This page provides information about the various GeneCards sections and tables.
|protein-coding||Protein-coding according to HGNC, Ensembl, or Entrez Gene*|
|pseudogene||Pseudogene according to HGNC, Ensembl, or Entrez Gene*|
|ncRNA gene||RNA gene according to HGNC, Ensembl, or Entrez Gene*|
or genes that are mined from RNAcentral and its external sources.
|Biological regions||Non-genic functional elements (e.g. enhancers, promoters) that have been described in the literature and are experimentally validated, according to Entrez Gene|
|gene cluster||includes piwi-interacting RNA clusters (PIRCs) and symbols ending with '@'|
|genetic locus||none of the above, but there is disease information , or 'QTL' in the symbol|
|uncategorized||none of the above|
* In cases of conflict, HGNC overrides Ensembl which overrides Entrez Gene.
This section provides the gene's symbol, category, GIFtS score (see below), and GCid
The header also contains a short description of the gene, approved by HUGO Gene Nomenclature Committee (HGNC) database on the left side of the header.
Next to the gene symbol and category, there is a star symbol allowing the user to mark the gene for future reference. All marked genes can be viewed in the My Genes page.
On the right side of the header, there are links to Gene and Participant pages at the Undiagnosed Diseases Network (UDN) .
Our goal is to use these two types of annotations in order to measure the functionality of GeneCards genes. Our first step, was to produce for each gene, a binary vector of 67 elements , indicating presence or absence of data in each relevant source. The GIFtS score of a particular gene is a percentage which is derived from the sum of these binary values divided by the number of sources (the vector length).
Improved GIFtS includes experimenting with increased resolution by using sub-sectioning of data sources and adjusting scores based on the presence or absence of detailed annotations within a source (currently SwissProt). In addition we have introduced weights related to the quantitative aspects of annotations items, enabling better evaluation of the data relevant to annotation levels (currently orthologs and publications). In order to enrich GIFtS with respect to protein data, we selected the pivotal bioinformatics source for such data, namely SwissProt, and dissected it into 6 sub sources: protein subunit, sub cellular location, post-translational modification, function, catalytic activity, and other. Each of these subfields received a binary score as described above, thereby increasing the GIFtS vector size by 5. To weight proteins effectively in the new vectors, the sum of the binary data was still divided by the original number of sources (with SwissProt treated as 1 source for this denominator, in spite of its sub sources contributions to the numerator). To enrich GIFtS by orthologs or publications data, we define a new score for each of those components, which is then added to the default GIFtS. Specifically, the orthologs and publications scores for each gene are calculated as round (logxsum(i)), where x equals 3 for orthologs and 5 for publications, and sum(i) is the number of relevant orthologs or publications. Genes with no orthologs or publications receive score of zero for the relevant component(s); scores rounded down to 0 (for low counts) are normalized to 1.
Harel A, Inger A, Stelzer G, Strichman-Almashanu L, Dalah I, Safran M and Lancet D. GIFtS: annotation landscape analysis with GeneCards BMC Bioinformatics 2009, 10:348
This section displays synonyms and aliases for the relevant GeneCards gene, as extracted from OMIM, HGNC, Entrez Gene, UniProtKB (Swiss-Prot/TrEMBL), GeneLoc, Ensembl, DME, miRBase, ENA, GtRNAdb, LncBase, LncBook, Lncipedia, Modomics, Noncode, PDBe, Rfam, SILVA, snopy, SRPDB and TarBase. Also shown are accessions from HGNC, EntrezGene, UniProtKB, OMIM, HORDE, and/or Ensembl and previous symbols where relevant (for cases that GeneLoc deems it necessary to assign a new identifier to a gene based on updated information about its chromosomal location). Although gene symbols may change, GC ids will always remain with their original genes and will not be reused with other symbols.
For each alias, a list of its sources is listed (source names in parenthesis for RNA Genes, superscripts for all other categories).
For genes with the 'RNA gene' category, the 'Data sources' subsection shows a list of sources contributing to defining the gene , including transcript sources that do not assign aliases.
Subcategories for genes with the 'RNA gene' category are derived from Ensembl's biotype, Entrez Gene's gene type, HGNC's locus type, and RNAcentral’s RNA type.
Finally, this section contains an option to search for the gene in outside databases by selecting from its aliases, asociated disorders and/or other keywords.
