The overarching goal and motto for all Allen Institute projects is "fueling discovery". The project strives to fulfill this goal and advance science in a few ways. First, they create brain atlases to better understand the connections between genes and brain functioning. They aim to advance the research and knowledge about neurobiological conditions such as  Parkinson's ,  Alzheimer's , and  Autism  with their mapping of gene expression throughout the brain.
The Brain Atlas projects also follow the "Allen Institute" motto with their open release of data and findings. This policy is also related to another goal of the Institute: collaborative and multidisciplinary research. Thus, any scientist from any discipline is able to look at the findings and take them into account while designing their own experiments. Also available to the public is the Brain Explorer application.


The  Allen Institute for Brain Science  uses a project-based philosophy for their research. Each brain atlas focuses on its own project, made up of its own team of researchers. To complete an atlas, each research team collects and synthesizes brain scans, medical data, genetic information and psychological data. With this information, they are able to construct the 3-D biochemical architecture of the brain and figure out which proteins are expressed in certain parts of the brain. To gather the needed data, scientists at the Allen Institute use various techniques. One technique involves the use of postmortem brains and brain scanning technology to discover where in the brain genes are turned on and off. Another technique, called  in situ hybridization , or ISH, is used to view gene expression patterns as in situ hybridization images.


Within the Brain Atlases, these 3-D ISH digital images and graphs reveal, in color, the regions where a given gene is expressed. In the Brain Explorer, any gene can be searched for and selected resulting in the in situ image appearing as an easily manipulated and explored fashion. Part of the creation of this anatomy-centred database of gene expression, includes aligning ISH data for each gene with a three-dimensional coordinate space through registration with a reference atlas created for the project. [ 2 ]


The different types of cells in the central nervous system originate from varying  gene expression . A map of gene expression in the brain allows researchers to correlate forms and functions. The Allen Brain Atlas lets researchers view the areas of differing expression in the brain which enables the viewing of neural connections throughout the brain. Viewing these pathways through differing gene expression as well as functional imaging techniques permits researchers to correlate between gene expression, cell types, and pathway function in relation to behaviors or phenotypes.


Even though the majority of research has been done in mice, 90% of genes in mice have a counterpart in humans. This makes the Atlas particularly useful for modeling neurological diseases. The gene expression patterns in normal individuals provide a standard for comparing and understanding altered phenotypes. Extending information learned from mouse diseases will help better the understanding of human neurological disorders. The atlas can show which genes and particular areas are effected in neurological disorders; the action of a gene in a disease can be evaluated in conjunction with general expression patterns and this data could shed light on the role of the particular gene in the disorder.


The Allen Brain Atlas website contains a downloadable 3-D interactive Brain explorer. The explorer is essentially a search engine for locations of gene expression; this is particularly useful in finding regions that express similar genes. Users can delineate networks and pathways using this application by connecting regions that co-express a certain gene. The explorer uses a multicolor scale and contains multiple planes of the brain that let viewers see differences in density and expression level. The images are a composite of many averaged samples so it is useful when comparing to individuals with abnormally low gene expression.


The Allen Mouse Brain Atlas is a comprehensive genome-wide map of the adult mouse brain that reveals where each gene is expressed. [ 3 ]  The mouse brain atlas was the original project of the Allen Brain Atlas and was finished in 2006. The purpose of the atlas is to aid in the development of neuroscience research. The hope of the project is that it will allow scientists to gain a better understanding of brain diseases and disorders such as  autism  and  depression . [ 1 ]


The Allen Human Brain Atlas was made public in May 2010. It was the first anatomically and genomically comprehensive three-dimensional human brain map. [ 4 ]  The atlas was created to enhance research in many neuroscience research fields including neuropharmacology, human brain imaging, human genetics, neuroanatomy, genomics and more. The atlas is also geared toward furthering research into mental health disorders and brain injuries such as  Alzheimer's disease , autism,  schizophrenia  and  drug addiction .


The Allen Developing Mouse Brain Atlas is an atlas which tracks gene expression throughout the development of a C57BL/6 mouse brain. The project began in 2008 and is currently ongoing. The atlas is based on  magnetic resonance imaging  (MRI). It traces the growth,  white matter , connectivity, and development of the  C57BL/6  mouse brain from embryonic day 12 to postnatal day 80. [ 5 ]
This atlas enhances the ability of neuroscientists to study how pollutants and  genetic mutations  effect the development of the brain. Thus, the atlas may be used to determine what  toxins  pose special threats to children and pregnant mothers.


The Allen Mouse Brain Connectivity Atlas was launched in November 2011. [ 6 ]  Unlike other atlases from the Allen Institute, this atlas focuses on the identification of neural circuitry that govern behavior and brain function. This neural circuitry is responsible for functions like behavior and perception. This map will allow scientists to further understand how the brain works and what causes brain diseases and disorders, such as  Parkinson's disease  and depression. [ 7 ]


Unveiled in July 2008, the Allen Mouse Spinal Cord Atlas was the first genome-wide map of the mouse spinal cord ever constructed. The spinal cord atlas is a map of genome wide gene expression in the spinal cord of adult and juvenile C57 black mice. The initial unveiling included data for 2,000 genes and an anatomical reference section. A plan for the future includes expanding the amount of data to about 20,000 genes spanning the full length of the spinal cord.


The aim of the spinal cord atlas is to enhance research in the treatment of spinal cord injury, diseases, and disorders such as Lou Gehrig's diseases and spinal muscular atrophy. The project was funded by an array of donors including the Allen Research Institute,  Paralyzed Veterans of America  Research Foundation, the  ALS Association ,  Wyeth Research , PEMCO Insurance,  National Multiple Sclerosis Society , International Spinal Research Trust, and many other organizations, foundations, corporate and private donors. [ 8 ]


Alloenzymes  (or also called  allozymes ) are variant forms of an  enzyme  which differ structurally but not functionally from other allozymes coded for by different  alleles  at the same  locus . These are opposed to  isozymes , which are enzymes that perform the same function, but which are coded by genes located at different loci. [ 1 ]


Alloenzymes are common biological  enzymes  that exhibit high levels of functional  evolutionary conservation  throughout specific  phyla  and  kingdoms . They are used by  phylogeneticists  as molecular markers to gauge evolutionary histories and relationships between different species. This can be done because allozymes do not have the same structure. They can be separated by  capillary electrophoresis . However, some species are monomorphic for many of their allozymes which would make it difficult for phylogeneticists to assess the evolutionary histories of these species. [ 2 ]  In these instances, phylogeneticists would have to use another method to determine the evolutionary history of a species.


These enzymes generally perform very basic functions found commonly throughout all lifeforms, such as  DNA polymerase , the enzyme that repairs and copies  DNA . Significant changes in this enzyme reflect significant events in evolutionary history of organisms. As expected DNA polymerase shows relatively small differences in its  amino acid  sequence between phyla and even kingdoms.


The key to choosing which alloenzyme to use in a comparison between multiple species is to choose one that is as variable as possible while still being present in all the organisms. By comparing the amino acid sequence of the enzyme in the species, more amino acid similarities should be seen in species that are more closely related, and fewer between those that are more distantly related. The less well conserved the enzyme is, the more amino acid differences will be present in even closely related species. [ 3 ]


Barbara Ambrose  is a  botanist  working in the field of Plant Evolutionary Developmental Biology ( Evo-Devo ). As the Director of Laboratory Research at the  New York Botanical Garden , Ambrose is a prolific scholar and leader and mentor in her field who is interested in patterns in plant diversity on macro and micro scales. [ 4 ]


Ambrose earned a Ph.D. in  Biology  from the  University of California, San Diego  in June 2000, starting her career as a  developmental geneticist  working on  maize  ( Zea mays )  flowers . She also holds a B.Sc. in  Biology  from  University of the Virgin Islands , St. Thomas which she earned in 1994. [ 4 ]


One of Ambrose's Ph.D. articles  Molecular and genetic analyses of the Silky1 gene reveal conservation in floral organ specification between eudicots and monocots , [ 5 ]  published in  Molecular Cell  was one of the first Plant  Evo-Devo  papers, and has been cited nearly 500 times, as per  Google Scholar  metrics. [ 6 ] [ better source needed ]


Ambrose began her career first as a  National Science Foundation  International Postdoctoral fellow (2000-2002) and then as a Postdoctoral Fellow (2002–2003) at the  National Autonomous University of Mexico  (UNAM) in  Mexico . During this time, her work was also focused on floral  evo-devo  in  Lacandonia schismatica , a  monocot  with inside-out  flowers . [ 4 ]


Ambrose was next a Lecturer in the Institute of Molecular BioScience (IMBS) at  Massey University  in  New Zealand  from October 2003 until August 2008, [ 4 ]  where here research focused on the control of plant development at the molecular level, including  Evo-Devo  research related to  fruit . At Massey, Ambrose was involved in the planning of the Manawatu Microscopy & Imaging Centre (MMIC). She also developed a course in  Plant Development  and worked on a project with Plant Biology and Massey gardening staff to label plants in the living collection, making this collection more accessible. [ 3 ]


Since 2008, Ambrose has been an  adjunct professor  for the  Graduate Center ,  City University of New York  (CUNY) [ 4 ]  in the Plant Science subprogram where she has taught courses on  Plant Development  and Comparative  Morphology  of  Vascular Plants . [ 7 ]


Ambrose has worked at the  New York Botanical Garden  since 2008. Hired as an Assistant Curator in the Plant Genomics department in 2008, she was promoted to Associate Curator in 2014. In 2017, Ambrose was named Director of Laboratory Research. [ 4 ]  Ambrose's research interest is in  Plant Evolutionary Developmental Genomics with research foci in  Evolution  as it relates to  Molecular Biology  and  Genomics  and  Biochemistry  as they relate to  Gene Expression / Gene Regulation  and  Genetics /Development. [ 7 ]  Her  Evo-Devo  research has continued to expand into work with  leaves  and  seeds , using the latest scientific techniques to understand the evolution of morphological novelties. Recent projects also include the use of  machine learning  to identify  herbarium  specimens through  kaggle  competitions, notably in 2019 and 2020. [ 4 ]


In addition to her academic work, Ambrose is very active in scientific outreach activities in  New York City , through  CUNY ,  NYBG , and other local organizations including  Cooper Hewitt . [ 4 ] [ 8 ]


The  Archon Genomics X PRIZE presented by Express Scripts  for  Genomics , the second  X Prize  offered by the  X Prize Foundation , based in  Playa Vista ,  California , was announced on October 4, 2006 stating that the prize of "$10 million will be awarded to the first team to rapidly, accurately and economically sequence 100 whole human genomes to an unprecedented level of accuracy." [ 2 ]  The 30 day evaluation phase of the competition to begin on September 5, 2013, was canceled August 22, 2013 and this cancellation was debated on March 27, 2014. [ 3 ]


In November 2011 the prize goals were stated as: "The $10 million grand prize will be awarded 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." [ 1 ]


The $10 million was donated by  Canadian   geologist  and philanthropist  Stewart Blusson , who co-discovered the  Ekati Diamond Mine . The name "Archon" is the name of Blusson's company, which refers to the type of  lithosphere  beneath northern Canada. Upon cancellation, the money was returned to the Blussons because no Master Team Agreements were in place.


