Literature DB >> 15114375

TEAM: a tool for the integration of expression, and linkage and association maps.

Lude Franke1, Harm van Bakel, Begoña Diosdado, Martine van Belzen, Martin Wapenaar, Cisca Wijmenga.   

Abstract

The identification of genes primarily responsible for complex genetic disorders is a daunting task. Despite the assignment of many susceptibility loci, there has only been limited success in identifying disease genes based solely on positional information from genome-wide screens. The incorporation of several complementary strategies in a single integrated approach should facilitate and further enhance the efficacy of this search for genes. To permit the integration of linkage, association and expression data, together with functional annotations, we have developed a Java-based software tool: TEAM (tool for the integration of expression, and linkage and association maps). TEAM includes a genome viewer, capable of overlaying karyobands, genes, markers, linkage graphs, association data, gene expression levels and functional annotations in one composite view. Data management, analysis and filtering functionality was implemented and extended with links to the Ensembl, Unigene and Gene Ontology databases to facilitate gene annotation. Filtering functionality can help prevent the exclusion of poorly annotated, but differentially expressed, genes that reside in candidate regions that show linkage or association. Here we demonstrate the program's functionality in our study on coeliac disease (OMIM 212750), a multifactorial gluten-sensitive enteropathy. We performed a combined data analysis of a genome-wide linkage screen in 82 Dutch families with affected siblings and the microarray expression profiles of 18,110 cDNAs in 22 intestinal biopsies.

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Year:  2004        PMID: 15114375     DOI: 10.1038/sj.ejhg.5201215

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  6 in total

Review 1.  Computational tools for prioritizing candidate genes: boosting disease gene discovery.

Authors:  Yves Moreau; Léon-Charles Tranchevent
Journal:  Nat Rev Genet       Date:  2012-07-03       Impact factor: 53.242

2.  Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.

Authors:  Marinka Zitnik; Francis Nguyen; Bo Wang; Jure Leskovec; Anna Goldenberg; Michael M Hoffman
Journal:  Inf Fusion       Date:  2018-09-21       Impact factor: 12.975

3.  Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes.

Authors:  Lude Franke; Harm van Bakel; Like Fokkens; Edwin D de Jong; Michael Egmont-Petersen; Cisca Wijmenga
Journal:  Am J Hum Genet       Date:  2006-04-25       Impact factor: 11.025

4.  Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus.

Authors:  Hemang Parikh; Valeriya Lyssenko; Leif C Groop
Journal:  BMC Med Genomics       Date:  2009-12-31       Impact factor: 3.063

5.  Gene Prioritization by Compressive Data Fusion and Chaining.

Authors:  Marinka Žitnik; Edward A Nam; Christopher Dinh; Adam Kuspa; Gad Shaulsky; Blaž Zupan
Journal:  PLoS Comput Biol       Date:  2015-10-14       Impact factor: 4.475

6.  An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

Authors:  Giorgio Valentini; Alberto Paccanaro; Horacio Caniza; Alfonso E Romero; Matteo Re
Journal:  Artif Intell Med       Date:  2014-03-20       Impact factor: 5.326

  6 in total

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