This section displays descriptions of a gene's function, cellular localization and a gene's effect on phenotype for the relevant GeneCards gene, as extracted from Entrez Gene, CIViC, UniProtKB (UniprotKB/Swiss-Prot/ UniprotKB/TrEMBL), Tocris Bioscience, PharmGKB, Gene Wiki, and Rfam. The GeneCards-generated summary compiles significant annotations for the gene (such as aliases, diseases, paralogs, and pathways) into a descriptive text.
This section displays the chromosome, cytogenetic band and map location of the GeneCards gene as extracted from GeneLoc, HGNC, Entrez Gene, Nature (405, 311-319) and miRBase, as well as genomic views from UCSC and Ensembl, RefSeq DNA sequence links and transcription factor binding sites from Qiagen and links to SPP. The GeneLoc integrated location is shown in red on the image. If this differs from the location provided by Entrez Gene and/or Ensembl, their locations are shown on the image in green and/or blue respectively. Also provided are links to the GeneLoc gene density information for this gene's chromosome, which shows the number of genes in each 1 Mb interval along the chromosome, and to detailed exon information as provided by GeneLoc.
We also integrated non-coding transcripts from RNAcentral. Transcripts with the same RNA type, same strand and at least one overlapping exon were grouped into clusters. Clusters overlapping with ncRNA genes from the main sources described above were added as annotations. Other clusters are defined as novel ncRNA genes.
This subsection describes genomic regulatory elements related to the gene from GeneHancer. GeneHancer is a database of genome-wide enhancer-to-gene and promoter-to-gene associations, embedded in GeneCards. Regulatory elements were mined from the following sources:
The GeneHancer table lists a set of enhancers and promoters associated with the gene. Gene-GeneHancer associations and likelihood-based scores were generated using information that helps link regulatory elements to genes:
GeneHancer elements have unique, informative and persistent GeneHancer identifiers (GHids). The id begins with GH, which is followed by the chromosome number, a single letter related to the GeneHancer version (constant since version 4.8, ‘J’), and approximate kilobase start coordinate. Example: GH0XJ101383 is located on chromosome X, with starting position (in kb) of 101383.
|GeneCards Version||GH id letter|
Each GeneHancer has a confidence score which is computed based on a combination of evidence annotations: (1) Number of sources; (2) Source scores; (3) TFBSs (from ENCODE). GeneHancers supported by two or more evidence sources were defined as elite and annotated accordingly with an asterisk. For every GeneHancer, the following annotations are included: GH id, GH type (promoter, enhancer or both), the sources with evidence for the GeneHancer, genomic size, GeneHancer confidence score, and a list of TFs (Transcription Factors) having TFBSs (Transcription Factor Binding Sites) within the GeneHancer (based on ChIP-Seq evidence). The expanded view also provides genomic location of the GeneHancer, and additional source-specific annotations such as identifiers, genomic locations, enhancer type (proximal/distal), a list of biological samples with evidence for the GeneHancer, a list of super-enhancers the enhancer belongs to, eRNA expression strength (maximum pooled expression of eRNA CAGE tag clusters), tissue pattern, and tissue pattern reproducibility.
For every gene-GeneHancer association the following annotations are displayed: Gene- GeneHancer Score, a general score for the gene-GeneHancer association (combined score based on all methods, associations supported by two or more methods were defined as elite and annotated accordingly with an asterisk), total score - multiplication of the GeneHancer confidence score and the GeneHancer-gene association score, gene-GeneHancer distance (calculated between the GeneHancer midpoint and the gene TSS, positive for downstream and negative for upstream), number of genes having a TSS between the gene and the GeneHancer, and a list of all genes associated with the GeneHancer. The expanded view also provides method-specific scores for the gene-GeneHancer association (p-values for eQTLs and co-expression, log(observed/expected) for C-HiC, and distance-inferred probability) and annotation of Topologically Associated Domains (TADs) shared by the GeneHancer and the gene (mined from the ENCODE project). A link to a UCSC GeneCards custom track presenting all GeneHancers within 100kb from the gene is located below the GeneHancers table.
Disease-GeneHancer associations: GeneHancer-gene pairs were associated to diseases by integrating manually curated disease-associated variants within regulatory elements from (1) DiseaseEnhancer, PMID:29059320; (2) PMID:27569544.
This section provides annotated information of the proteins encoded by GeneCards genes
the capability to view phosphorylation sites using
Specific Peptides from DME,
a link to the Protein Expression image from
MOPED,PaxDb, and MaxQB
and reference sequences (RefSeq) according to NCBI.
