Ensembl Genomes makes no attempt to include all possible genomes, rather the genomes that are included on the site are those that are deemed to be scientifically important. [ 35 ]  Each site contains the following number of species:


Ensembl Genomes continuously expands the annotation data through collaboration with other organisations involved in genome annotation projects and research. The following organisations are collaborators of Ensembl Genomes: [ 42 ]


EPD  ( Eukaryotic Promoter Database ) is a  biological database   and  web resource  of  eukaryotic   RNA polymerase II   promoters  with experimentally defined  transcription  start sites. [ 1 ]  Originally, EPD was a manually curated resource relying on transcript mapping experiments (mostly  primer extension  and  nuclease protection assays ) targeted at individual  genes  and published in  academic journals .  More recently, automatically generated promoter collections derived from electronically distributed high-throughput data produced with the  CAGE  or TSS-Seq  [ 2 ] [ 3 ]  protocols were added as part of a special subsection named  EPDnew . [ 4 ]  The EPD web server offers additional services, including an entry viewer which enables users to explore the genomic context of a promoter in a  UCSC Genome Browser  window, and direct links for uploading EPD-derived promoter subsets to associated web-based promoter analysis tools of the  Signal Search Analysis (SSA) [ 5 ]  and  ChIP-Seq  servers. EPD also features a collection of  position weight matrices  (PWMs) for common promoter  sequence motifs .


EPD was created in 1986 as an electronic version of a eukaryotic promoter compilation published in an article [ 6 ]  and has been regularly updated since then. The database was initially distributed on  magnetic tapes  as part of the EMBL data library [ 7 ]  and later via the  Internet . The collaboration between EPD and the EMBL library was cited as a pioneering example of  remote nucleotide sequence annotation by domain experts. [ 8 ]  EPD has played an instrumental role in the development and evaluation of promoter prediction algorithms [ 9 ]  as it is broadly considered the most accurate promoter resource. [ 10 ]   As of November 2014, it has been cited about 2500 times in scientific literature. [ 11 ]  EPD has also received ample coverage by  textbooks  in  bioinformatics  ( e.g.   [ 12 ] ) and  systems biology  ( e.g.   [ 13 ] ).


European Forest Genetic Resources Programme  ( EUFORGEN ) is an international network that promotes the conservation and sustainable use of  forest genetic resources  in  Europe [ 1 ]  as an integral part of sustainable forest management. It was established in 1994 as a result of a resolution adopted in 1990 by the first  Ministerial Conference of the Forest Europe process .


The programme's tasks include coordination and promotion of  in situ  and  ex situ   conservation  of forest  genetic resources , facilitation of the exchange of information, and increasing public awareness of the need to conserve forest genetic resources. [ 2 ]


EUFORGEN is funded by member countries and operates through working groups formed by experts from across Europe who meet to exchange knowledge, analyse policies and practice, and develop science-based strategies to improve the management of forest genetic resources. EUFORGEN was established in 1994. [ 1 ]  Its secretariat, hosted by the  European Forest Institute , is located in  Barcelona ,  Spain .


As of June 2023 [update] , there are 28 member countries in Europe:


The  European Nucleotide Archive  ( ENA ) is a repository providing free and unrestricted access to annotated  DNA and RNA sequences . It also stores complementary information such as experimental procedures, details of  sequence assembly  and other  metadata  related to  sequencing projects . [ 1 ]
The archive is composed of three main databases: the  Sequence Read Archive , the Trace Archive and the EMBL Nucleotide Sequence Database (also known as EMBL-bank). [ 2 ]  The ENA is produced and maintained by the  European Bioinformatics Institute  and is a member of the  International Nucleotide Sequence Database Collaboration  (INSDC) along with the  DNA Data Bank of Japan  and  GenBank .


The ENA has grown out of the EMBL Data Library which was released in 1982 as the first internationally supported resource for nucleotide sequence data. [ 3 ]  As of early 2012, the ENA and other INSDC member databases each contained complete  genomes  of 5,682 organisms and sequence data for almost 700,000. [ 4 ]
Moreover, the volume of data is  increasing exponentially  with a doubling time of approximately 10 months. [ 5 ]


The European Nucleotide Archive originated from separate databases, the earliest of which was the EMBL Data Library, established in October 1980 at the  European Molecular Biology Laboratory  (EMBL),  Heidelberg . [ 3 ]  The first release of this  database  was made in April 1982 and contained a total of 568 separate entries consisting of around 500,000  base pairs . [ 6 ]  In 1984, referring to the EMBL Data Library, Kneale and Kennard remarked that "it was clear some years ago that a large computerized database of sequences would be essential for research in Molecular Biology". [ 6 ]


Despite the primary distribution method at the time being via  magnetic tape , by 1987, the EMBL Data Library was being used by an estimated 10,000 scientists internationally. [ 7 ]  The same year, the EMBL File Server was introduced to serve database records over  BITNET ,  EARN  and the early  Internet . [ 8 ]  In May 1988 the journal  Nucleic Acids Research  introduced a policy stating that "manuscripts submitted to [Nucleic Acids Research] and containing or discussing sequence data must be accompanied by evidence that the data have been deposited with the EMBL Data Library." [ 9 ]


During the 1990s the EMBL Data Library was renamed the EMBL Nucleotide Sequence Database [ 10 ]  and was formally relocated to the  European Bioinformatics Institute  (EBI) from Heidelberg. [ 11 ]  In 2003, the Nucleotide Sequence Database was extended with the addition of the Sequence Version Archive (SVA), which maintains records of all current and previous entries in the database. [ 1 ]   A year later in June 2004, limits on the maximum sequence length for each record (then 350  kilobases ) were removed, allowing entire genome sequences to be stored as a single  database  entry. [ 12 ]


Following the uptake of  Sanger sequencing , the  Wellcome Trust Sanger Institute  (then known as The Sanger Centre) had begun cataloguing sequence reads along with quality information in a database called The Trace Archive. [ 13 ]  The Trace Archive grew substantially with the commercialisation of high-throughput parallel sequencing technologies by companies such as  Roche  and  Illumina . [ 14 ]
In 2008, the EBI combined the Trace Archive, EMBL Nucleotide Sequence Database (now also known as EMBL-Bank) [ 2 ]  and a newly developed Sequence (or Short) Read Archive (SRA) to make up the ENA, aimed at providing a comprehensive  nucleotide  sequence archive. [ 13 ]  As a member of the  International Nucleotide Sequence Database Collaboration , the ENA exchanges data submissions each day with both the  DNA Data Bank of Japan  and  GenBank . [ 15 ]


The EMBL Nucleotide Sequence Database (also known as EMBL-Bank) is the section of the ENA which contains high-level  genome assembly  details, as well as assembled sequences and their functional  annotation . [ 12 ] [ 17 ]  EMBL-Bank is contributed to by direct submission from genome  consortia  and smaller research groups as well as by the retrieval of sequence data associated with  patent applications . [ 2 ] [ 18 ]


As of release 114 (December 2012), the EMBL Nucleotide Sequence Database contains approximately 5×10 11  nucleotides with an uncompressed filesize of 1.6  terabytes . [ 16 ]


The EMBL Nucleotide Sequence Database supports a variety of data derived from different sources including, but not limited to: [ 19 ]


The EMBL Nucleotide Sequence Database uses a  flat file  plaintext  format  to represent and store data which is typically referred to as EMBL-Bank format. [ 20 ]  EMBL-Bank format uses a different  syntax  to the records in DDBJ and GenBank, though each format uses certain standardised nomenclature, such as  taxonomies  as defined by the  NCBI  Taxon database. Each line of an EMBL-format file begins with a two-letter code, such as  AC  to label the  accession number  and  KW  for a list of keywords relevant to the record; each record ends with  // . [ 20 ]


The ENA operates an instance of the Sequence Read Archive (SRA), an archival repository of sequence reads and analyses which are intended for public release. [ 23 ]  Originally called the Short Read Archive, the name was changed in anticipation of future sequencing technologies being able to produce longer sequence reads. [ 24 ]  Currently, the archive accepts sequence reads generated by next-generation  sequencing platforms  such as the Illumina Genome Analyzer and  ABI SOLiD  as well as some corresponding analyses and  alignments . [ 25 ]  The SRA operates under the guidance of the  International Nucleotide Sequence Database Collaboration  (INSDC) [ 23 ]  and is the fastest-growing repository in the ENA. [ 14 ]


In 2010 the Sequence Read Archive made up approximately 95% of the  base pair  data available through the ENA, [ 13 ]  encompassing over 500,000,000,000 sequence reads made up of over 60 trillion (6×10 13 ) base pairs. [ 23 ]  Almost half of this data was deposited in relation to the  1000 Genomes Project [ 23 ]  wherein the researchers published their sequence data to the SRA in  real-time . [ 26 ]  In total, as of September 2010, 65% of the Sequence Read Archive was  human genomic  sequence, with another 16% relating to human  metagenome  sequence reads. [ 23 ]


The preferred  data format  for files submitted to the SRA is the BAM format, which is capable of storing both aligned and unaligned reads. [ 23 ]  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 . [ 22 ]


The data contained in the ENA can be accessed manually or programmatically via  REST URL  through the ENA browser. Initially limited to the Sequence Read Archive, [ 14 ]   the ENA browser now also provides access to the Trace Archive and EMBL-Bank, allowing file retrieval in a range of formats including  XML ,  HTML ,  FASTA  and FASTQ. [ 13 ]  Individual records can be accessed using their accession numbers and other text queries are enabled through the  EB-eye  search engine. [ 13 ]  Additionally,  sequence similarity -based searches implemented using  De Bruijn graphs  offer another method of retrieving records from the ENA. [ 14 ]


The ENA is accessible via the EBI  SOAP  and REST APIs, which also offer access to other databases hosted at the EBI, such as  Ensembl  and  InterPro . [ 27 ]


The European Nucleotide Archive handles large volumes of data which pose a significant storage challenge. [ 5 ] [ 28 ]  As of 2012, the ENA's storage requirements continue to  grow exponentially , with a doubling time of approximately 10 months. [ 5 ]  To manage this increase, the ENA selectively discards less-valuable sequencing platform data and implements advanced  compression  strategies. [ 23 ] [ 29 ]  The CRAM reference-based compression toolkit was developed to help reduce ENA storage requirements. [ 5 ] [ 30 ]


Currently the ENA is funded jointly by the  European Molecular Biology Laboratory , the  European Commission  and the  Wellcome Trust . [ 13 ]  The emerging ELIXIR framework, coordinated by EBI director  Janet Thornton , aims to secure a sustainable European funding infrastructure to support the continued availability of  life science  databases such as the ENA. [ 29 ] [ 31 ] [ 32 ]


The  Expression Atlas  is a  database  maintained by the  European Bioinformatics Institute  that  provides information on  gene expression  patterns from  RNA-Seq  and  Microarray  studies, and protein expression from  Proteomics  studies. [ 1 ]  The Expression Atlas allows searches by  gene ,  splice variant , protein attribute, disease, treatment or organism part (cell types/tissues). Individual genes or gene sets can be searched for. All datasets in Expression Atlas have its metadata manually curated and its data analysed through standardised analysis pipelines. There are two components to the Expression Atlas, the Baseline Atlas and the Differential Atlas:


The Baseline Atlas provides information about which  gene products  are present (and at what abundance) under "normal" conditions. This component of the Expression Atlas consists of  RNA-seq  experiments from ArrayExpress repositories. It aims to answer questions such as:


The Differential Atlas allows users to identify genes that are up- or down-regulated in different experimental conditions.


