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Database update
GeneCards Version 3: the human gene
integrator
Marilyn Safran
1,2,
*, Irina Dalah
1
, Justin Alexander
1
, Naomi Rosen
1
,
Tsippi Iny Stein
1
, Michael Shmoish
1,3
, Noam Nativ
1
, Iris Bahir
1
, Tirza Doniger
1
,
Hagit Krug
1
, Alexandra Sirota-Madi
1,4
, Tsviya Olender
1
, Yaron Golan
5
, Gil Stelzer
1
,
Arye Harel
1
and Doron Lancet
1
1
Department of Molecular Genetics,
2
Department of Biological Services, Weizmann Institute of Science, Rehovot, Israel,
3
Bioinformatics Knowledge
Unit, Lorry I. Lokey Interdisciplinary Center for Life Sciences and Engineering, Technion - Israel Institute of Technology, Haifa, Israel,
4
The Sackler
School of Medicine, Tel Aviv University, Tel Aviv, Israel and
5
Xennex Inc, Cambridge, MA, USA
*Corresponding author: Tel: +972 8 934 3455; Fax: +972 8 934 4487. Email: marilyn.safran@weizmann.ac.il
Submitted 25 March 2010; Revised and Accepted 22 July 2010
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GeneCards (www.genecards.org) is a comprehensive, authoritative compendium of annotative information about human
genes, widely used for nearly 15 years. Its gene-centric content is automatically mined and integrated from over 80 digital
sources, resulting in a web-based deep-linked card for each of >73 000 human gene entries, encompassing the following
categories: protein coding, pseudogene, RNA gene, genetic locus, cluster and uncategorized. We now introduce GeneCards
Version 3, featuring a speedy and sophisticated search engine and a revamped, technologically enabling infrastructure,
catering to the expanding needs of biomedical researchers. A key focus is on gene-set analyses, which leverage GeneCards’
unique wealth of combinatorial annotations. These include the GeneALaCart batch query facility, which tabulates
user-selected annotations for multiple genes and GeneDecks, which identifies similar genes with shared annotations,
and finds set-shared annotations by descriptor enrichment analysis. Such set-centric features address a host of applications,
including microarray data analysis, cross-database annotation mapping and gene-disorder associations for drug targeting.
We highlight the new Version 3 database architecture, its multi-faceted search engine, and its semi-automated quality
assurance system. Data enhancements include an expanded visualization of gene expression patterns in normal and cancer
tissues, an integrated alternative splicing pattern display, and augmented multi-source SNPs and pathways sections.
GeneCards now provides direct links to gene-related research reagents such as antibodies, recombinant proteins, DNA
clones and inhibitory RNAs and features gene-related drugs and compounds lists. We also portray the GeneCards Inferred
Functionality Score annotation landscape tool for scoring a gene’s functional information status. Finally, we delineate
examples of applications and collaborations that have benefited from the GeneCards suite.
Database URL: www.genecards.org
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Introduction
With the recent accumulation of data from worldwide
genome projects, the individual scientist faces the time con-
suming and laborious task of sifting through the expanding
labyrinth of gene information. This can be partly alleviated
by the use of sophisticated integrated and searchable data-
bases. For many years, GeneCardsÕ(www.genecards.org)
(1–3) has provided such a remedy, with carefully selected,
comprehensive information about human genes, mined
and integrated from over 80 data sources. By bringing to-
gether gene information from large public sources such as
HGNC (4), NCBI (5), ENSEMBL (6) and UniProtKB (7), as well
as many other smaller resources (8), GeneCards has pro-
vided concise genome, proteome, transcriptome, disease
and function data on all known and predicted human
genes. It has successfully overcome barriers of data
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ßThe Author(s) 2010. Published by Oxford University Press.
This is Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecom-
mons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the
original work is properly cited. Page 1 of 16
(page number not for citation purposes)
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format heterogeneity using standard nomenclature, espe-
cially HUGO nomenclature committee approved gene sym-
bols (4). The information is organized in a ‘card’ format for
each gene, in distinct functional sections and including a
variety of features such as textual summaries and links to
other genome-wide and specialized databases. GeneCards
has evolved significantly since initially described (1,9,10),
and its progress is documented in a number of past publi-
cations (2,3,11–15). In this article, we introduce the new
GeneCards Version 3 (V3) and describe its features in
detail. We place special emphasis on the novel set-centric
capabilities (beyond and in conjunction with the new
GeneCards search engine), which address a variety of appli-
cations, including microarray data analysis, cross-database
annotation mapping and gene-disorder associations for
drug targeting.
Readers who are new to GeneCards might want to read
the Applications section below first, familiarize themselves
with previous articles (1–3), and then read the rest of this
article, possibly skipping the ‘Methods’ section.