Direct links to three-dimensional visualization of PDB structures provided by the
and Proteopedia. Visualizations are also provided via the (3D)
for OCA Browser or the Proteopedia symbol hyperlink shown next to each PDB identifier.
Genes with similar ontologies can be seen using Genes Like Me (more information)
This subsection provides annotated information of post translational modifications according to UniprotKB, neXtProt, and GlyConnect and the capability to view phosphorylation sites using PhosphoSitePlus. Specific amino acid identity and position of glycosylation and ubiquitination modifications are mined from neXtProt. Amino acid position refers to the sequence of the canonical isoform as defined in neXtProt.
This section provides annotated information about protein domains and families according to
and Suggested Antigen Peptide Sequences from GenScript.
We also show a list of protein family terms, as mined from the Human Protein Atlas (HPA).
Original content and additional information can be found at the Human Protein Atlas available at www.proteinatlas.org (PubMed:
Genes with similar domains can be seen using Genes Like Me (more information)
This section provides annotated information about gene function
Human phenotypes from GenomeRNAi,
transcription factor targeting from Qiagen
miRNA Gene Targets from miRTarBase,
as well as molecular function ontologies visualized by the
Gene Ontology Consortium (more information).
Genes with similar ontologies can be seen using Genes Like Me (more information).
Information from MGI includes links to mouse knock-outs, phenotypes for mouse orthologs, and a popup table with information on phenotypic alleles of the orthologs. This table presents the following columns:
This table lists human phenotypes associated to the gene by GWAS (genome wide association studies) from the GWAS Catalog (PMID: 27899670). The GWAS Catalog is a quality controlled, manually curated, literature-derived collection of published genome-wide association studies assaying at least 100,000 SNPs and all SNP-trait associations with p-values < 1.0 x 10-5. Each record in the table summarizes the phenotype association evidence for a given gene, including the best p-value, the average p-value, count of SNPs, count of studies, and a list of SNPs. Phenotype-gene associations were created via 3 routes. (1) Associations reported by the source - marked as ‘GWAS’ in the ‘Gene Relation’ column. (2) Associations created by mapping SNPs to gene exons - marked as 'GeneExon'. (3) Associations created by mapping SNPs to GeneHancer regulatory elements - marked as ‘GeneHancer’.
This table lists human phenotypes that were found to be linked to the gene by the Human Phenotype Ontology project. The HPO project has generated a set of 10,088 classes (terms) describing human phenotypic abnormalities. Links between phenotypes to genes are generated using the information about the phenotypes of a particular syndrome and the corresponding genes that are known to cause this syndrome when mutated. For each gene, the table first displays the most specific relevant HPO-classes (in bold), followed by their ancestor terms. (This approach implements the transitivity of the HPO annotation method).
This section provides information about gene localization according to UniProtKB and COMPARTMENTS Subcellular localization database, as well as cellular component ontologies visualized by the Gene Ontology Consortium (more information).
COMPARTMENTS localization data is integrated from literature manual curation, high-throughput microscopy-based screens, predictions from primary sequence, and automatic text mining (see COMPARTMENTS: unification and visualization of protein subcellular localization evidence ). Unified confidence scores of the localization evidence are assigned based on evidence type and source, and visualized both in a table and in the schematic cell image. Confidence scale is color coded, ranging from light green (1) for low confidence to dark green (5) for high confidence. White (0) indicates an absence of localization evidence.
List of subcellular location terms, as mined from the Human Protein Atlas (HPA). Original content and additional information can be found at the Human Protein Atlas available at www.proteinatlas.org (PubMed: 28495876)
This section provides SuperPaths from PathCards, links to pathways according to information extracted from Kyoto Encyclopedia of Genes and Genomes (KEGG), Cell Signaling Technology, R&D Systems, GeneGo (Thomson Reuters), Reactome, BioSystems, Sino Biological, Tocris Bioscience, PharmGKB, Qiagen, and GeneTex interactions according to UniProtKB, IID, STRING MINT, and GPS-Prot, as well as biological process ontologies visualized by the Gene Ontology Consortium (more information).