The  Frequency of INherited Disorders  database ( FINDbase ) is a database of frequencies of causative genetic variations worldwide. [ 1 ] [ 2 ]   FINDbase  was founded in 2006 to be a  relational database  for these frequencies of causative genetic variations of inherited genetic disorders, as well as  pharmacogenetic  markers. Out of all the national/ethnic mutation databases (NEMDBs),  FINDbase  has the most content and since all the entries are collected from various populations worldwide, it is seen as a great resource for population-specific information. [ 3 ]


Genotype to Phenotype Databases: a Holistic Approach  ( GEN2PHEN ) is a European project aiming to develop a knowledge web portal integrating information from the  genotype  to the  phenotype  in a unifying portal: The Knowledge Centre]. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]


The GEN2PHEN project aims to unify human and model organism genetic variation databases towards increasingly holistic views into Genotype-To-Phenotype (G2P) data, and to link this system into other biomedical knowledge sources via genome browser functionality. The project will establish the technological building-blocks needed for the evolution of today’s diverse G2P databases into a future seamless G2P biomedical knowledge environment, by the projects end. This will consist of a European-centred but globally networked hierarchy of bioinformatics GRID-linked databases, tools and standards, all tied into the Ensembl genome browser. The project has the following specific objectives:


The GEN2PHEN Consortium members have been selected from a talented pool of European research groups and companies that are interested in the G2P database challenge. Additionally, a few non-EU participants have been included to bring extra capabilities to the initiative. The final constellation is characterised by broad and proven competence, a network of established working relationships, and high-level roles/connections within other significant projects in this domain...


By providing a complete Homo sapiens ‘parts list’ (the gene sequences) and a powerful ‘toolkit’ (technologies), the Human Genome Project has revolutionised mankind’s ability to explore how genes cause disease and other phenotypes. Studies in this domain are proceeding at a rapid and ever-increasing pace, generating unprecedented amounts of raw and processed data. It is now imperative that the scientific community finds ways to effectively manage and exploit this flood of information for knowledge creation and practical benefit to society. This fundamental goal lies at the heart of the “Genotype-To-Phenotype Databases: A Holistic Solution (GEN2PHEN)” project.


Previous genetics studies have shown that inter-individual genome variation plays a major role in differential normal development and disease processes. However, the details of how these relationships work are far from clear, even in the case of most Mendelian disorders where single genetic alterations are fully penetrant (essentially causative, rather than risk modifying). Background genetic effects (modifier genes), epistasis, somatic variation, and environmental factors all complicate the situation. This is particularly the case in complex, multi-factorial disorders (e.g., cancer, heart disease, diabetes, dementia) that will affect most of us at some stage in our lifetime. Strategies do, however, now exist to study the genetics of these disorders, and such investigations are a major focus of research throughout Europe and beyond. A common thread in these studies is the need to create ever-larger datasets and integrate these more effectively.


The  GenBank   sequence database  is an  open access , annotated collection of all publicly available  nucleotide  sequences and their  protein  translations. It is produced and maintained by the  National Center for Biotechnology Information  (NCBI; a part of the  National Institutes of Health  in the  United States ) as part of the  International Nucleotide Sequence Database Collaboration  (INSDC).


In October 2024, GenBank contained 34 trillion base pairs from over 4.7 billion nucleotide sequences and more than 580,000 formally described  species . [ 2 ] [ 3 ]


The database started in 1982 by  Walter Goad  and  Los Alamos National Laboratory . GenBank has become an important database for research in biological fields and has grown in recent years at an  exponential rate  by doubling roughly every 18 months. [ 4 ] [ 5 ] [ 3 ]


GenBank is built by direct submissions from individual laboratories, as well as from bulk submissions from large-scale  sequencing  centers.


Only original sequences can be submitted to GenBank. Direct submissions are made to GenBank using BankIt, which is a Web-based form, or the stand-alone submission program, Sequin. Upon receipt of a sequence submission, the GenBank staff examines the originality of the data and assigns an  accession number  to the sequence and performs quality assurance checks. The submissions are then released to the public database, where the entries are retrievable by  Entrez  or downloadable by  FTP . Bulk submissions of  Expressed Sequence Tag  (EST),  Sequence-tagged site  (STS),  Genome Survey Sequence  (GSS), and  High-Throughput Genome Sequence  (HTGS) data are most often submitted by large-scale sequencing centers. The GenBank direct submissions group also processes complete microbial genome sequences. [ 6 ] [ 7 ]


Walter Goad  of the Theoretical Biology and Biophysics Group at  Los Alamos National Laboratory  (LANL) and others established the Los Alamos Sequence Database in 1979, which culminated in 1982 with the creation of the public GenBank. [ 8 ]  Funding was provided by the  National Institutes of Health , the  National Science Foundation , the  Department of Energy , and the  Department of Defense . LANL collaborated on GenBank with the firm  Bolt, Beranek, and Newman , and by the end of 1983 more than 2,000 sequences were stored in it.


In the mid-1980s, the Intelligenetics bioinformatics company at  Stanford University  managed the GenBank project in collaboration with LANL. [ 9 ]  As one of the earliest  bioinformatics  community projects on the Internet, the GenBank project started  BIOSCI /Bionet news groups for promoting  open access  communications among bioscientists. During 1989 to 1992, the GenBank project transitioned to the newly created  National Center for Biotechnology Information (NCBI) . [ 10 ]


The GenBank  release notes  for release 250.0 (June 2022) state that "from 1982 to the present, the number of bases in GenBank has doubled approximately every 18 months". [ 11 ] [ 12 ]  As of 15 June 2022, GenBank release 250.0 has over 239 million  loci , 1,39 trillion nucleotide bases, from 239 million reported sequences. [ 11 ]


The GenBank database includes additional data sets that are constructed mechanically from the main sequence data collection, and therefore are excluded from this count.


An analysis of Genbank and other services for the molecular identification of clinical blood culture isolates using  16S rRNA  sequences [ 13 ]  showed that such analyses were more discriminative when GenBank was combined with other services such as  EzTaxon -e [ 14 ]  and the BIBI [ 15 ]  databases.


GenBank may contain sequences wrongly assigned to a particular species, because the initial identification of the organism was wrong. A recent study showed that 75% of mitochondrial  Cytochrome c oxidase subunit I  sequences were wrongly assigned to the fish  Nemipterus  mesoprion  resulting from continued usage of sequences of initially misidentified individuals. [ 16 ]  The authors provide recommendations how to avoid further distribution of publicly available sequences with incorrect scientific names.


Numerous published manuscripts have identified erroneous sequences on GenBank. [ 17 ] [ 18 ] [ 19 ]  These are not only incorrect species assignments (which can have different causes) but also include chimeras and accession records with sequencing errors. A recent manuscript on the quality of all  Cytochrome b  records of  birds  further showed that 45% of the identified erroneous records lack a voucher specimen that prevents a reassessment of the species identification. [ 20 ]


Another problem is that sequence records are often submitted as anonymous sequences without species names (e.g. as " Pelomedusa  sp. A CK-2014" because the species are either unknown or withheld for publication purposes. However, even after the species have been identified or published, these sequence records are not updated and thus may cause ongoing confusion. [ 21 ]


GENCODE  is a scientific project in  genome  research and part of the  ENCODE  (ENCyclopedia Of DNA Elements) scale-up project.


The GENCODE consortium was initially formed as part of the pilot phase of the ENCODE project to identify and map all protein-coding genes within the ENCODE regions (approx. 1% of Human genome). [ 2 ]  Given the initial success of the project, GENCODE now aims to build an “Encyclopedia of genes and genes variants”. [ 2 ]


The result will be a set of annotations including all protein-coding  loci  with  alternatively transcribed variants , [ 3 ]   non-coding  loci [ 4 ]   with transcript evidence, and  pseudogenes . [ 5 ]


GENCODE is currently progressing towards its goals in Phase 2 of the project. [ 6 ]


The most recent release of the Human geneset annotations is Gencode 36, with a freeze date of December 2020. This release utilises the latest GRCh38 human  reference genome  assembly. [ 7 ]


The latest release for the mouse geneset annotations is Gencode M25, also with a freeze date December 2020. [ 7 ]


Since September 2009, GENCODE has been the human gene set used by the  Ensembl  project and each new GENCODE release corresponds to an Ensembl release. [ 8 ]


2003 September


The project was designed with three phases - Pilot, Technology development and Production phase. [ 9 ]
The pilot stage of the ENCODE project aimed to investigate in great depth, computationally and experimentally, 44 regions totaling 30 Mb of sequence representing approximately 1% of the human genome. As part of this stage, the GENCODE consortium was formed to identify and map all protein-coding genes within the ENCODE regions. [ 2 ]  It was envisaged that the results of the first two phases will be used to determine the best path forward for analysing the remaining 99% of the human genome in a cost-effective and comprehensive production phase. [ 9 ]


2005 April
The first release of the annotation of the 44 ENCODE regions was frozen on 29 April 2005 and was used in the first ENCODE Genome Annotation Assessment Project (E-GASP) workshop. [ 2 ]  GENCODE Release 1 contained 416 known loci, 26 novel (coding DNA sequence) CDS loci, 82 novel transcript loci, 78 putative loci, 104 processed pseudogenes and 66 unprocessed pseudogenes.


