PHI-base has been aligned with  Ensembl Genomes  since 2011,  FungiDB  since 2016, and  Global Biotic Interactions (GloBI)  since 2018. [ 12 ]  All new PHI-base releases are integrated by these independent databases.


PHI-base is a resource for many applications including:


› The discovery of conserved genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention


› Comparative genome analyses


› Annotation of newly sequenced pathogen genomes


› Functional interpretation of RNA sequencing and microarray experiments


› The rapid cross-checking of phenotypic differences between pathogenic species when writing articles for peer review


PHI-base use has been cited in over 900 peer-reviewed articles. [ 1 ]


Since 2015, the website has linked to an online literature curation tool called  PHI-Canto , enabling community-driven literature curation for various pathogenic species. [ 13 ]  PHI-Canto employs a community curation framework that not only offers a curation tool but also includes a phenotype ontology and controlled vocabularies using unified languages and rules used in biology experiments. The central concept of this framework is the introduction of a 'Metagenotype', which allows the annotation and assignment of phenotypes to specific pathogen mutant-host interactions. PHI-Canto extends the single species curation tool developed for PomBase  [ 14 ]  ( https://www.pombase.org ), the model organism database for fission yeast.


PHI-base is a National Capability funded by the  Biotechnology and Biological Sciences Research Council  (BBSRC), a UK research council. [ 7 ]


PhylomeDB  is a public  biological database  for complete catalogs of gene phylogenies ( phylomes ). [ 1 ] [ 2 ] [ 3 ]  It allows users to interactively explore the evolutionary history of genes through the visualization of  phylogenetic trees  and  multiple sequence alignments . Moreover, phylomeDB provides genome-wide orthology and paralogy predictions which are based on the analysis of the phylogenetic trees. The automated pipeline used to reconstruct trees aims at providing a high-quality phylogenetic analysis of different genomes, including Maximum Likelihood tree inference, alignment trimming  [ 4 ]  and evolutionary model testing.


PhylomeDB includes also a public download section with the complete set of trees, alignments and orthology predictions, as well as a web API that facilitates cross linking trees from external sources. Finally, phylomeDB provides an advanced tree visualization interface based on the ETE toolkit, [ 5 ]  which integrates tree topologies, taxonomic information, domain mapping and alignment visualization in a single and interactive tree image.


The tree searching engine of PhylomeDB was updated to provide a gene-centric view of all phylomeDB resources. Thus, after a protein or gene search, all the available trees in phylomeDB are listed and organized by phylome and tree type. Users can switch among all available seed and collateral trees without missing the focus on the searched protein or gene.


In phylomeDB v4 all the information available for each tree is now shown using an integrated layout in which tree topology, taxonomy data, alignments and domain annotations, and event-age (phylostratigraphy) information are rendered in the same figure using the newest visualization features provided by the ETE toolkit v2.2:


Also, users can download relevant data, including the whole database, a specific phylome or, from the tree entry page, the relevant data corresponding to that tree. In this new release we have implemented the possibility to download orthology predictions from a tree in the recently developed  OrthoXML  standard format, in addition to a tabulated format.


The  Quest for Orthologs  (QfO) consortium involve more than 30 phylogenomic databases. The main of the consortium is improve and standardize orthology predictions through collaboration and discuss about new emerging methods.


The  Plant DNA C-values Database  ( https://cvalues.science.kew.org/ ) is a comprehensive catalogue of  C-value  ( nuclear   DNA  content, or in  diploids ,  genome size ) data for  land plants  and  algae .  The database was created by  Prof. Michael D. Bennett  and Dr. Ilia J. Leitch of the  Royal Botanic Gardens, Kew ,  UK .  The database was originally launched as the "Angiosperm DNA C-values Database" in April 1997, essentially as an online version of collected data lists that had been published by Prof. Bennett and colleagues since the 1970s.  Release 1.0 of the more inclusive Plant DNA C-values Database was launched in 2001, with subsequent releases 2.0 in January 2003 and 3.0 in December 2004.  In addition to the  angiosperm  dataset made available in 1997, the database has been expanded  taxonomically  several times and now includes data from  pteridophytes  (since 2000),  gymnosperms  (since 2001),  bryophytes  (since 2001), and algae (since 2004) (see  (1)  for update history).  (Note that each of these subset databases is cited individually as they may contain different sets of authors).


The most recent release of the database (release 7.1) went live in April 2019. It contains data for 12,273 species of plants comprising 10,770 angiosperms, 421 gymnosperms, 303 pteridophytes (246 ferns and fern allies and 57 lycophytes), 334 bryophytes, and 445 algae.


A similar  Animal Genome Size Database  was created in 2001 by  Dr. T. Ryan Gregory  of the  University of Guelph ,  Canada .


Polyposis registries  exists for the purpose of understanding the  genetic disease   familial adenomatous polyposis . [ 1 ]  The registries provide a service to doctors for identification, surveillance and management of families and individuals with high  colorectal cancer  risk from  Familial Adenomatous Polyposis  (FAP) and  Hereditary Non-Polyposis Colorectal Cancer  (HNPCC). The  Centers for Disease Control and Prevention  of the United States provides,  royalty-free ,  Registry Plus  software for collecting and processing cancer registry data compliant with national standards established by  health professionals  and regulators to understand and address the burden of cancer more effectively.


Polyposis  registries have been used in numerous  academic studies  to assess  morbidity  and  mortality  of  colorectal cancer  related to  FAP , and use of registry data has resulted in improved treatment and reduced mortality from polyposis-related colorectal cancer.


The  University of Texas M. D. Anderson Cancer Center  maintains an international  list of registries  related to  hereditary colon cancer .


Some of the registries include: -


PomBase  is a  model organism database  that provides online access to the  fission yeast   Schizosaccharomyces pombe   genome  sequence and annotated features, together with a wide range of manually curated functional gene-specific data. The PomBase website was redeveloped in 2016 to provide users with a more fully integrated, better-performing service (described in   [ 2 ] ).


PomBase staff manually curate a wide variety of data types using both primary literature and bioinformatics sources, and numerous mechanisms are employed to ensure both syntactical and biological content validity. [ 3 ]


Types of data curated include:


Gene annotation can be viewed either at a gene-specific level (on the gene pages) or at a term-specific level (on the ontology term pages). This makes it possible to either:


Genome-wide datasets (including protein datasets, all annotations, manually curated ortholog lists etc) can be accessed from the  datasets  page. Datasets suitable for display in a genome browser and that have been loaded can be accessed via the  PomBase JBrowse instance .


PomBase uses several biological ontologies to capture gene-specific information, including:


The  GO slim page  provides an overview of the "biological role" of all "known" fission yeast genes - these are proteins that have either been experimentally characterized in fission yeast, or in another species and transferred by orthology.


Remarkably, nearly 20% of eukaryotic proteomes, from yeast to human, are uncharacterized in terms of the pathways and processes that these proteins participate in, [ 6 ]  making it one of the great  unsolved problems in biology . The role that these proteins play in biology, have not yet been discovered in any species. To aid research into these unknown proteins, PomBase maintains an inventory of  uncharacterized fission yeast proteins . The  priority unstudied genes  list represents the subset of uncharacterized fission yeast genes that are conserved to man, making it an especially high priority research target.


To supplement the work of the small team of professional PomBase curators, fission yeast researchers contribute annotations directly to PomBase via an innovative community curation scheme, for which an online curation tool,  Canto , [ 7 ]  has been developed. Community curation is reviewed by PomBase staff, and this results in highly accurate, effectively co-curated, annotations. [ 8 ]


PomBase maintains an  annotation stats page .


Pombase provides both  documentation  and an  FAQ .


Usage of PomBase as a research tool is explored in the "Eukaryotic Genomic Databases" (Methods and Protocols) book chapter. [ 9 ]  Developments and updates are described in the NAR Database Issue papers. [ 10 ] [ 11 ] [ 2 ]
For a detailed overview of using  S. pombe  as a  model organism  see the genetics primer  [ 12 ]


In molecular biology,  REBASE  is a database of information about  restriction enzymes  and  DNA methyltransferases . [ 1 ]  REBASE contains an extensive set of references, sites of recognition and cleavage, sequences and structures.  It also contains information on the commercial availability of each enzyme. REBASE is one of the longest running  biological databases  having its roots in a collection of restriction enzymes maintained by  Richard J. Roberts  since before 1980. [ 2 ]  Since that time there have been regular descriptions of the resource in the journal  Nucleic Acids Research . [ 3 ] [ 4 ]


RECODE  is a database of "programmed"  frameshifts , bypassing and  codon  redefinition used for  gene expression . [ 1 ]


The Reference Sequence ( RefSeq )  database [ 1 ]  is an  open access , annotated and curated collection of publicly available  nucleotide  sequences ( DNA ,  RNA ) and their  protein  products. RefSeq was introduced in 2000. [ 2 ] [ 3 ]  This database is built by  National Center for Biotechnology Information  (NCBI), and, unlike  GenBank , provides only a single record for each natural biological molecule (i.e. DNA, RNA or protein) for major organisms ranging from  viruses  to  bacteria  to  eukaryotes .


For each  model organism ,  RefSeq  aims to provide separate and linked records for the genomic DNA, the gene transcripts, and the proteins arising from those transcripts.  RefSeq  is limited to major organisms for which sufficient data are available (121,461 distinct "named"  organisms  as of July 2022), [ 4 ]  while  GenBank  includes sequences for any organism submitted (approximately 504,000 formally described  species ). [ 5 ]


RefSeq collection comprises different data types, with different origins, so it is necessary to establish standard categories and identifiers to store each data type. The most important categories are:


For more details and more categories, see  Table 1  in  Chapter 18 of the book  The Reference Sequence (RefSeq) Database .


Several projects to improve  RefSeq  services are currently in development by the NCBI, often in collaboration with research centers such as EMBL-EBI:


According to the RefSeq release 213 (July 2022), the number of species represented in the database by counting distinct taxonomic IDs are as follows: [ 4 ]


The counts of accession and basepairs per molecule type are: [ 4 ]


Rfam  is a  database  containing information about  non-coding RNA  (ncRNA) families and other structured RNA elements.  It is an  annotated ,  open access  database originally developed at the  Wellcome Trust Sanger Institute  in collaboration with  Janelia Farm , [ 1 ] [ 2 ] [ 3 ] [ 4 ]  and currently hosted at the  European Bioinformatics Institute . [ 5 ]  Rfam is designed to be similar to the  Pfam  database for annotating protein families.