GeneCards version 3
The new home page
The new GeneCards V3 home page, shown in Figure 1,
hosts the new search facility, provides links to a sample
gene and its various sections on the card via labeled oval
buttons, and enables one to view a variety of differently
categorized and annotated genes, from pre-defined links
as well as by interacting with a random-gene generator,
customizable by category and/or GeneCards Inferred
Functionality Score (GIFtS). The GIFtS algorithm (11) uses
the wealth of GeneCards annotations to produce annota-
tion scores aimed at predicting the degree of a gene’s func-
tionality. Since the degree of known functionality is
correlated with the amount of research done on a particu-
lar gene or its product, these annotation scores are pre-
sented as inferred functionality measures. The extended
GIFtS tool, linked to from the home page, facilitates brows-
ing the human genome by searching for the annotation
level of a specified gene, retrieving a list of genes within
a specified range of GIFtS values, obtaining random genes
with a specific GIFtS value, and experimenting with the
GIFtS weighting algorithm for a variety of annotation cate-
gories. The left hand side of the home page retains the
logos and links to the GeneCards suites sites—GeneDecks,
GeneALaCart, GeneLoc, GeneNote, GeneAnnot and
GeneTide.
The new search engine
The new version 3 search engine is extremely fast, and is
capable of matching complex field-specific queries of the
entire database in milliseconds. For example, a search for a
Figure 1. GeneCards Version 3 home page, including search, sample gene, logos and links to the other suite sites, and category/
GIFtS-based random gene generator.
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very common keyword like ‘cancer’ returns 8000 results in
3 ms. In contrast, V2 could not handle such a query, or even
a more focused one such as ‘melanoma’ (‘too many results
to be efficiently displayed’); a considerably more restricted
search in V2 such as ‘schizophrenia’ yielded 1100 results
and took 80 s. Efficient V3 performance is achieved by
breaking the search process into distinct phases, and also
by returning results in limited pages of data. The two pri-
mary stages of each search are: (i) to first quickly identify
the list of genes that have information matching the search
term, and (ii) upon demand, discover the detailed relevant
context and annotation details of those hits, and highlight
them in ‘minicards’ (Figure 2). The ‘Methods’ section details
the design of the new search engine.
The upgraded GeneCards webcard
The ‘card’ presented for a GeneCards gene has grown con-
siderably since last described in the literature (1–3). The
colored ovals in Figure 1 depict the various sections (aliases,
summaries, location, proteins, domains, function, path-
ways, drugs, transcripts, expression, orthologs, paralogs,
SNPs, disorders, articles, databases, technologies and prod-
ucts) wherein relevant data sources are excerpted from
and/or deep-linked to in each GeneCards gene. We now
highlight some of the updated sections’ interesting content
and algorithms.
Header. The header at the top of each GeneCard
provides the gene’s symbol, category, GIFtS (11) and
GeneCards identifier (GCid) (12). Gene categories of protein
coding, pseudogene, RNA gene, cluster, genetic locus and
uncategorized, are color coded, with the gene’s symbol
painted accordingly. The background color of the header’s
box is indicative of which database the symbol is from
[HGNC (4), Entrez Gene (5) or Ensembl (6)]. The header
also contains a short description of the gene, and spells
out whether or not the gene symbol is HGNC approved.
GCids, provided by the GeneLoc algorithm (12) are
unique, informative and trackable. The id begins with GC,
which is followed by the chromosome number, ‘P’ or ‘M’ for
orientation (Plus or Minus strand), and approximate start
coordinate in kilobases if relevant. When a location is not
possible to determine, a sequential number is used in that
part of the GCid. If more than one gene falls on the same
kilobase, the closest free identifier is chosen. For example,
GC09P139152, the GCid for GRIN1, is on chromosome 9 on
the plus strand, starting at 139 152 kb. While GCids may
change from version to version to reflect the reality of new
Figure 2. GeneCards Version 3 search results, including detailed minicard expansions which highlight where in the ‘card’ the
hits occur.
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genome builds, once a GCid is assigned to a gene, the as-
sociation remains (as a ‘previous GCid’), and it cannot be
reassigned to another gene. Figure 3 shows examples of a
few GeneCards genes, comprising a variety of categories
and source databases, along with their GCids and GIFtS,
as well as statistics (available at the bottom of the
GeneCards home page) about the number of genes per
category, with examples from each one.
Proteins
This section provides annotated information of the proteins
encoded by GeneCards genes according to UniProtKB (7),
and/or Ensembl (6), the capability to view phosphorylation
sites using Phosphosite (16), reference sequences (RefSeq)
according to NCBI (17), cellular component ontologies
visualized by the Gene Ontology (GO) Consortium (18),
and links for ordering antibodies, recombinant proteins
and assays from numerous sources. Direct links to 3D visu-
alization of PDB structures are provided by the OCA brows-
er (19) and Proteopedia (20).
Gene function
This section provides annotated information about gene
function from UniProtKB (7) and Genatlas (21), animal
model information from MGI (22), RNAi, primers and
clones products from vendors, as well as molecular function
ontologies visualized by the GO Consortium (18). While the
‘Orthologies’ section in GeneCards presents a table of
orthologs from HomoloGene, SGD, euGenes and MGI,
showing symbol, locus, description, similarity to human
and NCBI accessions, the Gene Function section presents
animal model information from MGI, including mutant
phenotypes for mouse orthologs, and a popup table with
information on their alleles including: (i) allele name—the
official symbol for the allele with link to the MGI record,
(ii) the MGI identifier of the allele, (iii) type of allele by
mode of origin and (iv) phenotypic details for all genotypes
that include at least one of the alleles. We believe that the
use of mouse phenotypes to depict human gene function is
unique to GeneCards.