2005 October
A second version (release 02) was frozen on 14 October 2005, containing updates following discoveries from experimental validations using  RACE  and  RT-PCR  techniques. [ 2 ]  GENCODE Release 2 contained 411 known loci, 30 novel CDS loci, 81 novel transcript loci, 83 putative loci, 104 processed pseudogenes and 66 unprocessed pseudogenes.


2007 June
The conclusions from the  pilot project  were published in June 2007. [ 10 ]  The findings highlighted the success of the pilot project to create a feasible platform and new technologies to characterise functional elements in the human genome, which paves the way for opening research into genome-wide studies.


2007 October
New funding was part of NHGRI's endeavour to scale-up the ENCODE Project to a production phase on the entire genome along with additional pilot-scale studies.


2012 September
In September 2012, The GENCODE consortium published a major paper discussing the results from a major release – GENCODE Release 7, which was frozen in December 2011. [ 11 ]


2018
In 2018, one of the latest additions to the GENCODE project was the CRISPR/Cas9 track on human and model organism assemblies. CRISPR is a genome editing technique that uses sequences of RNA that successfully bind to the region edited with high specificity. The new track was designed to assist in the search for appropriate guide sentences by listing potential binding sites for the CRISPR/Cas9 complex that are next to transcribed regions, or within 200 bp of one. For each site, the track provides possible guide sequences along with a collection of predicted efficiency and specificity scores for those guide sequences. It also provides information about potential off-targets, grouped by the number of missmatches between the off-target and the guide. [ 11 ]


2020
Among other achievements, it has been completed the first pass manual annotation of the mouse reference genome, it has started a cooperation with RefSeq and Uniprot reference annotation databases toward achieving annotation convergence, and the annotation of lncRNAs has been improved via the discovery of novel loci and novel transcripts at existing loci. Also, given the COVID-19 pandemic during 2020, there has been an urge to support research responding to the situation, so GENCODE has reviewed and improved the annotation for a set of protein-coding genes associated with SARSCoV-2 infection. [ 12 ]


The key participants of the GENCODE project have remained relatively consistent throughout its various phases, with the Wellcome Trust Sanger Institute now leading the overall efforts of the project.


A summary of key participating institutions of each phase is listed below: [ 6 ] [ 13 ]


Source: [ 8 ]


Since its inception, GENCODE has released 36 versions of the Human gene set annotations (excluding minor updates).


The key summary statistics of the most recent GENCODE Human gene set annotation ( Release 36, December 2020 freeze ) is shown below: [ 14 ]


Through advancements in sequencing technologies (such as RT-PCR-seq), increased coverage from manual annotations (HAVANA group), and improvements to automatic annotation algorithms using Ensembl, the accuracy and completeness of GENCODE annotations have been continuously refined through its iteration of releases.


A comparison of key statistics from 3 major GENCODE releases until 2014 is shown below. [ 14 ]  It is evident that although the coverage, in terms of total number of genes discovered, is steady increasing, the number of protein-coding genes has actually decreased. This is mostly attributed to new experimental evidence obtained using  Cap Analysis Gene Expression (CAGE)  clusters, annotated  PolyA  sites, and  peptide  hits. [ 11 ]


Putative loci can be verified by wet-lab experiments and computational predictions are analysed manually. [ 15 ]
Currently, to ensure a set of annotation covers the complete genome rather than just the regions that have been manually annotated, a merged data set is created using manual annotations from HAVANA, together with automatic annotations from the Ensembl automatically annotated gene set. This process also adds unique full-length CDS predictions from the Ensembl protein coding set into manually annotated genes, to provide the most complete and up-to-date annotation of the genome possible. [ 16 ]


Ensembl transcripts are products of the Ensembl automatic gene annotation system (a collection of gene annotation pipelines), termed the Ensembl gene build. All Ensembl transcripts are based on experimental evidence and thus the automated pipeline relies on the mRNAs and protein sequences deposited into public databases from the scientific community. [ 17 ]


There are several analysis groups in the GENCODE consortium that run pipelines that aid the manual annotators in producing models in unannotated regions, and to identify potential missed or incorrect manual annotation, including completely missing loci, missing alternative isoforms, incorrect splice sites and incorrect biotypes. These are fed back to the manual annotators using the AnnoTrack tracking system. [ 18 ]  Some of these pipelines use data from other ENCODE subgroups including RNASeq data, histone modification and CAGE and Ditag data. RNAseq data is an important new source of evidence, but generating complete gene models from it is a difficult problem. As part of GENCODE, a competition was run to assess the quality of predictions produced by various RNAseq prediction pipelines (Refer to  RGASP  below). To confirm uncertain models, GENCODE also has an experimental validation pipeline using RNA sequencing and RACE. [ 16 ]


For GENCODE 7, transcript models are assigned a high or low level of support based on a new method developed to score the quality of transcripts. [ 2 ]


The current GENCODE Human gene set version (GENCODE Release 20) includes annotation files (in GTF and GFF3 formats), FASTA files and METADATA files associated with the GENCODE annotation on all genomic regions (reference-chromosomes/patches/scaffolds/haplotypes). The annotation data is referred on reference chromosomes and stored in separated files which include: Gene annotation, PolyA features annotated by HAVANA, (Retrotransposed) pseudogenes predicted by the Yale & UCSC pipelines, but not by HAVANA, long non-coding RNAs, and tRNA structures predicted by tRNA-Scan.
Some examples of the lines in the GTF format are shown below:


The columns within the GENCODE GTF file formats are described below.


Format description of GENCODE GTF file. TAB-separated standard GTF columns


Description of key-value pairs in 9th column of the GENCODE GTF file (format: key "value")


Also, the GENCODE website contains a Genome Browser for human and mouse where you can reach any genomic region by giving the chromosome number and start-end position (e.g. 22:30,700,000..30,900,000), as well as by ENS transcript id (with/without version), ENS gene id (with/without version) and gene name. The browser is powered by Biodalliance. [ 19 ]


The definition of a "gene" has never been a trivial issue, with numerous definitions and notions proposed throughout the years since the discovery of the human genome. First, genes were conceived in the 1900s as discrete units of heredity, then it was thought as the blueprint for protein synthesis, and in more recent times, it was being defined as genetic code that is transcribed into RNA. Although the definition of a gene has evolved greatly over the last century, it has remained a challenging and controversial subject for many researchers. With the advent of the ENCODE/GENCODE project, even more problematic aspects of the definition have been uncovered, including alternative splicing (where a series of exons are separated by introns), intergenic transcriptions, and the complex patterns of dispersed regulation, together with non-genic conservation and the abundance of noncoding RNA genes. As GENCODE endeavours to build an encyclopaedia of genes and gene variants, these problems presented a mounting challenge for the GENCODE project to come up with an updated notion of a gene. [ 20 ]


The  Human Genome Project  was an international research effort to determine the sequence of the human genome and identify the genes that it contains. The Project was coordinated by the National Institutes of Health and the U.S. Department of Energy. Additional contributors included universities across the United States and international partners in the United Kingdom, France, Germany, Japan, and China. The Human Genome Project formally began in 1990 and was completed in 2003, 2 years ahead of its original schedule. [ 21 ]


Ensembl  is part of the GENCODE project. [ 22 ]


A key research area of the GENCODE project was to investigate the biological significance of long non-coding RNAs (lncRNA). To better understand the lncRNA expression in Humans, a sub project was created by GENCODE to develop custom microarray platforms capable of quantifying the transcripts in the GENCODE lncRNA annotation. [ 4 ]  A number of designs have been created using the  Agilent Technologies  eArray system, and these designs are available in a standard custom Agilent format. [ 4 ]


The RNA-seq Genome Annotation Assessment Project (RGASP) project is designed to assess the effectiveness of various computational methods for high quality RNA-sequence data analysis. The primary goals of RGASP are to provide an unbiased evaluation for RNA-seq alignment, transcript characterisation (discovery, reconstruction and quantification) software, and to determine the feasibility of automated genome annotations based on transcriptome sequencing. [ 23 ]


RGASP is organised in a consortium framework modelled after the EGASP (ENCODE Genome Annotation Assessment Project) gene prediction workshop, and two rounds of workshops have been conducted to address different aspects of RNA-seq analysis as well as changing sequencing technologies and formats. One of the main discoveries from rounds 1 & 2 of the project was the importance of read alignment on the quality of gene predictions produced. Hence, a third round of RGASP workshop is currently being conducted (in 2014) to focus primarily on read mapping to the genome. [ 23 ]


In  bioinformatics , a  Gene Disease Database  is a systematized collection of data, typically structured to model aspects of reality, in a way to comprehend the underlying mechanisms of complex diseases, by understanding multiple composite interactions between  phenotype - genotype  relationships and gene-disease mechanisms. [ 1 ]  Gene Disease Databases integrate human gene-disease associations from various expert curated databases and  text mining  derived associations including Mendelian, complex and environmental diseases. [ 2 ] [ 3 ]


Experts in different areas of biology and  bioinformatics  have been trying to comprehend the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some illnesses, it has become apparent that it is the right amount of animosity is made for not enough to obtain an index of the disease-related genes but to uncover how disruptions of molecular grids in the cell give rise to disease phenotypes. [ 4 ]  Moreover, even with the unprecedented wealth of information available, obtaining such catalogues is extremely difficult.


