Literature DB >> 15181176

Genome-wide identification of genes likely to be involved in human genetic disease.

Núria López-Bigas1, Christos A Ouzounis.   

Abstract

Sequence analysis of the group of proteins known to be associated with hereditary diseases allows the detection of key distinctive features shared within this group. The disease proteins are characterized by greater length of their amino acid sequence, a broader phylogenetic extent, and specific conservation and paralogy profiles compared with all human proteins. This unique property pattern provides insights into the global nature of hereditary diseases and moreover can be used to predict novel disease genes. We have developed a computational method that allows the detection of genes likely to be involved in hereditary disease in the human genome. The probability score assignments for the human genome are accessible at http://maine.ebi. ac.uk:8000/services/dgp.

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Year:  2004        PMID: 15181176      PMCID: PMC434425          DOI: 10.1093/nar/gkh605

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  28 in total

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Journal:  Bioinformatics       Date:  2000-10       Impact factor: 6.937

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Authors:  A Bernal; U Ear; N Kyrpides
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Journal:  Nature       Date:  2001-02-15       Impact factor: 49.962

5.  The Ensembl genome database project.

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9.  Understanding human disease mutations through the use of interspecific genetic variation.

Authors:  M P Miller; S Kumar
Journal:  Hum Mol Genet       Date:  2001-10-01       Impact factor: 6.150

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Authors:  L D Hurst; N G Smith
Journal:  Curr Biol       Date:  1999-07-15       Impact factor: 10.834

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  101 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

Review 2.  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

3.  PGMapper: a web-based tool linking phenotype to genes.

Authors:  Qing Xiong; Yuhui Qiu; Weikuan Gu
Journal:  Bioinformatics       Date:  2008-01-18       Impact factor: 6.937

4.  CANDID: a flexible method for prioritizing candidate genes for complex human traits.

Authors:  Janna E Hutz; Aldi T Kraja; Howard L McLeod; Michael A Province
Journal:  Genet Epidemiol       Date:  2008-12       Impact factor: 2.135

5.  Walking the interactome for prioritization of candidate disease genes.

Authors:  Sebastian Köhler; Sebastian Bauer; Denise Horn; Peter N Robinson
Journal:  Am J Hum Genet       Date:  2008-03-27       Impact factor: 11.025

Review 6.  Hypothesis-driven candidate gene association studies: practical design and analytical considerations.

Authors:  Timothy J Jorgensen; Ingo Ruczinski; Bailey Kessing; Michael W Smith; Yin Yao Shugart; Anthony J Alberg
Journal:  Am J Epidemiol       Date:  2009-09-17       Impact factor: 4.897

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.  Advances in translational bioinformatics: computational approaches for the hunting of disease genes.

Authors:  Maricel G Kann
Journal:  Brief Bioinform       Date:  2009-12-10       Impact factor: 11.622

Review 9.  Kinase mutations in human disease: interpreting genotype-phenotype relationships.

Authors:  Piya Lahiry; Ali Torkamani; Nicholas J Schork; Robert A Hegele
Journal:  Nat Rev Genet       Date:  2010-01       Impact factor: 53.242

10.  Gene-disease relationship discovery based on model-driven data integration and database view definition.

Authors:  S Yilmaz; P Jonveaux; C Bicep; L Pierron; M Smaïl-Tabbone; M D Devignes
Journal:  Bioinformatics       Date:  2008-11-27       Impact factor: 6.937

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