a book by British science writer and founding editor of  Nature Genetics , Kevin Davies. [ 3 ]  By late 2015, the cost to generate a high-quality "draft" whole human genome sequence was just below  $ 1,500. [ 4 ]


The "$1,000 genome" catchphrase was first publicly recorded in December 2001 at a scientific retreat to discuss the future of biomedical research following publication of the first draft of the  Human Genome Project (HGP) , convened by the  National Human Genome Research Institute  at Airlie House in Virginia. [ 5 ]  The phrase neatly highlighted the chasm between the actual cost of the  Human Genome Project , estimated at $2.7 billion over a decade, and the benchmark for routine, affordable personal genome sequencing.


On 2 October 2002,  Craig Venter  introduced the opening session of GSAC (The Genome Sequencing and Analysis Conference) at the Hynes Convention Center in Boston: "The Future of Sequencing: Advancing Towards the $1,000 Genome." Speakers included  George M. Church  and executives from  454 Life Sciences ,  Solexa , U.S. Genomics, VisiGen and  Amersham plc . [ 6 ] [ 7 ]  In 2003, Venter announced that his foundation would earmark $500,000 for a breakthrough leading to the $1,000 genome. [ 8 ]  That sum was subsequently rolled into the  Archon X Prize .


In October 2004, NHGRI introduced the first in a series of '$1,000 Genome' grants designed to advance "the development of breakthrough technologies that will enable a human-sized genome to be sequenced for $1,000 or less." [ 9 ]


In a January 2006 article in  Scientific American  making the case for the  Personal Genome Project , George M. Church wrote


The "$1,000 genome" has become shorthand for the promise of DNA-sequencing capability made so affordable that individuals might think the once-in-a-lifetime expenditure to have a full personal genome sequence read to a disk for doctors to reference is worthwhile. [ 10 ]


In 2007, the journal  Nature Genetics  invited dozens of scientists to respond to its 'Question of the Year':


The sequencing of the equivalent of an entire human genome for $1,000 has been announced as a goal for the genetics community... What would you do if [the $1,000 genome was] available immediately? [ 11 ]


In May 2007, during a ceremony held at  Baylor College of Medicine ,  454 Life Sciences  founder  Jonathan Rothberg  presented  James D. Watson  with a digital copy of his personal genome sequence on a portable hard drive. Rothberg estimated the cost of the sequence—the first personal genome produced using a  next-generation sequencing  platform—at $1 million. [ 12 ]  Watson's genome sequence was published in 2008. [ 13 ]


A number of scientists have highlighted the cost of additional analysis after performing sequencing.  Bruce Korf , past president of the  American College of Medical Genetics , described "the $1-million interpretation." [ 14 ] [ failed verification ]  Washington University's  Elaine Mardis  prefers "the $100,000 analysis." [ 15 ]


At the end of 2007, the biotech company Knome debuted the first direct-to-consumer genome sequencing service at an initial price of $350,000 (including analysis). One of the first clients was Dan Stoicescu, a Swiss-based biotech entrepreneur. [ 16 ]  As the costs of sequencing continued to plummet, in 2008, Illumina announced that it had sequenced an individual genome for $100,000 in reagent costs. Applied Biosystems countered by saying the cost on its platform was $60,000. [ 17 ]   Pacific Biosciences  became the latest entrant in what  The New York Times  called "a heated race for the '$1,000 genome'". [ 18 ] [ 19 ]  In 2009,  Stanford University  professor  Stephen Quake  published a paper sequencing his own genome on an instrument built by  Helicos Biosciences  (a company he co-founded) for a reported cost in consumables of $48,000. [ 20 ]  That same year,  Complete Genomics  debuted its proprietary  whole-genome sequencing  service for researchers, charging as little as $5,000/genome for bulk orders. [ 21 ]


In 2010,  Illumina  introduced its individual genome sequencing service for consumers, who were required to present a doctor's note. The initial price was $50,000/person. One of the first clients was former  Solexa  CEO John West, who had his entire family of four sequenced. [ 22 ]  In January 2012,  Life Technologies  unveiled a new sequencing instrument, the Ion Proton Sequencer, which it said would achieve the $1,000 genome in a day within 12 months. [ 23 ]   Sharon Begley  wrote: "After years of predictions that the '$1,000 genome' – a read-out of a person's complete genetic information for about the cost of a dental crown—was just around the corner, a U.S. company is announcing... that it has achieved that milestone." [ 24 ]