The Archon X Prize in genomics began as a joint effort of the X Prize Foundation and the J. Craig Venter Science Foundation. [ 1 ] [ 4 ] [ 5 ]   The J. Craig Venter Science Foundation offered the $500,000 (US) Innovation in Genomics Science and Technology Prize in September 2003 aimed at stimulating development of less expensive and faster sequencing technology. To attract even more resources to this goal, Dr. Venter joined forces with the X Prize Foundation, wrapping his competition and prize purse into a later incarnation, The Archon Genomics X Prize presented by Express Scripts.


The 100 human genomes to be sequenced in this competition were donated by 100 centenarians (ages 100 or older) from all over the world, known as the 100 Over 100. Sequencing the genomes of the 100 Over 100 presented an unprecedented opportunity to identify those "rare genes" that protect against diseases, while giving researchers valuable clues to health and longevity. These centenarians’ genes would provide us with a window to the past, significantly impacting the future of healthcare. Although the contest is cancelled, the X PRIZE foundation collected blood samples and created cell-lines to preserve the DNA from more than 100 centenarians. Those genomes are expected to be sequenced nonetheless and put into an open data forum.


The only two pre-registered teams were  Ion Torrent  in Guilford, CT and  Wyss Institute for Biologically Inspired Engineering  in Boston, MA.  These teams were announced 23-Jul-2012 and 3-Oct-2012. [ 3 ] [ 6 ]


The result was going to be the world's first "medical grade” genome, a critically needed clinical standard that would transform genomic research into usable medical information to improve patient diagnosis and treatment. This global competition was expected to inspire breakthrough genome sequencing innovations and technologies that would usher in a new era of personalized medicine.


The evaluation phase of the competition was officially set to begin on September 5, 2013, and conclude 30 days later on October 5, but was canceled August 22, 2013 because it was "outpaced by innovation". [ 7 ]  A public debate concerning the validity and potential implications of the cancellation was published March 27, 2014. [ 3 ]


Bacterial genomes  are generally smaller and less variant in size among species when compared with  genomes  of  eukaryotes . Bacterial genomes can range in size anywhere from about 130  kbp [ 1 ] [ 2 ]  to over 14  Mbp . [ 3 ]  A study that included, but was not limited to, 478 bacterial genomes, concluded that as genome size increases, the number of genes increases at a disproportionately slower rate in eukaryotes than in non-eukaryotes. Thus, the proportion of  non-coding DNA  goes up with genome size more quickly in non-bacteria than in  bacteria . This is consistent with the fact that most eukaryotic nuclear DNA is non-gene coding, while the majority of prokaryotic, viral, and organellar genes are coding. [ 4 ]
Right now, we have genome sequences from 50 different bacterial phyla and 11 different archaeal phyla. Second-generation sequencing has yielded many draft genomes (close to 90% of bacterial genomes in GenBank are currently not complete);  third-generation sequencing  might eventually yield a complete genome in a few hours. The genome sequences reveal much diversity in bacteria. Analysis of over 2000  Escherichia coli  genomes reveals an  E. coli  core genome of about 3100 gene families and a total of about 89,000 different gene families. [ 5 ]  Genome sequences show that parasitic bacteria have 500–1200 genes, free-living bacteria have 1500–7500 genes, and archaea have 1500–2700 genes. [ 6 ]  A striking discovery by Cole et al. described massive amounts of gene decay when comparing  Leprosy  bacillus to ancestral bacteria. [ 7 ]  Studies have since shown that several bacteria have smaller genome sizes than their ancestors did. [ 8 ]  Over the years, researchers have proposed several theories to explain the general trend of bacterial genome decay and the relatively small size of bacterial genomes. Compelling evidence indicates that the apparent degradation of bacterial genomes is owed to a deletional bias.


As of 2014, there are over 30,000 sequenced bacterial genomes publicly available and thousands of  metagenome  projects. Projects such as the Genomic Encyclopedia of Bacteria and Archaea (GEBA) intend to add more genomes. [ 5 ]


The single gene comparison is now being supplanted by more general methods. These methods have resulted in novel perspectives on genetic relationships that previously have only been estimated. [ 5 ]


A significant achievement in the second decade of bacterial  genome sequencing  was the production of metagenomic data, which covers all DNA present in a sample. Previously, there were only two metagenomic projects published. [ 5 ]


Bacteria possess a compact genome architecture distinct from  eukaryotes  in two important ways: bacteria show a strong correlation between genome size and number of functional genes in a genome, and those genes are structured into  operons . [ 9 ] [ 10 ]  The main reason for the relative density of bacterial genomes compared to eukaryotic genomes (especially multicellular eukaryotes) is the presence of  noncoding DNA  in the form of  intergenic regions  and  introns . [ 10 ]   Some notable exceptions include recently formed pathogenic bacteria. This was initially described in a study by Cole  et al . in which  Mycobacterium leprae  was discovered to have a significantly higher percentage of  pseudogenes  to functional genes (~40%) than its free-living ancestors. [ 7 ]


Furthermore, amongst species of bacteria, there is relatively little variation in genome size when compared with the genome sizes of other major groups of life. [ 6 ]  Genome size is of little relevance when considering the number of functional genes in eukaryotic species. In bacteria, however, the strong correlation between the number of genes and the genome size makes the size of bacterial genomes an interesting topic for research and discussion. [ 11 ]


The general trends of bacterial evolution indicate that bacteria started as free-living organisms. Evolutionary paths led some bacteria to become  pathogens  and  symbionts . The lifestyles of bacteria play an integral role in their respective genome sizes. Free-living bacteria have the largest genomes out of the three types of bacteria; however, they have fewer pseudogenes than bacteria that have recently acquired  pathogenicity .


Facultative  and recently evolved pathogenic bacteria exhibit a smaller genome size than free-living bacteria, yet they have more pseudogenes than any other form of bacteria.


Obligate  bacterial symbionts or pathogens have the smallest genomes and the fewest pseudogenes of the three groups. [ 12 ]  The relationship between life-styles of bacteria and  genome size  raises questions as to the mechanisms of bacterial genome evolution. Researchers have developed several theories to explain the patterns of genome size evolution amongst bacteria.


As single-gene comparisons have largely given way to genome comparisons, phylogeny of bacterial genomes have improved in accuracy. The Average Nucleotide Identity (ANI) method quantifies genetic distance between entire genomes by taking advantage of regions of about 10,000 bp. With enough data from genomes of one genus, algorithms are executed to categorize species. This has been done for the  Pseudomonas avellanae  species in 2013 [ 5 ]  and for all sequenced bacteria and archaea since 2020. [ 13 ]  Observed ANI values among sequences appear to have an "ANI gap" at 85–95%, suggesting that a genetic boundary suitable for defining a species concept is present. [ 14 ]


To extract information about bacterial genomes, core- and  pan-genome  sizes have been assessed for several strains of bacteria. In 2012, the number of core gene families was about 3000. However, by 2015, with an over tenfold increased in available genomes, the pan-genome has increased as well. There is roughly a positive correlation between the number of genomes added and the growth of the pan-genome. On the other hand, the core genome has remain static since 2012. Currently, the  E. coli  pan-genome is composed of about 90,000 gene families. About one-third of these exist only in a single genome. Many of these, however, are merely gene fragments and the result of calling errors. Still, there are probably over 60,000 unique gene families in  E. coli . [ 5 ]


Bacteria lose a large amount of genes as they transition from free-living or facultatively parasitic life cycles to permanent host-dependent life. Towards the lower end of the scale of bacterial genome size are the mycoplasmas and related bacteria. Early molecular phylogenetic studies revealed that mycoplasmas represented an evolutionary derived state, contrary to prior hypotheses. Furthermore, it is now known that mycoplasmas are just one instance of many of genome shrinkage in obligately host-associated bacteria. Other examples are  Rickettsia ,  Buchnera aphidicola , and  Borrelia burgdorferi . [ 15 ]


Small genome size in such species is associated with certain particularities, such as rapid evolution of polypeptide sequences and low GC content in the genome. The convergent evolution of these qualities in unrelated bacteria suggests that an obligate association with a host promotes genome reduction. [ 15 ]


Given that over 80% of almost all of the fully sequenced bacterial genomes consist of intact ORFs, and that gene length is nearly constant at ~1 kb per gene, it is inferred that small genomes have few metabolic capabilities. While free-living bacteria, such as  E. coli ,  Salmonella  species, or  Bacillus  species, usually have 1500 to 6000 proteins encoded in their DNA, obligately pathogenic bacteria often have as few as 500 to 1000 such proteins. [ 15 ]


One candidate explanation is that reduced genomes maintain genes that are necessary for vital processes pertaining to  cellular growth  and  replication , in addition to those genes that are required to survive in the bacteria's  ecological niche . However, sequence data contradicts this hypothesis. The set of universal orthologs amongst eubacteria comprises only 15% of each genome. Thus, each lineage has taken a different evolutionary path to reduced size. Because universal cellular processes require over 80 genes, variation in genes imply that the same functions can be achieved by exploitation of nonhomologous genes. [ 15 ]


Host-dependent bacteria are able to secure many compounds required for  metabolism  from the host's  cytoplasm  or tissue. They can, in turn, discard their own biosynthetic pathways and associated genes. This removal explains many of the specific gene losses. For example, the  Rickettsia  species, which relies on specific energy substrate from its host, has lost many of its native energy metabolism genes. Similarly, most small genomes have lost their amino acid  biosynthesizing  genes, as these are found in the host instead. One exception is the  Buchnera , an obligate maternally transmitted symbiont of aphids. It retains 54 genes for biosynthesis of crucial amino acids, but no longer has pathways for those amino acids that the host can synthesize. Pathways for nucleotide biosynthesis are gone from many reduced genomes. Those anabolic pathways that evolved through niche adaptation remain in particular genomes. [ 15 ]


