The success of gene therapy depends on the efficient insertion of therapeutic  genes  at an appropriate  chromosomal  target site within the human  genome , without causing cell injury,  oncogenic  mutations, or an  immune response . The construction of  plasmid   vectors  is simple and straightforward. Custom-designed ZFNs that combine the non-specific cleavage domain (N) of  Fok I endonuclease with zinc-finger proteins (ZFPs) offer a general way to deliver a site-specific DSB to the genome, and stimulate local homologous recombination by several orders of magnitude. This makes targeted gene correction or genome editing a viable option in human cells. Since ZFN-encoding plasmids could be used to transiently express ZFNs to target a DSB to a specific gene locus in human cells, they offer an excellent way for targeted delivery of the therapeutic genes to a pre-selected chromosomal site. The ZFN-encoding plasmid-based approach has the potential to circumvent all the problems associated with the viral delivery of therapeutic genes. [ 36 ]  The first therapeutic applications of ZFNs are likely to involve  ex vivo  therapy using a patient's own stem cells. After editing the stem cell genome, the cells could be expanded in culture and reinserted into the patient to produce differentiated cells with corrected functions. Initial targets likely include the causes of monogenic diseases, such as the IL2Rγ gene and the β-globin gene for gene correction and CCR5 gene for mutagenesis and disablement. [ 33 ]


If the zinc finger domains are not specific enough for their target site or they do not target a unique site within the genome of interest, off-target cleavage may occur.  Such off-target cleavage may lead to the production of enough double-strand breaks to overwhelm the repair machinery and, as a consequence, yield chromosomal rearrangements and/or cell death.  Off-target cleavage events may also promote random integration of donor DNA. [ 33 ]
Two separate methods have been demonstrated to decrease off-target cleavage for 3-finger ZFNs that target two adjacent 9-basepair sites. [ 37 ]
Other groups use ZFNs with 4, 5 or 6 zinc fingers that target longer and presumably rarer sites and such ZFNs could theoretically yield less off-target activity.  A comparison of a pair of 3-finger ZFNs and a pair of 4-finger ZFNs detected off-target cleavage in human cells at 31 loci for the 3-finger ZFNs and at 9 loci for the 4-finger ZFNs. [ 38 ]  Whole genome sequencing of  C. elegans  modified with a pair of 5-finger ZFNs found only the intended modification and a deletion at a site "unrelated to the ZFN site" indicating this pair of ZFNs was capable of targeting a unique site in the  C. elegans  genome. [ 19 ]


As with many foreign proteins inserted into the human body, there is a risk of an immunological response against the therapeutic agent and the cells in which it is active. Since the protein must be expressed only transiently, however, the time over which a response may develop is short. [ 33 ]


Liu et al. respectively target ZFNickases to the endogenous b-casein(CSN2) locus stimulates lysostaphin and human lysozyme gene addition by homology-directed repair and derive secrete lysostaphin cows. [ 39 ] [ 40 ]


The ability to precisely manipulate the genomes of plants and animals has numerous applications in basic research, agriculture, and human therapeutics. Using ZFNs to modify endogenous genes has traditionally been a difficult task due mainly to the challenge of generating zinc finger domains that target the desired sequence with sufficient specificity. Improved methods of engineering zinc finger domains and the availability of ZFNs from a commercial supplier now put this technology in the hands of increasing numbers of researchers.  Several groups are also developing other types of engineered nucleases including engineered homing endonucleases [ 41 ]
 [ 42 ]  and nucleases based on engineered  TAL effectors . [ 43 ] [ 44 ]
TAL effector nucleases (TALENs) are particularly interesting because TAL effectors appear to be very simple to engineer [ 45 ]
 [ 46 ]
and TALENs can be used to target endogenous loci in human cells. [ 47 ]  But to date no one has reported the isolation of clonal cell lines or transgenic organisms using such reagents.  One type of ZFN, known as SB-728-T, has been tested for potential application in the treatment of HIV. [ 48 ]


Zinc-finger nickases (ZFNickases) are created by inactivating the catalytic activity of one ZFN monomer in the ZFN dimer required for double-strand cleavage. [ 49 ]  ZFNickases demonstrate strand-specific nicking activity  in vitro  and thus provide for highly specific single-strand breaks in DNA. [ 49 ]  These SSBs undergo the same cellular mechanisms for DNA that ZFNs exploit, but they show a significantly reduced frequency of mutagenic NHEJ repairs at their target nicking site. This reduction provides a bias for HR-mediated gene modifications. ZFNickases can induce targeted HR in cultured human and livestock cells, although at lower levels than corresponding ZFNs from which they were derived because nicks can be repaired without genetic alteration. [ 39 ] [ 50 ]  A major limitation of ZFN-mediated gene modifications is the competition between NHEJ and HR repair pathways. Regardless of the presence of a DNA donor construct, both repair mechanisms can be activated following DSBs induced by ZFNs. Thus, ZFNickases is the first plausible attempt at engineering a method to favor the HR method of DNA repair as opposed to the error-prone NHEJ repair. By reducing NHEJ repairs, ZFNickases can thereby reduce the spectrum of unwanted off-target alterations. The ease by which ZFNickases can be derive from ZFNs provides a great platform for further studies regarding the optimization of ZFNickases and possibly increasing their levels of targeted HR while still maintain their reduced NHEJ frequency.


NM_178457 NM_001384354


Zinc finger protein 831  is a  protein  that in humans is encoded by the ZNF831  gene .
 [ 5 ]


This article on a  gene  on human  chromosome 20  is a  stub . You can help Wikipedia by  expanding it .


23andMe Holding Co.  is an American  personal genomics  and  biotechnology  company based in  South San Francisco, California . [ 1 ]  It is best known for providing a  direct-to-consumer genetic testing  service in which customers provide a  saliva sample  that is laboratory analysed, using  single nucleotide polymorphism genotyping , [ 2 ]  to generate reports relating to the customer's ancestry and genetic predispositions to health-related topics. The company's name is derived from the 23 pairs of  chromosomes  in a  diploid  human  cell . [ 3 ]


Founded in 2006, 23andMe soon became the first company to begin offering  autosomal  DNA testing for ancestry, which all other major companies now use. [ 4 ]  Its saliva-based direct-to-consumer genetic testing business was named "Invention of the Year" by  Time  in 2008. [ 5 ] [ 6 ]


The company had a previously fraught relationship with the United States  Food and Drug Administration  (FDA) due to its genetic health tests; as of October 2015,  DNA tests  ordered in the US include a revised health component, per FDA approval. [ 7 ] [ 8 ]  23andMe has been selling a product with both ancestry and health-related components in Canada since October 2014, [ 9 ] [ 10 ] [ 11 ]  and in the UK since December 2014. [ 12 ]


23andMe became a publicly traded company, via a merger with a  Special Purpose Aquisition Company (SPAC)  in 2021 and soon had a market capitalization of US$6 billion. [ 13 ]  By 2024, its valuation had fallen to 2% of that peak. [ 13 ]  On September 17, 2024, all seven independent directors of the company resigned, voicing concerns about the strategic direction of the company and Wojcicki's stated intention to take the company private. [ 14 ] [ 15 ]  Users were concerned about the security of their genetic data, and were trying to delete it from the company's archives.


Linda Avey , Paul Cusenza and  Anne Wojcicki  founded 23andMe in 2006 to offer  genetic testing  and interpretation to individuals. [ 2 ]  Investment documents from 2007 also suggest that 23andMe hoped to develop a database to pursue research efforts. [ 16 ]  In 2007,  Google  invested $3.9 million in the company, along with  Genentech ,  New Enterprise Associates , and Mohr Davidow Ventures. [ 17 ]  Wojcicki and Google co-founder  Sergey Brin  were married at the time. [ 10 ]


In 2007, Cusenza left to join Nodal Exchange as CEO. [ 18 ]  Avey left in 2009 and co-founded Curious, Inc. in 2011. [ 19 ]


In 2012, 23andMe raised $50 million in a Series D  venture round , almost doubling its capital of $52.6 million. [ 20 ]  In 2015, 23andMe raised $115 million in a Series E offering, increasing capital to $241 million. [ 8 ]


In June 2017, 23andMe created a  brand marketing  advertisement featuring Gru from  Despicable Me . [ 21 ]  In 2018, the company launched advertisements narrated by  Warren Buffett . [ 22 ]


In September 2017, it was rumored the company was raising another $200 million with a $1.5 billion valuation. As of that time, the company had raised $230 million since its inception. [ 23 ]  Afterwards, it was reported the company raised $250 million, at a $1.75 billion valuation. [ 24 ]


On July 25, 2018, 23andMe announced a partnership with  GlaxoSmithKline  to allow the pharmaceutical company to use test results from 5 million customers to design new drugs. GlaxoSmithKline invested $300 million in the company. [ 25 ]  In January 2022, this partnership was extended until July 2023 with an additional $50 million payment from GlaxoSmithKline. [ 26 ]


The company was based in  Mountain View, California , initially in North Bayshore and then downtown, [ 27 ]  until 2019, when it moved to a larger headquarters in  Sunnyvale . [ 28 ] [ 29 ]  In January 2020, 23andMe announced it would lay off about 100 employees in its consumer testing division. [ 30 ] [ 31 ]


In July 2020, 23andMe and GlaxoSmithKline announced their partnership's first clinical trial: a joint asset being co-developed by the two companies for cancer treatment. [ 32 ]  In December 2020, the company raised around $82.5 million in a series F round, bringing the total raised over the years to over $850M. The post-money valuation was not reported. [ 33 ]


In February 2021, the company announced that it had entered into a definitive agreement to merge with Sir  Richard Branson 's  special-purpose acquisition company , VG Acquisition Corp, in a $3.5 billion transaction. [ 34 ]  In June 2021, the company completed the merger with VG Acquisition Corp. The combined company was renamed to 23andMe Holding Co. and began trading on the  Nasdaq  stock exchange on June 17, 2021, under the ticker symbol "ME". [ 35 ] [ 36 ]


In October 2021, 23andMe announced that it would acquire Lemonaid Health, a  telehealth  company, for $400 million with the deal closing in November. [ 37 ] [ 38 ]


After 23andMe became a publicly traded company in 2021, it reached a market capitalization of $6 billion. [ 13 ]  By 2024, its valuation had fallen to 2% of that peak. [ 13 ]  Suggested factors include the fact that the company has never been profitable, with lack of recurring revenue from 23andMe's retail customers of its DNA kits (who only need to take the test once). [ 39 ]  In 2022, the company moved from Sunnyvale to a smaller headquarters in  South San Francisco . [ 29 ] [ 40 ]  In June 2023, it announced another round of layoffs. [ 41 ]


