Free for academic non-profit institutions. Other users need a Commercial license

What's in a GeneCard?


This page provides information about the various GeneCards sections and tables.

General Comments

GeneCards Categories

CategoryDescription
protein-codingProtein-coding according to HGNC, Ensembl, or Entrez Gene*
pseudogene Pseudogene according to HGNC, Ensembl, or Entrez Gene*
RNA geneRNA gene according to HGNC, Ensembl, or Entrez Gene*
or genes that are mined from fRNAdb, miRBase, H-invDB, NONCODE, or Rfam
gene clusterincludes 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
uncategorizednone of the above

* In cases of conflict, HGNC overrides Ensembl which overrides Entrez Gene.

  • Categories are based on Entrez Gene type and status, as well as several other factors.
  • The former categories 'predicted' and 'predicted with support' are manifested now in the attibute 'predicted', which appears in the upper left box, where the category and GCid appear. This attribute means that the Entrez Gene status is 'PREDICTED', 'INFERRED', or 'MODEL' or the symbol source is Ensembl.
  • The former category "reserved symbol" no longer exists because it is no longer used by HUGO.

GeneCard Header

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.

On the right side of the header, above the GCID, there is a star symbol allowing the user to mark the gene for future refference. All marked genes can be viewed in the My Genes page.

GeneCards Inferred Functionality Scores (GIFtS)

The GIFtS algorithm uses the wealth of GeneCards annotations to produce scores aimed at predicting the degree of a gene's functionality. Since the degree of known functionality is correlated with the amount of research done on a particular gene or its product, we use these annotations in a scoring system aimed at inferring functionality. Note that while the accumulation of data for a specific gene in certain databases is merely correlated with functionality, many GeneCards sources, like the Gene Ontology (GO) Consortium and Genatlas provide definitive information about functionality.

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

GeneCards Sections

Aliases

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, NONCODE, CROW21 and/or RNAdb. Also shown are accessions from HGNC, EntrezGene, UniProtKB, OMIM, 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.

Subcategory for genes with category 'RNA gene' was taken from Ensembl's biotype, Entrez Gene's gene type, HGNC's locus type, fRNAdb sequence ontology, and descriptors from Rfam and H-invDB.

Overlapping RNA Genes unified location (ORGUL)

RNA genes from fRNAdb, HinvDB, Rfam and other sources are grouped into ORGULs. We strived to overcome the problem of having many ncRNA entries originating from fRNAdb and other sources that map to appreciably overlapping positions. To cope with such redundant entries, and to unite presumed parallel versions of the same gene, a clustering algorithm was applied to join entries with overlaps greater than 70% of the genomic territory of the smaller partner, when occurring on the same strand. Only entries belonging to the same RNA class were unified (unless a unique class was not assigned to a given entry in which case it could be unified to more than one class). The above procedure allowed us to define Overlapping RNA Genes with Unified Location (ORGUL) clusters [see Belinky, F., Bahir, I., Stelzer, G., Zimmerman, S., Rosen, N., Nativ, N., Dalah, I., Iny Stein, T., Rappaport, N., Mituyama, T., Safran, M., and Lancet, D. Non-redundant compendium of human ncRNA genes in GeneCards. Bioinformatics 15;29(2):255-61 (2013).[Abstract]]. Bellow is the full legend for a graphical representation of the overlapping entries available in the genomic locations section

Full legend of RNA genes sources

Quality score

The score indicates how many RNA databases have information about this gene, and whether the RNA gene is expressed or is known to be functional. An RNA gene that is known to be expressed will have a score of at least 5, and an RNA gene that is known to be functional will have a score of at least 10. The quality score Q is computed as the sum Q=10SF+5SE+0.2SP+0.5SN, where Si denote the count of data sources of the following kind: SF, showing functional annotation; SE, showing expression; SP, reporting prediction; SN, none of the above. In this respect, GeneCards does not simply unify information about ncRNAs from other resources, but also attempts to convey evidence parameters.

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.