The hypothesis that unused genes are eventually removed does not explain why many of the removed genes would indeed remain helpful in obligate pathogens. For example, many eliminated genes code for products that are involved in universal cellular processes, including replication,  transcription , and  translation . Even genes supporting  DNA recombination  and repair are deleted from every small genome. However some genes, such as those encoding the  RecA  protein, were found to be nearly ubiquitous, indicating that a large majority of bacterial genomes are probably capable of  homologous recombination . [ 16 ]   In addition, small genomes have fewer  tRNAs , utilizing one for several amino acids. So, a single  codon  pairs with multiple codons, which likely yields less-than-optimal translation machinery. It is unknown why obligate intracellular pathogens would benefit by retaining fewer tRNAs and fewer DNA repair enzymes. [ 15 ]


Another factor to consider is the change in population that corresponds to an evolution towards an obligately pathogenic life. Such a shift in lifestyle often results in a reduction in the genetic population size of a lineage, since there is a finite number of hosts to occupy. This genetic drift may result in fixation of mutations that inactivate otherwise beneficial genes, or otherwise may decrease the efficiency of gene products. Hence, not will only useless genes be lost (as mutations disrupt them once the bacteria has settled into host dependency), but also beneficial genes may be lost if genetic drift enforces ineffective  purifying selection . [ 15 ]


The number of universally maintained genes is small and inadequate for independent cellular growth and replication, so that small genome species must achieve such feats by means of varying genes. This is done partly through nonorthologous gene displacement. That is, the role of one gene is replaced by another gene that achieves the same function. Redundancy within the ancestral, larger genome is eliminated. The descendant small genome content depends on the content of chromosomal deletions that occur in the early stages of genome reduction. [ 15 ]


The very small genome of  M. genitalium  possesses dispensable genes. In a study in which single genes of this organism were inactivated using  transposon-mediated mutagenesis , at least 129 of its 484 ORGs were not required for growth. A much smaller genome than that of the  M. genitalium  is therefore feasible. [ 15 ]


One theory predicts that bacteria have smaller genomes due to a selective pressure on genome size to ensure faster replication. The theory is based upon the logical premise that smaller bacterial genomes will take less time to replicate. Subsequently, smaller genomes will be selected preferentially due to enhanced fitness. A study done by Mira et al. indicated little to no correlation between genome size and  doubling time . [ 17 ]  The data indicates that selection is not a suitable explanation for the small sizes of bacterial genomes. Still, many researchers believe there is some  selective pressure  on bacteria to maintain small  genome size .


Selection  is but one process involved in evolution. Two other major processes ( mutation  and  genetic drift ) can account for the genome sizes of various types of bacteria. A study done by Mira et al. examined the size of insertions and deletions in bacterial pseudogenes. Results indicated that mutational deletions tend to be larger than insertions in bacteria in the absence of  gene transfer  or  gene duplication . [ 17 ]  Insertions caused by horizontal or lateral  gene transfer  and  gene duplication  tend to involve transfer of large amounts of genetic material. Assuming a lack of these processes, genomes will tend to reduce in size in the absence of selective constraint. Evidence of a deletional bias is present in the respective genome sizes of free-living bacteria,  facultative  and recently derived  parasites  and obligate parasites and  symbionts .


Free-living bacteria tend to have large population-sizes and are subject to more opportunity for gene transfer. As such, selection can effectively operate on free-living bacteria to remove deleterious sequences resulting in a relatively small number of  pseudogenes . Continually, further selective pressure is evident as free-living bacteria must produce all gene-products independent of a host. Given that there is sufficient opportunity for gene transfer to occur and there are selective pressures against even slightly deleterious deletions, it is intuitive that free-living bacteria should have the largest bacterial genomes of all bacteria types.


Recently-formed parasites undergo severe bottlenecks and can rely on host environments to provide gene products. As such, in recently-formed and facultative parasites, there is an accumulation of pseudogenes and  transposable elements  due to a lack of selective pressure against deletions. The population bottlenecks reduce gene transfer and as such, deletional bias ensures the reduction of genome size in parasitic bacteria.


Obligatory parasites and symbionts have the smallest genome sizes due to prolonged effects of deletional bias. Parasites which have evolved to occupy specific niches are not exposed to much selective pressure. As such, genetic drift dominates the evolution of niche-specific bacteria. Extended exposure to deletional bias ensures the removal of most superfluous sequences. Symbionts occur in drastically lower numbers and undergo the most severe bottlenecks of any bacterial type. There is almost no opportunity for gene transfer for endosymbiotic bacteria, and thus genome compaction can be extreme. One of the smallest bacterial genomes ever to be sequenced is that of the  endosymbiont   Carsonella rudii . [ 18 ]
At 160 kbp, the genome of  Carsonella  is one of the most streamlined examples of a genome examined to date.


Molecular phylogenetics  has revealed that every clade of bacteria with genome sizes under 2 Mb was derived from ancestors with much larger genomes, thus refuting the hypothesis that bacteria evolved by the successive doubling of small-genomed ancestors. [ 19 ]
Recent studies performed by Nilsson et al. examined the rates of bacterial genome reduction of obligate bacteria. Bacteria were cultured introducing frequent bottlenecks and growing cells in serial passage to reduce gene transfer so as to mimic conditions of endosymbiotic bacteria. The data predicted that bacteria exhibiting a one-day generation time lose as many as 1,000 kbp in as few as 50,000 years (a relatively short evolutionary time period). Furthermore, after deleting genes essential to the methyl-directed  DNA mismatch repair  (MMR) system, it was shown that bacterial genome size reduction increased in rate by as much as 50 times. [ 20 ]  These results indicate that genome size reduction can occur relatively rapidly, and loss of certain genes can speed up the process of bacterial genome compaction.


This is not to suggest that all bacterial genomes are reducing in size and complexity. While many types of bacteria have reduced in genome size from an ancestral state, there are still a huge number of bacteria that maintained or increased genome size over ancestral states. [ 8 ]  Free-living bacteria experience huge population sizes, fast generation times and a relatively high potential for gene transfer. While deletional bias tends to remove unnecessary sequences, selection can operate significantly amongst free-living bacteria resulting in evolution of new genes and processes.


Unlike eukaryotes, which evolve mainly through the modification of existing genetic information, bacteria have acquired a large percentage of their genetic diversity by the  horizontal transfer of genes . This creates quite dynamic genomes, in which DNA can be introduced into and removed from the chromosome. [ 21 ]


Bacteria have more variation in their metabolic properties, cellular structures, and lifestyles than can be accounted for by point mutations alone. For example, none of the phenotypic traits that distinguish  E. coli  from  Salmonella enterica  can be attributed to point mutation. On the contrary, evidence suggests that horizontal gene transfer has bolstered the diversification and speciation of many bacteria. [ 21 ]


Horizontal gene transfer is often detected via DNA sequence information. DNA segments obtained by this mechanism often reveal a narrow phylogenetic distribution between related species. Furthermore, these regions sometimes display an unexpected level of similarity to genes from taxa that are assumed to be quite divergent. [ 21 ]


Although gene comparisons and phylogenetic studies are helpful in investigating horizontal gene transfer, the DNA sequences of genes are even more revelatory of their origin and ancestry within a genome. Bacterial species differ widely in overall GC content, although the genes in any one species' genome are roughly identical with respect to base composition, patterns of codon usage, and frequencies of di- and trinucleotides. As a result, sequences that are newly acquired through lateral transfer can be identified via their characteristics, which remains that of the donor. For example, many of the  S. enterica  genes that are not present in  E. coli  have base compositions that differ from the overall 52% GC content of the entire chromosome. Within this species, some lineages have more than a megabase of DNA that is not present in other lineages. The base compositions of these lineage-specific sequences imply that at least half of these sequences were captured through lateral transfer. Furthermore, the regions adjacent to horizontally obtained genes often have remnants of translocatable elements, transfer origins of  plasmids , or known attachment sites of phage  integrases . [ 21 ]


In some species, a large proportion of laterally transferred genes originate from plasmid-,  phage -, or  transposon -related sequences. [ 21 ]


Although sequence-based methods reveal the prevalence of horizontal gene transfer in bacteria, the results tend to be underestimates of the magnitude of this mechanism, since sequences obtained from donors whose sequence characteristics are similar to those of the recipient will avoid detection. [ 21 ]


Comparisons of completely sequenced genomes confirm that bacterial chromosomes are amalgams of ancestral and laterally acquired sequences. The hyperthermophilic Eubacteria  Aquifex aeolicus  and  Thermotoga maritima  each has many genes that are similar in protein sequence to homologues in thermophilic Archaea. 24% of  Thermotoga's  1,877 ORFs and 16% of  Aquifex's  1,512 ORFs show high matches to an Archaeal protein, while mesophiles such as  E. coli  and  B. subtilis  have far lesser proportions of genes that are most like Archaeal homologues. [ 21 ]


The genesis of new abilities due to horizontal gene transfer has three requirements. First, there must exist a possible route for the donor DNA to be accepted by the recipient cell. Additionally, the obtained sequence must be integrated with the rest of the genome. Finally, these integrated genes must benefit the recipient bacterial organism. The first two steps can be achieved via three mechanisms: transformation, transduction and conjugation. [ 21 ]


Transformation involves the uptake of named DNA from the environment. Through transformation, DNA can be transmitted between distantly related organisms. Some bacterial species, such as  Haemophilus influenzae  and  Neisseria gonorrhoeae , are continuously competent to accept DNA. Other species, such as  Bacillus subtilis  and  Streptococcus pneumoniae , become competent when they enter a particular phase in their lifecycle.


Transformation in  N. gonorrhoeae  and  H. influenzae  is effective only if particular recognition sequences are found in the recipient genomes (5'-GCCGTCTGAA-3' and 5'-AAGTGCGGT-3'. respectively). Although the existence of certain uptake sequences improve transformation capability between related species, many of the inherently competent bacterial species, such as  B. subtilis  and  S. pneumoniae , do not display sequence preference.