In July 2024, Wojcicki announced her intention to take 23andMe private, by paying 40 cents each for all outstanding shares not already owned by her directly or through affiliates. [ 42 ]  The proposal was rejected by a committee set up to evaluate options for the company's future, [ 43 ]  and in September the seven independent members of the board of directors all resigned. [ 14 ] [ 15 ] [ 44 ] [ 45 ]  The company subsequently filed with the  SEC  indicating that it was no longer open to third-party acquisition offers. [ 42 ]


23andMe began offering direct-to-consumer genetic testing in November 2007. Customers provide a  saliva testing  sample that is partially  single nucleotide polymorphism (SNP) genotyped  and results are posted online. [ 2 ] [ 46 ] [ 47 ]  In 2008, when the company was offering estimates of "predisposition for more than 90 traits and conditions ranging from baldness to blindness",  Time  magazine named the product "Invention of the Year". [ 5 ]


After the sample is received by the lab, the DNA is extracted from the saliva and  amplified  so that there is enough to be  genotyped . The DNA is then cut into small pieces, and applied to a glass  microarray chip , which has many microscopic beads applied to its surface. Each bead has a  gene probe  on it that matches the DNA of one of the many variants the company test for. If the sample has a match in the microarray, the sequences will  hybridize , or bind together, letting researchers know that this variant is present in the customer's genome by a  fluorescent label  located on the probes. Tens of thousands of variants are tested out of the 10 to 30 million located in the entire genome. These matches are then compiled into a report that is supplied to the customer, allowing them to know if the variants associated with certain diseases, such as Parkinson's, celiac and Alzheimer's, are present in their own genome. [ 48 ]


Uninterpreted raw genetic data may be downloaded by customers. [ 49 ]  This provides customers with the ability to choose one of the 23 chromosomes, as well as  mitochondrial DNA , and see which base is located in certain positions in  genes , and see how these compare to other common variants. Customers who bought tests with an ancestry-related component have online access to  genealogical DNA test  results and tools, including a relative-matching database. Customers can also view their  mitochondrial haplogroup  (maternal) and, if they are male or a relative shared a patriline that has also been tested,  Y chromosome haplogroup  (paternal). US customers who bought tests with a health-related component and received health-related results before November 22, 2013, have online access to an assessment of  inherited   traits  and  genetic disorder  risks. [ 7 ] [ 50 ] [ 51 ]  Health-related results for US customers who purchased the test from November 22, 2013, were suspended until late 2015 while undergoing an  FDA regulatory review . [ 8 ] [ 52 ]  Customers who bought tests from 23andMe's Canadian and UK locations have access to some, but not all, health-related results. [ 9 ] [ 12 ]


As of February 2018, 23andMe has  genotyped  over 3,000,000 individuals. [ 53 ]  FDA marketing restrictions reduced customer growth rates. [ 54 ]  In February 2024, 23andMe said they had genotyped more than 14,000,000 individuals. [ 55 ]


23andMe is commonly used for  donor conceived people  to find their biological siblings and in some cases their  sperm  or  egg donor . [ 56 ]


In late 2009, 23andMe split its genotyping service into three products with different prices: an Ancestry Edition, a Health edition, and a Complete Edition. [ 57 ]  This decision was reversed a year later, when the different products were recombined. [ 58 ]  In late 2010, the company introduced a monthly subscription fee for updates based on new  medical research  findings. [ 58 ] [ 59 ]  The subscription model proved unpopular with customers and was eliminated in mid-2012. [ 60 ]


23andMe only sold raw genetic data and ancestry-related results in the US due to FDA restrictions from November 22, 2013, until October 21, 2015, [ 7 ] [ 52 ]  when it announced that it would resume providing health information in the form of carrier status and wellness reports with FDA approval. [ 61 ]


The price of the full direct-to-consumer testing service in the US reduced from $999 in 2007 to $399 in 2008 [ 62 ]  and to $99 in 2012, [ 20 ]  and was effectively being sold as a  loss leader  in order to build a valuable customer database. [ 49 ] [ 63 ] [ 64 ]  In October 2015, the US price was raised to $199. [ 61 ]  In September 2016, an ancestry-only version was once again offered at a lower price of $99 with an option to upgrade to include the health component for an additional $125 later. [ 65 ]


The initial price of the product sold in Canada from October 2014, which includes health-related results, was  CA$99 . [ 9 ] [ 10 ]  The initial price of the product sold in the UK from December 2014, which includes health-related results, was £125. [ 66 ]


In February 2018, 23andMe announced that its ancestry reporting would tell people what country they were from, not just what region, and increased the number of regions by 120. Like other companies, it still lacked data about Asia and Africa, which the African Genetics Program (launched in October 2016 with a grant from the US  National Institutes of Health ) will rectify by recruiting  sub-Saharan Africans  to increase the genomic data on racial and ethnic minorities. [ 67 ] [ 68 ]  Building off of the African Genetics Program, the Global Genetics Program was also announced in February 2018. This program aims to increase the genomic data of 61 underrepresented countries in their database by providing free tests to individuals that have all 4 grandparents from one of the countries. In April 2018, 23andMe announced the Populations Collaboration Program, which sets up formal collaborations between the company and researchers that are investigating underrepresented countries. [ 69 ]


Since October 1, 2020, the company has offered a new service called "23andMe+", priced at $29/year, for the customers of the "Health + Ancestry" service, who completed genotyping on version 5 of the  microarray chip  used by the company. The new service makes available additional reports on health and pharmacogenetics, and commits to provide ongoing new reports and features. [ 70 ]


At the end of 2021, 23andMe acquired leading digital healthcare company Lemonaid Health for $400m to "...give patients and healthcare providers better information about health risks and treatment". Paul Johnson, CEO and co-founder of Lemonaid Health became COO of the 23andMe consumer business. [ 71 ] [ 72 ] [ 73 ]


Up until 2010,  Illumina  only sold instruments that were labeled "for research use only"; in early 2010, Illumina obtained FDA approval for its BeadXpress system to be used in clinical tests. [ 74 ] [ 75 ]


The new genetic testing service and ability to map significant portions of the  genome  has raised controversial questions, including whether the results can be interpreted meaningfully and whether they will lead to  genetic discrimination . [ 5 ] [ 49 ]  The regulatory environment for genetic testing companies has been uncertain, and anticipated risk-based regulation for different types of genetic tests has not yet materialized. [ 58 ] [ 76 ] [ 77 ]


In 2008,  New York  and  California  each provided notice to 23andMe and similar companies that those companies needed to obtain a  CLIA  license in order to sell tests in those states. [ 5 ] [ 78 ] [ 79 ]  By August 2008, 23andMe had received licenses that allow them to continue to do business in California. [ 80 ]


According to  Anne Wojcicki , 23andMe has been in dialogue with the FDA since 2008. [ 77 ]  In 2010, the FDA notified several genetic testing companies, including 23andMe, that their genetic tests are considered  medical devices  and federal approval is required to market them; a similar letter was sent to  Illumina , which makes the instruments and chips used by 23andMe in providing its service. [ 58 ] [ 81 ] [ 82 ]  23andMe first submitted applications for FDA clearance in July and September 2012. [ 83 ]


In November 2013, the FDA published guidance on how it classified genetic analysis and testing services offered by companies using instruments and chips labelled for "research use only" and instruments and chips that had been approved for clinical use. [ 84 ]


At around the same time, after not hearing from 23andMe for six months, the FDA ordered 23andMe to stop marketing its saliva collection kit and personal genome service (PGS), as 23andMe had not demonstrated that they have "analytically or clinically validated the PGS for its intended uses" and that the "FDA is concerned about the public health consequences of inaccurate results from the PGS device". [ 83 ] [ 85 ] [ 86 ]  As of December 2, 2013 [update] , 23andMe had stopped all advertisements for its PGS test but is still selling the product. [ 87 ] [ 88 ]  As of December 5, 2013 [update] , 23andMe was selling only raw  genetic data  and ancestry-related results. [ 7 ] [ 52 ]


23andMe publicly responded to media reports on November 25, 2013, stating, "We recognize that we have not met the FDA's expectations regarding timeline and communication regarding our submission. Our relationship with the FDA is extremely important to us and we are committed to fully engaging with them to address their concerns." [ 89 ] [ 90 ] [ 91 ]  CEO Anne Wojcicki subsequently posted an update on the 23andMe website, stating: "This is new territory for both 23andMe and the FDA. This makes the regulatory process with the FDA important because the work we are doing with the agency will help lay the groundwork for what other companies in this new industry do in the future. It will also provide important reassurance to the public that the process and science behind the service meet the rigorous standards required by those entrusted with the public's safety." [ 77 ]


On December 5, 2013, 23andMe announced that it had suspended health-related genetic tests for customers who purchased the test from November 22, 2013, in order to comply with the FDA warning letter, while undergoing regulatory review. [ 7 ] [ 52 ]


In May 2014, it was reported that 23andMe was exploring alternative locations abroad, including Canada, Australia, and the United Kingdom, in which to offer its full genetic testing service. [ 92 ]  23andMe had been selling a product with both ancestry and health-related components in Canada since October 2014, [ 9 ] [ 10 ] [ 11 ]  and in the UK since December 2014. [ 12 ]


In 2014, 23andMe submitted a  510(k)  application to the FDA to market a  carrier test  for  Bloom syndrome , which included data showing that 23andme's results were consistent and reliable and that the saliva collection kit and instructions were easy enough for people to use without making mistakes that might affect their results, and included citations to the  scientific literature  showing that the specific tests that 23andMe offered were associated with Blooms. [ 93 ] [ 94 ]  The FDA cleared the test in February 2015; in the clearance notice, the FDA said that it would not require similar applications for other carrier tests from 23andMe. [ 93 ] [ 95 ]  The FDA sent further clarification about regulation of the test to 23andMe on October 1, 2015. [ 96 ]


On October 21, 2015, 23andMe announced that it would begin marketing carrier tests in the US again. [ 8 ]  Wojcicki said, "There was part of us that didn’t understand how the regulatory environment works" in regards to the distributed laboratory regulatory functions of FDA and  Centers for Medicare and Medicaid Service  (CMS). [ 97 ]


