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Genomics. 2014 Jan;103(1):1-10. doi: 10.1016/j.ygeno.2013.11.005. Epub 2013 Dec 4.

DuctApe: a suite for the analysis and correlation of genomic and OmniLog™ Phenotype Microarray data.

Author information

1
Department of Biology, University of Florence, Florence, Italy. Electronic address: marco.galardini@unifi.it.
2
Department of Biology, University of Florence, Florence, Italy.
3
Interdisciplinary Research Institute USR3078, CNRS-Université Lille Nord de France, Villeneuve d'Ascq, France.
4
Consiglio per la Ricerca e la sperimentazione in Agricoltura, Centro di Ricerca per lo studio delle Relazioni tra Pianta e Suolo (CRA-RPS), Rome, Italy.
5
Consiglio per la Ricerca e la sperimentazione in Agricoltura, Centro di Ricerca per l'Agrobiologia e la Pedologia (CRA-ABP), Florence, Italy.

Abstract

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™ 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. The source code and tutorials are available at http://combogenomics.github.io/DuctApe/.

KEYWORDS:

Genomics; KEGG; Metabolism; Phenomics; Phenotype Microarray

PMID:
24316132
DOI:
10.1016/j.ygeno.2013.11.005
[Indexed for MEDLINE]
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