New genes may be introduced into bacteria by a bacteriophage that has replicated within a donor through generalized transduction or specialized transduction. The amount of DNA that can be transmitted in one event is constrained by the size of the phage  capsid  (although the upper limit is about 100 kilobases). While phages are numerous in the environment, the range of microorganisms that can be transduced depends on receptor recognition by the bacteriophage. Transduction does not require both donor and recipient cells to be present simultaneously in time nor space. Phage-encoded proteins both mediate the transfer of DNA into the recipient cytoplasm and assist integration of DNA into the chromosome. [ 21 ]


Conjugation involves physical contact between donor and recipient cells and is able to mediate transfers of genes between domains, such as between bacteria and yeast. DNA is transmitted from donor to recipient either by self-transmissible or mobilizable plasmid. Conjugation may mediate the transfer of chromosomal sequences by plasmids that integrate into the chromosome.


Despite the multitude of mechanisms mediating gene transfer among bacteria, the process's success is not guaranteed unless the received sequence is stably maintained in the recipient. DNA integration can be sustained through one of many processes. One is persistence as an episome, another is homologous recombination, and still another is illegitimate incorporation through lucky double-strand break repair. [ 21 ]


Natural transformation  is a DNA transfer process that depends on the expression of numerous bacterial genes. [ 22 ]   In order for a bacterium to bind, take up and recombine exogenous DNA into its chromosome, it must enter a special physiological state referred to as  “competence” .  Competence development in the bacterium  Bacillus subtilis  requires expression of about 40 genes. [ 23 ]   In general, the DNA integrated into the host chromosome is (with rare exceptions) derived from another bacterium of the same species, and is therefore homologous to the resident chromosome.  In  B. subtilis  the length of the transferred DNA is more than 1 million bases, is likely double stranded DNA, and is often more than a third of the total chromosome length of 4215 kb. [ 24 ]   Approximately 7-9% of the recipient cells take up an entire chromosome. [ 25 ]


The capacity for natural transformation appears to be common among prokaryotes, and thus far 67 prokaryotic species (in seven different phyla) are known to undergo this process. [ 26 ]   Competence for transformation is typically induced by high cell density and/or nutritional limitation, conditions associated with the stationary phase of bacterial growth.  Competence is also specifically induced by conditions that  damage DNA .  For example, transformation is induced in  Streptococcus pneumoniae  by the DNA damaging agents mitomycin C (a DNA cross-linking agent) and fluoroquinolone (a topoisomerase inhibitor that causes double-strand breaks). [ 27 ]   In  Bacillus subtilis , transformation is stimulated by exposure to UV light, a DNA damaging agent. [ 28 ]   In  Helicobacter pylori , ciprofloxacin, an agent that interacts with DNA gyrase and causes double-strand breaks, induces expression of competence genes, thus increasing the frequency of transformation [ 29 ]   Using  Legionella pneumophila , Charpentier et al. [ 30 ]  examined 64 toxic molecules to find out which of these induce competence. Of these toxic compounds, only six, all DNA damaging agents, caused strong induction.


Bacteria that are growing logarithmically differ from stationary phase bacteria with regard to the number of genome copies present in the cell, and this has implications for the ability to carry out an important  DNA repair  process.  During logarithmic growth, two or more copies of any particular region of the chromosome are ordinarily present in a bacterial cell, as cell division is not precisely matched with chromosome replication.  Homologous recombinational  repair is an important DNA repair process that is particularly effective for repairing double-strand damages, such as double-strand breaks. This DNA repair process depends on a second homologous chromosome in addition to the damaged chromosome.  During logarithmic growth, a  DNA damage  in one chromosome may be removed by homologous recombinational repair using sequence information from the other homologous chromosome.  However, when cells approach stationary phase they typically have just one copy of the chromosome, and homologous recombinational repair then requires input of an homologous template from outside the cell by transformation. [ 31 ]
To determine whether the adaptive function of transformation is repair of DNA damages, a series of experiments were performed using  B. subtilis  irradiated by UV light as the damaging agent (reviewed by Michod et al. [ 32 ]  and Bernstein et al. [ 31 ] )  These experiments produced results indicating that transforming DNA acts to repair potentially lethal DNA damages caused by UV light in the recipient DNA.  The particular process likely responsible for repair was homologous recombinational repair.  Thus transformation in bacteria can be regarded as a primitive sexual process, in the sense that it involves interaction of homologous DNA from two individuals to form recombinant DNA that is then passed on to succeeding generations. Bacterial transformation in prokaryotes may have been the ancestral process that evolved into meiotic sexual reproduction in eukaryotes (see  Evolution of sexual reproduction ;  Meiosis .)


Antimicrobial resistance  genes grant an organism the ability to grow its ecological niche, since it can now survive in the presence of previously lethal compounds. As the benefit to a bacterium earned from receiving such genes are time- and space-independent, those  sequences that are highly mobile are selected for. Plasmids are quite mobilizable between taxa and are the most frequent way by which bacteria acquire antibiotic resistance genes.


Adoption of a pathogenic lifestyle often yields a fundamental shift in an organism's ecological niche. The erratic phylogenetic distribution of pathogenic organisms implies that bacterial virulence is a consequence of the presence, or obtainment of, genes that are missing in avirulent forms. Evidence of this includes the discovery of large 'virulence' plasmids in pathogenic  Shigella  and  Yersinia , as well as the ability to bestow pathogenic properties onto  E. coli  via experimental exposure to genes from other species. [ 21 ]


In April 2019, scientists at  ETH Zurich  reported the creation of the world's first bacterial genome, named  Caulobacter ethensis-2.0 , made entirely by a computer, although a related viable form of  C. ethensis-2.0  does not yet exist. [ 33 ] [ 34 ]


The  Bacteroides thetaiotaomicron   genome  contains hundreds of  small RNAs  (sRNAs), [ 1 ]  discovered through  RNA sequencing . These include canonical housekeeping RNA species such as the  6S RNA  (SsrS),  tmRNA  (SsrA),  M1 RNA  (RnpB) and  4.5S RNA  (Ffs) as well as several hundred  cis  and  trans  encoded small RNAs. [ 1 ] [ 2 ]  More than 20 candidates have been validated with northern blots and the  structures  of several members  have been characterized  through  in silico  analyses and chemical probing experiments. [ 1 ] [ 2 ]


Two  B. thetaiotaomicron  sRNAs that have been functionally characterized are RteR and GibS.  RteR  is a 78  nucleotide  (nt) long sRNA that is  conserved  in closely related species and likely serves as a repressor of a  transposon   operon . [ 3 ] [ 4 ]  Analyses based on  secondary structure  conservation, taking into consideration nucleotide covariation and in-vitro chemical probing have revealed a structure that consists of a 5’ hairpin and a  Rho-independent terminator  that are separated by an 8 nt sequence. [ 3 ] [ 1 ]   GibS  is a 145 nt long sRNA that is also conserved in several closely related species within phylum  Bacteroidota  and has been hypothesized to play a role in  carbon metabolism . [ 1 ]  Structural analyses have revealed this sRNA to possess an extended 5’ single stranded region (38 nt) followed by two meta-stable hairpins and a Rho-independent terminator at the 3’ end. It is maximally expressed when  B. thetaiotaomicron  is grown in  N -acetyl-D-glucosamine  as the sole carbon source and has been shown to both induce and repress target mRNAs involved in  metabolic  regulation. [ 1 ]


The  B. thetaiotaomicron  genome also contains a large subset of  antisense  sRNAs that bear resemblance to the  B. fragilis   DonS  RNA. [ 5 ]  This family of 78 to 128 nt long sRNAs are encoded antisense to several of their target genes, that are members of PULs ( Polysaccharide Utilization Loci ). [ 5 ]


Binary Alignment Map  (BAM) is the comprehensive raw data of  genome sequencing ; [ 1 ]  it consists of the  lossless , compressed  binary  representation of the  Sequence Alignment Map -files. [ 2 ] [ 3 ]


BAM is the compressed binary representation of SAM (Sequence Alignment Map), a compact and index-able representation of nucleotide sequence alignments. [ 4 ]  The goal of indexing is to retrieve alignments that overlap a specific location quickly without having to go through all of them. Before indexing, BAM must be sorted by reference ID and then leftmost coordinate. [ 5 ]  BAM is in compressed BGZF format.


The structure of BAM files include a header section and an alignment section: [ 6 ]


Bam format uses 0-based  coordinate system , where as SAM uses 1-based coordinate system. BAM can represent values in the range [−2^31 , 2^32). [ 5 ]


To view a list of sequencing and analysis tools that work with SAM/BAM  click here .


In  molecular biology ,  biochips  are engineered substrates ("miniaturized laboratories") that can host large numbers of simultaneous biochemical reactions. One of the goals of biochip technology is to efficiently screen large numbers of biological analytes, with potential applications ranging from disease diagnosis to detection of  bioterrorism  agents. For example,  digital microfluidic biochips  are under investigation for applications in biomedical fields. In a digital microfluidic biochip, a group of (adjacent) cells in the microfluidic array can be configured to work as storage, functional operations, as well as for transporting fluid droplets dynamically. [ 1 ]


The development started with early work on the underlying  sensor  technology. One of the first portable, chemistry-based sensors was the  glass pH electrode , invented in 1922 by Hughes. [ 2 ]   The basic concept of using exchange sites to create permselective membranes was used to develop other ion sensors in subsequent years. For example, a K +  sensor was produced by incorporating  valinomycin  into a thin membrane. [ 3 ]


In 1953,  Watson  and  Crick  announced their discovery of the now familiar  double helix  structure of  DNA  molecules and set the stage for  genetics  research that continues to the present day. [ 4 ]  The development of  sequencing  techniques in 1977 by  Gilbert [ 5 ]  and  Sanger [ 6 ]  (working separately) enabled researchers to directly read the genetic codes that provide instructions for  protein synthesis . This research showed how  hybridization  of complementary single  oligonucleotide  strands could be used as a basis for DNA sensing. Two additional developments enabled the technology used in modern DNA-based. First, in 1983  Kary Mullis  invented the  polymerase chain reaction  (PCR) technique, [ 4 ]  a method for amplifying DNA concentrations. This discovery made possible the detection of extremely small quantities of DNA in samples. Secondly in 1986 Hood and co-workers devised a method to label DNA molecules with  fluorescent tags  instead of radiolabels, [ 7 ]  thus enabling hybridization experiments to be observed optically.