23andMe submitted a " de novo " application to the FDA to market tests that provide people with information about whether they have  gene mutations  or  alleles  that put them at risk for getting or having certain diseases; the applications included data showing that 23andMe's results were consistent and reliable. In April 2017, the FDA approved the applications for ten tests: late-onset  Alzheimer's disease ,  Parkinson's disease ,  celiac disease ,  hereditary thrombophilia ,  alpha-1 antitrypsin deficiency ,  glucose-6-phosphate dehydrogenase deficiency , early-onset of  dystonia ,  factor XI deficiency , and  Gaucher's disease . [ 98 ] [ 99 ]  The FDA also said that it intended to exempt further 23andMe genetic risk tests from the needing 510(k) applications, and it clarified that it was only approving genetic risk tests, not  diagnostic tests . [ 98 ]


In March 2018, the FDA approved another  de novo  application from the company, this one for a  direct-to-consumer  test for three specific  BRCA  mutations  that are the most common  BRCA  mutations in people of  Ashkenazi descent ; they are not however the most common  BRCA  mutations in the general population, and the test is only for three of the approximately 1,000 known mutations. [ 100 ]  These mutations increase the risk of  breast  and  ovarian cancer  in women, and the risk of breast and  prostate cancer  in men. [ 101 ]


In June 2020, 23andMe published results from a study that claimed that people with  type O  blood may be at lower risk of catching COVID-19. Out of more than 750,000 participants, those with type O blood were 9–18% less likely to contract the virus, while those who had been exposed were 13–26% less likely to test positive. [ 102 ] [ 103 ] [ 104 ]  The study was published in April 2021. [ 105 ]


Questions have been raised since at least 2013 as to whether the company can obtain  informed consent  through its web-based interactions with people who want to submit samples for  sequencing . [ 106 ] [ 107 ]


The company collects not only genetic and personal information from customers who order DNA tests, but also data about other web behavior information that 23andMe captures through the use of its website, products, software,  cookies , and through its smartphone app. [ 108 ] [ 109 ]  A combination of several individual policies within the terms of service and privacy policy (cookies, disclosure of aggregate data, targeted advertising) makes 23andMe a valuable  data mine  for third parties such as  health insurance companies ,  pharmaceutical companies ,  advertising companies ,  biotechnology companies , law enforcement, or other interested parties. [ 110 ] [ 111 ] [ 112 ]  People may not actually be aware of how the company uses the data, and there are always risks of  data breaches . [ 113 ] [ 114 ]  Personal information of customers of 23andMe “may be accessed, sold or transferred”. [ 115 ]


Depending on which state an individual resides in, 23andMe must follow that state's laws regarding privacy and disclosing information. Since 23andMe is not a medical provider, the company does not have to abide by standard privacy policies that must be followed at a doctor's office, such as the  Health Insurance Portability and Accountability Act  (HIPAA). [ 114 ]  Research by Deloitte has shown that only 9% of consumers actually read the terms and conditions, and research from ProPrivacy concluded that only 1% of consumers read the policies, which suggests that consent to be included in research may have been given without full knowledge of the permissions being given. [ 116 ]  In addition, 23andMe's privacy policy can be confusing for consumers to understand. [ 117 ]  Despite confusion, 23andMe’s informed consent practices are approved by an  institutional review board . Several sections of the privacy policy allows data to be disclosed to third parties, regardless whether the consent is signed:


Section 4(b) "We permit third party advertising networks and providers to collect Web-Behavior Information regarding the use of our Services to help us to deliver targeted online advertisements ('ads') to you." [ 108 ]


Section 4(c): "Regardless of your consent status, we may also include your data in aggregate data that we disclose to third-party research partners who will not publish that information in a scientific journal." [ 118 ]


Section 4(d): "We may share some or all of your Personal Information with other companies under common ownership or control of 23andMe, which may include our subsidiaries, our corporate parent, or any other subsidiaries owned by our corporate parent in order to provide you better service and improve user experience." [ 108 ]


The  Genetic Information Nondiscrimination Act  (GINA) protects a person against discrimination based on genetic information by their employer(s) or insurance companies in  most  situations. However, GINA does not extend to discrimination based on genetic information for long-term care or disability-insurance providers.


Effective 25 May 2018, 23andMe must abide by the  General Data Protection Regulation  (GDPR). [ 119 ] [ 120 ]  The GDPR is a set of rules/regulations that helps an individual take control of their data information that is collected, used and stored digitally or in a structured filing system on paper, and restricts a company's use of personal data. [ 120 ]   The regulation also applies to companies who offer the company's products/services outside of the EU. [ 120 ]


Over 80% of 23andMe customers have opted in to having their data used in research. [ 121 ] [ 122 ]
Aggregated customer data is studied by scientific researchers employed by 23andMe for research on inherited disorders; rights to use customers' data is also sold to  pharmaceutical  and  biotechnology  companies for use in their research. [ 8 ] [ 49 ] [ 123 ]  The company also collaborates with academic and government scientists. [ 124 ] [ 54 ]  In July 2012, 23andMe acquired the startup CureTogether, a crowdsourced treatment ratings website with data on over 600 medical conditions. [ 125 ]   23andMe has an optional consent that enables the individual's genetic information to be included in medical research that may be published in a scientific journal.  However, if an individual chooses not to consent for their 'personal information' to be used, their 'genetic information' and 'self-reported information' may still be used and shared with the company's third party service providers. [ 108 ] [ 112 ]


In 2010, 23andMe said that it was able to use its database to validate work published by the NIH: identifying mutations in the gene that codes for  glucocerebrosidase  as a risk factor for Parkinson's disease. [ 124 ]


In 2015, 23andMe made a business decision to pursue  drug discovery  themselves, under the direction of former  Genentech  executive  Richard Scheller . [ 8 ] [ 126 ]  One of their main focuses is Parkinson's disease, and they are utilizing the 23andMe database to search for rare variants associated with Parkinson's in the hope of developing a drug for the disease. The company also set up research agreements with the pharmaceutical company  Pfizer  to explore the genetic causes of  inflammatory bowel disease , namely  ulcerative colitis  and  Crohn's disease . [ 127 ] [ 128 ]


In 2016, a project that the company was developing to provide customers with next generation sequencing was ended, because of the fear that the results would be too complicated or vague to fit into the company's goal of providing useful information, both quickly and precisely, directly to consumers, according to Wojcicki. [ 129 ]  Also in 2016, 23andMe used self-reported data from customers to locate 17 genetic  loci  that seem to be associated with  depression . [ 130 ]


In 2017, 23andMe, the  Lundbeck  pharmaceutical company, and the  Milken Institute  think tank started collaborations to focus on psychiatric disorders, such as  bipolar disorder  and  major depression . Their goals are to determine the genetic roots of such disorders, as well as pursue drug discovery in those areas. [ 131 ]


23andMe's transparency report states that they had received requests from law enforcement for 15 US users' data since 2015 as of September 2024, and none from other countries. Data Produced (By User Account) was zero. [ 132 ]  The 23andMe Privacy Policy states that customers "can rest assured, we will not voluntarily share your Personal Information with... Law enforcement, absent a valid court order, subpoena, or search warrant". [ 133 ]


In October 2023, a significant data breach occurred and sensitive information of 23AndMe users was stolen. Hackers stole 7 million people's data, about half of 23andMe's customers at the time. Stolen information included people's names, addresses and genetic data and was sold online. [ 134 ] [ 135 ]


The company claimed that it was a  credential stuffing  incident rather than a breach into their systems. The hackers targeted  Ashkenazi Jews  and users of Chinese descent and are attempting to sell the stolen information. The affected accounts had enabled the platform's  "DNA Relatives" feature, which helps users find genetic relatives and connect with each other. [ 136 ] [ 137 ]


At the end of 2024 23AndMe was in serious financial trouble, losing 98% of its  US$ 6bn value. Many of its 15 million customers, concerned about security of their DNA data stored by the company given its uncertain future, were attempting to delete it. [ 138 ]


The  Alternative Splicing and Transcript Diversity database  ( ASTD ) was a database of  transcript variants  maintained by the  European Bioinformatics Institute  from 2008 to 2012. [ 2 ]  It contained  transcription initiation ,  polyadenylation  and  splicing variant  data. [ 1 ]


Bgee  is a  database  maintained by the  SIB Swiss Institute of Bioinformatics  and the  University of Lausanne  for retrieval and comparison of  gene expression  patterns from  RNA-Seq ,  scRNA-Seq ,  Microarray ,  In situ hybridization  and  EST  studies, across multiple  animal   species . [ 1 ] [ 2 ]  Bgee provides an intuitive answer to the question  where is a gene expressed?  and supports research in  cancer  and  agriculture , as well as  evolutionary biology .


Bgee is based exclusively on curated, healthy  wild-type , expression data (i.e., no  gene knock-out , no treatment, no  disease ), to provide a comparable reference of healthy wild-type gene expression.


Bgee produces calls of presence/absence of expression, and of differential over-/under-expression, integrated along with information of gene  orthology , and of  homology  between  organs . This allows comparisons of expression patterns between  species .


Bgee allows searches by  gene ,  organ  /  tissue  /  cell type  and  developmental stage .


Bgee is a part of  Global Core Biodata Resources (GCBRs)  representing "critical components for ensuring the  reproducibility  and  integrity  of life sciences research."
Bgee is also an  ELIXIR  Recommended Interoperability Resources that facilitate the  FAIR -supporting activities in scientific research.


BioModels  is a free and  open-source  repository for storing, exchanging and retrieving quantitative models of biological interest created in 2006. [ 1 ] [ 2 ] [ 3 ]  All the models in the curated section of BioModels Database have been described in  peer-reviewed  scientific literature.


The models stored in BioModels' curated branch are compliant with  MIRIAM , the standard of model curation and annotation. The models have been simulated by curators to check that when run in simulations, they provide the same results as described in the publication. Model components are annotated, so the users can conveniently identify each model element and retrieve further information from other resources.


Modellers can submit the models in  SBML  and  CellML . Models can subsequently be downloaded in  SBML ,  VCML   Archived  2006-12-09 at the  Wayback Machine ,  XPP ,  SciLab ,  Octave ,  BioPAX  and  RDF/XML . The reaction networks of models are presented in some graphic formats, such as  PNG ,  SVG  and graphic  Java   applet , in which some networks were presented by following  Systems Biology Graphical Notation . And a human readable summary of each model is available in  PDF .


