Detecting natural selection for translational efficiency in bacterial and archaeal genomes: a machine learning approach (CROSBI ID 362986)
Ocjenski rad | doktorska disertacija
Podaci o odgovornosti
Supek, Fran
Tomislav Šmuc
engleski
Detecting natural selection for translational efficiency in bacterial and archaeal genomes: a machine learning approach
Unequal use of synonymous codons in prokaryotes is largely a consequence of mutational biases, but highly expressed genes exhibit a preference towards codons that enable more efficient translation. I have tested and implemented in a freely accessible software package (INCA) several distance-based approaches to codon usage analysis and proposed a novel method (MILC) robust to changes in gene length and composition. Additionally, I have introduced a classifier-based computational framework that can distinguish between the two principal influences on codon usage. Evidence for translational selection was found to be universal in prokaryotic genomes, and the extent of such selection was quantified. I have performed extensive statistical testing for enrichment of specific gene functional categories with codon-optimized genes. Since presence of codon optimizations can be used as a proxy for expression levels in every sequenced genome, I have contrasted predicted gene activity within groups of Bacteria and Archaea defined by phenotype, environment or genotype, outlining microbial 'adaptomes'. Additionally, I have examined the interplay of codon usage and 5' mRNA secondary structures in determining the success of heterologous gene expression in an E. coli host.
codon usage bias; Random Forest; Support Vector Machines; non-coding DNA; highly expressed genes; translational selection; adaptome
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
149
18.10.2010.
obranjeno
Podaci o ustanovi koja je dodijelila akademski stupanj
Prirodoslovno-matematički fakultet, Zagreb
Zagreb