Figure 1 shows the make up of a typical biochip platform. The actual sensing component (or "chip") is just one piece of a complete analysis system.  Transduction  must be done to translate the actual sensing event (DNA binding,  oxidation/reduction ,  etc. ) into a format understandable by a computer ( voltage , light intensity, mass,  etc. ), which then enables additional analysis and processing to produce a final,  human-readable  output. The multiple technologies needed to make a successful biochip—from sensing chemistry, to  microarraying , to signal processing—require a true multidisciplinary approach, making the barrier to entry steep. One of the first commercial biochips was introduced by  Affymetrix . Their "GeneChip" products contain thousands of individual DNA sensors for use in sensing defects, or single nucleotide polymorphisms (SNPs), in genes such as  p53  (a tumor suppressor) and  BRCA1  and  BRCA2  (related to breast cancer). [ 8 ]  The chips are produced by using  microlithography  techniques traditionally used to fabricate  integrated circuits  (see below).


The microarray—the dense, two-dimensional grid of biosensors—is the critical component of a biochip platform. Typically, the sensors are deposited on a flat substrate, which may either be passive ( e.g.  silicon or glass) or active, the latter
consisting of integrated electronics or  micromechanical  devices that perform or assist signal transduction.  Surface chemistry  is used to  covalently bind  the sensor molecules to the substrate medium. The fabrication of microarrays is non-trivial and is a major economic and technological hurdle that may ultimately decide the success of future biochip platforms. The primary manufacturing challenge is the process of placing each sensor at a specific position (typically on a  Cartesian  grid) on the substrate. Various means exist to achieve the placement, but typically robotic micro-pipetting [ 9 ]  or micro-printing [ 10 ]  systems are used to place tiny spots of sensor material on the chip surface. Because each sensor is unique, only a few spots can be placed at a time. The low-throughput nature of this
process results in high manufacturing costs.


Fodor and colleagues developed a unique fabrication process (later used by  Affymetrix ) in which a series of microlithography steps is used to  combinatorially synthesize  hundreds of thousands of unique, single-stranded DNA sensors on a substrate one  nucleotide  at a time. [ 11 ] [ 12 ]   One lithography step is needed per base type; thus, a total of four steps is required per nucleotide level. Although this technique is very powerful in that many sensors can be created simultaneously, it is currently only feasible for creating short DNA strands (15–25 nucleotides). Reliability and cost factors limit the number of photolithography steps that can be done. Furthermore, light-directed combinatorial synthesis techniques are not currently possible for proteins or other sensing molecules.


As noted above, most microarrays consist of a Cartesian grid of sensors. This approach is used chiefly to map or "encode" the coordinate of each sensor to its function. Sensors in these arrays typically use a universal signalling technique ( e.g.  fluorescence), thus making coordinates their only identifying feature. These arrays must be made using a serial process ( i.e.  requiring multiple, sequential steps) to ensure that each sensor is placed at the correct position.


"Random" fabrication, in which the sensors are placed at arbitrary positions on the chip, is an alternative to the serial method. The tedious and expensive positioning process is
not required, enabling the use of parallelized self-assembly techniques. In this approach, large batches of identical sensors can be produced; sensors from each batch are then combined and assembled into an array. A non-coordinate based encoding scheme must be used to identify each sensor. As the figure shows, such a design was first demonstrated (and later commercialized by Illumina) using functionalized beads placed randomly in the wells of an etched  fiber optic  cable. [ 13 ] [ 14 ]  Each bead was uniquely encoded with a fluorescent signature. However, this encoding scheme is limited in the number of unique dye combinations that can be used and successfully differentiated.


Microarrays  are not limited to  DNA  analysis;  protein microarrays ,  antibody microarray ,  chemical compound microarray  can also be produced using biochips.  Randox Laboratories Ltd. launched Evidence, the first protein Biochip Array Technology analyzer in 2003.  In protein Biochip Array Technology, the biochip replaces the  ELISA  plate or  cuvette  as the reaction platform.  The biochip is used to simultaneously analyze a panel of related tests in a single sample, producing a  patient  profile.  The patient profile can be used in disease screening,  diagnosis , monitoring disease progression or monitoring treatment.  Performing multiple analyses simultaneously, described as multiplexing, allows a significant reduction in processing time and the amount of patient sample required.  Biochip Array Technology is a novel application of a familiar methodology, using sandwich, competitive and antibody-capture  immunoassays . The difference from conventional immunoassays is that, the capture ligands are covalently attached to the surface of the biochip in an ordered array rather than in solution.


In sandwich assays an enzyme-labelled antibody is used; in competitive assays an enzyme-labelled antigen is used.  On antibody-antigen binding a  chemiluminescence  reaction produces light.  Detection is by a  charge-coupled device  (CCD) camera.  The CCD camera is a sensitive and high-resolution sensor able to accurately detect and quantify very low levels of light.  The test regions are located using a grid pattern then the chemiluminescence signals are analysed by imaging software to rapidly and simultaneously quantify the individual analytes.


Biochips are also used in the field of  microphysiometry  e.g. in skin-on-a-chip [ 15 ]  applications.


For details about other array technologies, see  Antibody microarray .


Blast2GO , first published in 2005, [ 1 ] [ 2 ] [ 3 ] [ 4 ]  is a  bioinformatics  software tool for the automatic, high-throughput  functional annotation  of novel sequence data ( genes   proteins ). It makes use of the  BLAST [ 5 ]  algorithm to identify similar sequences to then transfers existing functional annotation from yet characterised sequences to the novel one. The functional information is represented via the  Gene Ontology  (GO), a  controlled vocabulary of functional attributes. [ citation needed ]  The  Gene Ontology , or  GO , is a major  bioinformatics  initiative to unify the representation of  gene  and  gene product  attributes across all  species . [ 6 ]


Chemogenomics , or  chemical genomics , is the systematic  screening  of targeted  chemical libraries  of  small molecules  against individual  drug target  families (e.g.,  GPCRs ,  nuclear receptors ,  kinases ,  proteases , etc.) with the ultimate goal of identification of novel  drugs  and drug targets. [ 1 ]   Typically some members of a target library have been well characterized where both the function has been determined and compounds that modulate the function of those targets ( ligands  in the case of  receptors ,  inhibitors  of  enzymes , or  blockers  of  ion channels )  have been identified. Other members of the target family may have unknown function with no known ligands and hence are classified as  orphan receptors . By identifying screening hits that modulate the activity of the less well characterized members of the target family, the function of these novel targets can be elucidated.  Furthermore, the  hits  for these targets can be used as a starting point for  drug discovery . The completion of the human genome project has provided an abundance of potential targets for therapeutic intervention.  Chemogenomics strives to study the intersection of all possible drugs on all of these potential targets. [ 2 ]


A common method to construct a targeted chemical library is to include known ligands of at least one and preferably several members of the target family. Since a portion of ligands that were designed and synthesized to bind to one family member will also bind to additional family members, the compounds contained in a targeted chemical library should collectively bind to a high percentage of the target family. [ 3 ]


Chemogenomics integrates target and  drug discovery  by using active compounds, which function as ligands, as probes to characterize  proteome  functions.  The interaction between a small compound and a protein induces a phenotype.  Once the phenotype is characterized, we could associate a protein to a molecular event.  Compared with  genetics , chemogenomics techniques are able to modify the function of a protein rather than the gene.  Also, chemogenomics is able to observe the interaction as well as reversibility in real-time.  For example, the modification of a  phenotype  can be observed only after addition of a specific compound and can be interrupted after its withdrawal from the medium.


Currently, there are two experimental chemogenomic approaches: forward (classical) chemogenomics and reverse chemogenomics. Forward chemogenomics attempt to identify drug targets by searching for molecules which give a certain phenotype on cells or animals, while reverse chemogenomics aim to validate phenotypes by searching for molecules that interact specifically with a given protein. [ 4 ]   Both of these approaches require a suitable collection of compounds and an appropriate model system for screening the compounds and looking for the parallel identification of biological targets and biologically active compounds.  The biologically active compounds that are discovered through forward or reverse chemogenomics approaches are known as modulators because they bind to and modulate specific molecular targets, thus they could be used as ‘targeted therapeutics’. [ 1 ]


In forward chemogenomics, which is also known as classical chemogenomics, a particular phenotype is studied and small compound interacting with this function are identified.  The molecular basis of this desired phenotype is unknown.  Once the modulators have been identified, they will be used as tools to look for the protein responsible for the phenotype.  For example, a loss-of-function phenotype could be an arrest of tumor growth.  Once compounds that lead to a target phenotype have been identified, identifying the gene and protein targets should be the next step. [ 5 ]  The main challenge of forward chemogenomics strategy lies in designing phenotypic assays that lead immediately from screening to target identification.


In reverse chemogenomics, small compounds that perturb the function of an enzyme in the context of an in vitro enzymatic test will be identified.  Once the modulators have been identified, the phenotype induced by the molecule is analyzed in a test on cells or on whole organisms.  This method will identify or confirm the role of the enzyme in the biological response. [ 5 ]  Reverse chemogenomics used to be virtually identical to the target-based approaches that have been applied in drug discovery and molecular pharmacology over the past decade.  This strategy is now enhanced by parallel screening and by the ability to perform lead optimization on many targets that belong to one target family.


Chemogenomics has been used to identify  mode of action  (MOA) for  traditional Chinese medicine  (TCM) and  Ayurveda . Compounds contained in traditional medicines are usually more soluble than synthetic compounds, have “privileged structures” (chemical structures that are more frequently found to bind in different living organisms), and have more comprehensively known safety and tolerance factors.  Therefore, this makes them especially attractive as a resource for lead structures in when developing new molecular entities.  Databases containing chemical structures of compounds used in alternative medicine along with their phenotypic effects, in silico analysis may be of use to assist in determining MOA for example, by predicting ligand targets that were relevant to known phenotypes for traditional medicines. [ 6 ]  In a case study for TCM, the therapeutic class of ‘toning and replenishing medicine” was evaluated.  Therapeutic actions (or phenotypes) for that class include anti-inflammatory, antioxidant, neuroprotective, hypoglycemic activity, immunomodulatory, antimetastatic, and hypotensive.   Sodium-glucose transport proteins  and  PTP1B  (an insulin signaling regulator) were identified as targets which link to the hypoglycemic phenotype suggested.  The case study for Ayurveda involved anti-cancer formulations.  In this case, the target prediction program enriched for targets directly connected to cancer progression such as  steroid-5-alpha-reductase  and synergistic targets like the efflux pump  P-gp .  These target-phenotype links can help identify novel MOAs.


