DuctApe: A suite for the analysis and correlation of genomic and OmniLog™ Phenotype Microarray data
2014
Galardini, Marco | Mengoni, Alessio | Biondi, Emanuele G. | Semeraro, Roberto | Florio, Alessandro | Bazzicalupo, Marco | Benedetti, Anna | Mocali, Stefano | Università degli Studi di Firenze = University of Florence = Université de Florence (UniFI) | Université Lille Nord de France (COMUE) | Laboratoire d'Ecologie Microbienne - UMR 5557 (LEM) ; Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Université de Lyon-Ecole Nationale Vétérinaire de Lyon (ENVL)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS) | Centro di Ricerca per lo Studio delle relazioni fra pianta e suolo | Research Centre for Agrobiology and Pedology (CRA-ABP) | Italian Ministry of Research TCKNJL; project MARKERINBIO DM 8348
International audience
Mostrar más [+] Menos [-]Inglés. Addressing the functionality of genomes is one of the most important and challenging tasks of today's biology. In particular the ability to link genotypes to corresponding phenotypes is of interest in the reconstruction and biotechnological manipulation of metabolic pathways. Over the last years, the OmniLog (TM) Phenotype Microarray (PM) technology has been used to address many specific issues related to the metabolic functionality of microorganisms. However, computational tools that could directly link PM data with the gene(s) of interest followed by the extraction of information on gene-phenotype correlation are still missing. Here we present DuctApe, a suite that allows the analysis of both genomic sequences and PM data, to find metabolic differences among PM experiments and to correlate them with KEGG pathways and gene presence/absence patterns. As example, an application of the program to four bacterial datasets is presented.
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