Literature DB >> 21278374

A guide to web tools to prioritize candidate genes.

Léon-Charles Tranchevent1, Francisco Bonachela Capdevila, Daniela Nitsch, Bart De Moor, Patrick De Causmaecker, Yves Moreau.   

Abstract

Finding the most promising genes among large lists of candidate genes has been defined as the gene prioritization problem. It is a recurrent problem in genetics in which genetic conditions are reported to be associated with chromosomal regions. In the last decade, several different computational approaches have been developed to tackle this challenging task. In this study, we review 19 computational solutions for human gene prioritization that are freely accessible as web tools and illustrate their differences. We summarize the various biological problems to which they have been successfully applied. Ultimately, we describe several research directions that could increase the quality and applicability of the tools. In addition we developed a website (http://www.esat.kuleuven.be/gpp) containing detailed information about these and other tools, which is regularly updated. This review and the associated website constitute together a guide to help users select a gene prioritization strategy that suits best their needs.

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Mesh:

Year:  2010        PMID: 21278374     DOI: 10.1093/bib/bbq007

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  78 in total

Review 1.  Bioinformatics for personal genome interpretation.

Authors:  Emidio Capriotti; Nathan L Nehrt; Maricel G Kann; Yana Bromberg
Journal:  Brief Bioinform       Date:  2012-01-13       Impact factor: 11.622

2.  Prioritization of candidate genes for attention deficit hyperactivity disorder by computational analysis of multiple data sources.

Authors:  Suhua Chang; Weina Zhang; Lei Gao; Jing Wang
Journal:  Protein Cell       Date:  2012-07-10       Impact factor: 14.870

Review 3.  A guide on gene prioritization in studies of psychiatric disorders.

Authors:  Sven Stringer; Kim C Cerrone; Wim van den Brink; Julia F van den Berg; Damiaan Denys; Rene S Kahn; Eske M Derks
Journal:  Int J Methods Psychiatr Res       Date:  2015-07-31       Impact factor: 4.035

4.  Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature.

Authors:  Rong Xu; Li Li; Quanqiu Wang
Journal:  Bioinformatics       Date:  2013-07-04       Impact factor: 6.937

Review 5.  Candidate gene prioritization.

Authors:  Ali Masoudi-Nejad; Alireza Meshkin; Behzad Haji-Eghrari; Gholamreza Bidkhori
Journal:  Mol Genet Genomics       Date:  2012-08-15       Impact factor: 3.291

6.  Disease gene prioritization using network and feature.

Authors:  Bingqing Xie; Gady Agam; Sandhya Balasubramanian; Jinbo Xu; T Conrad Gilliam; Natalia Maltsev; Daniela Börnigen
Journal:  J Comput Biol       Date:  2015-04       Impact factor: 1.479

7.  Beegle: from literature mining to disease-gene discovery.

Authors:  Sarah ElShal; Léon-Charles Tranchevent; Alejandro Sifrim; Amin Ardeshirdavani; Jesse Davis; Yves Moreau
Journal:  Nucleic Acids Res       Date:  2015-09-17       Impact factor: 16.971

8.  The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis.

Authors:  Sofie Van Landeghem; Stefanie De Bodt; Zuzanna J Drebert; Dirk Inzé; Yves Van de Peer
Journal:  Plant Cell       Date:  2013-03-26       Impact factor: 11.277

9.  Improving disease gene prioritization using the semantic similarity of Gene Ontology terms.

Authors:  Andreas Schlicker; Thomas Lengauer; Mario Albrecht
Journal:  Bioinformatics       Date:  2010-09-15       Impact factor: 6.937

10.  Candidate gene prioritization by network analysis of differential expression using machine learning approaches.

Authors:  Daniela Nitsch; Joana P Gonçalves; Fabian Ojeda; Bart de Moor; Yves Moreau
Journal:  BMC Bioinformatics       Date:  2010-09-14       Impact factor: 3.169

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