Pathways and interactions
This section provides links to—according to information ex-
tracted from Invitrogen (23), Millipore (24), Sigma-Aldrich
(25), Applied Biosystems (26), Cell Signaling Technology
(CST) (27) and the Kyoto Encyclopedia of Genes and
Genomes (KEGG) (28). For each of the pathway sources,
one can also view other genes that participate in these
pathways via the GeneDecks (15) Partner Hunter link.
Next, a link to the relevant SABiosciences (29) interacting
genes and proteins network is provided. Finally, interacting
proteins are presented in a table which merges protein–
protein interaction data from UniprotKB (7), EBI-IntAct
(30), String (31) and MINT (32) including links to the
GeneCards gene and the UniProtKB (7) and/or Ensembl
(6) protein entry for the interacting protein, as well as
Figure 3. Assorted GeneCards genes, of different color-coded categories, source databases, GC identifiers and GIFtS, with
associated statistics and examples.
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detailed annotation about the interactions, including all
supporting experiments and/or confidence scores about
predicted interactions. Finally, biological process ontologies
visualized by the GO Consortium (GO)(18) are presented.
Drugs and compounds
This section provides relationships between GeneCards
genes and chemical compounds and drugs, in a similar
manner as described below for disorders for the
NovoSeek (33) and PharmGKB (34) sources. It also juxta-
poses compound names, actions and chemical abstract
numbers provided by commercial sources, with links for
ordering products. We have found that the richness of
the integrated descriptor set of drugs and compounds has
enabled unique results for our GeneDecks partner hunting
and set distillation subsystems (described below) as com-
pared with comparable gene-set analysis tools (15).
Transcripts
Figure 4 presents the ‘Alternative Splicing’ subsection, with
alternative splicing information and isoforms from ASD
(35). Exons with alternative splice sites in different isoforms
were broken into Exonic Units (coined 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 mou-
seovers on the light blue squares show its transcript coord-
inates. When showing ASD’s splice variants, GeneCards
subtracts the 3000-bp flank that ASD adds to the transcript
coordinates. The section also displays multiple transcripts
from RefSeq and Ensembl.
Gene expression
Figure 5 depicts the enhanced GeneCards experimental
tissue vectors. The same set of non-fetal normal and
cancer human tissues are also analyzed and presented in
upgraded electronic northern and Serial Analysis of Gene
Expression (SAGE) graphs (3). For the experimental data,
duplicate measurements were obtained for 12 normal
human tissues (out of 28 tissues shown) hybridized against
Affymetrix GeneChips HG-U95A-E (GeneNote data) and for
22 normal human tissues hybridized against HG-U133A
(GNF data (36)). The intensity values (shown on the y-axis)
were first averaged between duplicates; then, probe-set
values were averaged per gene, global median-normalized
and scaled to have the same median of about 70 (half-way
between GeneNote and GNF medians). GNF HG-U133A
expression data for 18 NCI60 cancer cell lines was processed
and added to the display (a single measurement taken;
normalized according to the GNF normal data). The corres-
pondence between cell lines and tissues is given in a table
(37). Note that the diamonds along the x-axis of each
graph hint that the tissue (cell line) expression values
are available for a given gene, while empty ‘diamonds‘
denote that either there is no such tissue for a specific
microarray, SAGE or electronic northern platform or
that the current gene has no matching probe sets
(or tags/ESTs for SAGE/electronic northern). If there is a
filled diamond along the x-axis but no data shown in the
graph, it indicates that after threshholding and normal-
ization there is no meaningful expression data for that
tissue. Normalized intensities are drawn on a root scale
(3), which is an intermediate between log and linear
scales. The Affymetrix MAS5 algorithm was used for array
processing.
Figure 4. Alternative splicing diagram in the Transcripts section. Exons with alternative splice sites in different isoforms
are broken into Exonic Units (ExUns). The symbol ‘^’ between ExUns indicates an intron, while ‘’ indicates the junction of
two ExUns.
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SNPs
This section (Figure 6) integrates SNPs/variants data from
the NCBI SNP Database (38), Ensembl (6) and Pupasuite
(39), and adds descriptions from UniProtKB (7) and linkage
disequilibrium images from HapMap (40). 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 sorted first (shown in the select box as 1st)
by validation status (‘validated’ before ‘non-validated’),
then, within these groups, by ordered location type (first
‘coding non-synonymous’, then ‘coding synonymous’, fol-
lowed by ‘coding’, ‘splice site’, ‘mRNA-UTR’, ‘intron’,
‘locus’, ‘reference’ and/or ‘exception’), as the secondary
Figure 6. Snapshot of the SNPs section, highlighting the variety of annotation fields, availability of popups for more detailed
information, sort options.
Figure 5. Enhanced experimental tissue vectors, now including our GeneNote data integrated with normal and cancer data from
the Genomics Institute of the Novartis Research Foundation (GNF).
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(2nd) nested criterion, and finally, by the number of valid-
ations (up to 4). The user can change this default sort order
and define up to three hierarchical sorting priorities from
fields available as select boxes above the relevant columns
on the section’s button line as follows: rs-numbers (sorted
in ascending order), validation status, position on the
chromosome (ascending order), location type, allele fre-
quencies (existing info before non-existing), population
types (alphabetical order) and total sample size (largest to
smallest). Each displayed line includes genomic, expression
and allele frequency data sections. Only the summary is
shown for the expression and allele frequency sections,
with a link to the detailed information (via a magnifying
glass icon).