BioModels is composed of several branches. The curated branch hosts models that are well curated and annotated. The non-curated-branch provides models that are still not curated, are non-curatable (spatial models, steady-state models etc.), or too huge to be curated. Non-curated models can be later moved into the curated branch. The repository also hosts models which were automatically generated from pathways databases.


All the models are freely available under the  Creative Commons CC0 Public Domain Dedication , and can be easily accessed via the website or  Web Services . [ 4 ]  One can also download archives of all the models from the  EBI FTP server .


BioModels announced its 31st release on June 26, 2017. [ 5 ]  It now publicly provides 144,710 models. This corresponds to 1,640 models published in the literature and 143,070 models automatically generated from pathway resources.


Deposition of models in BioModels is advocated by many scientific journals, included  Molecular Systems Biology , all the journals of the  Public Library of Science , all the journals of  BioMed Central  and all the journals published by the  Royal Society of Chemistry .


BioModels is developed by the BioModels.net Team at the  EMBL - EBI , UK, the Le Novère lab at the  Babraham Institute , UK, and the  SBML  Team in  Caltech , USA. [ 6 ]


BioModels Development has benefited from the funds of the  European Molecular Biology Laboratory , the  Biotechnology and Biological Sciences Research Council , the  Innovative Medicines Initiative , the  Seventh Framework Programme (FP7) , the  National Institute of General Medical Sciences , the  DARPA , and the  National Center for Research Resources .


BioSamples  (BioSD) is a database at  European Bioinformatics Institute  for the information about the biological samples  used in  sequencing . [ 1 ]


It stores submitter-supplied  metadata  about the biological materials from which data stored in the  National Center for Biotechnology Information ’s (NCBI) primary data archives are derived. NCBI’s archives hosts data pertaining to diverse types of samples from many species, and as such the BioSample database is similarly diverse. Examples of a BioSample include a primary tissue  biopsy , an individual  organism  or an environmental isolate.


The BioSamples database captures sample metadata in a structured way by encouraging use of controlled sample attribute field name vocabularies. This metadata is key in giving the sample data context, allowing it to be more fully understood, reused,  and enables aggregation of disparate data sets.


Sample metadata is linked to relevant experimental data across many archival databases relieving submitter burden by enabling one-time submission of sample description. They then can reference that sample, when necessary, when making data deposits to other archives.


BioSample records are  indexed  and searchable, supporting cross-database queries by sample description.


The BioSamples database was launched in 2011 to help aggregate and standardise sample  metadata . Historically, each archive had created its own convention for sample metadata collection. These usually were limited in their standardisation and had no method to indicate when the a sample was used across multiple data sets.
In addition to this, there is a growing awareness amongst the research community that sample metadata is vital for understanding the underlying data. Further, chances for re-use, aggregation and integration of data are increased with improved metadata.
The database was initially populated with existing descriptions extracted from  SRA ,  EST ,  GSS  and  dbGaP . [ 2 ]  As of May 2013, the database hosts almost 2 million BioSample records encompassing 18,000 species. [ 3 ]


The BioSamples database has doubled in size since January 2012 when 1 million samples were described in the BioSamples database, as of October 2013 2,846,137 samples are available as 80,232 groups.
 [ 4 ]
The rapid growth is predominantly due to new data sources, and increased volume of data from existing sources. New data sources include 22,288 samples from The Cancer Genome Atlas, and 920,441 samples from the Catalogue of Somatic Mutation in Cancer (COSMIC).
 [ 5 ]


Attributes define the material under investigation using structured name: value pairs, for example:


tissue: liver
 collection date: 31-Jan-2013


After specifying the sample type, the user is presented with a list of required and optional attribute fields to fill in, as well as the opportunity to supply any number of custom descriptive attributes. The BioSample database is extendible in that new types and attributes can be added as new standards develop.
In addition to BioSample type and attributes, each BioSample record also contains:


The full list and definitions of BioSample types and attributes is available for preview and download. [ 6 ]


There are a number of ways in which the database can be accessed. The initial release of BioSD to the public only provided access to the database through a web interface. This web interface was subsequently updated in November 2012 and then again in March 2013 following the EBI site-wide re-launch.  In February 2013, a public  Application Programming Interface  (API) was released using a  Representational state transfer  (REST) system. In October 2013, as a part of the EBI's new  RDF  platform a  SPARQL  endpoint was released, providing access to the data in the RDF format. Additionally, the database can be downloaded through EBI's  FTP  service. [ 7 ]


The web interface allows users to access the BioSD database through a web browser. It provides functionality for both searching by sample groups and by samples themselves. The search features  incremental search  to assist users by providing them with possible search terms as they type. Advanced search is provided and allows users to search by applying the binary terms, AND, OR and NOT, to their search terms. Additionally, a  wildcard character  can be used to match any combination of characters including no characters. A question mark character can also be used to match any single character. [ 8 ]  Examples of these can be seen in the following table:


The web interface also allows users to select search results and view further details of that search result. The detailed view provides further information and makes available a link to the assay database(s) from which the data was sourced. Ordering by columns is also provided.


The API provides a suitable method for retrieving data in a programmatic way. It uses a RESTful system that allows users to query  URI  endpoints and receive  XML  as results. The API has URI endpoints for a number of different types of requests. These requests can be used to, find specific samples, find specific groups, search for groups, search for samples and to search for samples within a group. [ 9 ]


The SPARQL endpoint allows users to search the database in a more comprehensive way than the standard web interface whilst still being usable from a web browser. [ 10 ]  Through this interface, far more complex queries can be made to further enable users in their searches. However, there is an increased learning curve with this method of accessing the data. The SPARQL endpoint returns results in the RDF format which was initially designed with metadata in mind and is thus suited to the needs of BioSD. [ 11 ]


The development team forms a part of Helen Parkinson's team at EMBL-EBI and contains software engineers and web developers who are assisted with domain specific knowledge by ontologists and bioinformaticians.


The primary programming language used on the project is the  Java  programming language. To aid the development of the project, the development teams uses the  integrated development environment ,  IntelliJ IDEA  which is provided by  JetBrains . Other tools used in the project include  Bamboo  for  continuous integration  and the management of software releases. Additionally,  YourKit  is a Java profiler which helps optimise and eliminate bugs in the BioSD project. [ 12 ]


The project is developed as an  open-source  project with all source code being freely available on  GitHub . [ 13 ]


Currently the primary funding for the BioSD database development and maintenance is provided by the  European Molecular Biology Laboratory  (EMBL) core budget which is in turn funded by its 20 member countries. [ 1 ]  There has also been additional contributions from the  European Commission  in the form of a number of grants. [ 14 ]  Further funding has come from the  Human Induced Pluripotent Stem Cells Initiative  provided by the  Wellcome Trust  and the  Medical Research Council  and from the EBiSC  Innovative Medicines Initiative . [ 15 ]


Canto  is a web-based tool to support the curation of gene-specific scientific data, by both professional biocurators and publication authors. [ 1 ]  Canto was developed as part of the  PomBase  project, [ 2 ]  and is funded by the  Wellcome Trust .


Canto enables experts (biocurators and publication authors) to provide detailed, standardized, sharable annotation from research publications and was originally created for the fission yeast community. Canto is a generic tool that can be readily configured for use with other organisms and other databases and now supports pathogen-host interactions for  PHI-base  ( Rothamsted research ) and the curation of phenotypes and genetic interactions at  FlyBase  ( University of Cambridge ), and all gene-specific datatypes for the emerging model species  Schizosaccharomyces japonicus  in JaponicusDB.


Canto supports the use of bio-ontologies (including the  Gene Ontology ,  Protein Ontology , The Fission Yeast Phenotype Ontology FYPO, and the  Sequence Ontology   to describe attributes of gene products.  Complex ontology structures are hidden by an intuitive search, browse, and drill-down workflow. Canto workflow guides the user through the curation process with prompts for required qualifiers and metadata (for example evidence (provenance), annotation extensions, and experimental conditions). Prompts are tailored to different data types, and their individual specific domains and ranges.


Canto has been successful in supporting community curation, and most of the new curation in PomBase is provided by the community of researchers who use the fission yeast  Schizosaccharomyces pombe  as a  model organism .  The PomBase team demonstrate that co-curation by publication authors and professional curators provides higher quality curation to maximise the value and impact of scientific research. [ 3 ]


CaSNP  is database for storing data about  copy number alterations  from  SNP arrays  for different types of  cancer . [ 1 ]


The  Clinical Genome Resource  (ClinGen) is an initiative developed by the  National Human Genome Research Institute  (NHGRI) with the aim of creating a knowledge base of the clinical relevance of genes and variants for use in precision medicine and research. It was founded by the NHGRI in 2013, and is now funded by the  National Institutes of Health  (NIH). It is currently developing a resource that aims to define the relevance of genes and variants for utilisation clinically in precision medicine and other research purposes. [ 1 ] [ 2 ] [ 3 ]


The  Comparative Toxicogenomics Database  ( CTD ) is a public website and research tool launched in November 2004 that curates scientific data describing relationships between chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, GO annotations, pathways, and interaction modules.
The database is maintained by the Department of Biological Sciences at  North Carolina State University .


The Comparative Toxicogenomics Database (CTD) is a public website and research tool that curates scientific data describing relationships between chemicals, genes/proteins, diseases, taxa, phenotypes, GO annotations, pathways, and interaction modules, launched on November 12, 2004. [ 1 ] [ 2 ] [ 3 ] [ 4 ]
The database is maintained by the Department of Biological Sciences at North Carolina State University. [ citation needed ]


One of the primary goals of CTD is to advance the understanding of the effects of environmental chemicals on human health on the genetic level, a field called  toxicogenomics .


The  etiology  of many chronic diseases involves interactions between environmental factors and genes that modulate important physiological processes. Chemicals are an important component of the environment. Conditions such as  asthma , cancer,  diabetes ,  hypertension ,  immunodeficiency , and  Parkinson's disease  are known to be influenced by the environment; however, the molecular mechanisms underlying these correlations are not well understood. CTD may help resolve these mechanisms. The most up-to-date extensive list of peer-reviewed scientific articles about CTD is available at their publications page [ 5 ]


CTD is a unique resource where  biocurators [ 6 ] [ 7 ]  read the scientific literature and manually curate four types of core data:


By integrating the above four data sets, CTD automatically constructs putative chemical-gene-phenotype-disease networks to illuminate molecular mechanisms underlying environmentally-influenced diseases.


These inferred relationships are statistically scored and ranked and can be used by scientists and computational biologists to generate and verify testable hypotheses about toxicogenomic mechanisms and how they relate to human health.


