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Computational prediction of genomic functional cores specific to different microbes.

TitleComputational prediction of genomic functional cores specific to different microbes.
Publication TypeJournal Article
Year of Publication2006
AuthorsCarbone, A
JournalJ Mol Evol
Volume63
Issue6
Pagination733-46
Date Published2006 Dec
ISSN0022-2844
KeywordsBacillus subtilis, Biological Evolution, Codon, Computational Biology, Escherichia coli, Gammaproteobacteria, Genome, Bacterial, Synechocystis
Abstract

Computational and experimental attempts tried to characterize a universal core of genes representing the minimal set of functional needs for an organism. Based on the increasing number of available complete genomes, comparative genomics has concluded that the universal core contains < 50 genes. In contrast, experiments suggest a much larger set of essential genes (certainly more than several hundreds, even under the most restrictive hypotheses) that is dependent on the biological complexity and environmental specificity of the organism. Highly biased genes, which are generally also the most expressed in translationally biased organisms, tend to be over represented in the class of genes deemed to be essential for any given bacterial species. This association is far from perfect; nevertheless, it allows us to propose a new computational method to detect, to a certain extent, ubiquitous genes, nonorthologous genes, environment-specific genes, genes involved in the stress response, and genes with no identified function but highly likely to be essential for the cell. Most of these groups of genes cannot be identified with previously attempted computational and experimental approaches. The large variety of life-styles and the unusually detectable functional signals characterizing translationally biased organisms suggest using them as reference organisms to infer essentiality in other microbial species. The case of small parasitic genomes is discussed. Data issued by the analysis are compared with previous computational and experimental studies. Results are discussed both on methodological and biological grounds.

DOI10.1007/s00239-005-0250-9
Alternate JournalJ. Mol. Evol.
PubMed ID17103060