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Nucleic Acids Research 2006 34(Web Server issue):W285-W292; doi:10.1093/nar/gkl340
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© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (
http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.


Article

TOM: a web-based integrated approach for identification of candidate disease genes

Simona Rossi*, Daniele Masotti1, Christine Nardini2, Elena Bonora3, Giovanni Romeo3, Enrico Macii1, Luca Benini2 and Stefano Volinia

Functional Genomics Laboratory and Telethon Facility, DAMA Data Mining for Analysis of DNA Microarrays, Dipartimento di Morfologia ed Embriologia Via Fossato di Mortara 64b, 44100 Ferrara, Italy 1 Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino Corso Duca degli Abruzzi 24, 10129 Torino, Italy 2 Dipartimento di Elettronica, Informatica e Sistemistica (DEIS), University of Bologna Viale Risorgimento 2, 40136 Bologna, Italy 3 Unità di Genetica Medica, Policlinico S. Orsola via Massarenti 9, 40138 Bologna, Italy

*To whom correspondence should be addressed. Tel: +39 0532 291714; Fax: +39 0532 291533; Email: simona.rossi{at}gmail.com

Received February 14, 2006. Revised March 2, 2006. Accepted April 18, 2006.

The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals. The algorithm uses the information stored in public resources, including mapping, expression and functional databases. Given the queries, TOM will select and list one or more candidate genes. This approach allows the geneticist to bypass the costly and time consuming tracing of genetic markers through entire families and might improve the chance of identifying disease genes, particularly for rare diseases. We present here the tool and the results obtained on known benchmark and on hereditary predisposition to familial thyroid cancer. Our algorithm is available at http://www-micrel.deis.unibo.it/~tom/.


The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors


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