alexa LeaderGene: A Fast Data-mining Tool for Molecular Genomics
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

LeaderGene: A Fast Data-mining Tool for Molecular Genomics

Nicola Luigi Bragazzi, Luca Giacomelli, Victor Sivozhelezov and Claudio Nicolini*

Chair of Biophysics University of Genova and Nanoworld Institute Fondazione EL.B.A.Nicolini , Italy

*Corresponding Author:
Prof Claudio Nicolini
University of Genova
corso Europa 30, 16132 Genoa, Italy
Fax: +39 010 353 38215
E-mail: [email protected]

Received Date: April 02, 2011; Accepted Date: April 23, 2011; Published Date: April 25, 2011

Citation: Bragazzi NL, Giacomelli L, Sivozhelezov V, Nicolini C (2011) Leader Gene: A Fast Data-mining Tool for Molecular Genomics. J Proteomics Bioinform 4: 083-086. doi: 10.4172/jpb.1000171

Copyright: © 2011 Bragazzi NL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



DNA microarrays are one of the most promising methods for molecular genomics, but this technique is often associated with experimental complications and difficulties in the analysis. Moreover, the greatest part of genes displayed on an array is often not directly involved in the cellular process being studied. Recently, we proposed a data mining algorithm, based on the identification of genes involved in a given process, the calculation of interactions among them and their ranking according to number of interactions. Genes in the highest cluster are defined as "leader genes". These findings may lead to an ad hoc and therefore more significant experimentation. However, at present this complex process is performed manually. In this work, we present the general architecture of LeaderGene, an automated tool for ab-initio molecular genomics. Three different and independent parts: (1) Identification of gene list; (2) Calculation of weighted number of links; (3) Genes clustering. Initial inputs are provided by user; then, output of part 1 and part 2, respectively, become inputs of parts 2 and 3. The development of an user-friendly software capable to automatically compute leader genes in a given cellular system will allow further progresses in this field of molecular genomics.


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