OBJECTIVES: Animal models are a key resource for the investigation of human diseases. In contrast to functional annotation, phenotype annotation is less standard, and comparing phenotypes across species remains challenging. The objective of this paper is to propose a framework for comparing phenotype annotations of orthologous genes based on the Medical Subject Headings (MeSH) indexing of biomedical articles in which these genes are discussed. METHODS: 17,769 pairs of orthologous genes (mouse and human) are downloaded from the Mouse Genome Informatics (MGI) system and linked to biomedical articles through Entrez Gene. MeSH index terms corresponding to diseases are extracted from Medline. RESULTS: 11,111 pairs of genes exhibited at least one phenotype annotation for each gene in the pair. Among these, 81% have at least one phenotype annotation in common, 80% have at least one annotation specific to the human gene and 84% have at least one annotation specific to the mouse gene. Four disease categories represent 54% of all phenotype annotations. CONCLUSIONS: This framework supports the curation of phenotype annotation and the generation of research hypotheses based on comparative studies.
OBJECTIVES: Animal models are a key resource for the investigation of human diseases. In contrast to functional annotation, phenotype annotation is less standard, and comparing phenotypes across species remains challenging. The objective of this paper is to propose a framework for comparing phenotype annotations of orthologous genes based on the Medical Subject Headings (MeSH) indexing of biomedical articles in which these genes are discussed. METHODS: 17,769 pairs of orthologous genes (mouse and human) are downloaded from the Mouse Genome Informatics (MGI) system and linked to biomedical articles through Entrez Gene. MeSH index terms corresponding to diseases are extracted from Medline. RESULTS: 11,111 pairs of genes exhibited at least one phenotype annotation for each gene in the pair. Among these, 81% have at least one phenotype annotation in common, 80% have at least one annotation specific to the human gene and 84% have at least one annotation specific to the mouse gene. Four disease categories represent 54% of all phenotype annotations. CONCLUSIONS: This framework supports the curation of phenotype annotation and the generation of research hypotheses based on comparative studies.
Authors: William A Baumgartner; K Bretonnel Cohen; Lynne M Fox; George Acquaah-Mensah; Lawrence Hunter Journal: Bioinformatics Date: 2007-07-01 Impact factor: 6.937
Authors: Jan O Korbel; Tobias Doerks; Lars J Jensen; Carolina Perez-Iratxeta; Szymon Kaczanowski; Sean D Hooper; Miguel A Andrade; Peer Bork Journal: PLoS Biol Date: 2005-04-05 Impact factor: 8.029
Authors: John M Hancock; Ann-Marie Mallon; Tim Beck; Georgios V Gkoutos; Chris Mungall; Paul N Schofield Journal: Mamm Genome Date: 2009-08-02 Impact factor: 2.957
Authors: Eric W Sayers; Tanya Barrett; Dennis A Benson; Stephen H Bryant; Kathi Canese; Vyacheslav Chetvernin; Deanna M Church; Michael DiCuccio; Ron Edgar; Scott Federhen; Michael Feolo; Lewis Y Geer; Wolfgang Helmberg; Yuri Kapustin; David Landsman; David J Lipman; Thomas L Madden; Donna R Maglott; Vadim Miller; Ilene Mizrachi; James Ostell; Kim D Pruitt; Gregory D Schuler; Edwin Sequeira; Stephen T Sherry; Martin Shumway; Karl Sirotkin; Alexandre Souvorov; Grigory Starchenko; Tatiana A Tatusova; Lukas Wagner; Eugene Yaschenko; Jian Ye Journal: Nucleic Acids Res Date: 2008-10-21 Impact factor: 16.971