Unlike  proteins , ncRNAs often have similar  secondary structure  without sharing much similarity in the  primary sequence .  Rfam divides ncRNAs into families based on evolution from a common ancestor. Producing  multiple sequence alignments  (MSA) of these families can provide insight into their structure and function, similar to the case of protein families.  These MSAs become more useful with the addition of secondary structure information. Rfam researchers also contribute to  Wikipedia 's  RNA WikiProject . [ 4 ] [ 6 ]


The Rfam database can be used for a variety of functions.  For each ncRNA family, the interface allows users to: view and download multiple sequence alignments; read annotation; and examine species distribution of family members.  There are also links provided to literature references and other RNA databases.
Rfam also provides links to Wikipedia so that entries can be created or edited by users.


The interface at the Rfam website allows users to search ncRNAs by keyword, family name, or genome as well as to search by ncRNA sequence or  EMBL   accession number . [ 7 ]
The database information is also available for download, installation and use using the INFERNAL software package. [ 8 ] [ 9 ] [ 10 ]  The INFERNAL package can also be used with Rfam to annotate sequences (including complete genomes) for homologues to known ncRNAs.


In the database, the information of the  secondary structure  and the  primary sequence , represented by the  MSA , is combined in statistical models called profile  stochastic context-free grammars  (SCFGs), also known as covariance models.  These are analogous to  hidden Markov models  used for protein family annotation in the  Pfam  database. [ 1 ]  Each family in the database is represented by two multiple sequence alignments in  Stockholm format  and a SCFG.


The first MSA is the "seed" alignment.  It is a hand-curated alignment that contains representative members of the ncRNA family and is annotated with structural information.  This seed alignment is used to create the SCFG, which is used with the Rfam software INFERNAL to identify additional family members and add them to the alignment.  A family-specific threshold value is chosen to avoid false positives.


Until release 12, Rfam used an initial  BLAST  filtering step because profile SCFGs were too computationally expensive. However, the latest versions of INFERNAL are fast enough [ 10 ]  so that the BLAST step is no longer necessary. [ 11 ]


The second MSA is the “full” alignment, and is created as a result of a search using the covariance model against the sequence database.  All detected  homologs  are aligned to the model, giving the automatically produced full alignment.


Version 1.0 of Rfam was launched in 2003 and contained 25 ncRNA families and annotated about 50 000 ncRNA genes.  In 2005, version 6.1 was released and contained 379 families annotating over 280 000 genes. In August 2012, version 11.0 contained 2208 RNA families, while the current version (14.9, released in November 2022) annotates 4108 [ 7 ]  families.


RNA Modification Base  ( RMBase ) [ 1 ] [ 2 ]  is designed for decoding the landscape of  RNA modifications  identified from high-throughput sequencing data (MeRIP-seq, m6A-seq, miCLIP, m6A-CLIP, Pseudo-seq, Ψ-seq, CeU-seq, Aza-IP, RiboMeth-seq). It contains ~124200 N6-Methyladenosines (m6A), ~9500  pseudouridine  (Ψ) modifications, ~1000  5-methylcytosine  (m5C) modifications, ~1210 2′-O-methylations (2′-O-Me) and ~3130 other types of RNA modifications. RMBase demonstrated thousands of RNA modifications located within mRNAs, regulatory ncRNAs (e.g. lncRNAs, miRNAs, pseudogenes, circRNAs, snoRNAs, tRNAs), miRNA target sites and disease-related SNPs.


This  RNA modification databases  are a compilation of databases and web portals and servers used for RNA modification.  RNA modification  occurs in all living organisms, and is one of the most evolutionarily conserved properties of RNAs. [ 1 ] [ 2 ] [ 3 ]  More than 100 different types of RNA modifications have been characterized across all living organisms. It can affect the activity, localization as well as stability of RNAs, and has been linked with human cancer and diseases. [ 1 ] [ 2 ] [ 3 ] [ 4 ]


The  Sequence Read Archive  ( SRA , previously known as the  Short Read Archive ) is a  bioinformatics   database  that provides a public repository for  DNA sequencing  data, especially the "short reads" generated by  high-throughput sequencing , which are typically less than 1,000  base pairs  in length. [ 1 ]  The archive is part of the  International Nucleotide Sequence Database Collaboration  (INSDC), and run as a collaboration between the NCBI, the  European Bioinformatics Institute  (EBI), and the  DNA Data Bank of Japan  (DDBJ).


The archive was established by the  National Center for Biotechnology Information  (NCBI) in 2007 in order to provide a repository for data produced by  RNA-Seq  and  ChIP-Seq  studies as well as large-scale studies including the  Human Microbiome Project  and the  1000 Genomes Project . [ 1 ] [ 2 ]  Originally called the Short Read Archive, the name was changed in anticipation of future sequencing technologies being able to produce longer sequence reads. [ 3 ]


The volume of data deposited in the Sequence Read Archive has grown rapidly. As of September 2010, 65% of the SRA was  human genomic  sequence, with another 16% relating to human  metagenome  sequence reads. [ 6 ]  Much of this data was deposited through the 1000 Genomes Project. In June 2011, the data contained within the SRA passed 100 Terabases of DNA in volume. [ 2 ]


The preferred  data format  for files submitted to the SRA is the  BAM format , which is capable of storing both aligned and unaligned reads. [ 6 ]  Internally the SRA relies on the NCBI SRA Toolkit, used at all three INSDC member databases, to provide flexible  data compression ,  API  access and conversion to other formats such as  FASTQ . [ 5 ]


NCBI announced their plan to close the NCBI SRA in February 2011 due to funding reduction. [ 2 ] [ 7 ]  However, EBI and DDBJ announced that they would continue to support the SRA. [ 8 ]  In October 2011, NCBI announced continuation of funding for the SRA. [ 2 ]


Deposition of data in the SRA is mandated by most funding agencies and  open access journals .  Nature Publishing Group  journals require that DNA and RNA sequencing data is made available through the SRA. [ 9 ]


SonicParanoid  is an algorithm for the de-novo prediction of  orthologous genes  among multiple species. [ 1 ]  It borrows the main idea from InParanoid [ 2 ]  with substantial changes to the algorithm that drastically reduce the time required for the analysis. Additionally, SonicParanoid generates groups of orthologous genes shared among the input proteomes using  single-linkage   hierarchical clustering  or markov clustering. The latest iteration of SonicParanoid uses machine learning to substantially reduce execution times, and language models to infer orthologs at the domain level. [ 3 ]


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


SpliceInfo  is a database for the four major  alternative-splicing  modes (exon skipping, 5'-alternative splicing, 3'-alternative splicing and intron retention) in the  human genome . [ 1 ]


This resource appears to be no longer available. [ 2 ]


StarBase [ 2 ]  is a database for decoding  miRNA - mRNA ,  miRNA-lncRNA, [ 3 ]   miRNA - sncRNA , miRNA-circRNA, [ 3 ]  miRNA- pseudogene , protein-lncRNA, [ 4 ]  protein-ncRNA, protein-mRNA interactions, and ceRNA networks [ 5 ]  from  CLIP-Seq  ( HITS-CLIP ,  PAR-CLIP ,  iCLIP , CLASH) and  degradome sequencing  data. [ 1 ] [ 6 ]  StarBase provides miRFunction and ceRNAFunction web tools to predict the function of ncRNAs (miRNAs, lncRNAs, pseudogenes) and protein-coding genes from the miRNA and ceRNA [ 7 ]  regulatory networks. StarBase also developed Pan-Cancer Analysis Platform to decipher  Pan-Cancer Analysis  Networks of lncRNAs, miRNAs, ceRNAs, and RNA-binding proteins (RBPs) by mining clinical and expression profiles of 14 cancer types (including more than six thousand samples) from  The Cancer Genome Atlas (TCGA)  Data Portal.


STRBase  in  computational biology  is a database of  Short Tandem Repeats   [ 1 ]


The  Synthetic Gene Database  ( SGDB ) is a database of  artificially engineered genes . [ 1 ]


The  Tandem Repeats Database  ( TRDB ) is a database of  tandem repeats  in genomic DNA. [ 1 ]


TassDB  ( TAndem Splice Site DataBase ) is a database of tandem  splice sites  of eight species [ 1 ]


The  Dragon Database for Human Transcription Co-Factors and Transcription Factor Interacting Proteins  ( TcoF-DB ) is a database that facilitates the exploration of proteins involved in the regulation of  transcription  in humans by binding to regulatory DNA regions ( transcription factors ) and proteins involved in the regulation of transcription in humans by  interacting  with transcription factors and not binding to regulatory DNA regions ( transcription co-factors ). [ 1 ]  The database describes a total of 529 (potential) human transcription co-factors interacting with a total of 1365 human transcription factors.


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


Three Million African Genomes  (3MAG) is a  human genetics  project inaugurated by  Ambroise Wonkam  of  Cape Town University  in  South Africa . [ 1 ] [ 2 ]  The project's aim is to correct for the systemic shortfall in the collection and analysis of  genomic  data of Africans, who have the widest genetic variation among human populations, via sequencing to capture "the full scope of variation to improve health care, equity and medical research globally". Three million is the initial rough estimate of the sample size required to capture the variation. [ 3 ]


ThYme  ( Thioester-active enzYme ) is database of  enzymes  constituting the  fatty acid synthesis  and  polyketide synthesis  cycles. [ 1 ]


The  Tohoku Medical Megabank Project  is a national project in Japan, which started in 2012. [ 1 ]  The mission of the Tohoku Medical Megabank (TMM) project is to carry out a long-term health survey in the  Miyagi  and  Iwate  prefectures, which were affected by the  Great East Japan Earthquake , and provide the research infrastructure for the development of  personalized medicine  by establishing a  biobank  and conducting  cohort studies . [ 2 ] [ 3 ]


The TMM project is conducted by  Tohoku University 's Tohoku Medical Megabank Organization (ToMMo) and  Iwate Medical University 's Iwate Tohoku Medical Megabank Organization (IMM).


The following  cohort studies  and research projects are in progress or completed.


The study recruited over 80,000 participants, with 50,000 from Miyagi Prefecture and 30,000 from Iwate Prefecture.


The study recruited over 70,000 participants, including over 23,000 newborns and their parents, siblings, grandparents, and extended family members. [ 3 ]


The project has been collecting various specimens, including peripheral and  cord blood  mononuclear cells,  Buffy coat , plasma,  serum ,  urine ,  breast milk , and  saliva . [ 2 ]


The project provides allele frequencies of  SNVs  (single nucleotide variants) and short  Indels  (insertions/deletions) detected by short-read  whole genome sequencing  (WGS) analysis of 54,000 Japanese individuals, as well as allele frequencies of  structural variants  detected by  long-read  WGS analysis of 222 individuals. [ 7 ]


The project designed ethnic-specific  SNP arrays  optimized for the Japanese population and performed a series of  GWAS  analyses and polygenic predictions with the  cohort  samples. [ 8 ] [ 9 ]


The project constructed a  reference genome  for Japanese by integrating  de novo  assemblies of three Japanese individuals.