Beyond TCM and Ayurveda, chemogenomics can be applied early in drug discovery to determine a compound's mechanism of action and take advantage of genomic biomarkers of toxicity and efficacy for application to Phase I and II clinical trials. [ 7 ]


Chemogenomics profiling can be used to identify totally new therapeutic targets, for example new antibacterial agents. [ 8 ]   The study capitalized on the availability of an existing ligand library for an enzyme called murD that is used in the peptidoglycan synthesis pathway.   Relying on the chemogenomics similarity principle, the researchers mapped the murD ligand library to other members of the  mur ligase family  (murC, murE, murF, murA, and murG) to identify new targets for the known ligands.   Ligands identified would be expected to be broad-spectrum  Gram-negative  inhibitors in experimental assays since peptidoglycan synthesis is exclusive to bacteria.  Structural and molecular docking studies revealed candidate ligands for murC and murE ligases.


Thirty years after the posttranslationally modified  histidine derivative  diphthamide  was determined, chemogenomics was used to discover the enzyme responsible for the final step in its synthesis. [ 9 ]   Dipthamide is a posttranslationally modified histidine residue found on the  translation elongation factor 2  (eEF-2).   The first two steps of the biosynthesis pathway leading to dipthine have been known, but the enzyme responsible for the amidation of dipthine to diphthamide remained a mystery.  The researchers capitalized on  Saccharomyces cerevisiae  cofitness data. Cofitness data is data representing the similarity of growth fitness under various conditions between any two different deletion strains. Under the assumption that strains lacking the diphthamide synthetase gene should have high cofitness with strain lacking other diphthamide biosynthesis genes,  they identified ylr143w as the strain with the highest cofitness to the all other strains lacking known diphthamide biosynthesis genes.  Subsequent experimental assays confirmed that YLR143W was required for diphthamide synthesis and was the missing diphthamide synthetase.


A  Chip description file   is a file that contains a possible annotation of a  microarray  chip type. The file typically specifies which probes map to the same genomic unit of interest. This mapping methodology solves the problem of a reliable reconstruction of expression levels as, if more than one probeset per gene exists, expression signals for the same transcript may be enhanced incorrectly. [ 1 ]


Genomic units of interest may include: Expression,  Genotyping , CustomSeq, Copy Number and/or Tag probe sets. All probe set names within an array are unique. Multiple copies of a probe set may exist on a single array as long as each copy has a unique name. CDFs
exist for  Gene expression  (mapping sets of probes to genes), for  genotyping  (mapping set of probes to  Single-nucleotide_polymorphisms  and  allele  type. Different CDFs may exist for a single chip type: one CDF may be used to interrogate gene transcripts and another CDF to interrogate  exons .


Initially,  Affymetrix  created the specification to describe the layout for an Affymetrix GeneChip array. Affymetrix still provides "official" CDFs for most of their chip types. [ 2 ]  In addition to these, various groups provide custom CDF files that are optimized for various genomic features. [ 3 ]  CDFs are typically updated when the genome annotation is updated.


A Chip Description file is divided up into sections. [ 4 ]  The start of each section is defined by a line containing a section name enclosed in square braces. The section names are:


The data in each section is of the format TAG=VALUE.


ChIP-exo  is a  chromatin immunoprecipitation  based method for mapping the locations at which a protein of interest ( transcription factor ) binds to the genome. It is a modification of the  ChIP-seq  protocol, improving the resolution of binding sites from hundreds of  base pairs  to almost one base pair. It employs the use of  exonucleases  to degrade strands of the protein-bound DNA in the 5'-3' direction to within a small number of nucleotides of the protein binding site. The nucleotides of the exonuclease-treated ends are determined using some combination of  DNA sequencing ,  microarrays , and  PCR . These sequences are then mapped to the genome to identify the locations on the genome at which the protein binds.


Chromatin immunoprecipitation ( ChIP ) techniques have been in use since 1984 [ 1 ]  to detect protein-DNA interactions. There have been many variations on ChIP to improve the quality of results. One such improvement,  ChIP-on-chip  (ChIP-chip), combines ChIP with microarray technology. This technique has limited sensitivity and specificity, especially  in vivo  where microarrays are constrained by thousands of proteins present in the nuclear compartment, resulting in a high rate of false positives. [ 2 ]  Next came  ChIP-sequencing  (ChIP-seq), which combines ChIP with high-throughput sequencing. [ 3 ]  However, the heterogeneous nature of sheared DNA fragments maps binding sites to within ±300 base pairs, limiting specificity. Secondly, contaminating DNA presents a grave problem since so few genetic loci are cross-linked to the protein of interest, making any non-specific genomic DNA a significant source of background noise. [ 4 ]


To address these problems, Rhee and Pugh revised the classic  nuclease protection assay  to develop ChIP-exo. [ 5 ]  This new ChIP technique relies on a lambda  exonuclease  that degrades only, and all, unbound double-stranded DNA in the 5′-3′ direction. Briefly, a protein of interest (engineering one with an epitope tag can be useful for immunoprecipitation) is crosslinked in vivo to its natural binding locations across a genome using formaldehyde.


Cells are then collected, broken open, and the chromatin sheared and solubilized by  sonication . An antibody is then used to immunoprecipitate the protein of interest, along with the crosslinked DNA. DNA PCR adaptors are then ligated to the ends, which serve as a priming point for second strand DNA synthesis after the exonuclease digestion.  Lambda exonuclease then digests double DNA strands from the 5′ end until digestion is blocked at the border of the protein-DNA covalent interaction. Most contaminating DNA is degraded by the addition of a second single-strand specific exonuclease. After the  cross-linking  is reversed, the primers to the PCR adaptors are extended to form double stranded DNA, and a second adaptor is ligated to 5′ ends to demarcate the precise location of exonuclease digestion cessation. The library is then amplified by PCR, and the products are identified by  high throughput sequencing . This method allows for resolution of up to a single base pair for any protein binding site within any genome, which is a much higher resolution than either ChIP-chip or ChIP-seq.


ChIP-exo has been shown to give up to single base pair resolution in identifying protein binding locations. This is in contrast to ChIP-seq which can locate a protein's binding site only to with ±300 base pairs. [ 4 ]


Contamination of non-protein-bound DNA fragments can result in a high rate of false positives and negatives in ChIP experiments. The addition of exonucleases to the process not only improves resolution of binding-site calling, but removes contaminating DNA from the solution before sequencing. [ 4 ]


Proteins that are inefficiently bound to a nucleotide fragment are more likely to be detected by ChIP-exo. This has allowed, for example, the recognition of more CTCF transcription factor binding sites than previously discovered. [ 5 ]


Due to the higher resolution and reduced background, less depth of sequencing coverage is needed when using ChIP-exo. [ 4 ]


If a protein-DNA complex has multiple locations of cross-linking within a single binding event, then it can appear as though there are multiple distinct binding events. This likely results from these proteins being denatured and cross-linking at one of the available binding sites within the same event. The exonuclease would then stop at one of the bound sites, depending on which site the protein is cross-linked to. [ 5 ]


As with any ChIP-based method, a suitable antibody for the protein of interest needs to be available in order to use this technique.


Rhee and Pugh introduce ChIP-exo by performing analyses on a small collection of transcription factors: Reb1, Gal4, Phd1, Rap1 in yeast and CTCF in human. Reb1 sites were often found in clusters and these clusters had ~10-fold higher occupancy than expected. Secondary sites in clusters were found ~40 bp from a primary binding site. Binding motifs of Gal4 showed a strong preference for three of the four nucleotides, suggesting a negative interaction between Gal4 and the excluded nucleotide. Phd1 recognizes three different motifs which explains previous reports of the ambiguity of Phd1's binding motif. Rap1 was found to recognize four motifs.


Ribosomal protein genes bound by this protein had a tendency to use a particular motif with a stronger consensus sequence. Other genes often used clusters of weaker consensus motifs, possibly to achieve a similar occupancy. Binding motifs of CTCF employed four "modules". Half of the bound CTCF sites used modules 1 and 2, while the rest used some combination of the four. It is believed that CTCF uses its zinc fingers to recognize different combinations of these modules. [ 5 ]


Rhee and Pugh analyzed pre-initiation complex (PIC) structure and organization in  Saccharomyces  genomes. Using ChIP-exo, they were able to, among other discoveries, precisely identify TATA-like features in promoters reported to be TATA-less. [ 6 ]


Chordate genomics  is the study of the evolution of the  chordate   clade  based on a comparison of the  genomes  of several  species  within the clade. The field depends on whole genome data (the entire  DNA sequence ) of organisms. It uses comparisons of  synteny  blocks,  chromosome translocation , and other genomic rearrangements to determine the evolutionary history of the clade, and to reconstruct the genome of the founding species.


The deep branching of chordate  phylogeny  has been clarified by chordate genomics. [ 1 ]  Chordate genomics demonstrates that the  Lancelets  are the most basal living clade within the chordates, while the  Tunicates  are the sister clade to the  Craniata .


A comparison of the genomes of the Lancelet  Branchiostoma floridae ,  a  fish , the  chicken   Gallus gallus , and  humans   Homo sapiens  revealed extensive macro- synteny  with little or no micro-synteny. That is, across the Chordate clade the same genes are found clustered near each other although the order of the genes within the clusters has been shuffled. [ 2 ]  There are 135 identifiable segments in the human genome which retain synteny with the ancestral chordate  karyotype .


Synteny analysis indicates that there were 17 chromosomes in the last common ancestor to the Chordates. [ 3 ]


Multiple lines of experimental evidence strongly suggest that twice in the lineage leading to the  teleost   fish  the ancestral Chordate genome was duplicated. [ 4 ] [ 5 ] [ 6 ] [ 7 ]
A comparison of the genomes of the Lancelet  Branchiostoma floridae , the Tunicates  Ciona intestinalis  and  Oikopleura dioica , the  lamprey   Petromyzon marinus , the  fish   Fugu rubripes  and  Gasterosteus aculeatus , the  chicken   Gallus gallus , and  human   Homo sapiens  confirmed that two  whole-genome duplications  occurred in the early history of the  Vertebrata  clade. [ 8 ]


This  chordate -related article is a  stub . You can help Wikipedia by  expanding it .