Disorders and mutations
This section contains disorders and mutations in which
GeneCards genes are involved, according to a variety of
sources including OMIM (41), UniProtKB (7), NovoSeek
(33), PharmGKB (34), Genatlas (21), GeneTests (42), HGMD
(43) and GAD (44). Included are two disease relationships
table: The Novoseek table includes: (i) Disease—the name
of the disease related to this GeneCards gene. (ii) Score—
the Novoseek score of the relevance of the disease to this
gene, based on their literature text-mining algorithms.
(iii) Articles—the number of articles in which both the
gene’s symbol or description and the disease appear. (iv)
PubMed IDs for Articles with Shared Sentences (# sen-
tences) - 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. Similarly, the
PharmGKB table includes: (i) disease—the name of the dis-
ease related to this GeneCards gene, (ii) the PharmGKB de-
scription of the relationship between the gene and the
disease, one of the following types: CO, clinical outcome;
PD, pharmacodynamics and drug response; PK, pharmaco-
kinetics; FA, molecular and cellular functional assays; or GN,
genotype and (iii) PubMed IDs for articles supporting these
relationships since both the gene symbol and the disease
are discussed.
Research reagents
Distributed among the various sections, and highlighted in
the Services section, GeneCards provides directly targeted
deep links to cutting edge research reagents and tools such
as antibodies, recombinant proteins, primers, clones, ex-
pression assays and RNAi reagents. Adding this functional-
ity has proven to be a win–win strategy for the product
providers, for the GeneCards project and for researchers;
the links have been received very favorably by our users.
GeneCards suite
Gene set analyses via GeneDecks
GeneDecks exploits GeneCards’ unique wealth of combina-
torial annotations to identify similar (partner) genes, and to
perform quantitative descriptor enrichment analyses for
identifying set-shared annotations. Some of these capabil-
ities have been implemented on the GeneCards web site,
some as independent research studies, and others are in
preparation for upcoming releases:
Annotation combinatorics. Given a particular
GeneCards gene, one can ‘GeneDecks‘ it with respect to a
selected combinatorial annotation in order to obtain a set
of similar genes. The resulting GeneDecks summary table
ranks the degree of similarity between the identified genes
and the probe gene, taking into consideration all shared
combinations of annotations. Thus, if a particular probe
gene has N annotations in a given category (e.g. is involved
in N pathways or has N domains), GeneDecks separately
depicts sets of genes associated with any combination of
M of the annotations, 1 MN, in descending order of M,
thereby highlighting a rank order of similarity.This feature
is available on the V2 web-site and within V3’s Partner
Hunter’s algorithm.
Annotation unification. GeneCards is replete with an-
notations from different sources, often with heteroge-
neous naming conventions. For example, there are several
independent systems for pathway definition, each having
its own nomenclature and relevant sets of genes, and cur-
rently addressed separately when GeneDecksing from the
V2 webcard. Figure 7 describes a pilot study that uses simi-
larity measures within GeneDecks gene sets to see if and
how the nomenclatures and entities of KEGG (28),
Invitrogen (23) and CST (27) independent systems of path-
way annotations could be unified. We found that some of
the pathways (named identically or differently) had consid-
erable overlap in their gene-set composition, but none
were complete, that some of the pathways were closely
related, and that a few inter and intra-source subsets
could be identified. Annotation unification of this sort,
based on the similarity in GeneCards gene-content space
detection algorithms, could be expanded to include
other [e.g. our Millipore (24) and Sigma-Aldrich (25)] path-
ways, and perhaps also be generalized to include other at-
tributes such as chemical compounds, phenotypes and/or
orthologies.
Partner hunting. GeneDecks’s Partner Hunter (15) seeks
similar genes based on combinatorial similarity of weighted
attributes. It currently addresses gene sets using informa-
tion for pathways, protein domains, GO terms, mouse
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phenotype, mRNA expression patterns, disorders, drug
relationships and (sequence-based) paralogs.
Set distillation. Set distiller ranks attributes by their
degree of sharing within a given gene set. Like Partner
Hunter, it enables sophisticated investigation of a variety
of gene sets, of diverse origins, for discovering and eluci-
dating relevant biological patterns, thus enhancing system-
atic genomics and systems biology scrutiny. Both
subsystems have been used in the study of synthetic lethal-
ity [see ‘Applications, advantages and future directions’
section below and (15)].
Batch queries via GeneALaCart
GeneALaCart provides batch query support, whereby the
user submits a gene list (e.g. from a microarray experiment
or from search query results), along with the desired
GeneCards annotation fields, and receives tabulated
output which can be visually examined or serve as input
to automated scripts for more sophisticated analyses.
Figure 8 is an example of a GeneALaCart results file (45),
showing a few of the 50 available annotation categories
for a small set of genes. The ‘Applications, advantages and
future directions’ section below details a variety of ex-
amples of research facilitated by GeneALaCart.