Genetic  Broadly speaking, genetic diseases are caused by aberrations in genes or  chromosomes . Many genetic diseases are developed from before birth. Genetic disorders account for a significant number of the health care problems in our society. Advances in the understanding of this diseases have increased both the life span and quality of life for many of those affected by genetic disorders. Recent developments in bioinformatics and laboratory genetics have made possible the better delineation of certain malformation and mental retardation syndromes, so that their mode of inheritance can be understood. This information enables the genetic counselor to predict the risk for occurrence of a large number of genetic disorders. [ 2 ]  Most genetic counseling is done, however, only after the birth of at least one affected individual has alerted the family to their predilection for having children with a genetic disorder. The association of a single gene to a disease is rare and a genetic disease may or may not be a transmissible disorder. [ 5 ]  Some genetic diseases are inherited from the parent's genes, but others are caused by new  mutations  or changes to the  DNA . In other occurrences, the same disease, for instance, some forms of  carcinoma  or  melanoma , may stem from an inbred condition in some people, from new changes in other people, and from non-genetic causes in still other individuals. [ 6 ]


There are more than six thousand known single-gene disorders (monogenic), which occur in about 1 out of every 200 births. [ 1 ]  As their term suggests, these diseases are caused by a mutation in one gene. By contrast, polygenic disorders are caused by several genes, regularly in combination with environmental factors. [ 7 ]  Examples of genetic phenotypes include  Alzheimer's disease , breast cancer, leukemia, Down syndrome, heart defects, and deafness; therefore, cataloguing to sort out all the diseases related to genes is needed.


At different stages of any gene disease project,  molecular biologists  need to choose, even after careful statistical data analysis, which genes or  proteins  to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets, such as expression data, sequence information, functional annotation and the biomedical literature, allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers. So one of the main concerns in biological and  biomedical  research is to recognise the underlying mechanisms behind this intricate genetic phenotypes. Great effort has been spent on finding the genes related to diseases [ 8 ]


However, increasingly evidences point out that most human diseases cannot be attributed to a single gene but arise due to complex interactions among multiple genetic variants and environmental risk factors. Several databases have been developed storing associations between genes and diseases such as the Comparative Toxicogenomics Database (CTD), Online Mendelian Inheritance in Man (OMIM), the genetic Association Database (GAD) or the Disease genetic Association Database (DisGeNET). Each of these databases focuses on different aspects of the phenotype-genotype relationship, and due to the nature of the database curation process, they are not complete, but in a way they are fully complementary between each other. [ 9 ]


Essentially, there are four types of databases: curated databases, predictive databases, literature databases and integrative databases [ 1 ]


The term curated data refers to information, that may comprise the most sophisticated computational formats for structured data, scientific updates, and curated knowledge, that has been composed and prepared under the regulation of one or more experts considered to be qualified to engage in such an activity [ 10 ]  The implication is that the resulting database is of high quality. The contrast is with data which may have been gathered through some automated process or using particularly low or inexpert unsupported data quality and possibly untrustworthy. [ 10 ]  Some of the most common examples include: CTD and UNIPROT.


The  Comparative Toxicogenomics Database , helps to understand about the effects of environmental compounds on human health by integrating data from curated scientific literature to describe biochemical interactions with genes and proteins, and links between diseases and chemicals, and diseases and genes or proteins. [ 11 ]  CTD contains curated data defining cross-species chemical–gene/protein interactions and chemical– and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data deliver insights into complex chemical–gene and protein interaction networks. One of the main sources in this Database is curated information from OMIM. [ 11 ]


CTD is a unique resource where bioinformatics specialists read the scientific literature and manually curate four types of core data:


The Universal Protein Resource ( UniProt ) is an inclusive resource for protein sequence and annotation data. It is a comprehensive, first-class and freely accessible database of protein sequence and functional information, that has many entries being derived from  genome  sequencing projects. It contains a large amount of information about the biological function of proteins derived from the study literature, which can hint to a direct connection between gene-protein-disease. [ 12 ]


A predictive database is one based on statistical inference. One particular approach to such inference is known as predictive inference, but the prediction can be undertaken within any of the several approaches to statistical inference. Indeed, one description of biostatistics is that it provides a means of transferring knowledge about a sample of a genetic population to the whole population ( genomics ), and to other related genes or genomes, which the same as prediction over time is not necessarily. [ 15 ]  When information is transferred across time, often to specific points in time, the process is known as forecasting. Three of the main examples of databases that can be considered in this category include: The Mouse genome Database (MGD), The Rat genome Database (RGD), OMIM and the SIFT Tool from Ensembl. [ 1 ]


The Mouse genome Database (MGD) is the international community resource for integrated genetic, genomic and biological data about the laboratory mouse. MGD provides full annotation of phenotypes and human disease associations for mouse models (genotypes) using terms from the Mammalian Phenotype Ontology and disease names from OMIM. [ 16 ]


The  Rat Genome Database  (RGD) began as a collaborative effort between leading research institutions involved in rat genetic and genomic research. The rat continues to be extensively used by researchers as a model organism for investigating the biology and pathophysiology of disease. In the past several years, there has been a rapid increase in rat genetic and genomic data. [ 17 ]  This explosion of information highlighted the need for a centralized database to efficiently and effectively collect, manage, and distribute a rat-centric view of this data to researchers around the world. The Rat Genome Database was created to serve as a repository of rat genetic and genomic data, as well as mapping, strain, and physiological information. It also facilitates investigators research efforts by providing tools to search, mine, and predict this data. [ 17 ]


Data at RGD that is useful for researchers investigating disease genes include disease annotations for rat, mouse and human genes. Annotations are manually curated from the literature, or downloaded via automated pipelines from other disease-related databases. Downloaded annotations are mapped to the same disease vocabulary used for manual annotations to provide consistency across the dataset. RGD also maintains disease-related quantitative phenotype data for the rat (PhenoMiner). [ 18 ]


Supported by the NCBI, The Online Mendelian Inheritance in Man (OMIM) is a database that catalogues all the known diseases with a genetic component, and predicts their relationship to relevant genes in the human genome and provides references for further research and tools for genomic analysis of a catalogued gene. [ 19 ]  OMIM is a comprehensive, authoritative compendium of human genes and genetic phenotypes that is freely available and updated daily. The database has been used as a resource for predicting relevant information to inherited conditions. [ 19 ]


This one of the largest resources available for all genomic and genetic studies, it provides a centralized resource for geneticists, molecular biologists and other researchers studying the genomes of our own species and other vertebrates and model disease organisms.  Ensembl  is one of several well-known genome browsers for the retrieval of genomic-disease information. Ensembl imports variation data from a variety of different sources, Ensembl predicts the effects of variants. [ 21 ]  For each variation that is mapped to the reference genome, each Ensembl transcript is identified that overlap the variation. Then it uses a rule-based approach to predict the effects that each allele of the variation may have on the transcript. The set of consequence terms, defined by the Sequence Ontology (SO) can be currently assigned to each combination of an allele and a transcript. Each allele of each variation may have a different effect in different transcripts. A variety of different tools are used to predict human mutations in the Ensembl database, one of the most widely used is SIFT, that predicts whether an amino acid substitution is likely to affect protein function based on sequence homology and the physic-chemical similarity between the alternate amino acids. The data provided for each amino acid substitution is a score and a qualitative prediction (either 'tolerated' or 'deleterious'). The score is the normalized probability that the amino acid change is tolerated so scores near 0 are more likely to be deleterious. The qualitative prediction is derived from this score such that substitutions with a score < 0.05 are called 'deleterious' and all others are called 'tolerated'. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations, that will lead to build relationships in phenotype characteristics,  proteomics  and genomics. [ 21 ]


This sort of databases summarize books, articles, book reviews, dissertations, and annotations about gene-disease databases. Some of the following are examples of this type: GAD, LGHDN and BeFree Data.


The Genetic Association Database is an archive of human genetic association studies of complex diseases. GAD is primarily focused on archiving information on common complex human disease rather than rare Mendelian disorders as found in the OMIM. It includes curated summary data extracted from published papers in peer reviewed journals on candidate gene and genome Wide Association Studies ( GWAS ). [ 22 ]  The GAD was frozen as of 09/01/2014 but is still available for download. [ 23 ]


The literature-derived human gene-disease network (LHGDN) is a text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a textual source of interest. It is part of the Linked Life Data, of the LMU in Munchen, Germany. [ 1 ]


Extracts gene-disease associations from MEDLINE abstract using the BeFree system. BeFree is composed of a biomedical Named Entity Recognition (BioNER) module to detect diseases and genes and a relation extraction module based on morphosyntactic information. [ 24 ]


This sort of databases include Mendelian, compound and environmental diseases in an integrated gene-disease association archive and show that the concept of modularity applies for all of them They provide a functional analysis of diseases in case of important new biological insights, which might not be discovered when considering each of the gene-disease associations independently. Hence, they present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The best example for this sort of database is DisGeNET. [ 8 ] [ 25 ]


DisGeNET  is a comprehensive gene-disease association database that integrates associations from several sources that covers different biomedical aspects of diseases. [ 25 ]  In particular, it is focused on the current knowledge of human genetic diseases including Mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, this database performs a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. [ 1 ]  The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including Mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. Obtaining similar results when studying  clusters  of diseases, the findings in this database suggest that related diseases might arise due to dysfunction of common biological processes in the cell. The network analysis of this integrated database points out that data integration is needed to obtain a comprehensive view of the genetic landscape of human diseases and that the genetic origin of complex diseases is much more common than expected. [ 1 ]


Some of the most interesting cases using Gene-Disease Databases can be found in the following papers: [ 1 ] [ 8 ]


The completion of the human genome has changed the way the search for disease genes is performed. In the past, the approach was to focus on one or a few genes at a time. Now, projects like the DisGeNET exemplify the efforts to systematically analyze all the gene alterations involved in a single or multiple diseases. [ 26 ]  The next step is to produce a complete picture of the mechanistic aspects of the diseases and the design of drugs against them. For that, a combination of two approaches will be needed: a systematic search and in-depth study of each gene.
The future of the field will be defined by new techniques to integrate large bodies of data from different sources and to incorporate functional information into the analysis of large-scale data generated by bioinformatics studies. [ 1 ]


Bioinformatics is both a term for the body of biological gene disease studies that use computer programming as part of their methodology, as well as a reference to specific analysis pipelines that are repeatedly used, particularly in the fields of genetics and genomics. [ 1 ]  Common uses of bioinformatics include the identification of candidate genes and nucleotides,  SNPs . Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties, or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences. [ 1 ]


