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Database Commons - GeneCards

GeneCards

Citations: 900

z-index 18.45

Short name GeneCards
Full name GeneCards
Description GeneCards is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes. It automatically integrates gene-centric data from ~125 web sources, including genomic, transcriptomic, proteomic, genetic, clinical and functional information.
URL http://www.genecards.org/
Year founded 1998
Last update & version 2017-03-06    V 4.4.0
Availability Free to all users
University/Institution hosted Weizmann Institute of Science
Address Department of Molecular Genetics
City Rehovot
Province/State
Country/Region Israel
Contact name Simon Fishilevich
Contact email simon.fishilevich@weizmann.ac.il
Data type(s)
Major organism(s)
Keyword(s)
  • human gene
Publication(s)
  • Genic insights from integrated human proteomics in GeneCards. [PMID: 27048349]

    Simon Fishilevich, Shahar Zimmerman, Asher Kohn, Tsippi Iny Stein, Tsviya Olender, Eugene Kolker, Marilyn Safran, Doron Lancet
    Database : the journal of biological databases and curation 2016:2016
    9 Citations (Google Scholar as of 2017-03-27)

    Abstract: GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite's next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein-RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome. Database URL:http://www.genecards.org/. © The Author(s) 2016. Published by Oxford University Press.

  • GeneCards Version 3: the human gene integrator. [PMID: 20689021]

    Marilyn Safran, Irina Dalah, Justin Alexander, Naomi Rosen, Tsippi Iny Stein, Michael Shmoish, Noam Nativ, Iris Bahir, Tirza Doniger, Hagit Krug, Alexandra Sirota-Madi, Tsviya Olender, Yaron Golan, Gil Stelzer, Arye Harel, Doron Lancet
    Database : the journal of biological databases and curation 2010:2010
    408 Citations (Google Scholar as of 2017-03-27)

    Abstract: 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.

  • GeneCards 2002: towards a complete, object-oriented, human gene compendium. [PMID: 12424129]

    Marilyn Safran, Irina Solomon, Orit Shmueli, Michal Lapidot, Shai Shen-Orr, Avital Adato, Uri Ben-Dor, Nir Esterman, Naomi Rosen, Inga Peter, Tsviya Olender, Vered Chalifa-Caspi, Doron Lancet
    Bioinformatics (Oxford, England) 2002:18(11)
    160 Citations (Google Scholar as of 2017-03-27)

    Abstract: In the post-genomic era, functional analysis of genes requires a sophisticated interdisciplinary arsenal. Comprehensive resources are challenged to provide consistently improving, state-of-the-art tools. GeneCards (Rebhan et al., 1998) has made innovative strides: (a). regular updates and enhancements incorporating new genes enriched with sequences, genomic locations, cDNA assemblies, orthologies, medical information, 3D protein structures, gene expression, and focused SNP summaries; (b). restructured software using object-oriented Perl, migration to schema-driven XML, and (c). pilot studies, introducing methods to produce cards for novel and predicted genes.

  • GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. [PMID: 9789091]

    M Rebhan, V Chalifa-Caspi, J Prilusky, D Lancet
    Bioinformatics (Oxford, England) 1998:14(8)
    323 Citations (Google Scholar as of 2017-03-27)

    Abstract: Modern biology is shifting from the 'one gene one postdoc' approach to genomic analyses that include the simultaneous monitoring of thousands of genes. The importance of efficient access to concise and integrated biomedical information to support data analysis and decision making is therefore increasing rapidly, in both academic and industrial research. However, knowledge discovery in the widely scattered resources relevant for biomedical research is often a cumbersome and non-trivial task, one that requires a significant amount of training and effort. To develop a model for a new type of topic-specific overview resource that provides efficient access to distributed information, we designed a database called 'GeneCards'. It is a freely accessible Web resource that offers one hypertext 'card' for each of the more than 7000 human genes that currently have an approved gene symbol published by the HUGO/GDB nomenclature committee. The presented information aims at giving immediate insight into current knowledge about the respective gene, including a focus on its functions in health and disease. It is compiled by Perl scripts that automatically extract relevant information from several databases, including SWISS-PROT, OMIM, Genatlas and GDB. Analyses of the interactions of users with the Web interface of GeneCards triggered development of easy-to-scan displays optimized for human browsing. Also, we developed algorithms that offer 'ready-to-click' query reformulation support, to facilitate information retrieval and exploration. Many of the long-term users turn to GeneCards to quickly access information about the function of very large sets of genes, for example in the realm of large-scale expression studies using 'DNA chip' technology or two-dimensional protein electrophoresis. Freely available at http://bioinformatics.weizmann.ac.il/cards/ cards@bioinformatics.weizmann.ac.il

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Tags

DNA Protein RNA
Homo sapiens
human gene

Record metadata

  • Created on: 2015-06-20
  • Curated by:
    • Shixiang Sun [2017-03-27]
    • Lin Liu [2016-03-26]
    • Jian Sang [2015-12-11]
    • Jian Sang [2015-06-28]
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