Transcription factors  are proteins that bind genomic regulatory sites. Identification of  genomic regulatory  elements is essential for understanding the dynamics of developmental, physiological and pathological processes. Recent advances in  chromatin immunoprecipitation  followed by sequencing ( ChIP-seq ) have provided powerful ways to identify genome-wide profiling of  DNA-binding proteins  and  histone  modifications. [ 1 ] [ 2 ]  The application of ChIP-seq methods has reliably discovered transcription factor binding sites and histone modification sites.


Comprehensive List of transcription factor binding sites (TFBSs) databases based on ChIP-seq data as follows:


TRANSFAC  (TRANScription FACtor database) is a manually curated database of  eukaryotic   transcription factors , their genomic binding sites and  DNA  binding profiles. The contents of the database can be used to predict potential  transcription factor binding sites .


The origin of the database was an early data collection published 1988. [ 2 ]  The first version that was released under the name TRANSFAC was developed at the former German National Research Centre for Biotechnology and designed for local installation (now:  Helmholtz Centre for Infection Research ). [ 3 ]  In one of the first publicly funded bioinformatics projects, launched in 1993, TRANSFAC developed into a resource that became available on the Internet. [ 4 ]


In 1997, TRANSFAC was transferred to a newly established company,  BIOBASE , in order to secure long-term financing of the database. Since then, the most up-to-date version has to be licensed, whereas older versions are free for non-commercial users. [ 5 ] [ 6 ]  Since July 2016, TRANSFAC is maintained and distributed by geneXplain GmbH, Wolfenbüttel, Germany. [ 7 ]


The content of the database is organized in a way that it is centered around the interaction between transcription factors (TFs) and their DNA binding sites (TFBS).  TFs are described with regard to their structural and functional features, extracted from the original scientific literature. They are classified to families, classes and superclasses according to the features of their  DNA binding domains . [ 8 ] [ 9 ] [ 10 ] [ 11 ]


Binding of a TF to a genomic site is documented by specifying the localization of the site, its sequence and the experimental method applied. All sites that refer to one TF, or a group of closely related TFs, are aligned and used to construct a  position-specific scoring matrix  (PSSM), or count matrix. Many matrices of the TRANSFAC matrix library have been constructed by a team of  curators , others were taken from scientific publications.


The TRANSFAC database can be used as an encyclopedia of eukaryotic transcription factors. The target sequences and the regulated genes can be listed for each TF, which can be used as benchmark for TFBS recognition tools or as training sets for new transcription factor binding sites (TFBS) recognition algorithms. [ 12 ]  The TF classification enables to analyze such data sets with regard to the properties of the DNA-binding domains. [ 13 ]  Another application is to retrieve all TFs that regulate a given (set of) gene(s). In the context of systems-biological studies, the TF-target gene relations documented in TRANSFAC were used to construct and analyze transcription regulatory networks. [ 14 ] [ 15 ]
By far the most frequent use of TRANSFAC is the computational prediction of potential TFBS. A number of algorithms exist which either use the individual binding sites or the matrix library for this purpose:


Comparison of matrices with the matrix library of TRANSFAC and other sources:


A number of servers provide genomic annotations computed with the aid of TRANSFAC. [ 37 ] [ 38 ]  Others have used such analyses to infer target gene sets. [ 39 ] [ 40 ]


TranspoGene  in  computational biology  is a database of  transposed elements   located inside  protein-coding genes  of seven species. [ 1 ]


TreeFam  (Tree families database) is a database of  phylogenetic trees  of animal  genes . It aims at developing a curated resource that gives reliable information about  ortholog  and  paralog  assignments, and  evolutionary  history of various  gene  families. [ 1 ] [ 2 ]


TreeFam defines a  gene family  as a group of genes that evolved after the speciation of single- metazoan  animals. It also tries to include outgroup genes like  yeast  ( S.cerevisiae  and  S. pombe ) and  plant  ( A. thaliana ) to reveal these distant members.


TreeFam is also an  ortholog  database. Unlike other pairwise alignment based ones, TreeFam infers  orthologs  by means of gene trees. It fits a gene tree into the universal  species  tree and finds historical duplications, speciations and losses events. TreeFam uses this information to evaluate tree building, guide manual  curation , and infer complex  ortholog  and  paralog  relations.


The basic elements of TreeFam are gene families that can be divided into two parts: TreeFam-A and TreeFam-B families. TreeFam-B families are automatically created. They might contain errors given complex  phylogenies . TreeFam-A families are manually  curated  from TreeFam-B ones. Family names and node names are assigned at the same time. The ultimate goal of TreeFam is to present a curated resource for all the families.


TreeFam is being run as a project at the  Wellcome Trust Sanger Institute , and its software is housed on  SourceForge  as "TreeSoft".


A  twin registry  is a  database  of information about both  identical twins  and  fraternal twins , which is often maintained by an academic institution, such as a university, or by other research institutions.


The use of twins can improve the statistical power of a genetic study by reducing the amount of genetic and/or environmental variability. [ 1 ]  "Identical twins" ( monozygotic  (MZ) twins) share virtually all their  genes  with each other, and "fraternal twins" ( dizygotic  (DZ) twins), on average, share about 50% of their genes with each other (about the same amount of sharing as non-twin siblings). Both types of twin pairs in twin registries almost always share similar  prenatal  and  early childhood  environments as well. By determining what are called " concordance " rates for a disease or trait among identical and fraternal twin pairs, researchers can estimate whether contributing factors for that disease or trait are more likely to be hereditary, environmental, or some combination of these. A concordance rate is a statistical measure of probability - if one twin has a specific trait or disease, what is the probability that the other twin has (or will develop) that same trait or disease. In addition, with  structural equation modeling  and  multivariate analyses  of twin data, researchers can offer estimates of the extent to which  allelic  variants and environment may influence  phenotypic  traits. [ 1 ]


Some twin registries seek to cover all twins in an entire country, [ 1 ]  including  Sweden , [ 2 ]   Denmark , [ 3 ]   Norway , [ 4 ]   Finland ,  Australia , [ 5 ]   Sri Lanka [ 6 ]  and the  United Kingdom . [ 7 ]    The Swedish Twin Registry is the largest twin database in the world, with approximately 85,000 twin pairs. [ 2 ]


Other twin registries cover a more limited geographic scope and are maintained by researchers at academic institutions, such as the  Michigan State University Twin Registry , a registry of twins produced by researchers at  Michigan State University , the Washington State Twin Registry, [ 8 ]  a registry of twins produced by researchers at  Washington State University , and the  Minnesota Twin Registry  project by researchers at the  University of Minnesota  related to the  Minnesota Twin Family Study .  The largest twin registry in the  United States  is the Mid-Atlantic Twin Registry (MATR) at  Virginia Commonwealth University , which has  more than 51,000 registered participants, with approximately 46,000 of these representing intact twin pairs. [ 1 ]


Many twin registries depend on the voluntary participation of twins – that is, participation in these twin registries is not compulsory, and twins must voluntarily elect whether or not to register with a twin registry (and later, whether to participate in research projects).  This characteristic limitation of many twin registries leads to standard issues known as "recruitment bias" or "volunteer bias". [ 9 ]   Recruitment biases include an over-inclusion of twins who share similar characteristics, [ 4 ]  and over-inclusion of identical twins and female twins: [ 10 ]


Twin registries use a number of strategies to try to reduce the risk of recruitment bias.  Some twin registries are "mandatory" - that is, for example, under the public health laws of Norway, all births of twins since 1967 have been recorded in a twin registry maintained by the Norwegian government. [ 4 ]  By comparison, enlisting with the Australian Twin Registry is voluntary. [ 11 ]   While the twin registry in Sri Lanka is based on volunteer twins, that twin registry has made extensive out-reach efforts, such as examining hospital birth records, and then making multiple follow-up efforts (including in-person visits) to find the twins and have them (or their parents) agree to be registered. [ 12 ]


TwinsUK , also known as  TwinsUK Registry , is the biggest UK adult registry of  twins  in the  United Kingdom , ages 16 to 98 to study the genetic and environmental aetiology of age related  complex traits  and diseases. [ 1 ]  Established in 1993, it is based at  King's College London  with an intent of aiding genetic research. The registry is used to connect researchers to volunteers. [ 2 ] [ 3 ] [ 4 ]


It is the largest registry of twin adults in the UK. [ 5 ]  As of 2013 [update] , it hosted information on more than 13,000 volunteer twins, with approximately 50% having completed a baseline comprehensive assessment and more than 70% having completed a detailed health questionnaire. As of 2013 [update] , the cohort was predominantly female (83%) and middle-aged or older, with about an equal division of  monozygotic (identical)  and  dizygotic (fraternal) twins . [ 1 ]


UCbase  is a database of   ultraconserved sequences  (UCRs or UCEs) that were first described by Bejerano, G. et al. [ 2 ]  in 2004. They are highly conserved genome regions that share 100% identity among human, mouse and rat. UCRs are 481 sequences longer than 200 bases. They are frequently located at genomic regions involved in cancer, differentially expressed in human leukemias and carcinomas and in some instances regulated by microRNAs. [ 3 ]  The first release of UCbase was published by Taccioli, C. et al. in 2009. [ 4 ]  Recent updates include new annotation based on hg19 Human genome, information about disorders related to the chromosome coordinates using the  SNOMED CT  classification, a query tool to search for SNPs, and a new text box to directly interrogate the database using a MySQL interface. Moreover, a sequence comparison tool allows the researchers to match selected sequences against ultraconserved elements located in genomic regions involved in specific disorders. To facilitate the interactive, visual interpretation of UCR chromosomal coordinates, the authors have implemented the graph visualization feature of UCbase creating a link to the  UCSC Genome Browser . UCbase 2.0 does not provide microRNAs (miRNAs) information anymore focusing only on UCRs. The official release of UCbase 2.0 was published in 2014. [ 1 ]


UgMicroSatdb  ( UniGene   Microsatellites  database) is a database of  microsatellites  present in  uniGene . [ 1 ]


UK Biobank  is a long-term prospective  biobank  study in the United Kingdom (UK) [ 1 ]  that houses de-identified [ 2 ]   biological samples and health-related data [ 3 ]  on half a million people. Volunteer participants aged 40-69 were recruited between 2006 and 2010 [ 1 ]  from across Great Britain and consented to share their health data and to be followed for at least 30 years thereafter with the aim to enable scientific discoveries into the prevention, diagnosis, and treatment [ 3 ]  of disease.