This table provides links to pathways in a unified view. All pathways from the sources listed above were clustered into SuperPaths for a better understanding of how the different pathways relate to one another. The left column contains a name representing the SuperPath, based on the most connected pathway in the SuperPath (this name giving pathway may or may not contain the gene to which the GeneCard belongs). SuperPaths are linked to PathCards, an integrated database of human pathways and their annotations. Human pathways were clustered into SuperPaths based on gene content similarity. Each PathCard provides information on one SuperPath, which represents one or more human pathways. The right column contains all current gene's pathways that belong to this SuperPath. Each of the contained pathways (in the right column) is followed by a score which is the Jaccard similarity score (0-1) to the most similar pathway. The SuperPaths are sorted by abundance of sources and then by number of gene-related pathways in the SuperPaths.
Below this table, all relevant pathways are listed by source.
Each line in this table represents one interacting protein, according to MINT, UniProtKB, IID, and/or String. The following columns are presented:
Presents a deep link to the SIGnaling Network Open Resource (SIGNOR), as well as a list of interacting genes, all linked to their GeneCards. The interactions are categorized as Activates, Inactivates, Is activated by, Is inactivated by, or Other effect.
This section provides relationships between GeneCards genes and drugs from different sources, including DrugBank, ApexBio, DGIdb, FDA Approved Drugs, ClinicalTrials.gov, and/or PharmGKB in a unified table. This table is sorted by each drug’s approval status, number and quality of sources, and group. Following this table there is a unified ‘Additional Compounds’ table that displays compounds from IUPHAR, Novoseek, HMDB, BitterDB, and/or Tocris Biosciences that are not also found at the above drug sources. Both tables have an "Options" drop-down menu to exclude(default)/include inferred text-mined information (from Novoseek).
Drugs shown in this table are considered to be drugs by DrugBank, ApexBio, DGIdb, FDA Approved Drugs,
ClinicalTrials.gov, and/or PharmGKB. Superscripts in the Name column are provided only for sources giving
evidence for drug-gene relationships. Drug metadata in other columns provided by DrugBank, ApexBio,
DGIdb, FDA Approved Drugs, ClinicalTrials.gov, PharmGKB, IUPHAR, HMDB, BitterDB, Tocris Biosciences, and/or
This table presents the following columns:
This table presents the following columns for compounds associated with this gene that are not classified as "drugs" in the preceding table:
This section contains REFSEQ mRNAs, additional gene/cDNA sequences from GenBank, transcript links to Ensembl, an integrated table of ncRNA transcripts where relevant, CRISPR, miRNA, Inhibitory RNA, and Clone Products, exon structure information from GeneLoc and alternative splicing information.
Our integrated gene-centric compendium of human non-coding RNA (ncRNA) genes was created by integrating data of transcripts genomic locations into a comprehensive non-redundant gene-centric view of human ncRNA genes.
GeneCards ncRNA gene records and annotations are integrated from EBI’s RNAcentral, and its expert sources of human ncRNA transcripts (including ENA, GtRNAdb, LncBase, LncBook, Lncipedia, miRBase, MirGeneDB, Modomics, Noncode, PDBe, Rfam, SILVA, snopy, snoDB, SRPDB, TarBase and 5SrRNAdb), as well as from Ensembl, NCBI Entrez Gene and HGNC.
Using the gene-centric GeneCards model, we clustered overlapping transcript entries from the aforementioned sources, applying an algorithm based on transcript annotations and genomic coordinates.
First, established ncRNA genes are generated using the GeneLoc algorithm, integrating genes from HGNC, NCBI and Ensembl.
Then, RNAcentral transcripts are processed as follows: If possible, transcripts are associated with established ncRNA genes based on their genomic coordinates and annotations (gene symbol, alias and RNA class). Remaining transcripts are clustered, based on 3 parameters: 1. Belonging to the same RNA class. 2. Mapped to the same DNA strand 3. Overlapping with at least 70% of one exon. Each cluster is defined as a GeneCards gene, categorized as an RNA gene, and annotated with an ncRNA subcategory based on the RNA class of its clustered transcripts. Coordinates of these genes are defined to be the minimum and maximum coordinates of all of their respective clustered transcripts. Transcript annotations are used to determine the gene symbol and aliases.
For RNA genes, this section contains a table of integrated transcripts. Transcripts information is based on RNAcentral’s data of non-redundant ncRNA transcript sequences and their annotations from expert sources. Each row in the table represents a transcript associated with a given gene. Data for each transcript includes transcript subcategory, length (sum of the lengths of the transcript exons), the number of expert sources annotating the transcript, and linked source transcript identifiers.