The response of bioinformatics to new experimental techniques brings a new perspective into the analysis of the experimental data, as demonstrated by the advances in the analysis of information from gene disease databases and other technologies. It is expected that this trend will continue with novel approaches to respond to new techniques, such as next-generation sequencing technologies. For instance, the availability of large numbers of individual human genomes will promote the development of computational analyses of rare variants, including the statistical mining of their relations to lifestyles, drug interactions and other factors. [ 1 ]
Biomedical research will also be driven by our ability to efficiently mine the large body of existing and continuously generated biomedical data. Text-mining techniques, in particular, when combined with other molecular data, can provide information about gene mutations and interactions and will become crucial to stay ahead of the exponential growth of data generated in biomedical research. Another field that is benefiting from the advances in mining and integration of molecular, clinical and drug analysis is pharmacogenomics.  In silico  studies of the relationships between human variations and their effect on diseases will be key to the development of personalized medicine. [ 8 ]
In summary, Gene Disease Databases have already transformed the search for disease genes and has the potential to become a crucial component of other areas of medical research. [ 1 ]


Gene Expression Omnibus  ( GEO ) is a  database  for  gene expression profiling  and  RNA methylation  profiling managed by the  National Center for Biotechnology Information  (NCBI). [ 1 ]  These  high-throughput screening  genomics data are derived from  microarray  or  RNA-Seq  experimental data. [ 2 ]  These data need to conform to the  minimum information about a microarray experiment  (MIAME) format. [ 3 ]


GeneNetwork  is a combined database and  open-source   bioinformatics  data analysis software resource for  systems genetics . [ 1 ]  This resource is used to study  gene regulatory networks   that link DNA sequence differences to corresponding differences in gene and protein expression and to variation in traits such as health and disease risk. Data sets in GeneNetwork are typically made up of large collections of genotypes (e.g.,  SNPs ) and phenotypes from groups of individuals, including humans, strains of mice and rats, and organisms as diverse as  Drosophila melanogaster ,  Arabidopsis thaliana , and  barley . [ 2 ]  The inclusion of genotypes makes it practical to carry out web-based  gene mapping  to discover those regions of genomes  that contribute to differences among individuals in mRNA, protein, and metabolite levels, as well as differences in cell function, anatomy, physiology, and behavior.


Development of GeneNetwork started at the University of Tennessee Health Science Center in 1994 as a web-based version of the  Portable Dictionary of the Mouse Genome (1994) . [ 3 ]  GeneNetwork is both the first and the longest continuously operating web service in biomedical research [see  https://en.wikipedia.org/wiki/List_of_websites_founded_before_1995 ]. In 1999 the Portable Gene Dictionary was combined with Kenneth F. Manly's  Map Manager  QT mapping program to produce an online system for real-time genetic analysis. [ 4 ]  In early 2003, the first large  Affymetrix  gene expression data sets (whole  mouse brain  mRNA and hematopoietic stem cells) were incorporated and the system was renamed WebQTL. [ 5 ] [ 6 ]  GeneNetwork is now developed by an international group of developers and has mirror and development sites in Europe, Asia, and Australia. Production services are hosted on systems at  University of Tennessee Health Science Center  with a backup instance in Europe.


A the current production version of GeneNetwork (also known as GN2) was released in 2016. [ 7 ]  The current version of GeneNetwork uses the same database as its predecessor, GN1, but has much more modular and maintainable open source code (available on  GitHub ). GeneNetwork now also has significant new features including support for:


GeneNetwork consists of two major components:


Four levels of data are usually obtained for each family or population:


The  combined data types are housed together in a relational database and IPSF fileserver, and are conceptually organized and grouped by species, cohort, and family. The system is implemented as a  LAMP (software bundle)  stack. Code and a simplified version of the  MariaDB  database are available on  GitHub .


GeneNetwork is primarily used by researchers, but has also been adopted successfully for undergraduate and graduate courses in genetics and bioinformatics (see  YouTube example ), bioinformatics, physiology, and psychology. [ 11 ]  Researchers and students typically retrieve sets of genotypes and phenotypes from one or more families and use built-in statistical and mapping functions to explore relations among variables and to assemble networks of associations. Key steps include the analysis of these factors:


Traits and molecular expression data sets are submitted by researchers directly or are extracted from repositories such as  National Center for Biotechnology Information  Gene Expression Omnibus. Data cover a variety of cells and tissues—from single cell populations of the immune system, specific tissues (retina, prefrontal cortex), to entire systems (whole brain, lung, muscle, heart, fat, kidney, flower, whole plant embryos). A typical data set covers hundreds of fully genotyped individuals and may also include technical and biological replicates. Genotypes and phenotypes are usually taken from peer-reviewed papers. GeneNetwork includes annotation files for several RNA profiling platforms (Affymetrix, Illumina, and Agilent). RNA-seq and quantitative proteomic, metabolomic, epigenetics, and metagenomic data are also available for several species, including mouse and human.


There are tools on the site for a wide range of functions that range from simple graphical displays of variation in gene expression or other phenotypes, scatter plots of pairs of traits (Pearson or rank order), construction of both simple and complex network graphs, analysis of principal components and synthetic traits, QTL mapping using marker regression, interval mapping, and pair scans for epistatic interactions. Most functions work with up to 100 traits and several functions work with an entire  transcriptome .


The database can be browsed and searched at the main  search  page.  An on-line  tutorial  is available. Users can also  download  the primary data sets as text files, Excel, or in the case of network graphs, as  SBML . As of 2017,  GN2  is available as a beta release.


GeneNetwork is an open source project released under the  Affero General Public License  (AGPLv3). The majority of code is written in Python, but includes modules and other code written in C, R, and JavaScript. The code is mainly Python 2.4. GN2 is mainly written in Python 2.7 in a  Flask  framework with  Jinja 2 HTML templates) but with conversion to Python 3.X planned over the next few years. GN2 calls many statistical procedures written in the  R programming language . The original source code from 2010 along with a compact database are available on  SourceForge . While  GN1  was actively maintained through 2019  GitHub , as of 2020 all work is focused on  GN2 .


Other systems genetics and network databases


The  GWAS catalog  is a free online database that compiles data of  genome-wide association studies  (GWAS), summarizing unstructured data from different literature sources into accessible high quality data. [ 1 ]  It was created by the  National Human Genome Research Institute  (NHGRI) in 2008 and have become a collaborative project between the NHGRI and the  European Bioinformatics Institute  (EBI) since 2010. [ 1 ]  As of September 2018, it has included 71,673  SNP –trait associations in 3,567 publications. [ 2 ]


A GWAS identifies  genetic loci  associated with common traits and disease through the analysis of categorized variants across the genome and the catalog provides information from all published  GWAS  results that meet its criteria. [ 3 ]  The catalog contains publication information, study groups information (origin, size) and SNP-disease association information (including  SNP  identifier,  P-value ,  gene  and risk  allele ). [ 4 ]  Over the years, the GWAS catalog has enhanced its data release frequency by adding features such as graphical user interface, ontology-supported search functionality and a curation interface. [ 3 ]


The GWAS catalog is widely used to identify causal variants and understand disease mechanisms by biologists, bioinformaticians and other researchers. [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ]  Some GWAS identified common genomic loci that are associated diseases include:  cardiovascular disease ,  inflammatory bowel disease ,  type 2 diabetes  and  breast cancer . [ 3 ]


The public can gain access to the GWAS Catalog’ s data in three ways: [ 4 ]


Some current applications of the GWAS Catalog include the use of studies on the genetics of human diseases  [ 5 ] [ 6 ]  and the  heritability  of human traits. [ 7 ]  The GWAS catalog data can also be used as a pool of  markers  for  SNP  studies. [ 8 ]


GWAS Central  (previously HGBASE, HGVbase and HGVbaseG2P) is a publicly available  database  of summary-level findings from  genetic association  studies in humans, including  genome-wide association studies  (GWAS).


It is funded through the  GEN2PHEN  project by the European Union under their  Seventh Framework Programme .


GWAS Central contains the most comprehensive collection of summary-level  p-value  GWAS data. The web resource employs powerful graphical and text based data presentation methods for discovery of and simultaneous  visualisation  and  co-examination  of many studies, at genome-wide and region-specific levels. Studies of interest can now be identified using  chromosomal regions / genes  and markers; there is also the facility for researchers to view their own data alongside selected studies. [ citation needed ]


Current content includes ‘top’ p-values from collections;  supplementary data ; direct researcher submissions; and publicly available data. Consequently, the database now hosts >21 million p-values and 708 studies (vs 3,948 p-values and 798 studies in the  NHGRI   GWAS Catalog ), representing ~5% of all such data yet produced. GWAS Central makes parts of its data freely available for  download  by the research community. However, only parts of the data may be downloaded freely, the whole database content can be accessed as part of a collaboration. [ citation needed ]


The Human Genome Bi-Allelic SEquence ( HGBASE ) database [ 1 ]  was the first version of what is now GWAS Central. [ 2 ]  It was first released in August 1998, focusing on providing a centralized collection of known human single nucleotide polymorphisms and other simple  DNA  variants. It was the first publicly available SNP database. The project was expanded over the next year by a consortium including the  Karolinska Institute , the  European Bioinformatics Institute  and the  European Molecular Biology Laboratory . Corporate support was provided by  Pfizer  and  GlaxoSmithKline . The version released in November 2001 was renamed the Human Genome Variation database (HGVbase), [ 3 ]  as this was a better reflection of the scope of the database and its emphasis on collection from many different laboratories. In addition this also highlighted its new role as a central repository for  data collection  efforts in collaboration with the  Human Genome Variation Society [ citation needed ]


HGVbase was scaled back in 2004 to simply provide an alternative representation of the full marker list from  dbSNP , but development continued on its successor: the Human Genome Variation Genotype-to-Phenotype database (HGVbaseG2P), [ 4 ]  in many ways the natural evolution of HGVbase into a central database for summary-level genetic association data. The work was originally funded by  GlaxoSmithKline , the  University of Leicester , and the European Community's  Sixth Framework Programme  ('INFOBIOMED'  Network of Excellence ), but the GEN2PHEN project became the main source of funds in 2008. Early work in the project involved devising a powerful way of modeling  phenotype  and  genotype - phenotype data, which itself was adopted and adapted to become the global standard Phenotype And Genotype Experiment Object model. [ 5 ]  In 2008 HGVbaseG2P went live and extended the project's content and scope by adding a far broader and more comprehensive range of markers (i.e.,  SNPs , structural variants, and STSs), along with association data from many genetic association studies. [ citation needed ]


In February 2010, the project was once again renamed to GWAS Central, to reflect the growing focus on  genome-wide association studies . GWAS Central is a core component of the GEN2PHEN project and intends to provide an operational model, plus an open-source  software  package, so others can create similar databases across the world. These will be hosted by institutes, consortia, and even individual laboratories; providing those groups a toolkit for publicising and publishing their genetic association findings on the web and examine their data alongside others data from similar valuable resources. [ citation needed ]


This is a list of notable  software  for  haplotype estimation  and  genotype imputation .