Based in  Stockport ,  Greater Manchester , it is incorporated as a  limited company  and  registered charity [ 4 ]  in England and Wales, and  registered as a charity [ 5 ]  in Scotland. [ 6 ] [ 7 ] [ 8 ]


UK Biobank holds more than 10,000 variables of data on many of their 500,000 participants to inform research including biological samples, physical measurements, body and brain imaging data, bone density data, activity tracking and lifestyle questionnaire data. [ 9 ]  Participants continue to provide more data over time. They have over 15 million biological samples stored, which can be requested for use by researchers, and their online database holds over 30 petabytes of data. [ 10 ] [ 11 ] [ 12 ]  Their  human genome  sequencing database,  proteomic  database, and  human imaging  project are the largest in the world. [ 13 ] [ 14 ]  The project is enabling scientists to study the onset of diseases such as cancers, heart disease, and age-related conditions in the early stages of their development. [ 15 ] [ 16 ] [ 17 ]   Nature  has referred to UK Biobank as an "unprecedented open access database." [ 18 ]


Since 2012, [ 19 ]  30,000 researchers from over 90 countries [ 20 ]  have registered to use UK Biobank. As of November 2023 there have been over 9,000 [ 21 ]  peer-reviewed publications using UK Biobank data, including over 3,000 in 2023. [ 22 ]


UK Biobank is funded by the  Medical Research Council  (MRC), the  Wellcome Trust , UK government and charitable sources, and philanthropic donations. [ 23 ]


UK Biobank was conceived in the early 2000s, [ 24 ]  with Professor Sir  Rory Collins  appointed as the Principal Investigator and Chief Executive of UK Biobank in 2005. [ 25 ]  An incremental approach was adopted to developing the study procedures and technology, using systems designed and developed by the Clinical Trial Service Unit. This consisted of a series of pilot studies of increasing complexity and sophistication with interludes for assessment of results and additional scientific input. In-house trials were conducted during 2005, and a fully integrated clinic was run at  Altrincham , Greater Manchester throughout Spring 2006 where 3,800 individuals were assessed. On 22 August 2006, it was announced that the main programme would recruit men and women aged between 40 and 69 based from up to 35 regional centres. [ 26 ]


Following the initial pilot stage in the 2005-6 period, [ 27 ]  the main study began in April 2007 and by the end of that year 50,000 people had taken part. Recruitment reached 100,000 in April 2008, 200,000 in October 2008, 300,000 in May 2009, 400,000 in November 2009 and passed the 500,000 target in July 2010. The volunteers were largely healthy, wealthy and white European. Rather than recruiting more participants into the biobank, the organisation is helping other institutions establish and run similar initiatives. [ 28 ]  Participant enrolment was declared complete in August 2010. [ 29 ]  However recruitment proved more efficient than hoped and only 22 centres had been opened when the recruitment target of 500,000 was reached in 2010.


In May 2023,  UKRI  announced that UK Biobank was set to receive £127.6m to move to a purpose-built facility at  Bruntwood SciTech 's  Manchester Science Park . [ 30 ]


In October 2023, a number of funders including  Eric Schmidt  and  Ken Griffin  announced further funding for the next five years. [ 31 ] [ 32 ]


The study is following about 500,000 volunteers in the UK, enrolled at ages from 40 to 69. Initial enrolment took place over four years from 2006, and the volunteers will be followed for at least 30 years thereafter. [ 33 ]


Prospective participants were invited to visit an assessment centre, at which they completed an automated questionnaire and were interviewed about lifestyle, medical history and nutritional habits; basic variables such as weight, height, blood pressure etc. were measured; and blood and  urine  samples were taken. These samples were preserved so that it was possible to later extract  DNA  and measure other biologically important substances. During the whole duration of the study it was intended that all disease events, drug prescriptions and deaths of the participants are recorded in a database, taking advantage of the centralized UK  National Health Service . [ 34 ] [ 35 ]


During the initial physical examination, basic feedback was provided to the participant regarding their weight, height, BMI,  blood pressure , lung  vital capacity ,  bone density  and  intra-ocular pressure ; however if any other medical problems were detected, neither the participant nor their physician would be notified. Problems detected later, such as genetic risk factors, were not conveyed to either participant or physician ("to ensure that volunteers are not penalised by insurance companies, for example, which may require customers to disclose the results of any genetic tests."). [ 36 ]


From 2012, researchers were able to apply to use the database (though they are not given access
to the volunteers, who will remain strictly anonymous).
A typical study using the database might compare a sample of participants who developed a particular  disease , such as  cancer ,  heart disease ,  diabetes  or  Alzheimer's disease , with a sample of those that did not, in an attempt to measure the
benefits, risk contribution and interaction of specific  genes , lifestyles, and medications.


In 2017 researchers were able to access the database including genetic information. [ 37 ] [ 38 ]  By 2017 Biobank participants had approximately 1.3 million hospitalisations, 40,000 cancer incidents with 14,000 of them having died. [ 39 ]


The study was initially launched with a visit consisting of the following: [ 39 ]


Once the visit-based assessment method was proven, the range of investigations was extended to include: [ 39 ]


Since the completion of recruitment several new types of data have been added:


Following the initial pilot stage in the 2005-6 period, the main study began in April 2007 and by the end of that year 50,000 people had taken part.
Recruitment reached 100,000 in April 2008, 200,000 in October 2008, 300,000 in May 2009, 400,000 in November 2009 and passed the 500,000 target in July 2010. Participant enrolment was declared complete in August 2010. [ 55 ]  The volunteers were largely healthy, wealthy and white European. Rather than recruiting more participants into the biobank, the organisation is helping other institutions establish and run similar initiatives. [ 56 ]


The UK Biobank dataset was opened to applications from researchers in March 2012. [ 57 ]  The resource is available to scientists from the UK and outside, whether they work in the public or private sector, for industry, academia or a charity, subject to verification that the research is health-related and in the public interest. Researchers must register to be approved to use UK Biobank data. [ 58 ]  Researchers are required to publish their results in an open source publication site or in an academic journal and return their findings to the UK Biobank. [ 59 ]


In 2021, UK Biobank launched its cloud-based Research Analysis Platform (UKB-RAP), providing information technology infrastructure to store and analyse UK Biobank's large dataset regardless of the researcher's own technological capabilities. [ 60 ]  By 2023 the platform had over 5,000 users. [ 61 ]  The platform is hosted by Amazon Web Services, which also pledged $1.5 million in research credits for early career researchers and researchers from low and low-middle income countries to reduce limitations when collating, storing and securely accessing data. [ 62 ]


By 2023 30,000 researchers had registered to use the resource and over 9,000 peer-reviewed articles based on UK Biobank data had been published. [ 63 ]


A 2022 review of UK Biobank data found that road traffic noise exposure increases risk of CVD mortality, stroke and all-cause mortality. [ 64 ]


A 2023 review found that participants with sense of  meaning and purpose in life  have a decreased risk of dementia. [ 65 ]  Two other studies of participants in the UK Biobank study found that dementia risk was higher for those who were more socially isolated. [ 66 ]


A study from the UK Biobank showed a reduction in grey matter thickness, overall reduction in brain size and greater cognitive decline in patients after  COVID-19  compared with control groups. [ 67 ] [ 68 ]  The UK Biobank also reported on an increased risk of hospitalization for those who contracted COVID-19 with obesity. [ 69 ]


Reviews of UK Biobank data have found that pescatarians and vegetarians have a lower risk of colorectal and prostate cancer compared to red meat eaters. [ 70 ]  Consumption of processed meat increases risk of breast cancer. [ 71 ]  They have also found that men with higher total and central adiposity have an increased risk of prostate cancer death. [ 72 ]


The UK Biobank project operates within the terms of an Ethics and Governance Framework. [ 73 ] [ 74 ] [ 75 ]  The Framework describes a series of standards to which UK Biobank will operate during the creation, maintenance and use of the resource and it elaborates on the commitments that are involved to those participating in the project, researchers and the public more broadly. The independent UK Biobank Ethics and Governance Council provides advice to the project and monitors its conformity with the Framework. [ 76 ]  The Council also advises more generally on the interests of research participants and the general public in relation to the project. [ citation needed ]


The UK Biobank Board is accountable to the members of the company (the  Medical Research Council  and The  Wellcome Trust ) and acts as company directors and as charity trustees. It is chaired by  Lord Kakkar , [ 77 ]  who succeeded Sir  Michael Rawlins  in January 2020.


The project has been generally praised for its ambitious scope and unique potential. A  scientific review  panel concluded, the "UK Biobank has the potential, in ways that are not currently available elsewhere, to support a wide range of research". [ 55 ]   Colin Blakemore , chief executive of the  MRC , predicted it "will provide scientists with extraordinary information" [ 78 ]  and "grow into a unique resource for future generations." [ 55 ]  There was some early criticism, however. GeneWatch UK, a  pressure group  that claims to promote the responsible use of genetic information, asserted that the complexity of the programme could result in the finding of "false links between genes and disease", [ 55 ]  and expressed concern that the genetic information from patients could be  patented  for commercial purposes. Biobank's chief executive described such a risk as "extremely low, if it exists at all." [ 78 ]


Some literature has raised concerns that the UK Biobank is not representative of the diversity of the UK population or is not applicable to diverse populations. [ 79 ] [ 80 ]


Questions and concerns have been raised about the nature of some of the approved studies that have made use of UK Biobank data. Some of these studies are less obviously related to UK Biobank's self-stated mission of  "Improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses" . In 2016, a study on "genetic contributions to  social deprivation  and household income" was published using UK Biobank data. [ 81 ]  In 2019, researchers published a study on the genetics of same-sex behaviour, which made use of data from  23andMe  and UK Biobank. [ 82 ]  Following the publication, researchers and bioethicists voiced concerns about the approval and nature of the study, including how the findings were being exploited commercially, similar to a former UK Biobank-based study on educational attainment. [ 83 ]


In November 2023,  The Observer  reported that UK Biobank had approved access for companies in the insurance sector between 2020 and 2023 and that this runs counter some of the publicly made claims about how insurance companies and law enforcement would not access the data and records donated by volunteers. [ 84 ]  In response, UK Biobank released a response, stating that the quoted passages were only made in relation to identifiable data, not the de-identified data that was ultimately shared with insurance companies. [ 85 ]


In 2023 the  UK's Secretary of State for Science, Innovation and Technology   Michelle Donelan  described UK Biobank as "mak[ing] an unparalleled contribution to science across the whole world, by putting invaluable information at researchers' fingertips. It is without question a jewel in the crown of UK science, and an envy of the world". [ 86 ]


In October 2024,  The Guardian  published a story detailing that the far-right " Human Diversity Foundation " had gotten access to UK Biobank data to use it in pseudo-scientific  race studies , raising questions about the governance and processes for approving and controlling access to the data. [ 87 ]  Following the initial publication in  The Guardian , UK Biobank released a statement that criticised the report and dismissed the main findings, claiming to have conducted a full investigation that had "found no evidence of misuse of UK Biobank data" as only publicly accessible summary statistics where used. [ 88 ]  However, in a follow-up report by  The Guardian  after UK Biobank's statement, the newspaper reported on correspondence between a senior medic and UK Biobank chief executive, Prof Sir  Rory Collins , in which Collins said that the inquiries were continuing. [ 89 ]  In the same article,  The Guardian  also reported on the US-based startup, Heliospect Genomics that claims to use UK Biobank data to predict traits such as IQ, sex and height, as well as risk of obesity or mental illness, in human embryos for IVF treatment.