Shantanu Chowdhury  (born 1968) is an Indian structural biologist and a professor at  Institute of Genomics and Integrative Biology  of the  Council of Scientific and Industrial Research . [ 1 ]  He is known for developing a mechanism for  gene regulation mediated by DNA Secondary-Structure in diverse cellular contexts . [ 2 ]  An elected fellow of the  National Academy of Sciences, India , [ 3 ]  he is a recipient of the  National Bioscience Award for Career Development  of the  Department of Biotechnology  in 2010. [ 4 ]  The  Council of Scientific and Industrial Research , the apex agency of the Government of India for scientific research, awarded him the  Shanti Swarup Bhatnagar Prize for Science and Technology , one of the highest Indian science awards, in 2012, for his contributions to biological sciences. [ 5 ]


Shantanu Chowdhury, born on 9 September 1968 in the Indian state of  West Bengal , completed his master's degree in chemistry at  Jadavpur University , Kolkata before securing a PhD from the  Indian Institute of Chemical Technology , Hyderabad in 1998. [ 6 ]  His post-doctoral studies were at the department of biochemistry of  University of Nebraska  and later at  Max Planck Institute for Terrestrial Microbiology . He returned to India in 2001 and joined Insilico Sciences, Bangalore but his tenure there lasted only one year. His next move was to  Institute of Genomics and Integrative Biology  (IGIB) as a scientist in 2002 [ 7 ]  where he serves as a professor of functional genomics. [ 8 ]


Chowdhury established his own laboratory at IGIB and his team is involved in researches in functional genomics. [ 1 ]  The group focuses its studies on the control of  NME2 , a metastasis suppressor, on  telomerase  function and the biological action of DNA secondary structures like  G-quadruplex . [ 9 ]  He and his colleagues are reported to have developed a new mechanism for gene regulation and elucidated the process of gene transcription involving G-quadruplex. [ 10 ]  His researches have been published in a number of articles [ note 1 ]  and  PubMed , an online repository of scientific articles, has listed several of them. [ 11 ]  He is a member of the scientific advisory board of Bionivid, a  Bengaluru -based information technology company involved in  microarray   data analytics  and sequencing. [ 12 ]  and has delivered keynote addresses which included the discourse on  Engineering Reversal of Drug Resistance in Cancer Cells  at the  Drug Discovery India 2016  conference held in Bengaluru in September 2016 [ 1 ]  and the address on  The Emerging Story of DNA Quadruplexes in Genetic and Epigenetic Regulation  at Asian Epigenomics 2016. [ 13 ]  He sits on the editorial boards of two science journals, Human Genomics [ 14 ]  and  Journal of Biological Chemistry [ 15 ]  and has served as a peer-reviewer for JBC and  PLOS Computational Biology . [ 16 ]


In 2007, Chowdhury received the Swarnajayanthi Fellowship of the Department of Biotechnology (DBT) [ 17 ]  and three years later, DBT honored him again with the 2010  National Bioscience Award for Career Development . [ 4 ]  The  Central Drug Research Institute  awarded him the Excellence in Drug Research Award for Life Sciences category in 2011 [ 18 ]  and he also received the  Wellcome Trust -DBT Fellowship the same year. [ 19 ]  He was awarded the  Shanti Swarup Bhatnagar Prize for Science and Technology , one of the highest Indian science awards, by the  Council of Scientific and Industrial Research  the next year. [ 5 ]  He was also elected as a fellow by the  National Academy of Sciences, India  in 2012. [ 3 ]   Chemical & Engineering News  magazine of the  American Chemical Society  featured his researches twice, in 2007 and in 2009, citing their relevance as future drug targets. [ 9 ]


Clinical metagenomic next-generation sequencing  ( mNGS ) is the comprehensive analysis of microbial and host genetic material ( DNA  or  RNA ) in clinical samples from patients by  next-generation sequencing . It uses the techniques of  metagenomics  to identify and characterize the genome of  bacteria ,  fungi ,  parasites , and  viruses  without the need for a prior knowledge of a specific pathogen directly from clinical specimens. The capacity to detect all the potential  pathogens  in a sample makes metagenomic next generation sequencing a potent tool in the diagnosis of infectious disease especially when other more directed assays, such as  PCR , fail. Its limitations include clinical utility, laboratory validity, sense and sensitivity, cost and regulatory considerations. [ 1 ]


Outside of  clinical medicine ,  similar work  is done to identify genetic material in environmental samples, such as ponds or soil. [ 2 ]


Next-generation sequencing  uses the techniques of  metagenomics  to identify and characterize the genome of  bacteria ,  fungi ,  parasites , and  viruses  without the need for a prior knowledge of a specific pathogen directly from clinical specimens. [ 3 ]  The capacity to detect all the potential  pathogens  in a sample makes metagenomic next generation sequencing a potent tool in the diagnosis of infectious disease especially when other more directed assays, such as  PCR , fail. [ 4 ]


A typical mNGS workflow consists of the following steps:


One way to detect these pathogens is detect part of their genome by metagenomics sequencing ( Next Generation Sequencing -mNGS), which can be targeted or untargeted. [ 3 ]


Because of that, the sensitivity to detect microorganisms that are being targeted usually increases, but this comes with a limitation of the amount of identifiable pathogens. [ 3 ]


The untargeted analysis is a metagenomic  Shotgun sequencing  approach. The whole DNA and/or RNA is sequenced with this approach using universal  primers . The resultant mNGS reads can be assembled into partial or complete genomes. These genome sequences allow to monitor hospital outbreaks to facilitate infection control and public health surveillance. Also, they can be used for subtyping (identificate a specific genetic variant of a microorganism). [ citation needed ]


Untargeted mNGS is the most promising approach to analyse clinical samples and provide a comprehensive diagnosis of infections. Various groups have validated mNGS in Clinical Laboratory Improvement Amendments ( CLIA ), such as meningitis or encephalitis, sepsis and pneumonia. [ 3 ]  This method can be very helpful in the settings where no exact infectious etiology is suspected. [ 7 ]  For example, in patients with suspected pneumonia, identification of the underlying infectious etiology as in  COVID-19  has important clinical and public health implications. [ 8 ] [ 7 ]


The traditional method consists on formulating a differential diagnosis on the basis of the patient's history, a clinical presentation, imaging findings and laboratory testing. But here it is suggested a different way of diagnosis;  metagenomic  next-generation sequencing (NGS) is a promising method because a comprehensive spectrum of a potential causes (viral, bacterial; fungus and parasitic) can be identify by a single assay. [ 3 ]


Below are some examples of the metagenomic sequencing application in infectious diseases diagnosis. [ citation needed ]


The traditional method used to the diagnosis of infectious diseases has been challenged in some cases: neuroinflammatory diseases, lack of diagnostic tests for rare pathogens and the limited availability and volume of the  Central Nervous System  (CNS) samples, because of the requirement for invasive procedures. Owing to these problems, some assays suggest a different way of diagnosis, which is the  metagenomic  next-generation sequencing (NGS). Summarising, NGS can identify a broad range of pathogens in a single test. [ 9 ]


Some studies evaluate the clinical usefulness of metagenomic NGS for diagnosis neurologic infections, in parallel with conventional microbiologic testing. It has been seen that the highest diagnostic yield resulted from a combination of metagenomics NGS of CSF and conventional testing, including serologic testing and testing of sample types other than CSF. [ 9 ]  Sometimes neurologic infections remain undiagnosed in a proportion of patients despite conventional testing. [ 9 ]


The results of metagenomic NGS can also be valuable even when concordant with results of conventional testing, not only providing reassurance that the conventionally obtained diagnosis is correct but also potentially detecting or ruling out coinfections, specially in immunocompromised patients. [ 9 ]


Nowadays to detect resistances of different microbes is used a technique called  Antibiotic Sensitivity (AST) , but several studies have discovered that bacterial resistance is in the genoma and it is transferred by  horizontal way (HGT) , so sequencing methods are being developed to ease the identification and characterization of those genomes and metagenomes. For the moment exist the following methods to detect antimicrobial resistances: [ 10 ]


Metagenomic sequencing methods have provided better results than genomics, due to these present fewer false negatives. Within metagenomics sequencing, functional metagenomic is a powerful approach for characterizing  resistomes ; a  metagenomic library  is generated by cloning the total community DNA extracted from a sample into an  expression vector , this library is assayed for antimicrobial resistance by plating on selective media that are lethal to the wild-type host. The selected inserts from the surviving recombinant, antimicrobial-resistant host cells are then sequenced, and resulting sequences are subsequently assembled and annotated (PARFuMS). [ 10 ] [ 11 ]


Functional metagenomics has enabled the discovery of several new antimicrobial resistance mechanisms and their related genes, one such example is the recently discovered  tetracycline  resistance mechanism by tetracycline destructases. [ 10 ]  It is important to incorporate not only the antimicrobial resistance gene sequence and mechanism but also the genomic context, host bacterial species and geographic location ( metagenome ). [ 10 ]


Potentially dangerous pathogens such as  ebolaviruses ,  coronaviruses  etc., and the closest genetic relative for unknown pathogens, could be identified immediately, prompting further follow-up. Its role in the future of pandemic preparedness is anticipated and could exist as the earliest surveillance system we may have to detect outbreaks of unknown etiology and to respond in an opportune manner. [ 12 ]


The use of mNGS to characterize the microbiome has made possible the development of bacterial probiotics to be administrated as pills, for example, as a treatment of  Clostridioides difficile -associated diseases. [ 3 ]


The studying of genes expression allows to characterize a lot of infections, for example infections due to  Staphylococcus aureus ,  Lyme disease ,  candidiasis ,  tuberculosis  and  influenza . Also, this approach can be used for  cancer  classification. [ citation needed ]


RNAseq analysis have a lot of other purposes and applications such as to identify novel or under appreciated host–microbial interactions directly from clinical samples, to make indirect diagnosis on the basis of a pathogen specific human host response and to discriminate infectious versus noninfectious causes of acute illness. [ 3 ]


Most of the metagenomics outcomes data generated consist of  case reports  which belie the increasing interest on diagnostic metagenomics. [ 13 ]


Accordingly, there is an overall lack of penetration of this approach into the clinical microbiology laboratory, as making a  diagnosis  with metagenomics is still basically only useful in the context of case report but not for a true daily diagnostic purpose. [ 13 ]


As of 2018,  cost-effectiveness modelling of metagenomics in the diagnosis of  fever of unknown origin  concluded that, even after limiting the cost of diagnostic metagenomics to $100 – 1000 per test, it would require 2.5-4 times the diagnostic yield of  computed tomography of the abdomen and pelvis  in order to be cost neutral and cautioned against ‘widespread rush’ to deploy metagenomic testing. [ 13 ]