In January 2014,  Illumina  launched its HiSeq X Ten Sequencer, claiming to have produced the first $1,000 genome at 30× coverage. Some researchers hailed the HiSeq X Ten's release as a milestone – Michael Schatz of  Cold Spring Harbor Laboratory  said that "it is a major human accomplishment on par with the development of the telescope or the microprocessor". However, critics pointed out that the $10 million upfront investment required to purchase the system would deter customers. Furthermore, the $1,000 genome cost calculation left out overheads, such as the cost of powering the machine. [ 25 ]  In September 2015,  Veritas Genetics  (co-founded by  George Church ) announced $1,000 full-genome sequencing including interpretation for participants in the  Personal Genome Project . [ 26 ]


In April 2017, the newly formed European company Dante Labs started offering the WGS for $900. [ 27 ] [ 28 ]  In 2017,  Beijing Genomics Institute  began offering WGS for $600. [ 29 ]  In July 2018, on Amazon Prime Day, Dante Labs offered it for $349. [ 30 ]  In November 2018, around the time of  Black Friday , Dante Labs offered WGS for the first time less than $200, [ 31 ]  and  Veritas Genetics  for two days for the same price of $199 offered WGS limited to a thousand customers. [ 32 ]  In March of the same year, geneticist Matthew Hurles of  Wellcome Sanger Institute  noted that the private companies, including Illumina, [ 33 ]  are currently competing to reach a new target for WGS of only $100. [ 34 ]  On 18 February 2020,  Nebula Genomics  announced that has partnered up with BGI Group to start offering 30x WGS for $299. [ 35 ]


It was originally announced that the revamped Archon Genomics X PRIZE presented by Medco would hold a $10-million grand prize competition in January 2013 for the team that reaches (or comes closest to reaching) the $1,000 genome. The grand prize would go to "the team(s) able to sequence 100 human genomes within 30 days to an accuracy of 1 error per 1,000,000 bases, with 98% completeness, identification of insertions, deletions and rearrangements, and a complete haplotype, at an audited total cost of $1,000 per genome." [ 36 ]  In August 2013, the Archon Genomics X PRIZE was cancelled, as the founders felt it had been "Outpaced by Innovation," and "was not incentivizing the technological changes". [ 37 ]


Activity-based proteomics , or  activity-based protein profiling  ( ABPP ) is a functional  proteomic  technology that uses chemical probes that react with mechanistically related classes of enzymes. [ 1 ]


The basic unit of ABPP is the probe, which typically consists of two elements:  a reactive group (RG, sometimes called a "warhead") and a tag.  Additionally, some probes may contain a binding group which enhances selectivity.  The reactive group usually contains a specially designed  electrophile  that becomes  covalently -linked to a  nucleophilic  residue in the  active site  of an active  enzyme .  An enzyme that is   inhibited  or   post-translationally modified  will not react with an activity-based probe.  The tag may be either a reporter such as a  fluorophore  or an affinity label such as  biotin  or an  alkyne  or  azide  for use with the  Huisgen 1,3-dipolar cycloaddition  (also known as  click chemistry ). [ 2 ]


A major advantage of ABPP is the ability to monitor the availability of the enzyme active site directly, rather than being limited to protein or mRNA abundance.  With classes of enzymes such as the  serine hydrolases [ 3 ]  and  metalloproteases [ 4 ]  that often interact with endogenous inhibitors or that exist as inactive  zymogens , this technique offers a valuable advantage over traditional techniques that rely on abundance rather than activity. Furthermore, ABPP could be used to target specific proteins which were previously viewed as undruggable targets. [ 5 ]


In recent years ABPP has been combined with  tandem mass spectrometry  enabling the identification of hundreds of active enzymes from a single sample.  This technique, known as  ABPP-MudPIT  (multidimensional protein identification technology) is especially useful for profiling inhibitor selectivity as the potency of an inhibitor can be tested against hundreds of targets simultaneously. [ citation needed ]


ABPP were first reported in the 1990s in the study of proteases. [ 6 ] [ 7 ]


Adenovirus   genomes  are linear, non-segmented double-stranded (ds)  DNA  molecules that are typically 26-46 Kbp long, containing 23-46  protein-coding   genes . [ 1 ]  The example used for the following description is Human adenovirus E, a  mastadenovirus  with a 36 Kbp genome containing 38 protein-coding genes. [ 2 ]  While the precise number and identity of genes varies among adenoviruses, the basic principles of genome organization and the functions of most of the genes described in this article are shared among all adenoviruses.