This subsection contains alternative splicing information according to
ASD. Exons with
alternative splice sites in different isoforms were broken into Exonic Units (ExUns). The letters
indicate the order of the ExUns in the exon. The symbol ' ^ ' between ExUns indicates an intron,
while ' ·' indicates the junction of two ExUns. Mouseovers on the dark blue squares show the
Exun's genomic coordinates, while mouseovers on the light blue squares show its transcript coordinates.
When showing ASD's splice variants, GeneCards subtracts the 3000 bp flank that ASD adds to the transcript
Note: We currently do not have any links to ASD, as their data has been frozen and their site taken down. We plan to upgrade this subsection.
This section contains expression images based on data from GTEx,BioGPS, Illumina Human BodyMap, and SAGE, with SAGE tags from CGAP, followed by a list of over-expressed tissues based on GTEx data, a table with expression data from LifeMap Discovery™, Protein Expression data from ProteomicsDB, SPIRE MOPED, PaxDb, and MaxQB, links to SPP and SOURCE, tissue specificity data from UniProtKB, evidence on tissue expression from TISSUES, and phenotype-based relationships between genes and organs from Gene ORGANizer.
RNA-seq RPKM values were obtained from GTEx for 51 normal human tissues, cells and fluids, based on 2712 samples. Data was averaged across samples for each tissue, and rescaled by multiplying RPKM by 100 and then calculating the root. Multiple datasets describing different compartments of a tissue were further averaged to produce a single generalized dataset for the tissue (Adipose, Artery, Brain, Colon, Esophagus, Heart).
Measurements were obtained for 76 normal human tissues and compartments hybridized against HG-U133A. The Affymetrix MAS5 algorithm was used for array processing and probesets were averaged per gene.
RNA obtained from 16 normal human tissues was sequenced and mapped to genes via their transcripts. Fragments Per Kilobase of exon per Million fragments mapped (FPKM) were calculated using the Cufflinks program and thereupon rescaled by multiplying FPKM by 100 and then calculating the root.
Serial Analysis of Gene Expression: For 19 normal human tissues, CGAP datasets Hs.frequencies and Hs.libraries are mined for information about the number of SAGE tags per tissue. Tags are reassigned to a Unigene cluster and after that to a particular gene by mining Hs.best_gene, Hs.best_tag and Hs_GeneData. The expression level of a particular gene in a particular tissue was calculated as the number of appearances of the corresponding tag divided by the total number of tags in libraries derived from that tissue. These fractions were then rescaled by making the geometric mean of all tissues equal. Please note: Currently, only associations with minimal ambiguity participate in the analysis.
Tissues and anatomical compartments are colored according to 6 categories - Immune (red), Nervous (green), Muscle (yellow), Internal (blue), Secretory (violet) and Reproductive (turquoise).
Normalized intensities are drawn on a root scale, which is an intermediate between log and linear scales. Values are not comparable between datasets (i.e. Microarray, RNAseq and SAGE).
This sentence provides a list of tissues for which a gene is positively differentially expressed, based on RNA-seq reads from GTEx. Fold change values of each sample were calculated using DESeq software, each sample reads were compared with all GTEx samples reads. Genes with fold change value >4 in a tissue are defined as positively differentially expressed in that tissue. Genes with maximal read count across tissues lower than 5 were excluded from calculations.
This table provides links to developmental and in vitro expression information in LifeMap Discovery™, the Embryonic Development and Stem Cells Database. Linked in-vivo cells or anatomical compartments where the gene is expressed also provide the tissue/organ of origin (using arrows). Links to stem cell differentiation are noted as "in vitro cells" or as "protocol derived cells". Additionally, there are links to datasets from external sources comprising high throughput experiments, such as microarray and RNA sequencing. The expression level (selective marker (cell-identifying gene) , positive , negative ) is also presented for each of the gene expression links. The table is grouped by tissue and sorted by number of hits, so tissues with more information are shown first.
This sentence provides a list of anatomical entities for which a gene is positively differentially expressed, based on the 69 integrated normal proteomics datasets in HIPED (the Human Integrated Protein Expression Database, see below). Fold change values were calculated as the ratio between the tested dataset protein abundance and the average of all datasets. Genes with fold change value >6 and protein abundance value >0.1 PPM in an anatomical entity are defined as positively differentially expressed in that entity
This subsection provides a list of genes defined as expression partners with respect to protein and RNA expression. The 69 integrated normal proteomics datasets in HIPED (the Human Integrated Protein Expression Database, see below) were the platform used to calculate the pairwise similarity of across-tissue protein abundance patterns amongst all genes. Gene pairs having Pearson’s correlation coefficient of >0.7 are annotated as expression partners. A parallel analysis of RNA-seq data from GTEx was performed. Gene pairs having both RNA and protein correlations of >0.7 are annotated as ‘Elite’ expression partners.
