Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: GOToolBox: functional analysis of gene datasets based on Gene Ontology. | Software Open Access GOToolBox functional analysis of gene datasets based on Gene Ontology David Martin Christine Brun Elisabeth Remy Pierre Mouren Denis Thieffry and Bernard Jacq Addresses Laboratoire de Génétique et Physiologie du Développement IBDM CNRS INSERM Université de la Méditerranée Parc Scientifique de Luminy case 907 13288 Marseille France. Institut de Mathématiques de Luminy Parc Scientifique de Luminy 13288 Marseille France. Correspondence David Martin. E-mail martin@ Published 26 November 2004 Genome Biology 2004 5 R101 The electronic version of this article is the complete one and can be found online at http 2004 5 12 R101 Received 13 April 2004 Revised 31 August 2004 Accepted 25 October 2004 2004 Martin et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract We have developed methods and tools based on the Gene Ontology GO resource allowing the identification of statistically over- or under-represented terms in a gene dataset the clustering of functionally related genes within a set and the retrieval of genes sharing annotations with a query gene. GO annotations can also be constrained to a slim hierarchy or a given level of the ontology. The source codes are available upon request and distributed under the GPL license. Rationale Since complete genome sequences have become available the amount of annotated genes has increased dramatically. These advances have allowed the systematic comparison of the gene content of different organisms leading to the conclusion that organisms share the majority of their genes with only relatively few species-specific genes. On this basis one can develop strategies to infer gene annotations from model species to less experimentally .