The UK Biobank is funded by the  UK Department of Health , the Medical Research Council, the  Scottish Executive , and the  Wellcome Trust  medical research charity. The cost of the initial participant recruitment and assessment phase was 62 million  GBP . [ 90 ]


UniGene  was a  NCBI  database of the  transcriptome  and thus, despite the name, not primarily a database for  genes . Each entry is a set of  transcripts  that appear to stem from the same  transcription  locus (i.e. gene or expressed  pseudogene ). Information on  protein  similarities, gene expression,  cDNA   clones , and genomic location is included with each entry.


Descriptions of the UniGene transcript based and genome based build procedures are available.


The  UniGene  resource,  developed  at   NCBI ,  clusters   ESTs   and  other  mRNA  sequences,  along  with  coding sequences  (CDSs)  annotated  on  genomic  DNA,  into subsets of related sequences. In most cases, each cluster is made up of sequences  produced by  a single  gene,  including  alternatively  spliced  transcripts.  However, some genes may be represented  by more than one cluster. The clusters are organism specific and are currently available for  human ,  mouse ,  rat ,  zebrafish , and  cattle . They are  built  in several  stages,  using  an automatic  process  based  on special  sequence comparison   algorithms .  First,  the  nucleotide  sequences  are  searched  for  contaminants,  such  as  mitochondrial ,   ribosomal ,  and vector  sequence,   repetitive elements , and low-complexity  sequences. After a sequence is screened, it must contain at least 100 bases to be a candidate  for entry into UniGene.  mRNA and genomic  DNA are clustered  first into  gene  links. A second  sequence  comparison  links  ESTs  to each other and to the gene links. At this stage, all clusters  are ‘‘anchored,’’  and contain either a sequence  with a polyadenylation  site or two ESTs labeled  as coming from the 3   end of a clone. Clone-based  edges are added by linking  the 5  and 3   ESTs that  derive  from  the  same  clone.  In  some  cases,  this  linking  may  merge  clusters identified at a previous  stage. Finally, unanchored  ESTs and gene clusters of size 1 (which may represent  rare transcripts)  are compared  with other UniGene clusters at lower stringency. The UniGene build is updated weekly, and the sequences that make up a cluster  may change.  Thus,  it is not safe to refer  to a UniGene  cluster  by its cluster  identifier;  instead,  one  should  use  the   GenBank   accession  numbers  of the sequences  in the cluster.


As of July 2000, the human subset of UniGene contained 1.7 million sequences in 82,000 clusters; 98% of these clustered sequences  were ESTs, and the remaining 2% were from mRNAs or CDSs annotated on genomic DNA. These human clusters could represent fragments of up to    82,000 unique human genes, implying that many human genes are now represented in a UniGene cluster. (This number is undoubtedly an overestimate  of the number of genes in the human genome,  as some genes may be represented  by more than one cluster.)  Only 1.4% of clusters  totally lack ESTs, implying that most human genes are represented  by at least one EST. Conversely,  it appears  that the majority  of human  genes have been identified only by ESTs; only 16% of clusters  contain  either an mRNA or a CDS annotated  on a genomic  DNA. Because fewer ESTs are available for mouse, rat, and zebrafish, the UniGene clusters are not as representative of the unique genes in the genome. Mouse UniGene contains 895,000 sequences  in 88,000 clusters, and rat UniGene contains 170,000 sequences in 37,000 clusters.


A new UniGene resource, HomoloGene,  includes curated and calculated orthologs  and  homologs  for  genes  from  human,  mouse,  rat,  and  zebrafish.  Calculated orthologs  and homologs  are the result of nucleotide  sequence comparisons  between all UniGene clusters for each pair of organisms.  Homologs are identified as the best match between a UniGene  cluster in one organism  and a cluster in a second organism. When two sequences  in different organisms  are best matches to one another (a reciprocal  best match), the UniGene clusters corresponding  to the pair of sequences are considered  putative orthologs. A special symbol indicates that UniGene clusters in three  or more  organisms  share  a mutually  consistent  ortholog  relationship.  The calculated orthologs and homologs are considered putative, since they are based only on sequence  comparisons.  Curated  orthologs  are provided  by the Mouse  Genome Database (MGD) at the Jackson Laboratory  and the Zebrafish Information Database (ZFIN)  at the  University  of Oregon  and  can  also  be  obtained  from  the  scientific literature.
Queries  to UniGene  are entered  into a text box on any of the UniGene  pages. Query terms can be, for example,  the UniGene  identifier, a gene name, a text term that is found somewhere  in the UniGene record, or the accession number of an EST or gene sequence in the cluster. For example, the cluster entitled ‘‘A disintegrin and metalloprotease  domain 10’’ that contains the sequence for human ADAM10 can be retrieved  by entering  ADAM10,  disintegrin,  AF009615 (the GenBank  accession number of ADAM10),  or H69859 (the GenBank  accession number of an EST in the cluster). To query  a  specific  part  of  the  UniGene  record,  use  the  @  symbol.  For  example, @gene(symbol) looks  for  genes  with  the  name  of the  symbol  enclosed  in the parentheses,   @chr(num) searches   for  entries   that  map  to  chromosome   num, @lib(id) returns entries in a cDNA library identified by id, and @pid(id) se- lects entries associated  with a GenBank  protein identifier id.


The  query  results  page  contains  a list  of all UniGene  clusters  that  match  the query. Each cluster is identified by an identifier, a description,  and a gene symbol, if available. Cluster identifiers are prefixed with Hs for Homo sapiens, Rn for Rattus norvegicus,  Mm  for  Mus  musculus,  or  Dn  for  Danio  rerio.  The  descriptions  of UniGene  clusters  are taken from LocusLink,  if available,  or from the title of a sequence  in the cluster. The UniGene  report page for each cluster links to data from other  NCBI  resources  (Fig.  12.5). At the top of the page  are links  to LocusLink, which  provides  descriptive   information   about  genetic  loci  (Pruitt  et  al.,  2000), OMIM, a catalog of human genes and genetic disorders, and HomoloGene.  Next are listed similarities  between the translations  of DNA sequences in the cluster and protein  sequences  from  model  organisms,  including  human,  mouse,  rat, fruit fly, and worm. The subsequent section describes relevant mapping information. It is followed by  ‘‘expression  information,’’  which  lists  the  tissues  from  which  the  ESTs  in the cluster have been created,  along with links to the SAGE database. Sequences  making up the cluster are listed next, along with a link to download  these sequences.


It is important  to note that clusters  that contain  ESTs only (i.e., no mRNAs  or annotated  CDSs) will be missing  some of these fields, such as LocusLink,  OMIM, and mRNA/Gene  links. UniGene titles for such clusters, such as ‘‘EST, weakly similar to ORF2 contains a reverse transcriptase  domain [H. sapiens],’’ are derived from the title of a characterized  protein  with which  the translated  EST sequence  aligns. The  cluster  title  might  be  as  simple  as  ‘‘EST’’  if  the  ESTs  share  no  significant similarity with characterized  proteins. [ 1 ]


On February 1, 2019, the NCBI announced that it was retiring the UniGene database because "reference genomes are available for most organisms with a sizable research community. Consequently, the usage of and need for UniGene has dropped significantly." [ 2 ]  Access to the UniGene builds will remain available through FTP.


The  Vertebrate Genome Annotation  ( VEGA ) database is a  biological database  dedicated to assisting researchers in locating specific areas of the  genome  and annotating genes or regions of vertebrate genomes. [ 1 ]  The VEGA browser is based on  Ensembl  web code and infrastructure and provides a public curation of known vertebrate genes for the scientific community. [ 2 ] [ 3 ]   The VEGA website is updated frequently to maintain the most current information about vertebrate genomes and attempts to present consistently high-quality  annotation  of all its published vertebrate genomes or genome regions. [ 4 ]  VEGA was developed by the  Wellcome Trust Sanger Institute  and is in close association with other annotation databases, such as  ZFIN  (The Zebrafish Information Network), the Havana Group and  GenBank . [ 1 ] [ 5 ]  Manual annotation is currently more accurate at identifying splice variants,  pseudogenes ,  polyadenylation  features, non-coding regions and complex gene arrangements than automated methods. [ 5 ]


The Vertebrate Genome Annotation (VEGA) database was first made public in 2004 by the Wellcome Trust Sanger Institute. It was designed to view manual annotations of human, mouse and zebrafish genomic sequences, and it is the central cache for genome sequencing centers to deposit their annotation of human chromosomes. [ 6 ]  Manual annotation of genomic data is extremely valuable to produce an accurate reference gene set but is expensive compared with automatic methods and so has been limited to model organisms. Annotation tools that have been developed at the Wellcome Trust Sanger Institute (WTSI) [ 7 ]  are now being used to fill that gap, as they can be used remotely and so open up viable community annotation collaborations. [ 8 ]  The HAVANA and VEGA Projects were run by Dr. Jennifer Harrow of the Wellcome Sanger Institute. VEGA has been archived since February 2017 and the HAVANA team moved to EMBL-EBI in June 2017.


The Vega database is the central repository for the majority of genome sequencing centers to deposit their annotation of human chromosomes. [ 6 ]  Since the original VEGA publication, the number of human gene loci annotated has more than doubled to over 49,000 (September 2012 release), over 20,000 of which are predicted to be protein coding. [ 6 ] [ 9 ]  The Havana Group as part of the consensus-coding sequence (CCDS) collaboration and whole-genome extension of the ENCODE project have fully manually annotated the human genome—which is available for reference, comparative analysis and sequence searches on the VEGA database. [ 10 ] [ 11 ]
The final VEGA release was in February 2017 (release 68) and VEGA is now an archived site that will no longer be updated.