HIPED (the Human Integrated Protein Expression Database) is a unified database of protein abundance in human tissues, residing within GeneCards. HIPED is based on publically available mass spectrometry-based proteomics sources, integrating data for 69 normal anatomical entities (tissues, cells and fluids) and 125 cell lines. HIPED data sources:
The data was normalized as follows:
The protein expression images present a protein expression vector for each gene, based on normalized abundances
in 69 normal human anatomical entities. Selected 23 cell lines are shown in an additional image.
List of samples and their sources:
|CD4 T cells||✓|
|CD8 T cells||✓|
|Periph. blood mononuclear cells||✓|
|Bone marrow stromal cell||✓|
|Bone marrow mesench. stem cell||✓|
|Nasal respiratory epithelium||✓|
|Lung Alveolar lavage||✓|
|Islet of Langerhans||✓|
|T-cell leukemia, Jurkat||✓|
|Myeloid leukemia, K562||✓|
|Lymphoblastic leukemia, CCRF-CEM||✓|
|Brain cancer, U251||✓|
|Brain cancer, GAMG||✓|
|Bone cancer, U2OS||✓|
|Liver cancer, HuH-7||✓|
|Liver cancer, HepG2||✓|
|NSC lung cancer, NCI-H460||✓|
|Lung cancer, A549||✓|
|Kidney cancer, RXF393||✓|
|Colon cancer, RKO||✓|
|Colon cancer, Colo205||✓|
|Breast cancer, LCC2||✓|
|Breast cancer, MCF7||✓|
|Ovarian cancer, SKOV3||✓|
|Prostate cancer, LnCap||✓|
|Prostate cancer, PC3||✓|
|Cervical cancer, HeLa S3||✓|
|Cervical cancer, HeLa||✓|
TISSUES tissue expression data is integrated from literature manual curation, proteomics and transcriptomics screens, and automatic text mining (see TISSUES 2.0: an integrative web resource on mammalian tissue expression). The evidence is unified by assigning confidence scores that facilitate comparison of the different types and sources of evidence. The unified scores are visualized at the TISSUES website on a schematic human body. Pairs of tissue term and score are listed in this GeneCards subsection. The following 21 terms are included:
Heart, Intestine, Kidney, Liver, Lung, Lymph node, Muscle, Nervous system, Pancreas, Skin, Spleen, Stomach, Adrenal gland, Bone, Bone marrow, Eye, Gall bladder, Thyroid gland, Blood, Saliva, Urine. Terms with confidence score below 2 are filtered out.
*Ensembl pan taxonomic compara doesn't have its own pages on the Ensembl site.
The table presents the following columns:
The species presented from Ensembl pan taxonomic compara were chosen to constitute a diverse collection of taxa including model organisms and species of interest. Currently, all available species from the Homologene database (old and new) are included. Species with no ortholog for the gene can be viewed just below the orthologs table.
Superscripts represent the source from which this data was extracted. Data from HomoloGene can have one of two superscripts. If the second one is cited, it means that data for this species exists only in the older version of HomoloGene, which used unfinished genomes and where the homologs found might not be true orthologs.
This section contains Paralogs from HomoloGene, Ensembl (similarities shown on mouseover), and SIMAP (PMID: 19906725), and Pseudogenes from Pseudogene.org. Genes with similar paralogs can be seen using Genes Like Me (more information). Paralogs obtained from SIMAP were chosen according to a fixed similarity score, shown on mouseover, to allow an average of 30 paralogs per protein-coding gene.
This section contains SNPs/Variants from
with links to
This is followed by Structural Variations (CNVs/InDels/Inversions) from the
Database of Genomic Variants,
and links to mutations from HGMD,
The Human Cytochrome P450 Allele Nomenclature Database,
the BRCA Exchange.
This table presents SNPs/variants with clinical significance annotations. SNP information is currently extracted from ClinVar VCF and UniProt's Human polymorphisms and disease mutations files. The order of a gene's displayed SNPs can be modified by using the up/down arrows above the relevant columns: SNP ID, clinical significance and condition, and position on the chromosome.