Summaries

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, UniProtKB (UniprotKB/Swiss-Prot/ UniprotKB/TrEMBL), Tocris Bioscience, PharmGKB, and Gene Wiki, as well as sequence ontology from fRNAdb. The GeneCards-generated summary compiles significant annotations for the gene (such as aliases, diseases, paralogs, and pathways) into a descriptive text.

Genomics

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. 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.

Whenever a gene consists of a multi-membered ORGUL or is clustered with one, a figure showing the locations of these overlapping members is presented. See GeneCards ORGULs.

This section also provides links to promoters and Pyrosequencing assays for human and/or mouse/rat orthologs at Qiagen, and/or SwitchGear Genomics.

Proteins

This section provides annotated information of the proteins encoded by GeneCards genes according to UniProtKB, HORDE, neXtProt, Ensembl, and/or Reactome, the capability to view phosphorylation sites using PhosphoSitePlus, Specific Peptides from DME, a link to the Protein Expression image from SPIRE MOPED,PaxDb, and MaxQB reference sequences (RefSeq) according to NCBI, links for ordering antibodies from EMD Millipore, Cell Signaling Technology, OriGene, Novus Biologicals, R&D Systems, Abcam, Thermo Fisher Scientific, LSBio, Cloud-Clone Corp, and/or others, recombinant proteins from EMD Millipore, R&D Systems, Enzo Life Sciences, Novus Biologicals, OriGene, GenScript, Sino Biological, ProSpec, Cloud-Clone Corp., eBioscience and/or others, and assays from EMD Millipore, Cell Signaling Technology, R&D Systems, OriGene, GenScript, Enzo Life Sciences, Cloud-Clone Corp., eBioscience and/or others. Direct links to three-dimensional visualization of PDB structures provided by the OCA browser 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)

Post-translational modifications

This subsection provides annotated information of post translational modifications according to UniprotKB and neXtProt 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 isoform #1 as defined in neXtProt.

Domains

This section provides annotated information about protein domains and families according to HGNC, IUPHAR, InterPro, ProtoNet, UniProtKB and Blocks.
Genes with similar domains can be seen using Genes Like Me (more information)

Function

This section provides annotated information about gene function according to MGI, UniProtKB IUBMB, DME, Genatlas, and LifeMap Discovery™, including: Human phenotypes from GenomeRNAi, transcription factor targeting from Qiagen and/or HOMER, shRNA for human and/or mouse/rat from OriGene, siRNAs from OriGene and for human and/or mouse/rat from Qiagen, miRNA Gene Targets from miRTarBase, microRNA for human and/or mouse/rat orthologs from Qiagen, SwitchGear Genomics, Gene Editing from DNA2.0, Clones from GenScript, Sino Biological, Addgene, and for human and/or mouse/rat from OriGene and Vector BioLabs, Cell Lines from GenScript, ESI BIO, in situ hybridization assays from Advanced Cell Diagnostics, Inc. (ACD), Flow cytometry from eBioscience, Animal models from inGenious Targeting Laboratory (iTL), genOway, 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:

  • Experiment type - Type of allele by mode of origin
  • Name - Official symbol for the allele

Genes with similar phenotypes can be seen using Genes Like Me (more information)

Localization

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).

Subcellular locations from COMPARTMENTS:

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.

Pathways & Interactions

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 and Qiagen, interactions according to UniProtKB, I2D, STRING and MINT, as well as biological process ontologies visualized by the Gene Ontology Consortium (more information). The section also provides expression via Pathway & Disease-focused RT2 Profiler PCR Arrays for human and/or mouse/rat from Qiagen,

Genes with similar ontologies and those in the same pathways can be seen using Genes Like Me (more information)
Links to the Qiagen GeneGlobe Interaction Network and the STRING Interaction Network for the relevant gene are also provided.

SuperPaths: unified GeneCards pathways

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.

