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*|
|RNA gene||RNA gene according to HGNC, Ensembl, or Entrez Gene*|
or genes that are mined from fRNAdb, miRBase, H-invDB, NONCODE, or Rfam
|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, NONCODE, CROW21 and/or RNAdb. 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.
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.
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
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+SN, 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.
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.
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 subsection describes genomic regulatory elements related to the gene from GeneHancer. GeneHancer is a database of genome-wide enhancer-to-gene associations, embedded in GeneCards. Regulatory elements were mined from the following sources:
The enhancers table lists a set of enhancers associated with the gene. Gene-Enhancer likelihood-based scores were generated using information that helps link enhancers to genes:
Each enhancer has a confidence score which is computed based on a combination of evidence annotations: (1) Number of sources; (2) Source scores;
(3) Conserved regions (see paper); (4) TFBSs (from ENCODE). Enhancers supported by
two or more evidence sources were defined as elite enhancers and annotated accordingly with an asterisk.
For every enhancer, the following annotations are included: GH id (a unique, informative, and stable GeneHancer
enhancer identifier, provided by the
GeneLoc Algorithm), the sources with evidence for the enhancer, genomic size, enhancer confidence score, and a list of TFs (Transcription Factors)
having TFBSs (Transcription Factor Binding Sites) within the enhancer (based on ChIP-Seq evidence). The expanded
view also provides genomic location of the enhancer, and additional source-specific annotations such as identifiers,
genomic locations, enhancer type (proximal/distal), a list of biological samples with evidence for the enhancer,
eRNA expression strength (maximum pooled expression of eRNA CAGE tag clusters), tissue pattern, and tissue pattern
For every gene-enhancer association the following annotations are displayed: A general score for the gene-enhancer association (combined score based on all methods, associations supported by two or more methods were defined as elite and annotated accordingly with an asterisk), gene-enhancer distance (calculated between the enhancer midpoint and the gene TSS (transcription start site), positive for downstream and negative for upstream), number of genes having a TSS between the gene and the enhancer, and a list of other genes being associated with the enhancer. The expanded view also provides method-specific scores for the gene-enhancer association (p-values for eQTLs and co-expression, log(observed/expected) for C-HiC, and distance-inferred probability). A link to a UCSC GeneCards custom track presenting all enhancers within 100kb from the gene is located below the enhancers table.
The promoters table lists a set of promoters in the vicinity of the gene TSS (transcription start site). Promoters having absolute distance of ≤ 2kb from the TSS are included (distance was calculated between the element midpoint and the TSS, positive for downstream and negative for upstream). Additional annotations are shown for every promoter, including a link to the Ensembl regulatory element, promoter length and a list of TFs (Transcription Factors) having TFBSs (Transcription Factor Binding Sites) within the promoter (based on ChIP-Seq evidence). A link to a UCSC GeneCards custom track presenting all regulatory elements around a gene is located below the promoter table.
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 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.
This section provides annotated information about protein domains and families according to
and Suggested Antigen Peptide Sequences from GenScript.
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 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.
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 and MINT, 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 associated Unigene clusters and representative sequences, REFSEQ mRNAs, RNA secondary structures from fRNAdb, 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.
This subsection contains RNA secondary structures according to fRNAdb.
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
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 NURSA and SOURCE, and tissue specificity data from UniProtKB.
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||✓|
*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, 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 the
NCBI SNP Database and
descriptions from UniProtKB, Linkage Disequilibrium images from
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 Human Genome Variation Society's Locus Specific Mutation Databases (LSDB),
the BRCA Exchange.
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).
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, 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.
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.
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).