Methods
GeneCards system architecture
Figure 9 depicts the architecture of the offline and online
components that comprise the GeneCards system. This is
described in some detail in the subsections below.
Data collection and integration
The data collection process is a pipeline that starts with
defining the full set of GeneCards genes, obtained from
three primary sources as follows. First, the complete current
snapshot of HGNC-approved symbols (4) is used as the core
gene list. Next, human Entrez Gene (5) entries that are dif-
ferent from the HGNC genes are added. Finally, human
Ensembl (6) records are matched against the emerging
gene list via our GeneLoc’s exon-based unification algo-
rithm (12); those that are not found to be equivalent to
others in the set are included as novel Ensembl-based
GeneCards gene entries. These primary sources provide an-
notations for aliases, descriptions, previous symbols, gene
category, location, summaries, paralogs and ncRNA details.
Once the gene list is in place with these significant annota-
tions, over 80 data sources, including those noted above
and others (12,18,22,36,46,47) are mined for thousands of
additional descriptors.
Figure 7. GeneDecks pathway annotation unification study, aimed at matching differently-named pathways based on overlaps in
associated gene-set space.
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The data collection and integration process, which runs
periodically (typically every 3–5 months) to ensure ongoing
access to recent updates, culminates in producing an inte-
grated database, which is available in plain text and XML
files, as well as MySQL dumps.
The version 3 database
The GeneCards data model is complex. In legacy GeneCards
Versions 2.xx, information is stored in flat files, one file per
gene. Version 3 uses a persistent object/relational ap-
proach, attempting to model all of the data entities and
Figure 9. GeneCards architecture and data flow, including offline data collection/ integration and quality assurance processes,
relational database, sophisticated search engine and set-centric GeneALaCart and GeneDecks subsystems.
Figure 8. Small sample of GeneALaCart output to a batch query. The data can be examined in Excel or serve as input to
application-specific computer analysis programs.
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relationships in an efficient manner. This allows diverse
functions of displaying single genes, extracting attribute
slices and performing complex queries for sets of genes,
and performing well on both full text and field-specific
searches. Since the information is collected by interrogating
dozens of sources, it is initially organized on a source-
by-source basis. However, the relational database structure
also makes it possible to present the data to the users orga-
nized by topics of interest, e.g. with all diseases grouped
together, irrespective of the mined source. The V3 database
(Figure 10) consists of 84 entities and 28 relationships.
These database objects are implemented as 112 tables
and two views (data and system), interlinked by 87 foreign
keys. The central entity is the ‘genes’ entity, with attributes
that include symbol, GeneCards identifier and origin
(HGNC, Entrez Gene or ENSEMBL). The data model parallels
that of the web-card, with some of the intricate sections
(e.g. gene function) represented by several tables. An
object-oriented interface to the data is facilitated by
Propel (48), an open-source Object-Relational Mapping
(ORM) framework for the PHP programming language.
Our V2 to V3 migration path uses the project’s XML
files, already organized in both data source and functional
presentations (2) as the source for populating the rela-
tional database. The complete schema is available upon
request.
The new search engine
As described earlier, the new search engine is very fast.
Much of the speed stems from the selection of the
Lucene indexing technology (49) used also by sites like
Wikipedia (50) and Microsoft’s Bing (51). We have chosen
the Solr (52) server, which combines the Lucene library with
XML, HTTP, hit highlighting and faceted navigation [a
mechanism that enables a user to browse information
along multiple paths (53)], enabling support for both field
specific and full text searches, and having maturity, robust-
ness and open-source availability. One shortcoming is that
Lucene does not store information hierarchically, whereas
GeneCards gene-specific data is by nature hierarchical. For
example, the ‘Proteins’ section of the card contains infor-
mation from UniProtKB/Swiss-Prot which in turn contains
Figure 10. Sample of GeneCards Version 3 (revision 3.02) gene-centric relational database entities and their relationships, with
associated web-card sections.
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accession numbers, links to PDB structures, subunit infor-
mation and so on. To produce optimal search results,
both accurate and context-specific, the engine must iden-
tify the specific subsections of matched information in the
relevant genes. However, all current, mature, search en-
gines work at a higher level when identifying positive re-
sults; the only information that is returned by the search
engine is the identity of the ‘document’ (e.g. specific gene
in our case) that was found. Solr does offer the ability to
return more detailed data, including a portion of words
surrounding the hit, but in a flat form and without the
hierarchical structure needed to enable identification of
the specific subsection of the card that contains the query
string. Often the same text phrase is found in dozens of
places, making it impossible, in most cases, to identify
which specific card sections or subsections (e.g. disorders
and/or literature) define the complete hierarchical context
of the hit. Our solution is to provide two-phased searches,
enabled by two indexes, the primary index and the second-
ary index. The primary index is populated by an automat-
ically generated flattened version of complete GeneCards
textual information; the secondary index is populated with
each individual sub-element as its own ‘document’, anno-
tated with its associated genes. A typical work flow, say for
a keyword search for cancer, is as follows: (i) Query the
primary index to look for all gene records that contain
‘cancer’—to which the system quickly returns a list of
8000 genes, including MSH2. (ii) When the user requests
to open the minicard for that gene, query the secondary
index for a list of detailed database records, mapped to the
relevant subsections of the card, which are associated with
the gene MSH2 and contain the keyword cancer, and high-
light the hits coherently and within context, in the mini-
card. Since this step is done upon demand, for a limited
subset of the genes, valuable time is saved during the initial
quest for matched genes. (iii) When the user requests to
view the complete GeneCard for MSH2 by clicking on that
symbol from within the search results, highlight the search
term cancer in all places that it occurs in the card.