Interacting proteins

Each line in this table represents one interacting protein, according to MINT, UniProtKB, I2D, and/or String. The following columns are presented:

  • Symbol - Links to the GeneCards page (first sub-column) of the interacting protein
  • External ID(s) - Links to the the UniProtKB page and/or the Ensembl page for the interacting protein. Superscript links are to one of the following:
    • The comments section in the UniProtKB page for the interactant.
    • The page of all interactions between the two proteins, or all experiments supporting them, in the MINT database.
    • The page of all interactions with the interactant at I2D.
    • The interaction network of the interactant at String.
  • Details - Links to the interaction page in the database from which it was retrieved. In the case of IntAct, this page may include several different experiments supporting the same interaction. In the MINT database each distinct interaction definition or experiment supporting it is assigned a different mint id, all are presented. In the I2D database, the score given to the interaction is shown. In the String database each interaction is given an experimental score (based on experimental datasets from other protein-protein interaction databases) and a database score (based on information from curated databases). These scores indicate the confidence that the predicted interaction exists.

Drugs & Chemical Compounds

This section provides relationships between GeneCards genes and both chemical compounds, ligands and drugs, as well as links to drugs and compounds for ordering at EMD Millipore, Enzo Life Sciences, Tocris Biosciences, and ApexBio. Chemical compound relationships are from HMDB, BitterDB, and Novoseek. Drug compound relationships are from DrugBank, and PharmGKB. Ligand relationships are from IUPHAR. Pharmaceutical uses are provided by UniProtKB.

Tocris compounds and pharmacological data

This table presents the following columns:

  • Compound - The name of the Tocris product related to this GeneCards gene.
  • Action - Action (i.e. agonist, ligand, etc.) of the Tocris product related to this GeneCards gene.
  • CAS Number - Chemical Abstracts Registry number.

ApexBio compounds and pharmacological data

This table presents the following columns:

  • Compound - The name of the ApexBio product related to this GeneCards gene.
  • Action - Action (i.e. agonist, ligand, etc.) of the ApexBio product related to this GeneCards gene.
  • CAS Number - Chemical Abstracts Registry number.

HMDB chemical compound relationships

This table presents the following columns:

  • Compound - The name of the chemical compound related to this GeneCards gene.
  • Synonyms - other names for this compound.
  • CAS Number - Chemical Abstracts Registry number.
  • PubMed IDs - PubMed IDs of articles associated with this compound.

BitterDB Bitter Compounds

[ Wiener, A., Shudler, M., Levit, A. and Niv, M. Y. BitterDB: a database of bitter compounds , Nucleic Acids Res., 40: D413-D419 (2011) ].

This table presents the following columns:

  • Compound - The name of the chemical compound related to this GeneCards gene.
  • SMILES - Simplified Molecular Input Line Entry Specification, a specification for unambiguously describing the structure of chemical molecules using short ASCII strings.
  • CAS Number - Chemical Abstracts Registry number.

Novoseek chemical compound relationships

This table presents the following columns:

  • Compound - The name of the chemical compound related to this GeneCards gene.
  • -log (P-Val) - The Novoseek score of the relevance of the chemical compound to this gene, based on their literature text-mining algorithms.
  • Hits - The number of articles in which both the gene's symbol and the compound appear.
  • PubMed IDs - PubMed IDs of articles in which both the gene symbol and the compound appear in the same sentence, sorted by the number of sentences (shown in parentheses in the column) in which they both appear.

DrugBank drug compound relationships

This table presents the following columns:

  • Compound - The name of the chemical compound related to this GeneCards gene.
  • Synonyms - other names for this compound.
  • CAS Number - Chemical Abstracts Registry number.
  • Type - transporter, target, carrier, or enzyme.
  • Actions - inhibitor, substrate, antagonist, suppressor, inducer, and/or other/unkown.
  • PubMed IDs - PubMed IDs of articles associated with this compound.