Users can search CTD to explore scientific data for chemicals, genes, diseases, or interactions between any of these three concepts.  Currently, [ when? ]  CTD integrates toxicogenomic data for vertebrates and invertebrates.


CTD integrates data from or hyperlinks to these databases:


The  Consensus Coding Sequence (CCDS) Project  is a collaborative effort to maintain a dataset of protein-coding regions that are identically  annotated  on the human and mouse reference genome assemblies. The CCDS project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented by the National Center for Biotechnology Information  (NCBI) ,  Ensembl , and  UCSC Genome Browser . [ 1 ]  The integrity of the CCDS dataset is maintained through stringent  quality assurance testing  and on-going  manual curation . [ 2 ]


Biological and biomedical research has come to rely on accurate and consistent annotation of genes and their products on genome assemblies.  Reference annotations of genomes are available from various sources, each with their own independent goals and policies, which results in some annotation variation.


The CCDS project was established to identify a gold standard set of protein-coding gene annotations that are identically annotated on the human and mouse  reference genome  assemblies by the participating annotation groups.  The CCDS gene sets that have been arrived at by consensus of the different partners  [ 2 ]  now consist of over 18,000 human and over 20,000 mouse genes (see  CCDS release history ).  The CCDS dataset is increasingly representing more  alternative splicing  events with each new release. [ 3 ]


Participating annotation groups include: [ 3 ]


Manual annotation is provided by:


"Consensus" is defined as protein-coding regions that agree at the start codon, stop codon, and splice junctions, and for which the prediction meets quality assurance benchmarks. [ 1 ]  A combination of manual and automated genome annotations provided by  (NCBI)
and  Ensembl  (which incorporates manual HAVANA annotations) are compared to identify annotations with matching genomic coordinates.


In order to ensure that CDSs are of high quality, multiple quality assurance (QA) tests are performed (Table 1).  All tests are performed following the annotation comparison step of each CCDS build and are independent of individual annotation group QA tests performed prior to the annotation comparison. [ 3 ]


Annotations that fail QA tests undergo a round of manual checking that may improve results or reach a decision to reject annotation matches based on QA failure.


The CCDS database is unique in that the review process must be carried out by multiple collaborators, and agreement must be reached before any changes can be made.  This is made possible with a collaborator coordination system that includes a work process flow and forums for analysis and discussion. The CCDS database operates an internal website that serves multiple purposes including curator communication, collaborator voting, providing special reports and tracking the status of CCDS representations.  When a collaborating CCDS group member identifies a CCDS ID that may need review, a voting process is employed to decide on the final outcome.


Coordinated manual curation is supported by a restricted-access website and a discussion e-mail list.  CCDS curation guidelines were established to address specific conflicts that were observed at a higher frequency.  Establishment of CCDS curation guidelines has helped to make the CCDS curation process more efficient by reducing the number of conflicting votes and time spent in discussion to reach a consensus agreement.  A link to the CCDS curation guidelines can be found  here .


Curation policies established for the CCDS data set have been integrated in to the  RefSeq  and HAVANA annotation guidelines and thus, new annotations provided by both groups are more likely to be concordant and result in addition of a CCDS ID.  These standards address specific problem areas, are not a comprehensive set of annotation guidelines, and do not restrict the annotation policies of any collaborating group. [ 2 ]  Examples include, standardized curation guidelines for selection of the initiation codon and interpretation of upstream  ORFs  and transcripts that are predicted to be candidates for  nonsense-mediated decay .  Curation occurs continuously, and any of the collaborating centers can flag a CCDS ID as a potential update or withdrawal.


Conflicting opinions are addressed by consulting with scientific experts or other annotation curation groups such as the HUGO Gene Nomenclature Committee  (HGNC)  and Mouse Genome Informatics  (MGI) .  If a conflict cannot be resolved, then collaborators agree to withdraw the CCDS ID until more information becomes available.


Nonsense-mediated decay (NMD):
 NMD  is the most powerful  mRNA  surveillance process.  NMD  eliminates defective  mRNA  before it can be translated into protein. [ 4 ]  This is important because if the defective  mRNA  is translated, the truncated protein may cause disease. Different mechanisms have been proposed to explain  NMD ; one being the  exon junction complex  (EJC) model.  In this model, if the stop codon is >50 nt upstream of the last exon-exon junction, the transcript is assumed to be a  NMD  candidate. [ 2 ]  The CCDS collaborators use a conservative method, based on the EJC model, to screen mRNA transcripts.  Any transcripts determined to be  NMD  candidates are excluded from the CCDS data set except in the following situations: [ 2 ]


Previously,  NMD  candidate transcripts were considered to be protein coding transcripts by both  RefSeq  and HAVANA, and thereby, these  NMD  candidate transcripts were represented in the CCDS data set. The  RefSeq  group and the HAVANA project have subsequently revised their annotation policies.


Multiple in-frame translation start sites:
Multiple factors contribute to translation initiation, such as upstream  open reading frames  (uORFs), secondary structure and the sequence context around the translation initiation site.  A common start site is defined within Kozak consensus sequence: (GCC) GCCACCAUGG in vertebrates.  The sequence in brackets (GCC) is the motif with unknown biological impact. [ 5 ]  There are variations within Kozak consensus sequence, such as G or A is observed three nucleotides upstream (at position -3) of AUG. Bases between positions -3 and +4 of Kozak sequence have the most significant impact on translational efficiency. Hence, a sequence (A/G)NNAUGG is defined as a strong Kozak signal in the CCDS project.


According to the scanning mechanism, the small ribosomal subunit can initiate translation from the first reached start codon. There are exceptions to the scanning model:


According to the CCDS annotation guidelines, the longest  ORF  must be annotated except when there is experimental evidence that an internal start site is used to initiate translation. Additionally, other types of new data, such as ribosome profiling data, [ 6 ]  can be used to identify start codons. The CCDS data set records one translation initiation site per CCDS ID. Any alternative start sites may be used for translation and will be stated in a CCDS public note.


Upstream open reading frames:
AUG initiation codons located within transcript leaders are known as upstream AUGs (uAUGs). Sometimes, uAUGs are associated with u ORFs  . u ORFs  are found in approximately 50% of human and mouse transcripts. [ 7 ]  The existence of u ORFs  are another challenge for the CCDS data set. The scanning mechanism for translation initiation suggests that small ribosomal subunits (40S) bind at the 5’ end of a nascent  mRNA  transcript and scan for the first AUG start codon. [ 5 ]  It is possible that an uAUG is recognised first, and the corresponding uORF is then translated. The translated u ORF  could be a  NMD  candidate, although studies have shown that some u ORFs  can avoid  NMD . The average size limit for u ORFs  that will escape  NMD  is approximately 35  amino acids . [ 2 ] [ 8 ]  It also has been suggested that u ORFs  inhibit translation of the downstream gene by trapping a  ribosome  initiation complex and causing the  ribosome  to dissociate from the  mRNA  transcript before it reaches the protein-coding regions. [ 4 ] [ 7 ]  Currently, no studies have reported the global impact of u ORFs  on translational regulation.


The current CCDS annotation guidelines allow the inclusion of  mRNA  transcripts containing u ORFs  if they meet the following two biological requirements: [ 2 ]


Read-through transcripts:
Read-through transcripts are also known as  conjoined genes  or co-transcribed genes. Read-through transcripts are defined as transcripts combining at least part of one exon from each of two or more distinct known (partner) genes which lie on the same chromosome in the same orientation. [ 9 ]  The biological function of read-through transcripts and their corresponding protein molecules remain unknown. However, the definition of a read-through gene in the CCDS data set is that the individual partner genes must be distinct, and the read-through transcripts must share ≥ 1 exon (or ≥ 2 splice sites except in the case of a shared terminal exon) with each of the distinct shorter loci. [ 2 ]  Transcripts are not considered to be read-through transcripts in the following circumstances:


Quality of reference genome sequence:
As the CCDS data set is built to represent genomic annotations of human and mouse, the quality problems with the human and mouse  reference genome  sequences become another challenge. Quality problems occur when the reference genome is misassembled. Thereby the misassembled genome may contain premature  stop codons ,  frame-shift indels , or likely polymorphic  pseudogenes . Once these quality problems are identified, the CCDS collaborators report the issues to the Genome Reference Consortium, which investigates and makes the necessary corrections.


The CCDS project is available from the NCBI CCDS data set page  (here) , which provides FTP download links and a query interface to acquire information about CCDS sequences and locations. CCDS reports can be obtained by using the query interface, which is located at the top of the CCDS data set page. Users can select various types of identifiers such as CCDS ID, gene ID, gene symbol, nucleotide ID and protein ID to search for specific CCDS information. [ 1 ]  The CCDS reports (Figure 1) are presented in a table format, providing links to specific resources, such as a history report,  Entrez Gene [ 10 ]  or re-query the CCDS data set. The sequence identifiers table presents transcript information in  VEGA ,  Ensembl  and  Blink . The chromosome location table includes the genomic coordinates for each individual exon of the specific coding sequence. This table also provides links to several different genome browsers, which allow you to visualise the structure of the coding region. [ 1 ]  Exact nucleotide sequence and protein sequence of the specific coding sequence are also displayed in the section of CCDS sequence data.


The CCDS dataset is an integral part of the  GENCODE  gene annotation project [ 11 ]  and it is used as a standard for high-quality coding exon definition in various research fields, including clinical studies, large-scale  epigenomic  studies,  exome  projects and exon array design. [ 3 ]   Due to the consensus annotation of CCDS exons by the independent annotation groups,  exome  projects in particular have regarded CCDS coding exons as reliable targets for downstream studies (e.g., for  single nucleotide variant  detection), and these exons have been used as  coding region  targets in commercially available  exome  kits. [ 12 ]


The CCDS data set size has continued to increase with both the computational genome annotation updates, which integrate new data sets submitted to the International Nucleotide Sequence Database Collaboration  (INSDC ), and on ongoing curation activities that supplement or improve upon that annotation. Table 2 summarises the key statistics for each CCDS build where  Public CCDS IDs  are all those that were not under review or pending an update or withdrawal at the time of the current release date.


The complete set of release statistics can be found at the official CCDS website on their  Releases & Statistics  page.


Long-term goals include the addition of attributes that indicate where transcript annotation is also identical (including the  UTRs ) and to indicate splice variants with different  UTRs  that have the same CCDS ID.  It is also anticipated that as more complete and high-quality genome sequence data become available for other organisms, annotations from these organisms may be in scope for CCDS representation.