From an implementation standpoint, the GeneCards
database relationships sometimes involve five or more
joins, and there are several thousand relationship vari-
ations among the approximately 100 different tables.
Consequently, preprocessing the data by maintaining opti-
mized sets of typical queries is not feasible. The efficient
indexes described earlier are built by a recursive crawler,
which iterates over the relational table data structures asso-
ciated with each gene, and discovers associated annota-
tions. That data is categorized for faceted searching, and
then transformed into valid virtual documents for the rele-
vant index(es). When the database schema changes, say to
accommodate new GeneCards data sources, the table-
driven crawler code does not need to be modified. A chal-
lenge that was overcome was to ensure that the crawler
would not enter infinite loops; this was achieved by care-
fully defining the terminal nodes in the network-like data-
base schema. A major advantage of the crawler is its ability
to create custom ‘perspectives’ automatically. GeneCards
has traditionally been gene-centric in its organization of
information. In V2, this was reflected in the underlying
one gene per file technology. For the relational V3, other
views of the data are naturally available. Without major
changes to its architecture, the crawler is capable of
re-rendering the nature of the GeneCards search to
return hits that are not genes. One could then, for example,
search for keywords (e.g. muscle) and receive hits present-
ing lists of associated disorders instead of (or in addition to)
lists of associated genes. This will, one day, allow
GeneCards to create new ways of presenting its rich data,
without the need for major search-engine rewrites.
Version 2 infrastructure versus Version 3 infrastructure
V2 used the text cards as input for the web cards, for the
GeneALaCart batch query system and for the Glimpse index
(54) that served its searches. Version 3 uses its MySQL (55)
database as input to its novel searchable word-set collec-
tion process, which provides input to a Lucene (49)-based
search engine, and to drive the V3 GeneDecks partner
hunting and set distillation tools. (56). V3 web-cards are
currently implemented as a hybrid system, with the con-
tents and user interface based on V2, but with the addition
of a search bar that enables powerful V3 searches from
each card.
Quality assurance and statistics via GeneQArds
GeneQArds is a resident quality assurance (QA) tool,
enhanced in V3 to: (i) assess the integrity of the migration
from the V2 text file system into the V3 MySQL database,
and (ii) validate and quantify the results of the new V3
search engine. The GeneCards data transformation be-
tween versions is a multi-step pipeline which includes creat-
ing intermediate XML files as well as populating the large
set of tables (Figure 11). To ensure exactitude, we have
developed a mechanism, based on SQL queries and PHP
modules, which builds a binary matrix indicating the pres-
ence or absence of source data for all gene entries. This
matrix also serves as the foundation of the GeneCards
site’s statistical graphs and its GIFtS annotation scores.
This binary matrix is compared to its counterpart for the
V2 text files, built with a set of V2 Perl quality assurance
programs. The produced report provides a good initial as-
sessment of the integrity of the database, and also points
to the possible sources of errors. For example, paucity of V3
genes with annotation derived from a given source indi-
cates a data mining setback, and provides a clue regarding
the cause of error. This first-tier comparison alone is not
sufficient, since the binary matrices do not contain details
about each of the source-specific annotation fields
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(e.g. protein name, subunit and PDB identifiers within the
Swiss-Prot annotation source). Therefore, a set of very
detailed SQL queries are also run against the V3 database,
with results compared against similar checks of the text
cards, as well as previous version loads of the database.
The search engine comparison tool enables version com-
parisons both by single query (via a web interface) and
batch query (via command line) for all types of searches
(keywords, symbols only, symbol/alias and mixed external
identifiers). The results are summarized in a report, which
includes the amount of time each search took, lists of dis-
tinct genes found by one of the search engines but not the
other, and a list of genes found in both versions. To enable
tracking the discrepancies, the report also notes the context
for each of the hits (e.g. the keyword ‘cancer’ was found in
gene TP53 in the proteins, disorders and summary sections)
and provides a deep link for further scrutiny. The search
engine comparison tool uses internal persistent MySQL
tables which contain: (i) all single queries invoked by testers
in GeneQArds, (ii) the 500 most frequent queries against
the live GeneCards site, (iii) all queries that previously
involved errors and (iv) the results of all comparisons.
These tables help extend the power of GeneQArds,
affording accurate multifaceted QA performance. One
GeneQards output is a distribution of gene hits differences
between two versions (Figure 12), allowing the tester to
assess improvements or degradations. We have
analyzed the trends of the results, followed by detailed
inspection of 10% of the isolated anomalies. The fact
that GeneQArds combines both white box (by taking
advantage of knowing the internals of the system, e.g. by
interrogating specific database tables) as well as black
box (non-biased/external, e.g. by measuring hit counts)
testing, will enable us to zero in on the remaining deficien-
cies. Many of the GeneQArds tools are available upon
request.