PharmGKB related drug/compound annotations

This table presents the following columns:

  • Drug/Compound - The name of the drug compound related to this GeneCards gene.
  • PharmGKB Annotation - description of the annotation of the drug:
    • CA - High-level Clinical Annotation is available
    • VIP - VIP information is available
    • DG - Dosing Guideline information is available
    • DL - Drug Label information is available
    • VA - Variant Annotation is available
    • LA - Literature annotations are available
    • DS - Dataset is available

IUPHAR ligands relationships

This table presents the following columns:

  • Ligand - The name of the ligand related to this GeneCards gene.
  • Type - Agonist, Allosteric regulator, Antagonist, Channel blocker, Activator, Inhibitor, Pore Blocker, Gating inhibitor or None.
  • Action - This indicates how the ligand binds to the target.
  • Affinity - A measure of how strongly the ligand binds to the target.
  • PubMed IDs - PubMed IDs of articles associated with this ligand.

Genes with similar drug and compound relationships can be seen using Genes Like Me (more information)

Transcripts

This section contains associated Unigene clusters and representative sequences, REFSEQ mRNAs, RNA secondary structures from fRNAdb, siRNAs from OriGene and Qiagen, shRNA for human and/or mouse/rat from OriGene and microRNA for human and/or mouse/rat orthologs from Qiagen, SwitchGear Genomics, clones from GenScript, Sino Biological, Addgene, and for human and/or mouse/rat from OriGene and Vector BioLabs, primers for human and/or mouse/rat orthologs from OriGene, and/or Qiagen, Flow cytometry from eBioscience, assemblies (sorted by a scoring scheme that gives preferences to mRNAs over EST associations) from DOTS, transcript and alignment information from AceView, additional gene/cDNA sequences from GenBank, exon structure information from GeneLoc, alternative splicing information, and transcript links to Ensembl.

Secondary structures

This subsection contains RNA secondary structures according to fRNAdb.

Alternative Splicing

This subsection contains alternative splicing information according to ASD followed by alternative splicing isoforms from ECgene. 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 coordinates.
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.

Expression

RNA expression data (presence/absence) for RNA genes is according to H-InvDB, NONCODE, miRBase, and RNAdb.

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 SOURCE, tissue specificity data from UniProtKB, primers for human and/or mouse/rat orthologs from OriGene and/or Qiagen, and in situ hybridization assays from Advanced Cell Diagnostics, Inc. (ACD).

GTEx

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).

BioGPS

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.

Illumina body map

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.

CGAP: SAGE Normal

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).

Genes with similar binary patterns can be seen using Genes Like Me (more information)

mRNA Differential Expression

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.

LifeMap Discovery™ Table

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.

Protein Expression Images

Presentation of protein expression images for 69 tissues, fluids and cells and 23 cell lines. Data sources:

  1. ProteomicsDB- Bernhard Küster, TUM School of Life Sciences Weihenstephan, Technische Universität München.
  2. MOPED - Eugene Kolker, Bioinformatics & High-throughput Analysis Lab, Seattle Children's Research Institute.
  3. PaxDb - Christian von Mering, Bioinformatics Group, Institute of Molecular Life Sciences, University of Zurich.
  4. MaxQB - Matthias Mann, Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Germany.
The data was normalized as follows:
  1. For each sample, ppm protein values were calculated, if not provided so by data sources. For each sample from MaxQB and ProteomicsDB, iBAQ expression values were divided by sum of values of each sample, and multiplied by 1,000,000. iBAQ, intensity-based absolute quantification, is a proxy for protein abundance levels (see http://www.nature.com/nature/journal/v473/n7347/full/nature10098.html#supplementary-information). For all samples, data was gene centrically aggregated by summing expression values of all isoforms for each gene.
  2. Samples from similar tissues were averaged, using geometric mean.
  3. For better visualization of graphs, expression values are drawn on a root scale, which is an intermediate between log and linear scales as used for our mRNA expression graphs [see Safran, M., Chalifa-Caspi, V., Shmueli, O., Olender, T., Lapidot, M., Rosen, N., Shmoish, M., Peter, Y., Glusman, G., Feldmesser, E., Adato, A., Peter, I., Khen, M., Atarot, T., Groner, Y., and Lancet, D. Human Gene-Centric Databases at the Weizmann Institute of Science: GeneCards, UDB, CroW 21 and HORDE . Nucleic Acids Research 31,1:142-146 (2003). [PDF]].
List of samples and its sources:

Sample ProteomicsDB MOPED PaxDB MaxQB
Serum
Plasma
Monocyte
Neutrophil
B-lymphocyte
T-lymphocyte
CD4 T cells
CD8 T cells
NK cells
Periph. blood mononuclear cells
Platelet
Lymph node
Tonsil
Bone marrow stromal cell
Bone marrow mesench. stem cell
Brain
Brain, fetal
Frontal cortex
Cerebral cortex
Cerebrospinal fluid
Spinal cord
Retina
Heart
Heart, Fetal
Bone
Colon muscle
Oral epithelium
Nasopharynx
Nasal respiratory epithelium
Esophagus
Stomach
Cardia
Gut, fetal
Colon
Rectum
Liver
Liver, fetal
Liver secretome
Kidney
Spleen
Lung
Lung Alveolar lavage
Adipocyte
Synovial fluid
Amniocyte
Vitreous humor
Saliva
Salivary gland
Thyroid
Adrenal
Breast
Milk
Pancreas
Pancreatic juice
Islet of Langerhans
Gallbladder
Prostate
Urine
Urinary bladder
Skin
Hair follicle
Placenta
Uterus
Cervix
Ovary
Ovary, fetal
Testis
Testis, fetal
Seminal vesicle
T-cell leukemia, Jurkat
Myeloid leukemia, K562
Lymphoblastic leukemia, CCRF-CEM
Brain cancer, U251
Brain cancer, GAMG
Bone cancer, U2OS
Kidney, HEK293
Liver cancer, HuH-7
Liver cancer, HepG2
NSC lung cancer, NCI-H460
Lung cancer, A549
Kidney cancer, RXF393
Colon cancer, RKO
Colon cancer, Colo205
Melanoma, M14
Breast cancer, LCC2
Breast cancer, MCF7
Pancreas cancer
Ovarian cancer, SKOV3
Prostate cancer, LnCap
Prostate cancer, PC3
Cervical cancer, HeLa S3
Cervical cancer, HeLa

Orthologs

This section contains Orthologs from HomoloGene, Ensembl pan taxonomic compara, euGenes, SGD, and MGI Flybase and WormBase (through ensembl).

*Ensembl pan taxonomic compara doesn't have its own pages on the Ensembl site.

The table presents the following columns:

  • Organism - The names of the homologous species, using both scientific and popular terminology.
  • Taxonomy - The class of the species is presented (or a similar level if class is not defined). Higher taxonomic classifications are shown when hovering over the field (mouse-over).
  • Gene - The symbol for the gene in the homologous species. Its description is shown when hovering over the field (mouse-over).
  • Similarity - The percent similarity to the human gene, followed either by (n) where the comparison was based on nucleic acids or (a) for amino acid based comparisons.
  • Type - the type of orthology from Ensembl based on Ensembl gene trees:
    • 1 ↔ 1 (OneToOne) - one ortholog in the homologous species and one corresponding ortholog in human.
    • 1 → many - one ortholog in the homologous species and more than one corresponding orthologs in human.
    • many → 1 - more than one orthologs in the homologous species and one corresponding ortholog in human (changed from 1 → many type obtained from Ensembl, in cases where there is only one human ortholog).
    • 1 ↔ many - a more complex one to many relationships that can be better understood by examining the gene tree (changed from 1 → many type obtained from Ensembl, in cases where there is more than one ortholog in human as well as in the homologous species).
    • many ↔ many - more than one ortholog in the homologous species and more than one corresponding orthologs in human.
    • possible ortholog - type of orthology was not determined by Ensembl.
  • Details - The position of the gene in the homologous species, and IDs with links to sequences in other databases.

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.

Following the table are links to Ensembl and TreeFam gene trees.

Paralogs

This section contains Paralogs from HomoloGene, Ensembl (similarities shown on mouseover), and SIMAP, 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.