Alphabetical order:


laboratory:  autosome.org


HOCOMOCO [ 1 ] [ 2 ]
 [ 3 ] [ 4 ]  is an open-access database providing curated and benchmarked binding motifs of human and mouse  transcription factors . It captures the following data types:  Homo sapiens  (human) and  Mus musculus  (mouse)  transcription factors , their  DNA binding site  motifs, and motif subtypes.


Transcription factors  (TFs) are  proteins  that bind  DNA  and thus regulate the  trasncription  process. The binding is sequence-specific. A  sequence motif [ 5 ]  is a model that describes the common pattern of the  DNA binding sites [ 6 ]  that a particular  TF  prefers to bind. One of the possible representations of the model is the  Position-Weight Matrix (PWM) . [ 7 ]


According to the  Web of Science , the 2018 publication of HOCOMOCO  [ 2 ]  has been cited 396 times (as of January 2024). The publications  [ 3 ] [ 4 ]  have been cited 144 and 151 times.


Hollywood  is a  RNA splicing  database containing data for the splicing of  orthologous  genes in different species. [ 1 ]


The  HUGO Gene Nomenclature Committee  ( HGNC ) is a committee of the  Human Genome Organisation  (HUGO) that sets the  standards  for human  gene nomenclature . The HGNC approves a  unique  and  meaningful  name for every known human  gene , [ 4 ] [ 5 ]  based on a query of experts. In addition to the name, which is usually 1 to 10 words long, the HGNC also assigns a symbol (a short group of characters) to every gene. As with an  SI  symbol, a gene symbol is like an abbreviation but is more than that, being a second unique name that can stand on its own just as much as substitute for the longer name. It may not necessarily "stand for" the initials of the name, although many gene symbols do reflect that origin.


Full gene names, and especially gene abbreviations and symbols, are often not specific to a single gene. A marked example is CAP which can refer to any of 6 different genes ( BRD4   Archived  2013-10-27 at the  Wayback Machine ,  CAP1   Archived  2013-11-02 at the  Wayback Machine ,  HACD1   Archived  2013-10-07 at the  Wayback Machine ,  LNPEP   Archived  2012-09-13 at the  Wayback Machine ,  SERPINB6   Archived  2013-10-08 at the  Wayback Machine , and  SORBS1   Archived  2012-10-12 at the  Wayback Machine ).


The HGNC short gene names, or gene symbols, unlike previously used or published symbols, are specifically assigned to one gene only. This can result in less common abbreviations being selected but reduces confusion as to which gene is referred to.


The HGNC published its latest human gene naming guidelines in 2020. [ 5 ]  These may be summarized as: [ 6 ]


The HGNC states that "gene nomenclature should  evolve with new technology  rather than be restrictive, as sometimes occurs when historical and single gene nomenclature systems are applied." [ 7 ]  The HGNC has also issued guides to specific locus types such as endogenous retroviral loci, [ 8 ]  structural variants [ 9 ]   and non-coding RNAs. [ 10 ] [ 11 ] [ 12 ]


When assigning new gene nomenclature the HGNC make efforts to contact authors who have published on the human gene in question by email, and their responses to the proposed nomenclature are requested. HGNC also coordinates with the related Mouse and Rat Genomic Nomenclature Committees, other database curators, and experts for given specific gene families or sets of genes.


The gene name revision procedure is similar to the naming procedure, but changing a standardized gene name after establishment of a consensus can create confusion, therefore the merit of this is controversial. For this reason the HGNC aims to change a gene name only if agreement for that change can be reached among a majority of researchers working on that gene.


A complete list of all HGNC-approved gene symbols for protein-coding genes:


IMGT  or  the international ImMunoGeneTics information system  is a collection of databases and resources for  immunoinformatics , particularly the  V, D, J, and C gene  sequences, as well as a providing other tools and data related to the  adaptive immune system . [ 1 ]  IMGT/LIGM-DB, the first and still largest database hosted as part of IMGT contains reference nucleotide sequences for 360 species'  T-cell receptor  and  immunoglobulin  molecules, as of 2023. [ 2 ]  These genes encode the proteins which are the foundation of adaptive immunity, which allows highly specific recognition and memory of pathogens.


IMGT was founded in June, 1989, by  Marie-Paule Lefranc , an immunologist working at  University of Montpellier . The project was presented to the 10th Human Genome Mapping Workshop, and resulted in the recognition of V, D, J, and C regions as genes. [ 3 ]  The first resource created was IMGT/LIGM-DB, a reference for nucleotide sequences of T-cell receptor and immunoglobulin of humans, and later vertebrate species. IMGT was created under the auspices of Laboratoire d'ImmunoGénétique Moléculaire at the University of Montpellier as well as  French National Centre for Scientific Research  (CNRS).


As both T-cell receptors and immunoglobulin molecules are built through a process of recombination of nucleotide sequences, the annotation of the building block regions and their role is unique within the genome. To standardize terminology and references, the IMGT-NC was created in 1992 and recognized by the  International Union of Immunological Societies  as a nomenclature subcommittee. [ 4 ]  Other tools include IMGT/Collier-de-Perles, a method for two dimensional representation of receptor amino acid sequences, and IMGT/mAb-DB, a database of  monoclonal antibodies . Now maintained by the  HLA Informatics Group , the primary reference for human HLA, IPD-IMGT/HLA Database, originated in part with IMGT. [ 5 ]  It was merged with the Immuno Polymorphism Database in 2003 to form the current reference.


Since 2015, IMGT has been headed by Sofia Kossida. [ 6 ]


The  Leiden Open Variation Database (LOVD)  is a free, flexible web-based  open source  database developed in the  Leiden University Medical Center  in the Netherlands, designed to collect and display  variants in the DNA sequence . [ 1 ] [ 2 ]  The focus of an LOVD is usually the combination between a  gene  and a  genetic (heritable) disease . All sequence variants found in individuals are collected in the database, together with information about whether they could be causally connected to the disease (i.e. a disease-causing variant or  mutation ) or not (i.e. a non-disease causing variant). Specialized doctors ( clinical geneticists ) use LOVDs to diagnose and advise patients carrying a genetic disease. Ideally, if a patient has been screened for mutations and one has been found, information in LOVD can predict the progress of the disease.


In contrast to human genome databases, showing information on all DNA variants, LOVDs include information about the individuals in which the variants were found. This patient information is usually only accessible for registered users.


Currently, LOVD installations worldwide contain more than 515,500 variant observations (124,000 unique variants in 5175 genes) in 162,000 patients. [ 3 ]


As the  human genome project  has completed, the collection and study of all sequence variation between individuals is of increasing importance to understand the relationship between DNA variations and disease. Direct access to up-to-date information on sequence variation is currently provided most efficiently through web-based, gene-centered, locus-specific databases (LSDBs). While over 1600 of these LSDBs exist online, [ 4 ]  sharing information or combining data of these databases is extremely hard if they are not software-based or use custom software. LOVD is a member of the  GEN2PHEN  project, a European Commission funded project which aims to unify these LSDBs with each other. [ 5 ]
Currently, LOVD installations make out 57% [ 4 ]  to 90% [ 6 ]  of all LSDBs.


The LOVD software was developed to create an "LSDB-in-a-Box". It should allow the easy creation and maintenance of a gene sequence variation database using the internet. LOVD is  platform-independent  and uses  PHP  and  MySQL  open source software only. The gene-centered design of the database follows the recommendations of the Human Genome Variation Society (HGVS) [ 7 ]  and focuses on ease of use and flexibility. The newest LOVD version, released late 2012, also allows to process  Next-generation sequencing  data, which often results in large numbers of variants found in between genes as well. To ensure the use of unambiguous sequence variant descriptions in newly submitted data, LOVD interacts with  Mutalyzer , [ 8 ]  which applies the HGVS human nomenclature guidelines to check and, if necessary, correct sequence variant descriptions.


This list of  phylogenetic tree viewing software  is a compilation of software tools and web portals used in visualizing  phylogenetic trees .


1  "All" refers to Microsoft Windows, Apple OSX and Linux; L=Linux, M=Apple Mac, W=Microsoft Windows


This  list of phylogenetics software  is a compilation of  computational phylogenetics  software used to produce  phylogenetic trees . Such tools are commonly used in  comparative genomics ,  cladistics , and  bioinformatics . Methods for estimating phylogenies include  neighbor-joining ,  maximum parsimony  (also simply referred to as parsimony), unweighted pair group method with arithmetic mean ( UPGMA ),  Bayesian phylogenetic inference ,  maximum likelihood , and  distance matrix methods .


This is a  list of  long noncoding RNA databases , which provide information about  lncRNAs . [ 1 ] [ 2 ]


The  Mammalian Promoter Database  ( MPromDb ) is a curated  database  of  gene   promoters  identified from  ChIP-seq . [ 1 ]  The proximal promoter region (upstream of the  core-promoter  region) contains the  cis-regulatory elements  of most of the  transcription factors  (TFs). [ 2 ]


Recently, a better approach to annotate active promoters has been demonstrated with a combination of ChIP-seq and  computational  technique. [ 3 ]  This technique has been used to find the target genes of TFs in  mammalian  systems. [ 4 ]  The MPromDb is based on this technology.  Curated  promoter sequences for  eukaryotic  organisms are provided by an EPD database; [ 5 ]  however, promoter activity information at  tissue /  cell  centric level is not offered.


The MPromDb data base added active  RNAP-II  promoters identified after analyzing ten different mouse cell/tissue  ChIP -seq experiments performed with RNAP-II  antibodies  and six different  human cell  types. [ 1 ]  The data was acquired by a series of computational methods followed by manual correction to ensure its high level quality. [ 2 ]  In the newest version of MPromDb, about 507 million uniquely-aligned  RNA Pol-II  ChIP-seq reads have already been analyzed from 26 different databases, including six human cell-types and 10 distinct mouse cell/tissues. [ 1 ]


MethBase  is a database of  DNA methylation  data derived from  next-generation sequencing  data. [ 1 ]  MethBase provides a visualization of publicly available  bisulfite sequencing  and  reduced representation bisulfite sequencing  experiments through the  UCSC Genome Browser .  MethBase contents include single- CpG site  resolution methylation levels for each CpG site in the  genome  of interest, annotation of regions of hypomethylation often associated with  gene promoters , and annotation of  allele -specific methylation associated with  genomic imprinting .