The VEGA database combines the information from individual vertebrate genome databases and brings them all together to allow easier access and comparative analysis for researchers. The human and vertebrate analysis and annotation (Havana) team at the Wellcome Trust Sanger Institute (WTSI) manually annotate the human, mouse and zebrafish genomes using the Otterlace/ZMap genome annotation tool. [ 12 ]  The Otterlace manual annotation system comprises a relational database that stores manual annotation data and supports the graphical interface, Zmap and is based on the Ensembl schema. [ 8 ]


The Zebrafish Genome, which is being fully sequenced and manually annotated. [ 13 ]  The Zebrafish genome currently has 18,454 annotated VEGA genes—of which 16,588 are projected protein-coding genes (September 2012, release). [ 14 ]


The Mouse genome currently has 23,322 annotated VEGA genes—of which 14,805 are projected protein-coding genes (June 2012, release). [ 15 ]  The loci chosen for manual annotation are spread throughout the genome, but some regions have received more focus than others: Chromosomes 2, 4, 11 and X, which have been fully annotated. The annotation shown in this release of Vega is from a datafreeze taken on 19 March 2012 and the gene structures are presented in the merged mouse geneset shown in Ensembl release 67. Vega also shows artificial loci generated by the  mouse Knockout programs . [ 15 ]


The Pig genome currently has annotated 2,842 VEGA genes—of which 2,264 are projected protein-coding genes (September 2012, release). [ 16 ]  The pig major histocompatibility complex (MHC), also known as the swine leukocyte antigen complex (SLA), spans a 2.4Mb region of submetacentric chromosome 7 (SSC7p1.1-q1.1). Implicated in the control of immune response and susceptibility to a range of diseases, the pig MHC plays a unique role in histocompatibility. [ 16 ]  Chromosomes X-WTSI and Y-WTSI are currently being annotated by Havana. [ 16 ]


The Dog genome currently has 45 annotated VEGA genes—of which 29 are projected protein-coding genes (February 2005, release). [ 17 ]
The Chimpanzee genome currently has 124 annotated VEGA genes—of which 52 are projected protein-coding genes (January 2012, release). [ 18 ]
The Wallaby genome currently has 193 annotated VEGA genes—of which 76 are projected protein-coding genes (March 2009, release). [ 19 ]
The Gorilla genome currently has 324 annotated VEGA genes—of which 176 are projected protein-coding genes (March 2009, release). [ 20 ]


In addition to full genomes, and unlike other browsers, VEGA also displays small finished regions of interest from genomes of other vertebrates, human haplotypes and mouse strains. Currently this comprises the finished sequence and annotation of the major histocompatibility complex (MHC) from different human haplotypes, and dog and pig [the latter of which is currently otherwise only available in very limited form in Ensembl Pre!. [ 21 ]  Additionally there is mouse NOD (non-obese diabetes) strain annotation of IDD (insulin-dependent diabetes) candidate regions and two more pig regions. [ 6 ]


Vega contains comparative pairwise analysis between specific genomic regions from either different species or from different haplotypes / strains. This is in contrast to Ensembl where many all genome versus all genome comparisons are performed. [ 22 ]
The analysis in Vega involves:


1. The identification of genomic alignments using LastZ.
2. Prediction of the orthologue pairs using the Ensembl gene tree pipeline. Note that although the pipeline generates phylogenetic genetrees, the limited scope of the Vega comparative analysis means that these will necessarily be incomplete and consequently only orthologs are shown on the website.
3. The manual identification of alleles in either different human haplotypes or mouse strains.


There are five sets of analyses: [ 22 ]


1. The MHC region has been compared between dog, pig (two assemblies), gorilla, chimpanzee, wallaby, mouse and eight human haplotypes:


2. Comparisons between the LRC regions of pig, gorilla and human (nine haplotypes):


3. The regions of the CL57BL/6 reference assembly used in these comparisons are:


4. Comparisons between three specific regions:


5. Pairwise comparisons between three pairs of full length mouse and human chromosomes:


WormBase  is an online  biological database  about the biology and genome of the nematode  model organism   Caenorhabditis elegans  and contains information about other related nematodes. [ 1 ] [ 2 ]  WormBase is used by the  C. elegans  research community both as an information resource and as a place to publish and distribute their results. The database is regularly updated with new versions being released every two months. WormBase is one of the organizations participating in the  Generic Model Organism Database  (GMOD) project.


WormBase comprises the following main data sets:


In addition, WormBase contains an up-to-date searchable bibliography of  C. elegans  research and is linked to the  WormBook  project.


WormBase offers many ways of searching and retrieving data from the database:


Sequence curation at WormBase refers to the maintenance and annotation of the primary genomic sequence and a consensus gene set.


Even though the  C. elegans  genome sequence is the most accurate and complete eukaryotic genome sequence, it has continually needed refinement as new evidence has been created. Many of these changes were single nucleotide insertions or deletions, however several large mis-assemblies have been uncovered. For example, in 2005 a 39 kb cosmid had to be inverted. Other improvements have come from comparing genomic DNA to cDNA sequences and analysis of RNASeq high-throughput data. When differences between the genomic sequence and transcripts are identified, re-analysis of the original genomic data often leads to modifications of the genomic sequence.
The changes in the genomic sequence pose difficulties when comparing chromosomal coordinates of data derived from different releases of WormBase. There is a coordinate re-mapping program and mapping data are available to aid these comparisons.
 [ 6 ]


All the gene-sets of the WormBase species were initially generated by gene prediction programs. Gene prediction programs give a reasonable set of gene structures, but the best of them only predict about 80% of the complete gene structures correctly. They have difficulty predicting genes with unusual structures, as well as those with a weak translation start signal, weak splice sites or single exon genes. They can incorrectly predict a coding gene model where the gene is a pseudogene and they predict the isoforms of a gene poorly, if at all.


The gene models of  C. elegans ,  C. briggsae ,  C. remanei , and  C. brenneri  genes are manually curated. The majority of gene structure changes have been based on transcript data from large scale projects such as Yuji Kohara's EST libraries, Mark Vidal's Orfeome project (worfdb.dfci.harvard.edu/) Waterston and Hillier's Illumina data and Makedonka Mitreva's 454 data. However, other data types (e.g. protein alignments,  ab initio  prediction programs, trans-splice leader sites, poly-A signals and addition sites, SAGE and  TEC-RED  transcript tags, mass-spectroscopic peptides, and conserved protein domains) are useful in refining the structures, especially where expression is low and so transcripts are not sufficiently available. When genes are conserved between the available nematode species, comparative analysis can also be very informative.


WormBase encourages researchers to inform them via the help-desk if they have evidence for an incorrect gene structure. Any cDNA or mRNA sequence evidence for the change should be submitted to EMBL/GenBank/DDBJ; this helps in the confirmation and evidence for the gene model as WormBase routinely retrieve sequence data from these public databases. This also makes the data public, allowing appropriate reference and acknowledgement to the researchers.


When any change is made to a CDS (or Pseudogene), the old gene model is preserved as a ‘history’ object. This will have a suffix name like: “AC3.5:wp119”, where ‘AC3.5’ is the name of the CDS and the ‘119’ refers to the database release in which the change was made. The reason for the change and the evidence for the change are added to the annotation of the CDS – these can be seen in the Visible/Remark section of the CDS's ‘Tree Display’ section on the WormBase web site.


In WormBase, a Gene is a region that is expressed or a region that has been expressed and is now a Pseudogene. Genes have unique identifiers like ‘WBGene00006415’. All C. elegans WormBase genes also have a Sequence Name, which is derived from the cosmid, fosmid or YAC clone on which they reside, for instance  F38H4.7 , indicating it is on the cosmid ‘F38H4’, and there are at least 6 other genes on that cosmid. If a gene produces a protein that can be classified as a member of a family, the gene may also be assigned a  CGC  name like  tag-30  indicating that this is the 30th member of the  tag  gene family. Assignment of gene family names is controlled by WormBase. [ 7 ]  Before publication, requests for names should be made in WormBase. [ 8 ]


There are a few exceptions to this format, like the genes  cln-3.1 ,  cln-3.2 , and  cln-3.3  which all are equally similar to the human gene  CLN3 .
Gene GCG names for non-elegans species in WormBase have the 3-letter species code prepended, like  Cre-acl-5 ,  Cbr-acl-5 ,  Cbn-acl-5 .


A gene can be a Pseudogene, or can express one or more non-coding RNA genes (ncRNA) or protein-coding sequences (CDS).


Pseudogenes are genes that do not produce a reasonable, functional transcript. They may be pseudogenes of coding genes or of non-coding RNA and may be whole or fragments of a gene and may or may not express a transcript. The boundary between what is considered a  reasonable  coding transcript is sometimes subjective as, in the absence of other evidence, the use of weak splice sites or short exons can often produce a putative, though unsatisfactory, model of a CDS. Pseudogenes and genes with a problematic structure are constantly under review in WormBase and new evidence is used to try to resolve their status.


Coding Sequences (CDSs) are the only part of a Gene's structure that is manually curated in WormBase. The structure of the Gene and its transcripts are derived from the structure of their CDSs.


CDSs have a Sequence Name that is derived from the same Sequence Name as their parent Gene object, so the gene ‘F38H4.7’ has a CDS called ‘F38H4.7’. The CDS specifies coding exons in the gene from the START (Methionine) codon up to (and including) the STOP codon.


Any gene can code for multiple proteins as a result of alternative splicing. These isoforms have a name that is formed from the Sequence Name of the gene with a unique letter appended. In the case of the gene  bli-4  there are 6 known CDS isoforms, called K04F10.4a, K04F10.4b, K04F10.4c, K04F10.4d, K04F10.4e and K04F10.4f.


It is common to refer to isoforms in the literature using the CGC gene family name with a letter appended, for example  pha-4a , however this has no meaning within the WormBase database and searches for  pha-4a  in WormBase will not return anything. The correct name of this isoform is either the CDS/Transcript name:  F38A6.1a , or even better, the Protein name:  WP:CE15998 .


The transcripts of a gene in WormBase are automatically derived by mapping any available cDNA or mRNA alignments onto the CDS model. These gene transcripts will therefore often include the UTR exons surrounding the CDS. If there are no available cDNA or mRNA transcripts, then the gene transcripts will have exactly the same structure as the CDS that they are modelled on.


Gene transcripts are named after the Sequence Name of the CDS used to create them, for example,  F38H4.7  or  K04F10.4a .


However, if there is alternative splicing in the UTRs, which would not change the protein sequence, the alternatively spliced transcripts are named with a digit appended, for example:  K04F10.4a.1  and  K04F10.4a.2 . If there are no isoforms of the coding gene, for example  AC3.5 , but there is alternative splicing in the UTRs, there will be multiple transcripts named  AC3.5.1  and  AC3.5.2 , etc. If there are no alternate UTR transcripts the single coding_transcript is named the same as the CDS and does not have the .1 appended, as in the case of K04F10.4f.