Genomic Variants

This section contains SNPs/Variants from the NCBI SNP Database, Ensembl and DNA2.0, with descriptions from UniProtKB, Linkage Disequilibrium images from HapMap, Structural Variations (CNVs/InDels/Inversions) from the Database of Genomic Variants, Mutations from HGMD, The Human Cytochrome P450 Allele Nomenclature Database, the Human Genome Variation Society's Locus Specific Mutation Databases (LSDB), and BGMUT, PCR Resequencing Primers for human and/or mouse/rat orthologs from Qiagen, and Cancer Mutataion PCR Arrays and Assays and Copy Number PCR Arrays from Qiagen.

SNPs

SNP information is currently extracted from dbSNP XML and UniProt's Human polymorphisms and disease mutations files. Filtering is done to include only those that are not artifacts, not connected to gene duplication, not withdrawn by NCBI, fully specified, without ambiguous locations or low map quality, and having single Entrez Gene and contig ids. The order of a gene's displayed SNPs can be determined by the user. By default, SNPs are initially sorted first by validation status (validated before non-validated), then, within these groups, by ordered clinical significance (in the following order: drug-response, histocompatibility, non-pathogenic, pathogenic, probable-non-pathogenic, probable-pathogenic, untested, unknown, other, and none listed) as the secondary (2nd) nested criterion, and finally by location type (first coding non-synonymous, then coding synonymous, followed by coding, splice site, mRNA-UTR, intron, locus, reference, and/or exception). The user can change this default sort order above the relevant columns using the up/down arrows as follows: rs-numbers (sorted in ascending order), clinical significance, position on the chromosome, Sequence Context, location type and allele frequencies (existing info before non-existing).

This table presents the following columns:
  • SNP ID - The NCBI rs number or UniProt VAR number for this SNP
  • Clin - Clinical significance - clinical interpretation associated with this SNP can be drug-response, histocompatibility, non-pathogenic, pathogenic, probable-non- pathogenic, probable-pathogenic, untested, unknown, and other. Additionally, this column lists associated diseases provided by UniProt.
  • Chr pos - chromosomal position: position of variation(strand).
  • Sequence Context - The sequence flanking the base pair variation. Lower case letters indicate repetitive or low-complexity sequence. Alternatively, this column may provide a link to the variant page at UniProt's ExPASy, where the sequence is shown.
  • Type - The SNP type:
    mis - missense
    coding, non-synonymous: change in peptide with respect to contig sequence
    fra - frameshift-variant
    coding, non-synonymous: change in peptide with respect to contig sequence
    non - nonsense
    coding, non-synonymous: change in peptide with respect to contig sequence
    syn - synonymous-codon
    no change in peptide for allele with respect to contig seq
    cds - coding
    variation in coding region of gene, assigned if allele-specific class unknown
    spl - splice-site; includes splice 3 (sp3), splice 5 (sp5)
    variation in first 2 or last 2 bases of intron
    utr - mrna untranslated region; includes utr-variant-3-prime (ut3), utr-variant-5-prime (ut5)
    variation in transcript, but not in coding region interval
    int - intron-variant
    variation in intron, but not in first 2 or last 2 bases of intron
    exc - exception
    variation in coding region with exception raised on alignment.This occurs when protein with gap in sequence is aligned back to contig sequence. variations 3' of the gap have undefined functional inference.
    loc - locus-region; includes near gene 3 (ng3), near gene 5 (ng5)
    variation in region of gene, but not in transcript
    stg - stop-gained
    changes to STOP codon
    ds500 - downstream-variant-500B
    sequence variant within a half KB of the end of gene
    spa - splice-acceptor-variant
    splice acceptor variant
    spd - splice-donor-variant
    splice donor variant
    us2k - upstream-variant-2KB
    sequence variant within 2KB 5' of gene
    us5k - upstream-variant-5KB
    sequence variant within 5KB 5' of gene
  • AA info - View individual records at dbSNP
  • MAF - Allele Frequency - Average frequency of the allelles for all populations, displayed as a pie-chart (only if 2 alleles). Alleles are in the same orientation as the displayed SNP sequence. Numeric info about the frequencies is available using the mouseover.

