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This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2005

Open Access Research article

Inherited disorder phenotypes: controlled annotation and statistical analysis for knowledge mining from gene lists

Marco Masseroli1*, Osvaldo Galati1, Mauro Manzotti1, Karina Gibert2 and Francesco Pinciroli1

Author Affiliations

1 BioMedical Informatics Laboratory, Bioengineering Department, Politecnico di Milano, piazza Leonardo da Vinci 32, 20133 Milano, Italy

2 Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya, C. Pau Gargallo 5, 08028 Barcelona, Spain

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BMC Bioinformatics 2005, 6(Suppl 4):S18  doi:10.1186/1471-2105-6-S4-S18

Published: 1 December 2005

Abstract

Background

Analysis of inherited diseases and their associated phenotypes is of great importance to gain knowledge of underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose, mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To allow performing phenotype analyses of inherited disorder related genes we implemented new original modules within GFINDer http://www.bioinformatics.polimi.it/GFINDer/ webcite, a Web system we previously developed that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining.

Results

New GFINDer modules allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease phenotypes and their locations of occurrence, and statistically analyzing the obtained classifications. To achieve this we exploited, normalized and structured the information present in textual form in the Clinical Synopsis sections of the Online Mendelian Inheritance in Man (OMIM) databank. Such valuable information delineates numerous signs and symptoms accompanying many genetic diseases and it is divided into phenotype location categories, either by organ system or type of finding.

Conclusion

Supporting phenotype analyses of inherited diseases and biomolecular functional evaluations, GFINDer facilitates a genomic approach to the understanding of fundamental biological processes and complex cellular mechanisms underlying patho-physiological phenotypes.