Groups of genes which are co-transcribed as operons are curated as Operon objects. These have names like  CEOP5460  and are manually curated using evidence from the SL2 trans-spliced leader sequence sites.


There are several classes of non-coding RNA gene classes in WormBase:


There is also one scRNA gene.


Transposons are not classed as genes and so do not have a parent gene object. Their structure is curated as a Transposon_CDS object with a name like  C29E6.6 .


The non-elegans species in WormBase have genomes that have been assembled from sequencing technologies that do not involve sequencing cosmids or YACs. These species therefore do not have sequence names for CDSs and gene transcripts that are based on cosmid names. Instead they have unique alphanumeric identifiers constructed like the names in the table below.


The protein products of gene are created by translating the CDS sequences. Each unique protein sequence is given a unique identifying name like  WP:CE40440 . Examples of the protein identifier names for each species in WormBase is given in the table, below.


It is possible for two CDS sequences from separate genes, within a species, to be identical and so it is possible to have identical proteins coded for by separate genes. When this happens, a single, unique identifying name is used for the protein even though it is produced by two genes.


WormBase ParaSite [ 9 ]  is a sub-portal for approximately 100 draft genomes of parasitic helminths ( nematodes  and  platyhelminthes ) developed at the  European Bioinformatics Institute  and  Wellcome Trust Sanger Institute . All genomes are assembled and annotated. Additional information such as protein domains and  Gene Ontology  terms are also available. Gene trees allow the alignment of orthologues between parasitic worms, other nematodes and non-worm comparator species. A  BioMart  data-mining tool is offered to permit large scale access to the data.


WormBase is a collaboration among the  European Bioinformatics Institute ,  Wellcome Trust Sanger Institute ,  Ontario Institute for Cancer Research ,  Washington University in St. Louis , and the  California Institute of Technology . It is supported by the grant  P41-HG002223  from the  National Institutes of Health  and the grant  G0701197  from the  British Medical Research Council  . [ 10 ]  Caltech carries out the biological curation and develops the underlying ontologies, the EBI carries out sequence curation and computation as well as database builds, the Sanger is primarily involved in curation and display of parasitic nematode genomes and genes, and the OICR develops the website and main data mining tools.


The  Y Chromosome Haplotype Reference Database  ( YHRD ) is an open-access, annotated collection of population samples typed for Y chromosomal sequence variants. [ 1 ]  Two important objectives are pursued: (1) the generation of reliable frequency estimates for  Y-STR haplotypes  and  Y-SNP haplotypes  to be used in the quantitative assessment of matches in forensic and kinship cases and (2) the characterization of male lineages to draw conclusions about the origins and history of human populations. The database is endorsed by the  International Society for Forensic Genetics (ISFG) .
By May 2023 about 350,000 Y chromosomes typed for 9-29 STR loci have been directly submitted by worldwide forensic institutions and universities. In geographic terms, about 53% of the YHRD samples stem from Asia, 21% from Europe, 12% from North America, 10% from Latin America, 3% from Africa, 0.8% from Oceania/Australia and 0.2% from the Arctic. The 1.406 individual sampling projects are described in more than 800 peer-reviewed publications  [ 2 ]


YHRD is built by direct submissions of population data from individual laboratories. Upon receipt of a submission, the YHRD staff examines the originality, relevance, plausibility and validity of the data and assigns an accession number to the population sample if these criteria are met. The submissions are then registered to the public database, where the entries can be filtered for haplotypes, contributors or accession numbers. All population data published in forensic journals as  FSI: Genetics  or  International Journal of Legal Medicine [ 3 ]  are required to be validated by the YHRD custodians and are subsequently included in the YHRD. [ 4 ]


The database supports the most frequently used haplotype formats (e.g. Minimal (minHt), Powerplex Y12, [ 5 ]  YFiler, [ 6 ]  Powerplex Y23, [ 7 ]  YfilerPlus and Maximal (maxHt) for which differently-sized databases exist.


Because correlations exist between geographic areas, language groups and Y chromosomal variants, the YHRD population database was structured to display the geographic, linguistic and phylogenetic relationship of searched haplotype profiles. Currently the YHRD database recognizes four separate "metapopulation" structures: national, continental, linguistic/ethnic and phylogenetic affiliation with several categories within. In population genetics the term metapopulation describes discrete spatially distributed population groups which are interconnected by geneflow and migration. [ 8 ]  By analogy, the term metapopulation is used in forensic genetics to describe a set of geographically dispersed populations with shared ancestry and continuing geneflow. Thus, the population groups are more similar within the metapopulation than to groups outside the metapopulation. [ 9 ]


The concept of pooling data to build "national databases" has a very straightforward explanation: law enforcement agencies and forensic services rely on their national population to build  reference databases . In most instances offenders and victims stem from the national population, and their genetic profiles should thus be represented in the database. In countries like US, Brazil, UK or China which are characterized by strong population substructure national reference databases are often built on basis of a historical concept of ethnic affiliation, e.g. the US population is sub-structured in Caucasian, African, Hispanic, Asian and Native American populations or UK differentiates English, Afro-Caribbean, Indo-Pakistani and Chinese. National databases due to their importance in national legislation are thus searchable in the YHRD. Each national Metapopulation in the YHRD comprises all individuals sampled in a particular country regardless of the ancestry of the individuals.


Continental Metapopulations in the YHRD comprises all individuals sampled in a particular continent regardless of their  ancestries. The YHRD defines seven continental Metapopulations following the United Nations classification of geographical regions: Africa, Arctic, Asia, Europe, Latin America, North America, Oceania/Australia.


The Metapopulation structure built on basis of "ethnicity/linguistic affiliation" takes to a larger extent the ancestry of sampled individuals into account. "Ancestry" is a term collating historical, cultural, geographical and linguistic categories. Of course, a Metapopulation concept on basis of "ethnicity" is by no means ideal, fully rational or fully translatable, but simply takes the fact into account that on a global level categories other than "nation" or "geography" far better describe the observed genetic clustering and inhomogeneity of Y chromosome patterns.


For a global reference database the "major language group" criterion seems most appropriate to group data by taking the ancestry into account and produce subdatabases with respect to genetic similarity. The reasoning in doing so is twofold: first, language is an inherited cultural trait and thus the language phylae often correlate with genetic traits not the least to Y chromosome polymorphisms. Second, since languages are well examined by science and mostly understood by the public due to the long tradition of language research, the linguistic terminology is in principle more understandable and translatable into practice than their genetic pendant. Aside from the pure linguistic categorization (e.g. the  Altaic  language family comprising people speaking  Turk  and  Mongol languages ) we took also unifying geographic criteria ( Sub-Saharan Africa  comprising speakers of different  African  language groups which live south of the  Sahara ).


It is important to state, that the current Metapopulation structure is an a-priori categorization which needs a continuous evaluation and verification by means of statistical methods to quantify the genetic similarity/dissimilarity between the samples. While the current categorization of eight large Metapopulations gains some support from genetic distance analysis done on basis of ~41,000 haplotypes  [ 9 ]  a further subdivision of the "Eurasian – European Metapopulation" was implemented solely on basis of  Y-STR haplotypes . The analysis of ~12,000 European Haplotypes by AMOVA demonstrates that three larger pools of European haplotypes exist: the western, eastern and southeastern metapopulations. [ 10 ]


Currently the YHRD has seven non-overlapping broadly defined metapopulations: African, Afro-Asiatic, Native American, Australian Aboriginal, East Asian, Eskimo-Aleut, and Eurasian. Some of these metapopulations are further subdivided, e.g. Eurasian into six subcategories, from which the European subgroup splits further into three groups of Western, Eastern and Southeastern Europeans.


The DNA profiling of Y chromosomes submitted to the YHRD is now continuously extended for binary Y-SNP polymorphisms. The phylogeny of the Y chromosome defined by binary polymorphisms is well established and stable. [ 11 ] [ 12 ] [ 13 ] [ 14 ]  All Y chromosomes sharing a mutation are related by descent, until a further mutation splits the branch. Haplotypes within a haplogroup could be highly similar or even "identical by descent" (IBD). In thus, the haplogroup could be used as a criterion to substructure the database according to the phylogenetic descent of samples. Even though the chronology of the SNP mutations is far less certain than the structure of the tree, many haplogroups could be equated with events in human prehistory. The worldwide distribution of the patterns of the human Y-chromosome diversity has revealed clear geographically associated haplogroups. [ 11 ]


Analysis of molecular variance (AMOVA)  is a method for analyzing population variation using molecular data, e.g.  Y-STR haplotypes . [ 15 ]  With AMOVA it is possible to evaluate and quantify the extent of differentiation between two or more population samples. AMOVA is implemented as an online tool in the YHRD and provides a way of estimating Φ ST  and F ST  values. The online tool accepts Excel files and creates entry files from it. As much as 9 reference populations selected from the YHRD as well as population sets can be added to the AMOVA analysis. The online calculation returns as a result a *.csv table with pairwise  F ST  or Φ ST ( R ST ) values plus p-values as a test for significance (10,000 permutations). In addition, an  MDS plot  is generated to illustrate the genetic distance between the analyzed populations graphically. The program shows the references for the selected population studies which facilitates the correct citation.


The tool can be applied for forensic cases when a mixed trace (2 or more male contributors) should be analyzed. The result will be a likelihood ratio of donorship vs. non-donorship of the putative contributor to the trace.


The tool can be applied for kinship cases when a relationship between upstream and downstream relatives (e.g. father-son or grandfather-grandson) should be analyzed. The result will be a likelihood ratio (or kinship index)  of patrilineal relationship vs. patrilineal non-relationship of the analyzed persons.


Searching the YHRD will result in a match or a non-match between a searched haplotype and the databased reference samples. The relative number of matches is described as the profile frequency. In forensic casework the probability of a match which is based on the profile frequency is evaluated using different methods. Some of these are recommended by national guidelines, e.g. the augmented counting method with confidence intervals and/or theta subpopulation correction (SWGDAM Interpretation Guidelines for Y-Chromosome STR typing by Forensic Laboratories in the US, 2014) or the Discrete Laplace method (Andersen et al. 2013) as recommended in Germany (Willuweit et al. 2018). Both augmented counting and DL values are provided by the YHRD for different metapopulations.


Cloning  is the process of producing individual  organisms  with identical  genomes , either by natural or artificial means. In nature, some organisms produce clones through  asexual reproduction ; this reproduction of an organism by itself without a mate is known as  parthenogenesis . In the field of  biotechnology , cloning is the process of creating cloned organisms of  cells  and of DNA fragments.