The CCDS set will become more complete as the independent curation groups agree on cases where they initially differ, as additional experimental validation of weakly supported genes occurs, and as automatic annotation methods continue to improve. Communication among the CCDS collaborating groups is ongoing and will resolve differences and identify refinements between CCDS update cycles.  Human updates are expected to occur roughly every 6 months and mouse releases yearly. [ 3 ]


DAnCER  (disease-annotated  chromatin   epigenetics  resource) is a database for  chromatin  modifications and their relation to human disease. [ 1 ] [ 2 ]


It was developed by the  Wodak Lab  at the  Hospital for Sick Children .


It has been developed to serve as the core bioinformatics resource for seven experimental and bioinformatics laboratories working together to unravel the mechanisms of chromatin modifications and their relation to human disease. Since molecular networks are essential to the understanding of biological processes, this research effort strives to explore CM-related genes in the full context of protein complexes, gene-expression regulation and pathways. To gain additional insights into the CM process in human cells, it also explores patterns of evolutionary conservation across model organisms - from sequence, domain composition and 3D structure, to interaction patterns and regulatory mechanisms.


DAVID  (the  database for annotation, visualization and integrated discovery ) is a free online  bioinformatics  resource developed by the Laboratory of Human Retrovirology and Immunoinformatics ( LHRI ). [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ]  All tools in the DAVID Bioinformatics Resources aim to provide functional interpretation of large lists of  genes  derived from genomic studies, e.g.  microarray  and  proteomics  studies. DAVID can be found at  https://david.ncifcrf.gov/


The DAVID Bioinformatics Resources consists of the DAVID Knowledgebase and five integrated, web-based functional annotation tool suites: the DAVID Gene Functional Classification Tool, the DAVID Functional Annotation Tool, the DAVID Gene ID Conversion Tool, the DAVID Gene Name Viewer and the DAVID NIAID Pathogen Genome Browser. The expanded DAVID Knowledgebase now integrates almost all major and well-known public bioinformatics resources centralized by the DAVID Gene Concept, a  single-linkage  method to agglomerate tens of millions of diverse gene/protein identifiers and annotation terms from a variety of public bioinformatics databases. For any uploaded gene list, the DAVID Resources now provides not only the typical gene-term enrichment analysis, but also new tools and functions that allow users to condense large gene lists into gene functional groups, convert between gene/protein identifiers, visualize many-genes-to-many-terms relationships, cluster redundant and heterogeneous terms into groups, search for interesting and related genes or terms, dynamically view genes from their lists on bio-pathways and more.


DAVID 2021 update was released in December 2021. [ 8 ]  The knowledgebase has been scheduled to update quarterly.


DAVID provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes. For any given gene list, DAVID tools are able to:


The  Single Nucleotide Polymorphism Database [ 1 ]  ( dbSNP ) is a free public archive for  genetic variation  within and across different  species  developed and hosted by the  National Center for Biotechnology Information  (NCBI) in collaboration with the  National Human Genome Research Institute  (NHGRI). Although the name of the database implies a collection of one class of  polymorphisms  only (i.e.,  single nucleotide polymorphisms  (SNPs)), it in fact contains a range of molecular variation: (1)  SNPs , (2) short deletion and insertion polymorphisms ( indels /DIPs), (3)  microsatellite  markers or  short tandem repeats  (STRs), (4) multinucleotide polymorphisms (MNPs), (5) heterozygous sequences, and (6) named variants. [ 2 ]  The dbSNP accepts apparently neutral polymorphisms, polymorphisms corresponding to known phenotypes, and regions of no variation. It was created in September 1998 to supplement  GenBank , NCBI’s collection of publicly available nucleic acid and protein sequences. [ 2 ]


In 2017, NCBI stopped support for all non-human organisms in dbSNP. [ 3 ]  As of build 153 (released in August 2019), dbSNP had amassed nearly 2 billion submissions representing more than 675 million distinct variants for  Homo sapiens .


dbSNP is an online resource implemented to aid  biology  researchers. Its goal is to act as a single  database  that contains all identified genetic variation, which can be used to investigate a wide variety of genetically based natural phenomena. Specifically, access to the molecular variation cataloged within dbSNP aids basic research such as physical mapping,  population genetics , investigations into evolutionary relationships,  as well as being able to quickly and easily quantify the amount of variation at a given site of interest. In addition, dbSNP guides applied research in  pharmacogenomics  and the association of genetic variation with phenotypic traits. [ 4 ]  According to the NCBI website, “The long-term investment in such novel and exciting research [dbSNP] promises not only to advance human biology but to revolutionise the practice of modern medicine.”


Originally, dbSNP accepts submissions for any  organism  from a wide variety of sources including individual research laboratories, collaborative polymorphism discovery efforts, large scale genome sequencing centers, other SNP databases (e.g. the SNP consortium,  HapMap , etc.), and private businesses. [ 5 ]  On September 1, 2017, dbSNP stopped accepting non-human variant data submissions and two months later, its interactive websites and related NCBI services stopped presenting non-human variant data. Now dbSNP only accepts and presents human variant data.


Every submitted variation receives a submitted SNP ID number (“ss#”). [ 5 ]  This accession number is a stable and unique identifier for that submission. Unique submitted SNP records also receive a reference SNP ID number (“rs#”; "refSNP cluster"). However, more than one record of a variation will likely be submitted to dbSNP, especially for clinically relevant variations. To accommodate this, dbSNP routinely assembles identical submitted SNP records into a single reference SNP record, which is also a unique and stable identifier (see below). [ 4 ]


To submit variations to dbSNP, one must first acquire a submitter handle, which identifies the laboratory responsible for the submission. [ 4 ]  Next, the author is required to complete a submission file containing the relevant information and data. Submitted records must contain the ten essential pieces of information listed in the following table. [ 4 ]  Other information required for submissions includes contact information, publication information (title, journal, authors, year), molecule type (genomic  DNA ,  cDNA ,  mitochondrial  DNA,  chloroplast  DNA), and organism. [ 4 ]


New information obtained by dbSNP becomes available to the public periodically in a series of “builds” (i.e. revisions and releases of data). [ 4 ]  There is no schedule for releasing new builds; instead, builds are usually released when a new genome build becomes available, assuming that the genome has some cataloged variation associated with it. [ 6 ]  This occurs approximately every 3–4 months. Genome sequences may be improved over time so reference SNPs (“refSNP”) from previous builds, as well as new submitted SNPs, are re-mapped to the newly available genome sequence. Multiple submitted SNPs, if mapping to the same location, are clustered into one refSNP cluster and are assigned a reference SNP ID number. However, if two refSNP cluster records are found to map to the same location (i.e. are identical), dbSNP will also merge those records. In this case, the smaller refSNP number ID (i.e. the earliest record) would now represent both records, and the larger refSNP number IDs would become obsolete. These obsolete refSNP number IDs and are not used again for new records. When a merger of two refSNP records occurs, the change is tracked, and the former refSNP number IDs can still be used as a search query. This process of merging identical records reduces redundancy within dbSNP. [ 6 ]


There are two exceptions to the above merging criteria. First, variation of different classes (e.g. a SNP and a DIP) are not merged. Secondly, clinically important refSNPs that have been cited in the literature are termed “precious”; a merger that would eliminate such a refSNP is never performed, since it could later cause confusion. [ 6 ]


The dbSNP can be searched using the Entrez SNP search tool. A variety of queries can be used for searching: an ss number ID, a refSNP number ID, a gene name, an experimental method, a population class, a population detail, a publication, a marker, an allele, a chromosome, a base position, a heterozygosity range, or a build number. [ 6 ] [ 7 ]  In addition, many results can be retrieved simultaneously using batch queries. [ 6 ]  Searches return refSNP number IDs that match the query term and a summary of the available information for that refSNP cluster.


The information available for a refSNP cluster includes the basic information from each of the individual submissions (see “Submission”) as well as information available from combining the data from multiple submissions (e.g. heterozygosity, genotype frequencies).  Many tools are available to examine a refSNP cluster in greater depth. Map view shows the position of the variation in the genome and other nearby variations. Another tool, gene view reports the location of the variation within a gene (if it is in a gene), the old and new codon, the amino acids encoded by both, and whether the change is synonymous or non-synonymous. Sequence viewer shows the position of the variant in relation to  introns ,  exons , and other distant and close variants. 3D structure mapping, which shows 3D images of the encoded protein is also available.


The dbSNP is also linked to many other NCBI resources including the  nucleotide ,  protein ,  gene ,  taxonomy  and structure databases, as well as  PubMed , UniSTS,  PMC ,  OMIM , and UniGene.


The validation status list the categories of evidence that support a variant. These include: (1) multiple independent submissions; (2) frequency or genotype data; (3) submitter confirmation; (4) observation of all alleles in at least two chromosomes; (5) genotyped by  HapMap ; and (6) sequenced in the  1000 Genomes Project . [ 6 ]


The quality of the data found on dbSNP has been questioned by many research groups, [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ]  which suspect high  false positive  rates due to  genotyping  and base-calling errors. These mistakes can easily be entered into dbSNP if the submitter uses (1) uncritical  bioinformatic  alignments of highly similar but distinct DNA sequences, and/or (2)  PCRs  with  primers  that cannot discriminate between similar but distinct DNA sequences. [ 8 ]  Mitchell  et al.  (2004)  [ 9 ]  reviewed four studies  [ 10 ] [ 11 ] [ 12 ] [ 13 ]  and concluded that dbSNP has a false positive rate between 15-17% for SNPs, and also that the minor  allele  frequency is greater than 10% for approximately 80% of the SNPs that are not false positives. Similarly, Musemeci  et al.  (2010) [ 8 ]  states that as many as 8.32% of the biallelic coding SNPs in dbSNP are artifacts of highly similar DNA sequences (i.e. paralogous genes) and refer to these entries as single nucleotide differences (SNDs). The high error rates in dbSNP may not be surprising: of the 23.7 million refSNP entries for humans, only 14.5 million have been validated, leaving the remaining 9.2 million as candidate SNPs. However, according to Musemeci  et al.  (2010), [ 8 ]  even the validation code provided in the refSNP record is only partially useful: only HapMap validation reduced the number of SNDs (3% vs 8%), but only accepting this method removes more than half of the real SNPs in the dbSNP. These authors also note that one source of submissions from the Lee group are plagued with errors: 20% of these submissions are SNDs (vs. 8% for submissions). However, as the authors note, ignoring all of these submissions would remove many real SNPs.