Supporting Software
GeneCards Version 2.xx is implemented in Perl, with index-
ing provided by the University of Arizona’s Glimpse soft-
ware (54). GeneCards Version 3 uses XML, MySQL (with
default fast MyISAM tables), and PHP, together with the
Propel (48) Object-Relational Mapping (ORM) framework
for PHP. The latter provides foreign key metadata in its
configuration information, to compensate for MyISAM’s
lack of support for foreign key constraints. V3 uses
Smarty templates, and the Lucene (49) search engine pow-
ered by Solr (52). GeneDecks’s Partner Hunter and Set
Distiller server is written in Java. GeneQArds for V3 is
implemented in PHP and is fully integrated with the V3
MySQL database. The other components of the
GeneCards suite are written in Perl and MySQL.
Applications, advantages and
future directions
GeneCards and its suite of tools have been instrumental in
several recent collaborative projects with a biomedical
end-point. GeneCards’ contribution is detailed herein:
SYNLET—Regulatory control networks of synthetic
lethality.
This EU-funded project (http://synlet.izbi.uni-leipzig.de/)
addresses robustness of phenotypic function on the basis
of Synthetic Lethality—as proposed for novel cancer treat-
ment regimes. It derives novel concepts, methodologies
and algorithms for annotation and analysis of regulatory
networks, with focus on tumorigenesis and drug resistance.
It aims at identifying key proteins of cellular escape mech-
anisms that overcome lethality of drugs and find ways to
block them, utilizing siRNA. GeneDecks was one of the
main tools which served the consortium to select candi-
dates for the SiRNA experiments. Specifically, in the
Figure 11. The V2 and V3 database collection/integration pipeline and search application flow.
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Partner Hunter mode, Synlet searched for partners for
genes which underwent significant expression diminution
in microarray experiments done on resistant and
non-resistant neuroblastoma cell lines, seeking inactivation
targets among partners to which the tissue has been
‘addicted‘. The Set Distiller mode of GeneDecks enables
the identification of annotation descriptors shared by
experimentally-obtained gene sets, allowing the assess-
ment of their belonging to specific functional classes,
hence a judicious selection of inactivation targets.
SysKid—Systems biology towards novel chronic
kidney disease diagnosis and treatment
This EU-funded project focuses on chronic kidney disease,
with diabetes mellitus and hypertension being the most
prevalent causative conditions. It assumes that despite it
being diverse in etiology, the underlying molecular patho-
physiology of different manifestations of this condition
may be similar. The project aims at obtaining an integrated
view on the disease in the realm of systems biology, based
on integrated analyses of high-throughput OMICS data.
The GeneCards V3 database and advanced search engine
are instrumental for the integration process and in identify-
ing the most promising disease markers. As an example,
GeneCards will be used to identify relevant genes for meta-
bolomics pointers. In this framework, a ‘Genecards 100k’
effort is currently under way, aiming at increasing the
number of Gene entries towards 100 000. This will be
accomplished mainly through a significant expansion of
GeneCards’ scope in the realm of non-protein-coding RNA
genes, and resolving a large number of genes currently
entitled ‘uncategorized’. In parallel, a project-specific data-
base (GeneKid) will be created, to house incoming OMICs
data in GeneCards-compatible tables, thus facilitating the
systems biology analyses.
Research facilitated by GeneALaCart
GeneALaCart has contributed to numerous collaborative
efforts, and, based on user feedback, has been helpful to
hundreds of research groups. A point of strength is its cap-
acity to do cross-database identifier mappings of genes and
proteins, using the mixed identifier feature. GeneALaCart
provides systematic and detailed annotation of gene lists,
obtained, for example, from differential expression,
transcriptional regulation, siRNA screens or genome-wide
genetic association studies. Some users seek specific
information for their gene lists, such as the elucidation of
their potential drug targets, their orthologs in other spe-
cies, or their annotated SNP lists. Many of the specific uses
have clinical implications, for example assistance in the
choice of SNPs relevant to complex diseases studies, inte-
gration of phenotype and genotype information in clinical
patient information systems, or deciphering genes impli-
cated in clinical studies, including brain disorders or
immunity.
Research facilitated by GeneAnnot
An example of where GeneCards gene expression and an-
notation has been applied (at the University of Modena,
Italy) is the development of a novel set of custom Chip
Definition Files (CDF) and corresponding Bioconductor
libraries for Affymetrix human GeneChips, based on
information supplied by GeneAnnot (57).
Advantages of GeneCards V3 search results
We sampled fifteen single word queries, most extracted
from our list of popular GeneCards search terms, and com-
pared the number of hits found by GeneCards to those
found for human genes by similar systems [NCBI Entrez
Gene(5), Ensembl(6) and Harvester(58)]. GeneCards and
Entrez Gene offer users the option to easily download
the complete result set (in addition to the default paged
Figure 12. GeneQArds statistical comparison of GeneCards V2 and V3 search results, for each of the 500 most frequently
searched terms in 2008, showing vast improvements for V3. The cases where V2 finds more hits reflect many V2 false positives,
some V2 fields that haven’t as yet been incorporated into the V3 database, and some isolated anomalies that are still under
investigation.