The 38 genes in the Human  adenovirus  E genome are organized in 17  transcription  units, each containing 1-8 coding sequences. [ 3 ]  Alternative splicing during processing of the pre-mRNAs produced by each transcription unit enable multiple different  mRNAs  to be produced from one transcription unit. [ citation needed ]


The E1A, E1B, E2A, E2B, E3, and E4 transcription units are successively transcribed early in the  viral reproductive cycle . The proteins coded for by genes within these transcription units are mostly involved in regulation of viral transcription, in  replication  of viral DNA, and in suppression of the host response to infection. [ 4 ]


The L1-L5 transcription units are transcribed later in the viral reproductive cycle, and code mostly for proteins that make up components of the viral capsid or are involved in assembly of the capsid. The L1-L5 transcription units are all regulated by the same  promoter  region and share the same transcription start site. As a result, transcription of all five late transcription units begins at the same point in the viral reproductive cycle. [ 5 ]


Transcription of  pre-mRNAs  beginning at the late promoter is randomly terminated at one of five termination sites, producing a population of transcripts of five different lengths. The pre-mRNAs of any given length are then  alternatively spliced  to produce 1-4 different mRNAs coding for a corresponding number of proteins. [ citation needed ]


The names, locations, and properties of the 38 protein-coding genes in the Human Adenovirus E genome are given in the following table. [ 6 ] [ 7 ]


The functions of many adenovirus proteins are known: [ 5 ]


The  African BioGenome Project , or  AfricaBP , is an international effort to sequence the genomes of all  animals , all  plants , all  fungi , and all  protists  (and so, collectively, all  eukaryotes ) that are  native  to  Africa [ 1 ]  at an estimated cost of $1 billion  U.S. dollars . [ 2 ]  The project prioritizes doing its sequencing work and data storage within the African continent. [ 3 ]


The project was originally started by a group of scientists including  ThankGod Echezona Ebenezer , and has partnered with other major sequencing efforts such as the  Vertebrate Genomes Project  and  the 10,000 Plant Genomes Project . [ 4 ]


The  Allen Mouse and Human Brain Atlases  are projects within the  Allen Institute for Brain Science  which seek to combine  genomics  with  neuroanatomy  by creating  gene expression  maps for the  mouse  and  human brain . They were initiated in September 2003 with a $100 million donation from  Paul G. Allen  and the first atlas went public in September 2006. [ 1 ]
As of May 2012 [update] , seven  brain atlases  have been published: Mouse Brain Atlas, Human Brain Atlas, Developing Mouse Brain Atlas, Developing Human Brain Atlas, Mouse Connectivity Atlas, Non-Human Primate Atlas, and Mouse Spinal Cord Atlas. [ citation needed ]  There are also three related projects with data banks:  Glioblastoma , Mouse Diversity, and Sleep. [ citation needed ]  It is the hope of the Allen Institute that their findings will help advance various fields of science, especially those surrounding the understanding of neurobiological diseases. The atlases are free and available for public use online.


In 2001, Paul Allen gathered a group of scientists, including  James Watson  and  Steven Pinker , to discuss the future of neuroscience and what could be done to enhance neuroscience research (Jones 2009). During these meetings David Anderson from the  California Institute of Technology  proposed the idea that a three-dimensional atlas of gene expression in the mouse brain would be of great use to the neuroscience community. The project was set in motion in 2003 with a 100 million dollar donation by Allen through the  Allen Institute for Brain Science .
The project used a technique for mapping gene expression developed by Gregor Eichele and colleagues at the  Max Planck  Institute for Biophysical Chemistry in Goettingen, Germany. The technique uses colorimetric  in situ hybridization  to map gene expression. The project set a 3-year goal of finishing the project and making it available to the public.


An initial release of the first atlas, the mouse brain atlas, occurred in December 2004. Subsequently, more data for this atlas was released in stages. The final genome-wide data set was released in September 2006. However, the final release of the atlas was not the end of the project; the Atlas is still being improved upon. Also, other projects including the human brain atlas, developing mouse brain, developing human brain, mouse connectivity, non-human primate atlas, and the mouse spinal cord atlas are being developed through the Allen Institute for Brain Science in conjunction with the Allen Mouse Brain Atlas.