The artificial cloning of organisms, sometimes known as reproductive cloning, is often accomplished via  somatic-cell nuclear transfer  (SCNT), a cloning method in which a viable  embryo  is created from a  somatic cell  and an  egg cell . In 1996,  Dolly the sheep  achieved notoriety for being the first mammal cloned from a somatic cell. Another example of artificial cloning is  molecular cloning , a technique in  molecular biology  in which a single living cell is used to clone a large population of cells that contain identical DNA molecules.


In  bioethics , there are a variety of  ethical positions regarding the practice and possibilities of cloning . The use of  embryonic stem cells , which can be produced through SCNT, in some stem cell research  has attracted controversy . Cloning has been proposed as a means of  reviving extinct species . In popular culture, the concept of cloning—particularly human cloning—is often depicted in  science fiction ; depictions commonly involve themes related to identity, the recreation of historical figures or extinct species, or cloning for exploitation (e.g. cloning soldiers for warfare).


Coined by  Herbert J. Webber , the term  clone  derives from the Ancient Greek word  κλών  ( klōn ),  twig , which is the process whereby a new plant is created from a twig. In botany, the term  lusus  was used. [ 1 ]  In  horticulture , the spelling  clon  was used until the early twentieth century; the final  e  came into use to indicate the vowel is a "long o" instead of a "short o". [ 2 ] [ 3 ]  Since the term entered the popular lexicon in a more general context, the spelling  clone  has been used exclusively.


Natural cloning is the production of clones without the involvement of genetic engineering techniques or human intervention (i.e. artificial cloning). [ 4 ]  Natural cloning occurs through a variety of natural mechanisms, from single-celled organisms to complex multicellular organisms, and has allowed life forms to spread for hundreds of millions of years. Versions of this reproduction method are used by plants, fungi, and bacteria, and is also the way that  clonal colonies  reproduce themselves. [ 5 ] [ 6 ]  Some of the mechanisms are explored and used in plants and animals are binary fission, budding, fragmentation, and parthenogenesis. [ 7 ]  It can also occur during some forms of asexual reproduction, when a single parent organism produces genetically identical offspring by itself. [ 8 ] [ 9 ]


Many plants are well known for natural cloning ability, including  blueberry plants ,  Hazel  trees,  the Pando trees , [ 10 ] [ 11 ]  the  Kentucky coffeetree ,  Myrica , and the  American sweetgum.


It also occurs accidentally in the case of identical twins, which are formed when a fertilized egg splits, creating two or more embryos that carry identical DNA.


Molecular cloning refers to the process of making multiple molecules. Cloning is commonly used to amplify  DNA  fragments containing whole  genes , but it can also be used to amplify any DNA sequence such as  promoters , non-coding sequences and randomly fragmented DNA. It is used in a wide array of biological experiments and practical applications ranging from  genetic fingerprinting  to large scale protein production. Occasionally, the term cloning is misleadingly used to refer to the identification of the  chromosomal  location of a gene associated with a particular phenotype of interest, such as in  positional cloning . In practice, localization of the gene to a chromosome or genomic region does not necessarily enable one to isolate or amplify the relevant genomic sequence. To amplify any DNA sequence in a living organism, that sequence must be linked to an  origin of replication , which is a sequence of DNA capable of directing the propagation of itself and any linked sequence. However, a number of other features are needed, and a variety of specialised  cloning vectors  (small piece of DNA into which a foreign DNA fragment can be inserted) exist that allow  protein production ,  affinity tagging , single-stranded  RNA  or DNA production and a host of other molecular biology tools.


Cloning of any DNA fragment essentially involves four steps [ 12 ]


Although these steps are invariable among cloning procedures a number of alternative routes can be selected; these are summarized as a  cloning strategy .


Initially, the DNA of interest needs to be isolated to provide a DNA segment of suitable size. Subsequently, a ligation procedure is used where the amplified fragment is inserted into a  vector  (piece of DNA). The vector (which is frequently circular) is linearised using  restriction enzymes , and incubated with the fragment of interest under appropriate conditions with an enzyme called  DNA ligase . Following ligation, the vector with the insert of interest is transfected into cells. A number of alternative techniques are available, such as chemical sensitisation of cells,  electroporation ,  optical injection  and  biolistics . Finally, the transfected cells are cultured. As the aforementioned procedures are of particularly low efficiency, there is a need to identify the cells that have been successfully transfected with the vector construct containing the desired insertion sequence in the required orientation. Modern cloning vectors include selectable  antibiotic  resistance markers, which allow only cells in which the vector has been transfected, to grow. Additionally, the cloning vectors may contain colour selection markers, which provide blue/white screening (alpha-factor complementation) on  X-gal  medium. Nevertheless, these selection steps do not absolutely guarantee that the DNA insert is present in the cells obtained. Further investigation of the resulting colonies must be required to confirm that cloning was successful. This may be accomplished by means of  PCR , restriction fragment analysis and/or  DNA sequencing .


Cloning a cell means to derive a population of cells from a single cell. In the case of unicellular organisms such as bacteria and yeast, this process is remarkably simple and essentially only requires the  inoculation  of the appropriate medium. However, in the case of cell cultures from multi-cellular organisms, cell cloning is an arduous task as these cells will not readily grow in standard media.


A useful tissue culture technique used to clone distinct lineages of cell lines involves the use of cloning rings (cylinders). [ 13 ]  In this technique a single-cell suspension of cells that have been exposed to a  mutagenic  agent or drug used to drive  selection  is plated at high dilution to create isolated colonies, each arising from a single and potentially clonal distinct cell. At an early growth stage when colonies consist of only a few cells, sterile  polystyrene  rings (cloning rings), which have been dipped in grease, are placed over an individual colony and a small amount of  trypsin  is added. Cloned cells are collected from inside the ring and transferred to a new vessel for further growth.


Somatic-cell nuclear transfer , popularly known as SCNT, can also be used to create embryos for research or therapeutic purposes. The most likely purpose for this is to produce embryos for use in  stem cell research . This process is also called "research cloning" or "therapeutic cloning". The goal is not to create cloned human beings (called "reproductive cloning"), but rather to harvest stem cells that can be used to study human development and to potentially treat disease. While a clonal human blastocyst has been created, stem cell lines are yet to be isolated from a clonal source. [ 14 ]


Therapeutic cloning is achieved by creating embryonic stem cells in the hopes of treating diseases such as diabetes and Alzheimer's. The process begins by removing the nucleus (containing the DNA) from an egg cell and inserting a nucleus from the adult cell to be cloned. [ 15 ]  In the case of someone with Alzheimer's disease, the nucleus from a skin cell of that patient is placed into an empty egg. The reprogrammed cell begins to develop into an embryo because the egg reacts with the transferred nucleus. The embryo will become genetically identical to the patient. [ 15 ]  The embryo will then form a blastocyst which has the potential to form/become any cell in the body. [ 16 ]


The reason why SCNT is used for cloning is because somatic cells can be easily acquired and cultured in the lab. This process can either add or delete specific genomes of farm animals. A key point to remember is that cloning is achieved when the oocyte maintains its normal functions and instead of using sperm and egg genomes to replicate, the donor's somatic cell nucleus is inserted into the oocyte. [ 17 ]  The oocyte will react to the somatic cell nucleus, the same way it would to a sperm cell's nucleus. [ 17 ]


The process of cloning a particular farm animal using SCNT is relatively the same for all animals. The first step is to collect the somatic cells from the animal that will be cloned. The somatic cells could be used immediately or stored in the laboratory for later use. [ 17 ]  The hardest part of SCNT is removing maternal DNA from an oocyte at metaphase II. Once this has been done, the somatic nucleus can be inserted into an egg cytoplasm. [ 17 ]  This creates a one-cell embryo. The grouped somatic cell and egg cytoplasm are then introduced to an electrical current. [ 17 ]  This energy will hopefully allow the cloned embryo to begin development. The successfully developed embryos are then placed in surrogate recipients, such as a cow or sheep in the case of farm animals. [ 17 ]


SCNT is seen as a good method for producing agriculture animals for food consumption. It successfully cloned sheep, cattle, goats, and pigs. Another benefit is SCNT is seen as a solution to clone endangered species that are on the verge of going extinct. [ 17 ]  However, stresses placed on both the egg cell and the introduced nucleus can be enormous, which led to a high loss in resulting cells in early research. For example,  the cloned sheep Dolly  was born after 277 eggs were used for SCNT, which created 29 viable embryos. Only three of these embryos survived until birth, and only one survived to adulthood. [ 18 ]  As the procedure could not be automated, and had to be performed manually under a  microscope , SCNT was very resource intensive. The biochemistry involved in reprogramming the  differentiated  somatic cell nucleus and activating the recipient egg was also far from being well understood. However, by 2014 researchers were reporting cloning success rates of seven to eight out of ten [ 19 ]  and in 2016, a Korean Company Sooam Biotech was reported to be producing 500 cloned embryos per day. [ 20 ]


In SCNT, not all of the donor cell's genetic information is transferred, as the donor cell's  mitochondria  that contain their own  mitochondrial DNA  are left behind. The resulting hybrid cells retain those mitochondrial structures which originally belonged to the egg. As a consequence, clones such as Dolly that are born from SCNT are not perfect copies of the donor of the nucleus.


Organism cloning  (also called reproductive cloning) refers to the procedure of creating a new multicellular organism, genetically identical to another. In essence this form of cloning is an asexual method of reproduction, where fertilization or inter-gamete contact does not take place. Asexual reproduction is a naturally occurring phenomenon in many species, including most plants and some insects. Scientists have made some major achievements with cloning, including the asexual reproduction of sheep and cows. There is a lot of ethical debate over whether or not cloning should be used. However, cloning, or asexual propagation, [ 21 ]  has been common practice in the horticultural world for hundreds of years.


The term  clone  is used in horticulture to refer to descendants of a single plant which were produced by  vegetative reproduction  or  apomixis . Many horticultural plant  cultivars  are clones, having been derived from a single individual, multiplied by some process other than sexual reproduction. [ 22 ]  As an example, some European cultivars of grapes represent clones that have been propagated for over two millennia. Other examples are potato and banana. [ 23 ]


Grafting  can be regarded as cloning, since all the shoots and branches coming from the graft are genetically a clone of a single individual, but this particular kind of cloning has not come under  ethical  scrutiny and is generally treated as an entirely different kind of operation.


Many trees,  shrubs ,  vines ,  ferns  and other  herbaceous perennials  form  clonal colonies  naturally. Parts of an individual plant may become detached by  fragmentation  and grow on to become separate clonal individuals. A common example is in the vegetative reproduction of moss and liverwort gametophyte clones by means of  gemmae . Some vascular plants e.g.  dandelion  and certain  viviparous  grasses also form  seeds  asexually, termed  apomixis , resulting in clonal populations of genetically identical individuals.