Bioinformatics approaches for assembly and characterization of novel circular complete plasmids from publically available Escherichia coli genomes – Section of Microbiology - University of Copenhagen

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05. januar 2017

Bioinformatics approaches for assembly and characterization of novel circular complete plasmids from publically available Escherichia coli genomes

Project type: Master project

Nowadays the wide application of next generation sequencing technologies has provided us with huge amounts of primary nucleotide sequences of various bacterial species available in public databases. A number of recent studies focusing on comparative genomic characterization have discovered vital genetic mutations linked with bacterial adaptive evolution1. However, these studies rarely pay attention to recovery of putative circular plasmids, a class of important mobile genetic elements carrying resistance and virulence genes, from deep genomic sequencing of single species. The aim of this study is to utilize and improve an integrative bioinformatics approach to in silico identify complete plasmid sequences based on whole genome shotgun sequencing projects of Escherichia coli (1,606 WGS projects deposited in the GenBank database, Nov 2013). Novel plasmid type that occurs in Escherichia coli populations will be characterized and compared to the known ones. The spectrum of predicted circular plasmid sequences will provide promising targets for experimental validation and provide useful genetic context for future research into plasmid diversity of Escherichia coli.

Genetic organization of predicted plasmid contigs. Contigs labeled by asterisk are putative complete plasmids.

This project depends on one or two master students with a good working knowledge of genetics and programming (Perl, Python, R etc). Experience in analysis of high throughput sequencing data would be preferred.

Reference:
1)
MacLean D et al. (2013). Application of 'next-generation' sequencing technologies to microbial genetics. Nature Rev Microbial 7:287-296.

Supervisors:         
Søren J. Sørensen (sjs@bio.ku.dk)