Literature DB >> 15538263

Kidney gene database: a curated and integrated database of genes involved in kidney disease.

Hong Zhao1, Long-Cheng Li, Steven T Okino, Christopher J Kane, Peter R Carroll, Rajvir Dahiya.   

Abstract

PURPOSE: We have created a curated and integrated database, the Kidney Gene Database (KGDB) (http://www.urogene.org/kgdb) that contains current information about genes or genomic loci involved in human kidney disease.
MATERIALS AND METHODS: Genes that undergo molecular, genetic or epigenetic events that affect kidney function are identified through a biomedical literature search and catalogued in the database.
RESULTS: Events that are currently screened for are gene amplification, mutation, deletion, polymorphism, loss of heterozygosity, DNA methylation and DNA hypomethylation. In addition, genes that are uniquely expressed in the kidney, as determined by analyzing the expressed sequence tags database and the Serial Analysis of Gene Expression database, are also included in KGDB. For each gene KGDB provides basic information about the gene product, a tissue type gene expression profile, links to protein, mRNA and genomic DNA sequence information, relevant literature citations and cross-references to other databases.
CONCLUSIONS: We present KGDB, which is to our knowledge the first curated and integrated database of genes involved in human kidney disease. KGDB is free, widely accessible and easy to use, and it provides a wealth of relevant information. In addition, KGDB will be continuously updated every 6 months to include new information published in the biomedical literature or in gene expression databases. We envision that KGDB will serve as a valuable resource for scientists and clinicians.

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Year:  2004        PMID: 15538263     DOI: 10.1097/01.ju.0000144106.91876.7a

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  2 in total

Review 1.  Big Data in Nephrology.

Authors:  Navchetan Kaur; Sanchita Bhattacharya; Atul J Butte
Journal:  Nat Rev Nephrol       Date:  2021-06-30       Impact factor: 28.314

2.  Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease.

Authors:  Qingzhou Zhang; Bo Yang; Xujiao Chen; Jing Xu; Changlin Mei; Zhiguo Mao
Journal:  Database (Oxford)       Date:  2014-09-24       Impact factor: 3.451

  2 in total

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