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Updated: Apr 21, 2026
09:10
A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
Published on: May 22, 2018
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From pangenome to panphenome and back
Marco Galardini1, Alessio Mengoni, Stefano Mocali
1EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK, marco@ebi.ac.uk.
Methods in Molecular Biology (Clifton, N.J.)
|October 26, 2014
Summary
Linking bacterial genome differences to phenotype variability is crucial. DuctApe software enables joint analysis of genomic and phenomic data to identify gene-phenotype correlations.
Area of Science:
- Microbiology
- Systems Biology
- Bioinformatics
Background:
- Relating bacterial genomic variations to phenotypic differences is a significant biological challenge.
- Understanding these links is vital for biotechnological applications and pathway manipulation.
- Genome-wide metabolic pathway reconstruction and comprehensive phenotype measurement are essential prerequisites.
Purpose of the Study:
- To present the DuctApe software suite for joint analysis of bacterial genomic and phenomic data.
- To facilitate the identification of gene-phenotype correlations and associated pathways.
- To address the lack of computational tools for directly linking phenotype microarray data with specific genes.
Main Methods:
- Utilizing the KEGG database for reliable reconstruction of cellular metabolic pathways.
- Employing OmniLog™ Phenotype Microarray (PM) technology for extensive phenotype measurements across diverse conditions.
- Applying the DuctApe software suite for integrated analysis of genomic and phenomic datasets.
Main Results:
- DuctApe enables the joint analysis of genomic and phenomic data in bacterial species.
- The software highlights pathways and reactions strongly associated with observed phenotypic variability.
- A case study involving four Sinorhizobium meliloti strains demonstrates the software's utility.
Conclusions:
- The DuctApe software suite provides a valuable tool for correlating bacterial genotype with phenotype.
- This approach enhances the understanding of biological variability and aids in biotechnological pathway engineering.
- Further example datasets are available online for broader application and validation.
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