Structural Variation Table

Information from the Database of Genomic Variants (DGV) is provided, 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.

Disorders / Diseases

This section contains Disorders in which GeneCards genes are involved, according to MalaCards, OMIM, UniProtKB, the University of Copenhagen DISEASES database , Novoseek, Genatlas, GeneReviews, GeneTests, GAD, HuGENavigator, and/or TGDB. When possible, disorders are sorted by their relevance to the gene, with scores presented either explicitly in a table, or via mouseovers on disease names.

Novoseek disease relationships

This table presents the following columns:
  • Disease - The name of the disease related to this GeneCards gene.
  • -log (P-Val) - The Novoseek score of the relevance of the disease to this gene, based on their literature text-mining algorithms.
  • Hits - The number of articles in which both the gene's symbol or description and the disease appear.
  • PubMed IDs for Articles with Shared Sentences (# sentences) - PubMed IDs of articles in which both the gene symbol and the disease appear in the same sentence, sorted by the number of sentences (shown in parentheses in the column) in which they both appear.

Genes with similar disease relationships can be seen using Genes Like Me (more information)

Publications

This section provides titles of and links to research articles in PubMed, as associated via Novoseek, HGNC, Entrez Gene, UniProtKB, GAD, HMDB, and/or DrugBank.

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.

Products

This section provides links to reagents available from EMD Millipore, and/or R&D Systems, proteins, lysates, and/or antibodies available from Cell Signaling Technology, EMD Millipore, R&D Systems, OriGene, GenScript, Novus Biologicals, Sino Biological, Enzo Life Sciences, Abcam, ProSpec, Thermo Fisher Scientific, LSBio, Cloud-Clone Corp, eBioscience,and/or others, drugs and compounds available from EMD Millipore, Tocris Biosciences, ApexBio, and/or Enzo Life Sciences, Gene Editing from DNA2.0, clones, and/or primers available from OriGene, Qiagen, DNA2.0, Vector BioLabs, Addgene, SwitchGear Genomics, GenScript, and/or Sino Biological, Cell Lines from GenScript and/or ESI BIO, GPCR/Kinase Profiling, Assay development, GPCR & ELISA assays, & Flow cytometry available from GenScript, R&D Systems, Cloud-Clone Corp., and/or eBioscience, Animal models from inGenious Targeting Laboratory (iTL) , genOway, in situ hybridization kits from ACD, and links to reagents for mouse/rat orthologs from Qiagen.



Repeating Data Sources

Gene Ontology (GO) Tables

The Gene Ontology sections in Function, Localization, and Pathways & Interactions display a table with the following columns:

GO ID
The identifier used by GO and linked to the GO entry
Qualified GO term
The description of this entry, possibly qualified with "NOT", "colocalizes with", or "contributes to" (see TPX2)
Evidence
A 2 or 3 letter code
Curator-assigned Evidence Codes
   Experimental Evidence Codes:
IDA: Inferred from Direct Assay
IPI: Inferred from Physical Interaction
IMP: Inferred from Mutant Phenotype
IGI: Inferred from Genetic Interaction
IEP: Inferred from Expression Pattern
   Computational Analysis Evidence Codes:
ISS: Inferred from Sequence or Structural Similarity
IGC: Inferred from Genetic Context
RCA: Inferred from Reviewed Computational Analysis
   Author Statement Evidence Codes:
TAS: Traceable Author Statement
NAS: Non-traceable Author Statement
   Curator Statement Evidence Codes:
IC: Inferred by Curator
ND: No biological Data available
Automatically-assigned Evidence Codes
IEA: Inferred from Electronic Annotation
Obsolete Evidence Codes
NR: Not Recorded
PubMed ids
References in the literature, if relevant, obtained from EntrezGene

Genes Like Me

Genes Like Me partner hunter is available for ontologies, phenotypes, drugs and compounds, expression patterns, sequence-based paralogs, disorders, pathways, and domains. By clicking on the Genes Like Me Partner Hunter button for a particular section, one arrives at the Genes Like Me 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.

Novoseek Scoring Algorithm

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).

Content