Errors in the dbSNP can hamper candidate gene association studies [ 14 ]  and  haplotype -based investigations. [ 15 ]  Errors may also increase false conclusions in association studies: [ 8 ]  increasing the number of SNPs that are tested by testing false SNPs requires more hypothesis tests. However, these false SNPs cannot actually be associated with traits, so the alpha level is decreased more than is necessary for a rigorous test if only the true SNPs were tested and the false negative rate will increase. Musemeci  et al.  (2010) [ 8 ]  suggested that authors of negative association studies inspect their previous studies for false SNPs (SNDs), which could be removed from analysis.


Individual sequences can be referred to by their refSNP cluster ID numbers (e.g. rs206437). dbSNP should be referenced using the 2001 Sherry  et al.  paper: Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M., Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Research, 29: 308-311. [ 5 ]


DECIPHER  is a web-based resource and database of genomic variation data from analysis of patient  DNA . [ 1 ] [ 2 ] [ 3 ]  It documents submicroscopic  chromosome abnormalities  ( microdeletions  and  duplications ) and pathogenic sequence variants (single nucleotide variants - SNVs, Insertions, Deletions, InDels), from over 25000 patients and maps them to the  human genome  using  Ensembl  or  UCSC Genome Browser . [ 1 ] [ 2 ] [ 4 ]  In addition it catalogues the clinical characteristics from each patient and maintains a database of microdeletion/duplication  syndromes , together with links to relevant scientific reports and  support groups . [ 1 ] [ 5 ]


An  acronym  of  DatabasE of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources , DECIPHER was initiated in 2004 at the  Sanger Institute  in the United Kingdom, funded by the  Wellcome Trust . [ 1 ]  However it is supported by an international research consortium, with patient data contributed by more than 240  clinical genetics  centres from 33 countries. Each centre is represented by an experienced clinical geneticist and a senior  molecular cytogeneticist . [ 6 ]


DECIPHER was established in 2004 by Nigel Carter of the Wellcome Trust Sanger Institute and  Helen Firth , a clinical genetics consultant at  Addenbrooke's Hospital  in  Cambridge . It has three main aims: [ 6 ] [ 7 ]


As a tool for clinical geneticists, cytogeneticists and  molecular biologists , DECIPHER is used to determine whether gene  copy number variations  identified in patients are of clinical significance. Members can visualise the genes within the region of DNA altered in their patients, and ascertain whether any are known to be implicated in disease.  Chromosomal imbalances are a major cause of developmental delay, learning disabilities and congenital abnormalities and — according to Emily Niemitz writing in  Nature Genetics  — the database facilitates collaboration between researchers and clinicians who have patients with similar clinical characteristics, which can "assist in the discovery of new syndromes and in the recognition of genes of clinical importance." [ 4 ] [ 5 ]


Patients are entered into DECIPHER by registered consortium members. Typically a clinical geneticist arranges for a chromosome analysis (usually  microarray  based) of a patient's DNA. A potential microdeletion/microduplication may be identified, but the medical significance is not known. [ 8 ]  The clinician may enter the anonymised data into the restricted, password protected DECIPHER database and map the location and size of the chromosomal deletion/duplication to the  reference genome . Using DECIPHER, the clinician can then identify the specific genes affected by the deletion/duplication, determine whether any have known clinical significance (for example, whether  tumour suppressor genes  have been deleted), [ 9 ]  and view the region in the Ensembl genome browser to see whether there are any other consented patients in DECIPHER with overlapping deletion/duplications. [ 8 ]  This enables a better ascertainment of whether a copy number change is a normal  polymorphic  variant, or the likely cause of the patient's  clinical symptoms . The clinician can then counsel the patient on the likely significance of the deletion/duplication, and its implications for their health. [ 8 ]


Each patient's data is anonymized, and represented only by an ID with an associated  genotype  and set of clinical symptoms ( phenotypes ). Patient data is made accessible to other members of the consortium and viewable through Ensembl if a  consent  form is signed by the patient. [ 8 ]  With informed consent, the anonymized deletion/duplication and phenotypes become available for view to DECIPHER consortium members and public users, with different levels of access (e.g. only logged users can see the contact details of the centre that entered the data). Public users who wish to find more information about a patient may send a request to DECIPHER, which then will forward it to the clinician coordinator responsible for the submitting center.


Most patients deposited in DECIPHER display genetic mutations with a very low occurrence in the general population. Hence the probability of the same clinicians encountering similar patients are also low. Since DECIPHER is opened to any accredited clinician or cytogeneticist from around the world, the chances of finding similar rare cases are significantly increased. This on-line sharing of clinical genetic information not only promotes better understanding of microdeletions/microduplications and their associated pathogenic phenotypes, it has also facilitated the discovery of new  syndromes . [ 10 ] [ 11 ] [ 12 ] [ 13 ]  As of January 2014, over 23000 patients have been entered into the DECIPHER database of which over 10000 are consented.


The appropriate consent to enter patient data into DECIPHER is obtained by the submitting clinician. [ 14 ]  Patient consent can be withdrawn at any time, and their data is removed. [ 7 ]  Often children's records are displayed with the consent of their parents of guardians. DECIPHER advises that, when the child reaches the age of sixteen years, he or she be made aware of the entry and be given the opportunity to withdraw or continue as a participant. [ 7 ]   Each member centre that uses DECIPHER obtains ethical approval from a research  ethics committee  in their own institution or country, where applicable. In the UK, the  Information Commissioner's Office  has been notified about DECIPHER in accordance with the  Data Protection Act 1998 . [ 14 ]  The project is overseen by an  advisory board  representatives from the field of human genetics,  computational biology , ethics and law. [ 15 ]


To ensure  information privacy , data is served over an  encrypted   TLS/SSL  connection. Only trusted individuals from recognized medical research centres can access the identity of the center that submitted another patient (permitting them to contact the patient's clinicians should they wish to collaborate). Members of the public may browse consented anonymized patient data in DECIPHER and Ensembl, without the identity of the submitting centre being shown. [ 8 ] [ 14 ]


DisGeNET  is a discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET is one of the largest and comprehensive repositories of human gene-disease associations (GDAs) currently available. [ 1 ]  It also offers a set of bioinformatic tools to facilitate the analysis of these data by different user profiles. It is maintained by the  Integrative Biomedical Informatics (IBI) Group   Archived  2016-11-26 at the  Wayback Machine , of the (GRIB)-IMIM/UPF, based at the  Barcelona Biomedical Research Park  (PRBB), Barcelona,  Spain .


In the pursuit to gather different aspects of the current knowledge on the genetic basis of human diseases, DisGeNET covers information on all disease areas (Mendelian, complex and environmental diseases). With more than 400 000 genotype-phenotype relationships from different origins integrated and annotated with explicit provenance and evidence information, DisGeNET is a valuable knowledge and evidence-based discovery resource for  Translational Research .
DisGeNET is an open access resource that makes available a comprehensive knowledge base on disease genes and different tools for their exploitation and analysis. DisGeNET is available through a  Web interface , a  Cytoscape  plugin, [ 2 ]  as  linked data  for the Semantic Web, and supports programmatic access to its data. These valuable set of tools allows investigating the molecular mechanisms underlying diseases of genetic origin, [ 3 ]  and are designed to support the data exploitation from different perspectives and to fulfill the needs of different types of users, including bioinformaticians, biologists and healthcare practitioners.


The DisGeNET database integrates over 400 000 associations between > 17 000 genes and > 14 000 diseases from human to animal model expert curated databases with text mined GDAs from MEDLINE using a NLP-based approach. [ 4 ]  The highlights of DisGeNET are the data integration, standardisation and a fine-grained tracking of the provenance information. The integration is performed by means of gene and disease vocabulary mapping and by using the DisGeNET association type ontology. Furthermore, GDAs are organised according to their type and level of evidence as CURATED, PREDICTED and LITERATURE, and they are also scored based on the supporting evidence to prioritise and ease their exploration.


For a seamless integration of gene-disease association data, we developed the DisGeNET association type ontology. All association types as found in the original source databases are formally structured from a parent GeneDiseaseAssociation class if there is a relationship between the gene/protein and the disease, and represented as ontological classes. It is an OWL ontology that is integrated into the  Sematicscience Integrated Ontology (SIO) , which provides essential types and relations for the rich description of objects, processes and their attributes. [ 5 ]  You can check SIO gene-disease association classes from this  URL .


The DisGeNET Cytoscape plugin [ 2 ]  offers a network representation of the gene-disease associations. It represents gene-disease associations in terms of bipartite graphs and additionally provides gene centric and disease centric views of the data. It assists the user in the interpretation and exploration of human complex diseases with respect to their genetic origin by a variety of built-in functions. Using the DisGeNET Cytoscape plugin you can perform queries restricted to (i) the original data source, (ii) the association type, (iii) the disorder class of interest and (iv) specific diseases or genes.


The information contained in DisGeNET can also be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the  Linked Open Data  cloud. DisGeNET is distributed as RDF and Nanopublications linked datasets. The DisGeNET-RDF linked dataset is an alternative way to access the DisGeNET data and provides new opportunities for data integration, querying and integrating DisGeNET data to other external RDF datasets. The RDF and Nanopublication distributions of DisGeNET have been developed in the context of  the Open PHACTS project  to provide disease relevant information to the knowledge base on pharmacological data.


The  Earth Human STR Allele Frequencies Database [ 1 ]  is a scientific project based on a dynamic web interface and a relational database management system. Its main purpose is the management of STR populational data reported from all over the world, providing highly specialized population genetics tools and also an overview of world population genetic structure at global scale.


At the bottom of EHSTRAFD approach stays peer-review journals standardization trend in publishing populational data [ 2 ]  and most important, the allele frequencies gradient distribution over vast geographical areas. [ 3 ] [ 4 ] [ 5 ]


Allele Frequency Global Tracking  (AFGT) - allows searching for allele frequency distribution at global and regional level. STR Loci are available in AFGT if there are reported in at least ten EHSTRAFD populations. The AFGT locus list is permanently updated with each EHSTRAFD release.


Regional Profile Frequency  (RPF) - allows calculating the frequency of a genetic profile at global and regional level, base on EHSTRAFD current records. RPF is available just for ISSOL ( Interpol  Standard Set Of Loci), ESS (European Standard Set - recommended by  ENFSI ) and  CODIS  (Combined DNA Index System) loci.