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behavior that all share). The average response times in se-
conds (SD) were: GeneCards 5.5 (0.63), Entrez Gene 2.9
(0.59), Ensembl 1.9 (0.59), and Harvester 2.5 (0.51),
with the GeneCards results offering detailed ‘minicards‘.
In a majority of the cases, GeneCards finds more hits, due
to the unique richness of its contributing data sources. For
example, the search for EGFR finds: (i) a unique hit for the
gene AGK because its functional information from
Swiss-Prot (7) has the phrase ‘Overexpression increases the
formation and secretion of LPA, resulting in transactivation
of ‘EGFR’ and activation of the downstream MAPK signal-
ing pathway, leading to increased cell growth’ and (ii) a hit
for the gene A2 M because STRING (31) identifies a protein–
protein interaction between the two genes. Similarly, the
search for retinoblastoma in GeneCards finds a unique hit
for AANAT due to its association with this disorder via
Novoseek (33). Table 1 summarizes the benchmark’s details,
and Figure 13 presents qualitative differences for these two
specific examples. While assessing the quality of the extra
hits (see Quality assurance and statistics via GeneQArds, in
‘Methods’ section), in addition to the largely positive results
as demonstrated by these examples (right tail of the distri-
bution, Figure 12), we did find that the search engine’s
stemming is at times over-zealous. For example, when
searching for ‘batten disease’ (without quotes), a false hit
is found for the gene IL6, since one of its publications au-
thors is named Battenfeld. We will delve into Solr’s stem-
ming rules, improve our engine to not apply stemming to
author lists, and continue to probe the QA results in depth
as we enhance GeneCards in future versions.
Future directions
In addition to specific enhancements and improvements
directed by the above projects, we will continue to
enhance GeneCards core features. An intriguing future
challenge is to devise an algorithm for unifying disease
Figure 13. (A) Comparison of the number of hits (genes) found by GeneCards and Entrez Gene for two popular searches
(EGFR and Retinoblastoma) and (B) the distribution of those hits within the various sections in GeneCards.
Table 1. Search benchmark: comparison of the total number
of hits found when searching for 15 popular search terms
(selected from the list of top 100 terms queried in
GeneCards during 2008 and 2009) in GeneCards, Entrez
Gene, Ensembl and Harvester
Search term Total number of hits (genes)
GeneCards V3 Entrez Gene
a
Ensembl Harvester
Asthma 861 426 35 42
Estrogen 1912 572 152 383
Hippocampus
b
726 153 30 1197
Olfactory
b
1449 2755 1339 828
Retinoblastoma 643 209 38 29
Tongue 348 75 12 80
VIMENTIN 243 74 4 48
BRCA1 651 312 23 143
CDKN1A 79 104 11 75
CFTR 264 145 30 54
EGFR 870 495 7 43
GAPDH 78 164 55 34
MTHFR 116 56 2 11
TP53 884 419 26 401
VEGF 1012 512 9 61
a
Includes entries discontinued by NCBI—see examples in Figure 13.
b
Search terms which are not from the list of top 100 searches.
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Database update Database, Vol. 2010, Article ID baq020, doi:10.1093/database/baq020
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names/descriptions and enable these tables to be merged.
We’ would like to increase our pathway repertoire, and are
considering adding public domain (e.g. Reactome) and/or
additional commercial pathways. We hope to expand the
‘Function’ section to include animal models from species
other than mouse.
For GeneLoc, we are considering incorporating UCSC
and/or CCDS identifiers. We plan to migrate out of the cur-
rently implemented hybrid web-card system, with contents
and user interface still based on V2, to web-cards that fully
use the relational database and PHP/Propel infrastructure.
In parallel, we will continue to expand and improve the
GeneDecks algorithms.
Summary
GeneCards has evolved tremendously over the years, pro-
gressing from being an effective ‘one-stop shop‘ source of
information for scientists’ particular human genes of inter-
est, to becoming a facilitator for sophisticated systems-
biology efforts. We envision that its updated functionality
and new infrastructure will continue to provide an effective
research and development platform for many years to
come, and look forward to pursuing more adventures.
Acknowledgements
The authors thank the reviewers for crucial insights and
suggestions that have helped improve this manuscript,
Elena Matusevich and Yakov Perlman for their initial imple-
mentation of GeneCards Version 3, David Warshawsky for
providing the model and data sources for highly-targeted
reagents, Edna Ben-Asher and Orit Shmueli for defining the
initial SNP filtering algorithms, Ido Zak for improving its
implementation, Ohad Greenspan for implementing the al-
ternative splicing diagram, and Liora Strichman-Almashanu
for her mouse phenotype initiative, for pioneering
GeneQArds, and for initial V3 data modeling work.
Funding
The Weizmann Institute of Science Crown Human Genome
Center and the Phyllis and Joseph Gurwin Fund for
Scientific Advancement; EU Specific Targeted Research
Project consortium ‘Regulatory Control Networks
Synthetic Lethality’ (SYNLET—EU FP6 project number
043312); EU Systems Biology towards Novel Chronic
Kidney Disease Diagnosis and Treatment Project consor-
tium (SysKid—EU FP7 project number 241544); Xennex,
Inc., Cambridge MA. Funding for open access charge: The
Weizmann Institute of Science Crown Human Genome
Center.
Conflict of interest: None declared.
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