Variants from dbSNP that are not represented in the section above.
All consequence types are included: molecular consequences (e.g. missense, synonymous), and location-based (e.g. intron, upstream).
Information about healthy variants is provided from the Database of Genomic Variants (DGV), containing each variant ID with its type (CNV or OTHER), its subtype (deletion, duplication, insertion, loss, gain, inversion, gain+loss, CNV, or complex), and a PubMed ID.
This section also provides Linkage Disequilibrium (LD) information from HapMap and Mutation information from HGMD.
Genic intolerance – The ExAC RVIS (Residual Variation Intolerance Score based on the Exome Aggregation Consortium data) was mined from the Genic Intolerance database (see publication). The intolerance scoring system assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. Genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease. For each gene, the tolerance percentile is shown. Genes in the 25th percentile and below are considered intolerant to variation.
GDI values were mined from the Human Gene Damage Index (GDI) database (see publication). The GDI is the accumulated mutational damage of each human gene in the general human population. Highly damaged human genes are unlikely to be disease-causing, hence GDI might be used to filter out variants harbored in highly damaged (high GDI) genes that are unlikely to be disease-causing. For every gene the Phred-scale GDI score is shown along with the GDI percentile, using all disease causing genes as a reference set. More specific reference sets are provided at the GDI database.
This section contains Disorders in which GeneCards genes are involved, according to MalaCards. Gene-disease associations in MalaCards are generated by integration of GeneCards searches with information from multiple external sources: OMIM, ClinVar, Orphanet, UniProtKB/Swiss-Prot, Genetic Testing Registry, miR2Disease, LncRNADisease, the University of Copenhagen DISEASES database and Novoseek.
Additional information is shown from UniProtKB and Genatlas.
Finally, links to additional disease sources related to the gene are also provided: HuGENavigator, TGDB, the ATLAS of Genetics and Cytogenetics in Oncology and Haematology and the Open Targets Platform.
The MalaCards diseases table: This section provides a unified table of diseases associated with this gene by MalaCards. The table, sorted by MalaCards gene-association score, shows the disease name, linked to MalaCards, with superscripts indicating the sources for the disease annotation and a mouseover showing the MalaCards score. This score ranks diseases by how closely they are associated with the gene, factoring in the relative reliability of the sources that associate them. Elite associations are marked with an asterisk next to the disease name. The elite status is conferred when the gene-to-disease association is manually curated. For genes listed in The cancer Census Gene list, the cancer diseases they are associated with are marked with a CC icon. The cancer Census Gene list from COSMIC is an ongoing effort to catalogue those genes for which mutations have been causally implicated in cancer. The table further displays the most common alias for the disease, with a link to show all available aliases. A third column contains linked PubMed IDs associated with the disease. These can be seen by clicking on the magnifying glass icon, which appears whenever there are available PubMed IDs.
The articles are ranked, first according to the number of GeneCards sources that associate the article with this gene and then by date of publication, and then according to the Novoseek score for this article/gene relationship. The year of publication appears in parentheses after the title of each article. Lower ranked articles may also appear in initial results if their titles or authors contain your search term.
Finally, links for searching for articles related to the gene in PubMed and other databases, as well as for viewing recent publication in Mastermind, are also provided.
GenesLikeMe (formerly Partner Hunter) is available for ontologies, phenotypes, drugs and compounds, expression patterns, sequence-based paralogs, disorders, pathways, and domains. By clicking on the GenesLikeMe (formerly Partner Hunter) button for a particular section, one arrives at the GenesLikeMe home page, where the gene name has been entered and the appropriate fields selected from the attribute list. From this page, changes can be made to the data requested. Submitting this form brings up a result page containing a list of genes similar to the chosen gene and their descriptions.
The relevance scores of elements related to genes (chemical substances and diseases) are based on the analysis of co-occurrences of two elements in Medline documents. The observed number of documents where both elements appear together and the number of documents where both appear independently are compared to an expected value based on a hypergeometric distribution. The more co-occurrences are observed in relation to the number expected the more unlikely it is that this happened by chance and the higher will be the value. Unfortunately the absolute numbers are not meaningful but can only give an order of importance (i.e. in the list of chemicals related to a gene the order is meaningful and the first chemicals in the list are, statistically, stronger related to the gene than the following ones but the absolute values of the scores may change from one release to another).