Most Probable Geographical Origin  (MPGO) - allows searching for the most probable geographical origin of a given STR genetic profile. Geographical origin is estimated by the profile frequency in different populations. MPGO is available just for ISSOL ( Interpol  Standard Set Of Loci), ESS (European Standard Set - recommended by  ENFSI ) and  CODIS  (Combined DNA Index System) loci.


ECgene  in  computational biology  is a database of  genomic annotations  taking  alternative splicing  events into consideration. [ 1 ]


EDAS  was a database of  alternatively spliced  human genes. [ 1 ]  It doesn't seem to exist anymore.


EMAGE  (e-Mouse Atlas of Gene Expression [ note 1 ] ) is an online  biological database  of  gene expression  data in the developing mouse ( Mus musculus ) embryo. [ 1 ] [ 2 ] [ 3 ]  The data held in EMAGE is spatially annotated to a framework of 3D mouse embryo models produced by  EMAP  (e-Mouse Atlas Project).  These spatial annotations allow users to query EMAGE by spatial pattern as well as by gene name, anatomy term or  Gene Ontology (GO)  term. EMAGE is a freely available web-based resource funded by the  Medical Research Council (UK)  and based at the  MRC Human Genetics Unit  in the Institute of Genetics and Molecular Medicine, Edinburgh, UK.


EMAGE contains  in situ  hybridisation ,  immunohistochemistry , and  in situ  reporter (e.g.  knock-in  and  gene trap ) data.  It includes  wholemount  data,  section  data and full 3D OPT ( Optical Projection Tomography ) data.  The gene expression patterns are mapped into or onto the standard models by a team of  biocurators , using bespoke mapping software.  In addition to the spatial annotations, EMAGE data is also text annotated to provide a text based description of the expression patterns. This text annotation is carried out in collaboration with the  MGI Gene Expression Database (GXD)  using the EMAP mouse anatomy ontology.


EMAGE data comes primarily from peer reviewed, published journal articles, and from large scale screens, but also from direct submissions from researches working in the field. Data does not need to be published to be included in EMAGE, however EMAGE is a curated database. Biocurators check the accuracy of the meta-data included in the database entries and as well as performing the spatial annotations of the data.


EMAGE entries are designed to adhere to the  Minimum information specification  for in situ hybridization and immunohistochemistry experiments (MISFISHIE) [ 4 ]   specifications, and as such contain information about the submitter/author publication, detection reagent, assay specimen preparation,  and experimental procedures as well as the original data images and the spatial and text annotations. EMAGE entries also contain links to a variety of related resources based on the either the gene being assayed, or the assay itself.


Ensembl Genomes  is a scientific project to provide genome-scale data from non-vertebrate species. [ 1 ] [ 2 ]


The project is run by the  European Bioinformatics Institute , and was launched in 2009 using the  Ensembl  technology. [ 3 ]  The main objective of the Ensembl Genomes database is to complement the main Ensembl
database by introducing five additional web pages to include genome data for  bacteria ,  fungi , invertebrate  metazoa ,  plants , and  protists . [ 4 ]  For each of the domains, the  Ensembl  tools are available for manipulation, analysis and visualization of genome data. Most Ensembl Genomes data is stored in MySQL relational databases and can be accessed by the Ensembl REST interface, the Perl API, Biomart or online. [ 5 ]


Ensembl Genomes is an open project, and most of the code, tools, and data are available to the public. [ 6 ]  Ensembl and Ensembl Genomes software uses an Apache 2.0 license [ 7 ]  license.


The key feature of Ensembl Genomes is its graphical interface, which allows users to scroll through a  genome  and observe the relative location of features such as conceptual  annotation  (e.g.  genes ,  SNP  loci), sequence patterns (e.g. repeats) and experimental data (e.g. sequences and external sequence features mapped onto the  genome ). [ 1 ]  Graphical views are available for varying levels of resolution from an entire  karyotype , down to the sequence of a single  exon . Information for a  genome  is spread over four tabs, a species page, a ‘Location’ tab, a ‘ Gene ’ tab and a ‘ Transcript ’ tab, each providing information at a higher resolution.


Searching for a particular species using Ensembl Genomes redirects to the species page. Often, a brief description of the species is provided, as well as links to further information and statistics about the  genome , the graphical interface and some of the tools available.


A  karyotype  is available for some species in Ensembl Genomes. [ 8 ]  If the karyotype is available there will be a link to it in the Gene Assembly section of the species page. Alternatively if users are in the ‘Location’ tab they can also view the karyotype by selecting ‘Whole genome’ in the left-hand menu. Users can click on a location within the karyotype to zoom in to one specific chromosome or a genomic region. [ 8 ]  This will open the ‘Location’ Tab.


In the 'Location' tab, users can browse  genes , variations,  sequence conservation , and other types of  annotation  along the  genome . [ 9 ]  The 'Region in detail' is highly configurable and scalable, and users can choose what they want to see by clicking on the 'Configure this page' button at the bottom of the left-hand menu. By adding and removing tracks users will be able to select the type of data they want to have included in the displays. [ 9 ]  Data from the following categories can be easily added or removed from this 'Location' tab view: ' Sequence  and  assembly ', ' Genes  and  transcripts ', ' mRNA  and  protein  alignments', 'Other  DNA   alignments ', ' Germline  variation', ' Comparative genomics ', among others. [ 9 ]  Users can also change the display options such as the width. [ 9 ]  A further option allows users to reset the configuration back to the default settings. [ 9 ]


More specific information about a select  gene  can be found in the ‘Gene’ tab. Users can get to this page by searching for desired gene in the search bar and clicking on the gene ID or by clicking on one of the genes shown in the ‘Location’ tab view. The ‘Gene’ tab contains gene-specific information such as gene structure, number of  transcripts , position on the  chromosome  and  homology  information in the form of gene trees. [ 10 ]  This information can be accessed via the menu on the left-hand side.


A 'Transcript' tab will also appear when a user chooses to view a gene. The 'Transcript' tab contains much of the same information as the 'Gene' tab, however it is focused on only one transcript. [ 10 ]


Ensembl Genomes allows comparing and visualising user data while browsing karyotypes and genes. Most Ensembl Genomes views include an ‘Add your data’ or ‘Manage your data’ button that will allow the user to upload new tracks containing reads or sequences to Ensembl Genomes or to modify data that has been previously uploaded. [ 11 ]  The uploaded data can be visualised in region views or over the whole karyotype. The uploaded data can be localised using Chromosome Coordinates or BAC Clone Coordinates. [ 12 ]
The following methods can be used to upload a data file to any Ensembl Genomes page: [ 13 ]


The following file types are supported by Ensembl Genomes: [ 14 ]


The data is uploaded temporarily into the servers. Registered users can log in and save their data for future reference. It is possible to share and access the uploaded data using and an assigned URL. [ 15 ]  Users are also allowed to delete their custom tracks from Ensembl Genomes.


BioMart  is a programming free search engine incorporated in Ensembl and Ensembl Genomes (except for Ensembl Bacteria) for the purpose of mining and extracting genomic data from the Ensembl databases in table formats like HTML, TSV, CSV or XLS. [ 16 ]  Release 45 (2019) of Ensembl Genomes has the following data available at the BioMarts:


The purpose of the BioMarts in Ensembl Genomes is to allow the user to mine and download tables containing all the genes for a single species, genes in a specific region of a chromosome or genes on one region of a chromosome associated with an InterPro domain. [ 21 ]  The BioMarts also include filters to refine the data to be extracted and the attributes (Variant ID, Chromosome name, Ensembl ID, location, etc.) that will appear in the final table file can be selected by the user.


The BioMarts can be accessed online in each corresponding domain of Ensembl Genomes or the source code can be installed in UNIX environment from the BioMart  git  repository [ 22 ]


A  BLAST  interface is provided to allow users to search for  DNA  or  protein sequences  against the Ensembl Genomes. It can be accessed by the header, located on top of all Ensembl Genome pages, titled BLAST. The BLAST search can be configured to search against individual species or collections of species (maximum of 25). There is a  taxonomic  browser to allow the selection of taxonomically related species. [ 23 ]


Ensembl Genomes provides a second sequence search tool, that uses an algorithm based on Exonerate, that is provided by  European Nucleotide Archive . [ 23 ]  This tool can be accessed by the header, located on top of all Ensembl Genome pages, titled Sequence Search. Users can then choose whether they would like Exonerate to search against all species in the Ensembl Genomes division or against all species in Ensembl Genomes. They can also choose the 'Maximum E-value', which will limit the results that appear to those with E-values below the maximum. Finally users can choose to use an alternative search mode by selecting 'Use spliced query'.


The Variant Effect Predictor is one of the most used tools in Ensembl and Ensembl Genomes. It allows to explore and analyse what is the effect that the variants (SNPs, CNVs, indels or structural variations) have on a particular gene, sequence, protein, transcript or transcription factor. [ 24 ]  To use VEP, the users must input the location of their variants and the nucleotide variations to generate the following results: [ 25 ]


There are two ways in which the users can access the VEP. The first form is online-based. In this page, the user generates an input by selection the following parameters: [ 26 ]


Data upload to VEP supports VCF, pileup, HGVS notations and a default format. [ 27 ]  The default format is a whitespace-separated file that contains the data in columns. The first five columns indicate the chromosome, start location, end location, allele (pair of alleles separated by a '/', with the reference allele first) and the strand (+ for forward or – for reverse). [ 28 ]  The sixth column is a variation identifier and it is optional. If it is left in blank, VEP will assign an identifier to in output file.


VEP also provides additional identifier options to the users, extra options to complement the output and filtering. [ 29 ]  The filtering options allow features like removal of known variants from results, returning variants in exons only, and restriction of results to specific consequences of the variants. [ 30 ]


VEP users also have the possibility of viewing and manipulating all the jobs associated with their session by browsing the "Recent Tickets" tab. In this tab the users can view the status of their search (success, queued, running or failed) and save, delete or resubmit jobs. [ 31 ]


The second option to use VEP is by downloading the source code for its use in UNIX environments. [ 32 ]  All the features are equal between the online and script versions. VEP can also be used with online instances like Galaxy.


When a VEP job is completed the output is a tabular file that contains the following columns: [ 33 ]


Other common output formats for VEP include  JSON  and VDF formats. [ 34 ]


The Ensembl Genomes [REST] interface allows access to the data using your favourite programming language.


You can also access data using the Perl API and Biomart.