| Chapter title |
From pangenome to panphenome and back.
|
|---|---|
| Chapter number | 16 |
| Book title |
Bacterial Pangenomics
|
| Published in |
Methods in molecular biology, October 2014
|
| DOI | 10.1007/978-1-4939-1720-4_16 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-1719-8, 978-1-4939-1720-4
|
| Authors |
Galardini M, Mengoni A, Mocali S, Marco Galardini, Alessio Mengoni, Stefano Mocali, Galardini, Marco, Mengoni, Alessio, Mocali, Stefano |
| Abstract |
The ability to relate genomic differences in bacterial species to their variability in expressed phenotypes is one of the most challenging tasks in today's biology. Such task is of paramount importance towards the understanding of biotechnologically relevant pathways and possibly for their manipulation. Fundamental prerequisites are the genome-wide reconstruction of metabolic pathways and a comprehensive measurement of cellular phenotypes. Cellular pathways can be reliably reconstructed using the KEGG database, while the OmniLog™ Phenotype Microarray (PM) technology may be used to measure nearly 2,000 growth conditions over time. However, few computational tools that can directly link PM data with the gene(s) of interest followed by the extraction of information on gene-phenotype correlation are available.In this chapter the use of the DuctApe software suite is presented, which allows the joint analysis of bacterial genomic and phenomic data, highlighting those pathways and reactions most probably associated with phenotypic variability. A case study on four Sinorhizobium meliloti strains is presented; more example datasets are available online. |
X Demographics
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Demographic breakdown
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|---|---|---|
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Mendeley readers
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Demographic breakdown
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|---|---|---|
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| Student > Ph. D. Student | 4 | 22% |
| Professor > Associate Professor | 3 | 17% |
| Librarian | 1 | 6% |
| Student > Master | 1 | 6% |
| Other | 1 | 6% |
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| Computer Science | 1 | 6% |
| Immunology and Microbiology | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 1 | 6% |