MethDB  is a database for  DNA methylation  data. [ 1 ]


MICdb  ( Microsatellites database ) is a database of non-redundant microsatellites from   prokaryotic  genomes. [ 1 ]


A  microarray database  is a repository containing  microarray   gene expression  data. The key uses of a microarray database are to store the measurement data, manage a searchable index, and make the data available to other applications for analysis and interpretation (either directly, or via user downloads).


Microarray databases can fall into two distinct classes:


Some of the most known public, curated microarray  databases  are:


Competing endogenous RNAs ( ceRNAs , also refer as miRNA sponges) hypothesis :  ceRNAs  regulate other  RNA transcripts  (e.g., PTEN) by competing for shared  microRNAs . [ 1 ]  They are playing important roles in developmental,  physiological  and  pathological  processes, such as  cancer . Multiple classes of ncRNAs (lncRNAs, circRNAs, pseudogenes) and  protein -coding mRNAs function as key  ceRNAs  (sponges) and to regulate the expression of mRNAs in  plants  and  mammalian  cells. [ 2 ]


This competing endogenous RNA (ceRNA) databases and resources is a compilation of databases and web portals and servers used for ceRNA prediction and ceRNA networks.


This  microRNA  database and microRNA targets databases  is a compilation of databases and web portals and servers used for  microRNAs  and their targets.  MicroRNAs  (miRNAs) represent an important class of small non-coding RNAs (ncRNAs) that regulate gene expression by targeting messenger RNAs. [ 1 ]


The  laboratory mouse  has been instrumental in investigating the genetics of human disease, including cancer, for over 110 years. [ 1 ]  The laboratory mouse has physiology and genetic characteristics very similar to humans providing powerful models for investigation of the genetic characteristics of disease. [ 2 ]


The  Mouse Models of Human Cancer database (MMHCdb)  is a unique, comprehensive online knowledgebase of  mouse models  of human  cancer  hosted by The  Jackson Laboratory  with funding from the  National Cancer Institute (NCI) . [ 3 ]  MMHCdb is part of the  Mouse Genome Informatics  consortium (MGI) and was first released in 1998 as the Mouse Tumor Biology (MTB) database. [ 4 ]  MMHCdb  contains genetic and  genomic  information about  inbred mouse strains ,  genetically engineered mouse models (GEMMs)  and  Patient Derived Xenograft (PDX)  models of human cancer. Data in MMHCdb is expertly curated from peer-reviewed scientific publications or obtained as direct data submissions from individual investigators and large-scale research initiatives. [ 5 ]  MMHCdb, in collaboration with  EMBL-EBI , co-developed the PDCM Finder resource which serves as a global catalog of PDX models. [ 6 ]


Inbred mouse strains are the result of at least 20 generations of successive brother/sister matings which lead to a population with minimal segregating genetic variation.  Inbred strains  of mice allow researchers to investigate the role that genetics plays in cancer susceptibility and treatment response.  The first inbred strain, DBA (dilute brown agouti), was developed in 1909. [ 7 ]  Among the earliest references to the use of inbred mice in cancer was the observed susceptibility of the A family of inbred mice to spontaneous lung tumors. [ 8 ]  MMHCdb provides interactive graphical summaries of the characteristic cancers observed in over 700 different inbred mouse strains. Figure 1 shows one of these tools, the Tumor Frequency Grid, which displays the frequency of different types of tumors across different inbred strains.


Molecular biology  and  genome editing  technologies transformed the use of mice in cancer research by allowing researchers to develop  animal models  with specific  mutations  or that expressed human genes ( transgenics ).  The genetic background of genetically modified animals can influence the observed  phenotypes  and must be taken into account when mouse models of human cancer are developed and when interpreting the data generated by these models.  For example, transgenic mice on a mixed  C57BL/6J  and SJL background expressing human  HRAS  have a mammary gland  carcinoma  frequency of 45-50% whereas the same  transgene  expressed in FVB/N mice result in 100% of the animals having mammary gland carcinomas. [ 9 ]   MMHCdb provides search tools and displays to show the tumor frequencies of similar genetic modifications on different genetic backgrounds. Fig.2 shows the results of a search for lung adenocarcinomas with pathology images using the Advanced Search page and links to the results of that search.


A Patient Derived Xenograft (PDX) is generated by the implantation of human tumor tissue in an  immunodeficient  or  humanized  host mouse strain. PDXs are a powerful pre-clinical model system for evaluating efficacy of cancer therapy. [ 10 ]  Most of the PDX models in MMHCdb are from xenografts in the immunodeficient NSG host strain (aka, NOD scid gamma;   NOD.Cg-Prkdc<scid> Il2rg<tm1>Wjl/SzJ) mice  developed at The Jackson Laboratory. [ 11 ]   MMHCdb allows researchers to search for PDX models by such parameters as cancer site, diagnosis, genomic properties of the engrafted human tumor, and model treatment response. Fig. 3 shows the output of a search for PDX models whose engrafted tumors have amplified KRAS, the TP53 A159V mutation, a deletion of the ALB gene, and high KIT expression.


MMHCdb and EMBL-EBI co-developed PDCM Finder, an searchable catalog of PDX models available from repositories around the world.


The MMHCdb contains a large amount of varied, highly detailed data and is continually adding new data and data types. This section contains a summary of the content in MMHCdb as of February 2020;


MutationTaster  is a free web-based application to evaluate DNA sequence variants for their disease-causing potential. The software performs a battery of  in silico  tests to estimate the impact of the variant on the gene product / protein. Tests are made on both, protein and DNA level, MutationTaster is hence not limited to  substitutions of single amino acids  but can also handle  synonymous  or  intronic  variants. [ 1 ] [ 2 ]


Many  genetic disorders  can be caused by mutation of a single gene. New sequencing techniques however, have shown that a single individual can have up to 3.5 million alterations in the whole genome, most of which do not have a detrimental health effect. [ 3 ]  The challenge of prediction tools is thus to filter harmless mutations from disease-causing ones. It is important to note that these tools are not designed to predict sources of complex diseases such as cancer. The latter usually do not have a  monogenic causation  but are caused by multiple gene defects that develop cumulatively into a disease.


Mutation Taster is written in  Perl  and can process  Next-generation sequencing  data of all major platforms (Roche 454, Illumina Genome Analyzer and ABI SOLiD). The program first discards mutations that are known, harmless  polymorphisms  by comparison with the integrated databases. The remaining SNPs ( Single-nucleotide polymorphism ) are tested according to the gene alteration they are causing:


Multiple tests are performed to determine the nature of the given SNP. These tests comprise (among others):


Integrated data sources (among others):


The single results are then assessed by a  Naive Bayes classifier  which decides whether or not their combined effect might be deleterious for the protein. The 'raw' accuracy of MutationTaster is about 90%, with the inclusion of knowledge about common (harmless) polymorphisms and known disease mutations, the actual rate of correct classifications is much higher. The test output explains if the alteration is a known or predicted harmless or disease-causing mutation and gives detailed information about the mutation.


Importantly, the predictions of clinical effects of mutations suffer from a lack of specificity, which appears to be the common constraint of all recently used prediction methods, including those named above. Despite that, predictions mediated by these methods are associated with nearly absolute sensitivity. The outcomes of prediction methods are often uncritically used, particularly by non-specialists in the field. [ 4 ]


Development of MutationTaster has started in 2007, the software is available online since 2009. MutationTaster is hosted at the Charité Berlin and its current developers are Olivia Ebner, Daniela Hombach, Markus Schülke, Jana Marie Schwarz, Dominik Seelow.
Current efforts are focused on integrating mutations that do not directly alter protein coding genes but have an effect on gene regulation and expression.


The  Nucleic acid phylogenetic profiling  ( NAPP ) is a database of coding and non-coding sequences according to their pattern of  conservation  across the other genomes. [ 1 ]


NGSmethDB  is a database of  methylation  data derived from  next-generation sequencing  data. [ 1 ]


Nucleosome Positioning Region Database  ( NPRD ) is a database of  nucleosome  formation sites ( NFSs ). [ when? ] [ 1 ]


Online Mendelian Inheritance in Man  ( OMIM ) is a continuously updated catalog of  human genes  and  genetic disorders  and  traits , with a particular focus on the  gene-phenotype relationship . As of 28 June 2019 [update] , approximately 9,000 of the over 25,000 entries in OMIM represented  phenotypes ; the rest represented  genes , many of which were related to known phenotypes. [ 1 ]


OMIM is the online continuation of  Victor A. McKusick 's  Mendelian Inheritance in Man  (MIM), which was published in 12 editions between 1966 and 1998. [ 2 ] [ 3 ] [ 4 ]  Nearly all of the 1,486 entries in the first edition of MIM discussed phenotypes. [ 2 ]


MIM/OMIM is produced and curated at the  Johns Hopkins School of Medicine  (JHUSOM). OMIM became available on the internet in 1987 under the direction of the  Welch Medical Library  at JHUSOM with financial support from the  Howard Hughes Medical Institute . From 1995 to 2010, OMIM was available on the  World Wide Web  with  informatics  and financial support from the  National Center for Biotechnology Information . The current OMIM website ( OMIM.org ), which was developed with funding from JHUSOM, is maintained by  Johns Hopkins University  with financial support from the  National Human Genome Research Institute . [ 5 ] [ 6 ]


The content of MIM/OMIM is based on selection and review of the published  peer-reviewed  biomedical literature. Updating of content is performed by a team of science writers and curators under the direction of  Ada Hamosh  at the McKusick-Nathans Institute of Genetic Medicine of Johns Hopkins University. While OMIM is freely available to the public, it is designed for use primarily by physicians and other health care professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. [ 5 ]


The database may be used as a resource for locating literature relevant to inherited conditions, [ 7 ]  and its  numbering system  is widely used in the medical literature to provide a unified index for genetic diseases. [ 8 ]


Each OMIM entry is given a unique six-digit identifier [ 9 ]  as summarized below:


In cases of  allelic heterogeneity , the MIM number of the entry is followed by a decimal point and a unique 4-digit number specifying the variant. [ 9 ]  For example, allelic variants in the  HBB  gene (141900) are numbered 141900.0001 through 141900.0538. [ 10 ]


Because OMIM has responsibility for the classification and naming of genetic disorders, these numbers are stable identifiers of the disorders. [ 5 ]


Symbols preceding MIM numbers [ 11 ]  indicate the entry category:


OMA  ( Orthologous MAtrix ) is a database of  orthologs  extracted from available complete  genomes . [ 1 ] [ 2 ]  The orthology predictions of OMA are available in several forms:


In  bioinformatics , the  PANTHER  ( protein analysis through evolutionary relationships ) classification system is a large curated biological database of gene/protein families and their functionally related subfamilies that can be used to classify and identify the function of gene products. [ 1 ]  PANTHER is part of the  Gene Ontology  Reference Genome Project [ 2 ]  designed to classify proteins and their genes for  high-throughput  analysis.