Furthermore, in the case of the discovery of potential novel  infectious agents , usually only the positive results are published even though the vast majority of sequenced cases are negative, thus resulting in very biased information. Besides, most of the discovery work based in metagenomic that precedes the diagnostic-based work even mentioned the known agents detected while screening unsolved cases for completely novel causes. [ 13 ]


To date, most published testing has been run in an unvalidated, unreportable manner. The ‘standard microbiological testing’ that samples are subjected to prior to metagenomics is variable and has not included  reverse transcription-polymerase chain reaction (RT-PCR)  testing for common respiratory viruses or, routinely 16S/ITS PCR testing. [ 13 ]


Given the relative costs of validating and performing metagenomic versus 16S/ITS PCR testing, the second one is considered an easier and more efficient option. A potential exception to the 16S/ITS testing is blood, given the huge amount of 16S sequence available, making clean cutoffs for diagnostic purposes problematic. [ 13 ]


Furthermore, almost all of the organisms detected by metagenomics for which there is an associated treatment and thus would be truly actionable are also detectable by 16S/ITS testing (or 16S/ITS-NGS). This makes questionable the utility of metagenomics in many diagnostic cases. [ 13 ]


One of the main points to accomplish laboratory validity is the presence of  reference standards  and controls when performing mNGS assays. They are needed to ensure the quality and stability of this technique over time. [ 3 ]


In  clinical microbiology  labs, the quantitation of microbial burden is considered a routine function as it is associated with the severity and progression of the disease. To achieve a good  quantitation  a high  sensitivity  of the technique is needed. [ 13 ]


Whereas interfering substances represent a common problem to  clinical chemistry  or to PCR diagnostics, the degree of interference from host (for example, in  tissue biopsies ) or nonpathogen nucleic acids (for example, in stool) in metagenomics is a new twist. [ 3 ] [ 13 ]  In addition, due to the relative size of the  human genome  in comparison with microbial genomes the interference can occur at low levels of contaminating material. [ 13 ]


Another challenge for clinical metagenomics in regards to sensitivity is the diagnosis of  coinfections  where there are present high-titer pathogens that can generate biased results as they may disproportionately soak up reads and make difficult to distinguish the less predominant pathogens. [ 13 ]


In addition to issues with interfering substances, specially in the diagnosis area, accurate quantitation and sensitivities are essential as a confusion in the results can affect to a third person, the patient. For these reason, practitioners currently have to be keenly aware of the index-swapping issues associated with  Illumina sequencing  which can lead to trace incorrectly barcoded samples. [ 13 ]


Since metagenomics has typically been used on patients for whom every other test to date has been negative, questions surrounding analytical sensitivity haven been less germane. But, for ruling out infections causes being one of the more important roles for clinical metagenomics it is essential to be capable to perform a deep enough sequencing to achieve adequate sensitivities. One way could be developing novel library preparation techniques. [ 13 ]


As of 2018, the Illumina monopoly on high-quality next-generation sequencing reagents has meant that the sequencing reagents alone cost more than  FDA -approved syndromic testing panels. Also additional direct costs of  metagenomics  such as extraction, library preparation, and computational analysis have to be considered. [ 13 ]  In general, metagenomic sequencing is most useful and cost efficient for  pathogen  discovery when at least one of the following criteria are met:


Clinicogenomics , also referred to as  clinical genomics , is the study of  clinical outcomes  with  genomic  data.  Genomic factors have a causal effect on clinical data. Clinicogenomics uses the entire genome of a patient in order to diagnose diseases or adjust medications exclusively for that patient. Whole genome testing can detect more mutations and structural anomalies than targeted gene testing. [ 1 ] [ 2 ]  Furthermore, targeted gene testing can only test for the diseases for which the doctor screens, whereas testing the whole genome screens for all diseases with known markers at once. [ 1 ] [ 3 ]


Clinicogenomics is currently used in  personalized medicine  such as  pharmacogenomics  and  oncogenomics . By studying the whole genome, a physician is able to construct medical plans based on an individual patient's genome rather than generic plans for all patients with the same diagnosis. For example, researchers are able to identify the mutations that cause a particular kind of cancer by studying the genomes of many patients with that cancer type, such as in a study of renal tumors that were previously only diagnosed through morphological anomalies. [ 4 ]  Furthermore, researchers can identify the medications and treatments that work best on particular cancer-causing mutations, which can then be applied to treat future patients. [ 5 ]


Clinicogenomics can also be used in preventative medicine by sequencing a patient's genome prior to a diagnosis in order to identify the known mutations related to medical conditions. In the future, patients could be sequenced at birth and periodically throughout our lives to be cautious of potential health risks and prepare for probable future diagnoses. [ 6 ]  Through preventative care, patients will be able to change their lifestyles and behaviors to reflect their genetic predisposition to certain conditions. [ 7 ]  For example, if a woman knows she has mutation in the  BRCA1  gene, she can be more proactive about mammograms, Pap smears and other preventative care to help increase her odds of survival despite her likelihood of cancer. By detecting cancer earlier or preventing the development of diseases such as diabetes, health care costs for individuals implementing preventative medicine based on genomic data will decrease. [ 7 ]


Below are a few of the major challenges facing the usage of clinicogenomics by health care providers today. Other challenges also exist, such as the expense of  genome sequence analysis  and whether or not  insurance companies  provide coverage for sequencing.


One of the difficulties of genome testing is the amount of data from a sequence and the dozens of formats in which that data can come. This data needs to be standardized and added to  electronic health records . [ 8 ]  It also needs to be in a format that can be utilized by both health care providers for comparisons, second opinions and future study [ 8 ]  as well as by machines used for processing the data for further analysis. [ 9 ]


One of the concerns of utilizing clinicogenomics is the privacy of the patients throughout the process of collecting the DNA, analyzing the genome, and delivering the interpreted data to health care providers. In a study using HIV patients, the researchers encrypted the raw genetic data prior to analysis in order to maintain the anonymity of the patient. Then, a scientist without any previous knowledge of the patient interpreted the encrypted data. A report was produced and given to the physician for further study if applicable. [ 10 ]


Clinomics  is the study of -omics data along with its associated clinical data. The term  -omics  generally refers to a study of biology.  As an example,  genomics  is the study of the entire  genome  of an organism and was the first -omics term.


As personalized medicine advances, clinomics will be a bridge between basic biological data and its effect on human health.  As an example, there have been studies of the genes expressed in certain cancer tissues as a way of classification of the cancer and the putative best form of treatment.


Already we know that certain genes such as BRCA1 are associated with a higher probability of developing breast cancer.  Clinomics takes the next step by looking at not only the genetics of the patient, but also such data as  mRNA ,  metabolites , and  proteins  associated with a patient and a disease.


Cofactor Genomics  is a  biotech  company that primarily focuses on  drug development ,  medical research , and  personalized medicine .


Cofactor Genomics was founded in August 2008 by Jarret Glasscock, Matt Hickenbotham, and Ryan Richt. The technological advances brought on by  Next Gen sequencing  encouraged Glasscock, Hickenbotham and Richt to leave  Washington University Genome Center  and purchase their first Next Gen genome analyzer with  capital  raised from an  angel investor  in  California . [ 1 ] [ 2 ]


In November 2013, Cofactor Genomics opened a 10,000 ft 2  headquarters in  St. Louis, MO . The company also has offices in  San Diego  and  San Francisco . In July 2015, Cofactor Genomics was awarded a $1.5 million Phase II  Small Business Innovation Research  (SBIR) grant by the  National Institute on Drug Abuse  (NIDA) at the  National Institutes of Health  (NIH). [ 3 ]  In addition to working with clients such as  Ozzy Osbourne , [ 4 ]  Cofactor Genomics is involved in a number of research projects, including the preservation of the  White Rhino  and the  Black Footed Ferret .


Jarret Glasscock is a  co-founder  as well as the  chief executive officer  of Cofactor Genomics. [ 5 ]   Glasscock completed his undergraduate degree at the  University of Arizona , where he majored in  biology  with a focus in the  computer sciences . Upon graduation, Glasscock pursued his  doctorate  in  genetics  at  Washington University in St. Louis , where he studied under  Warren Gish , developer of the  NCBI   BLAST  sequence analysis program. [ 5 ] [ 6 ] [ 7 ]


Jon Armstrong is the  chief scientific officer  of Cofactor Genomics. [ 5 ]   After acquiring his  master's degree  in  neuroscience , Armstrong spent nine years in the Genome Center at Washington University's Technology Development Group. [ 5 ]


David Messina is the  chief operating officer  of Cofactor Genomics. He has spent the last 19 years in  computational biology  and genetics. He worked on the Human Genome Project at Washington University in St. Louis, trained in molecular biology and human genetics at the  University of Chicago , and earned his PhD in  computational biology  in  Stockholm ,  Sweden . [ citation needed ]


Cognitive genomics  (or  neurative genomics ) is the sub-field of  genomics  pertaining to cognitive function in which the  genes  and non-coding sequences of an  organism 's  genome  related to the health and activity of the  brain  are studied. By applying  comparative genomics , the genomes of multiple species are compared in order to identify genetic and  phenotypical  differences between species. Observed phenotypical characteristics related to the neurological function include  behavior ,  personality ,  neuroanatomy , and  neuropathology . The theory behind cognitive genomics is based on elements of  genetics ,  evolutionary biology ,  molecular biology ,  cognitive psychology ,  behavioral psychology , and  neurophysiology .


Intelligence  is the most extensively studied behavioral  trait . [ 1 ]  In humans, approximately 70% of all genes are expressed in the brain. [ 2 ]  Genetic variation accounts for 40% of phenotypical variation. [ 3 ]  Approaches in cognitive genomics have been used to investigate the genetic causes for many mental and  neurodegenerative  disorders including  Down syndrome ,  major depressive disorder ,  autism , and  Alzheimer's disease .


The most commonly used approach to genome-investigation is evolutionary genomics biology, or evo-geno, in which the genomes of two species which share a common ancestor are compared. [ 4 ]  A common example of evo-geno is comparative cognitive genomics testing between humans and chimpanzees which shared an ancestor 6-7 million years ago. [ 5 ]  Patterns in local  gene expression  and  gene splicing  are examined to determine genomic differentiation. Comparative transcriptomic analyses conducted on primate brains to measure gene expression levels have shown significant differences between human and chimpanzee genomes. [ 4 ]  The evo-geno approach was also used to verify the theory that humans and non-human primates share similar expression levels in energy metabolism-related genes which have implications for aging and neurodegenerative disease. [ 4 ]