The project consists of both manual curation and bioinformatics algorithms. [ 3 ]  Proteins are classified according to family (and subfamily), molecular function, biological process and pathway. It is one of the databases feeding into the European Bioinformatics Institute's  InterPro  database. [ 4 ] —Application of PANTHER—The most important application of PANTHER is to accurately infer the function of uncharacterized genes from any organism based on their evolutionary relationships to genes with known functions. [ 3 ]  By combining gene function,  ontology , pathways and statistical analysis tools, PANTHER enables biologists to analyze large-scale, genome-wide data obtained from the current advance technology including: sequencing,  proteomics  or  gene expression  experiments. [ 5 ]
Shortly, using the data and tools on the PANTHER, users will be able to: [ 6 ]


In PANTHER there is a  phylogenetic tree  for each of the protein families. The annotation of tree is done based on the following criteria:


To generate phylogenetic trees, PANTHER uses GIGA algorithm. GIGA uses species tree to develop tree construction. On every iteration it attempts to reconcile tree in event form of speciation and gene duplication.


The process for data generation is divided into three steps:


PANTHER trees depicts gene family evolution from a broad selection of genomes which are fully sequenced. PANTHER have one sequence per gene so that the tree can represent event occurred over the course of evolution i.e duplication, speciation.
PANTHER genomes set are selected based on the following criteria:


Following are the requirements for being family clusters in PANTHER:


For each family multiple sequence are aligned using a default setting of  MAFFT , any column which is aligned less than 75% of the sequence is removed. This data is then used as an input for GIGA program. The output tree from GIGA are labelled. Each internal node is labelled as whether divergence event happened as speciation or gene duplication.


Each node in PANTHER tree is annotated with heritable attribute. Heritable attribute can be of three types subfamily membership, gene function and protein class membership. These annotation of nodes applies to primary sequence which was used to construct tree. In applying these annotation to primary sequence simple evolutionary principle is used i.e. each node annotation is propagated by its decedent node. [ 3 ]


PANTHER/LIB (PANTHER library):  Library consists of collection of books. Each of these books represents a protein family. There are a  Hidden Markov Model  (HMM), a  multiple sequence alignment  (MSA) and a family tree for each protein family in the library. [ 1 ]


PANTHER/X (PANTEHR index):  Index contains abbreviated ontology which assist in summarizing, navigating molecular function and biological function. Although PANTHER/X ontology has a  hierarchical  organization,  it is a  directed acyclic graph  and so when it is biologically justified, child categories appear under more than one parent. PANTHER/X has been mapped to GO and arranged in a different way to facilitate large scale analysis of proteins. [ 1 ]


PANTHER includes 176 pathway using CellDesigner tool. PANTHER pathways can be downloaded in the following file formats.


Version 6 uses UniProt [ 11 ]  sequences as training sequences. There are 19132 UniProt training sequences directly associated with the pathway components. This version has ~1500 reactions in 130 pathways, and the number of pathways associated with subfamilies were expanded. PANTHER became a member of the InterPro Consortium. The availability of PANTHER data was improved (the HMMs can be downloaded by  FTP ). The PANTHER/LIB version 6.1 contains 221609 UniProt sequences from 53 organisms, grouped into 5546 families and 24561 subfamilies. [ 12 ]  (2006)


In this version the phylogenetic trees represent speciation and gene duplication events. Identification of gene  orthologs  is possible. There are more support for alternative database identifiers for genes, proteins and  microarray  probes. PANTHER version 7 uses the SBGN standard to depict biological pathways. It includes 48 set of genomes. To define the new families and in collaboration with the European Bioinformatics Institute’s InterPro group, [ 4 ]  approximately 1000 families of non-animal genomes were added in this version. The sources of gene sets included model organism databases,  Ensembl [ 13 ]  genome annotation and  Entrez  Gene. [ 14 ]  Since this version, a stable identifier to each node in the tree is used. This stable identifier is a nine-digit number with the prefix PTN (stand for PANTHER Tree Node). [ 3 ] [ 15 ]  (2009)


The reference proteome [ 16 ]  set maintained by the UniProt resource is used in this version of PANTHER and so the source of gene sets is UniProt. It includes 82 set of genomes (approximately double compared with version 7) and 991985 protein coding genes from which 642319 genes (64.75%) have been used for family clusters. PANTHER website is redesigned to facilitate common user workflow. [ 3 ]


This version contains 7180 protein families, divided into 52,768 functionally distinct protein subfamilies. Version 9.0 has genomes of all 85 organisms. [ 17 ] [ 6 ]


This version contains 78442 subfamilies and 1,064,054 genes annotated.


The home page of PANTHER website shows several folder tabs for major workflows, including: gene list analysis, browse, sequence search, cSNP scoring, and keyword search. The details about each of these workflow are provided below.


This tab is selected by default because this the most frequently used option. You can enter valid IDs in the box or upload a file, then select list type, choose organism of interest and select the type of analysis.


A practical example:
Let's try this workflow using an example of a small gene list containing three genes AKT1, AKT2, AKT3. We first type these gene names within the box and separate them by comma (or space). We select "ID list" as list type, "Homo Sapiens" (human) as organism, and " Functional classification viewed in gene list" as the type of operation; then click submit. It gives you the information for all the three genes which are:


Using this folder tab and by selecting the ontology you are interested in, you can browse different classification. It is also possible to select more than one ontology; in this case, the results will meet the criteria from all the selections.  You are able to see the association between ontology terms and PANTHER families, subfamilies and training sequences.


By putting the protein sequence in the Sequence Search box, PANTHER will search against a library of family and subfamily HMMs, and return the subfamily that best matches the sequence. If you click on the subfamily name, it will give some details, e.g. the genes related to that subfamily and the ability to view the subfamily within larger family tree. By downloading the PANTHER scoring tool from download page, you will be able to score many sequences against PANTHER HMMs.


Using this folder tab, you are able to do evolution analysis of coding  SNPs . You must enter a protein sequence in the first box and the substitutions relative to this protein sequence in the second box; this substitutions should be entered in the standard amino acid substitution format, e.g. L46P. PANTHER will use an alignment of evolutionarily related proteins, calculate the substitution position-specific evolutionary conservation (subPSEC) and estimate the  likelihood  of this nonsynonymous coding SNP to lead a functional effect on the protein. This tool uses data from PANTHER version 6.1 for technical reasons. One of the new features of PANTHER is that if you want to analyze a lot of SNPs, you can go to the download page and download the PANTHER Coding Snp Analysis tool.


Entering a search term in the keyword search box, PANTHER will give you the number of records matching your keyword for genes, families, pathways and ontology terms. You can filter them by determining the species of interest or by refining the search using other criteria. To view the details of the gene, you must click on the gene identifier.


PhEVER  is a  database  of  homologous gene  families between viral  sequences  and sequences from  cellular   organisms . [ 1 ]


PHIB-BLAST


The  Pathogen-Host Interactions database  ( PHI-base ) [ 1 ]  is a biological database that contains manually curated information on genes experimentally proven to affect the outcome of  pathogen-host interactions . The database has been maintained by researchers at  Rothamsted Research  and external collaborators since 2005. [ 2 ] [ 3 ] [ 4 ] [ 5 ]
PHI-base has been part of the UK node of  ELIXIR , the European life-science infrastructure for biological information, since 2016. [ 6 ]


The  Pathogen - Host  Interactions database was developed to utilise the growing number of verified genes that mediate an organism's ability to cause disease and/or trigger host responses. [ 7 ]


The web-accessible database catalogues experimentally verified pathogenicity, virulence, and effector genes from bacterial, fungal, and  oomycete  pathogens which infect animal, plant, and fungal hosts. PHI-base was the first online resource devoted to the identification and presentation of information on fungal and oomycete pathogenicity genes and their host interactions. PHI-base is a resource for the discovery of candidate targets in medically and agronomically important fungal and oomycete pathogens for intervention with synthetic chemistries and natural products ( fungicides ). [ 8 ] [ 9 ]


Each entry in PHI-base is curated by domain experts and supported by strong experimental evidence (gene disruption experiments) as well as literature references in which the experiments are described. Each gene in PHI-base is presented with its nucleotide and deduced amino acid sequence as well as a detailed structured description of the predicted protein's function during the host infection process. To facilitate data interoperability, genes are annotated using controlled vocabularies ( Gene Ontology  terms,  EC Numbers , etc.), and links to other external data sources such as  UniProt ,  EMBL , and the  NCBI  taxonomy services.


Version 4.17 (May 2024) of PHI-base  [ 1 ]  provides information on 9973 genes from 296 pathogens and 249 hosts and their impact on 22415 interactions as well on efficacy information on ~20 drugs and the target sequences in the pathogen. PHI-base currently focuses on plant pathogenic and human pathogenic organisms including fungi, oomycetes, and bacteria. The entire contents of the database can be downloaded in a tab delimited format. Since the launch of version 4, the PHI-base is also searchable using the PHIB-BLAST search tool, which uses the  BLAST  algorithm to compare a user's sequence against the sequences available from PHI-base. [ 10 ]  The database providers recently announced the launch of  PHI-base 5 , a new gene-centric version of PHI-base, through a  press release  on the Rothamsted Research website. A summary of the improvements made is also  available .


In 2016 the plant portion of PHI-base was used to establish a Semantic PHI-base search tool. [ 11 ]