At the CeMiSt center we work towards a greater understanding of the role of secondary metabolites in natural environments and microbial communities. By understanding how these compounds work we will be able to tackle problems such as antibiotic resistance and identify potential anti-microbial medical compounds. To investigate this we will be identifying and creating model communities of naturally occurring microbial communities and analyzing chemistry, morphology, genetics and many other points of interest. Part of the focus is the elucidation of genetic diversity and potential within these communities. If we are to understand the communities of microorganisms, we need to understand the individual species better. This task will, among other approaches, be partaken using comparative genomics techniques to analyze large sets of genomic data from several groups of bacteria and filamentous fungi. The knowledge of individual species and groups will aid in the understanding of future metagenomic analysis, allowing for selection of information that either resemble or deviate from the known species.
The questions to be addressed will include:
● What is the genetic diversity of the species found in communities?
● How much diversity of secondary metabolite gene clusters is there among different species?
● How different are secondary metabolite gene clusters in different species?
● Are some genes unique to species found in specific communities?
● Can we find types of gene clusters specific to the life with a specific “partner”-organism?
Student projects can be the form of special courses or thesis work. Projects can be geared to include more or less programming but will always include some level of scripted data handling. Basic skills in coding of R or Python is required.
Types of work will include
● Database work, locating, selecting, filtering and obtaining data
● Construction of datasets, analysis and validation, appropriate for the research question
● Research review, reading articles, identifying possible functional or genetic targets and verifying computational results
● Analysis of data using public pipelines and software
● Analysis of data using in-house pipelines and software (R, MySQL, Python code)
● Writing code for data visualization ( R )
● Writing code for data transformation/reformat (R, Python, shell, MySQL)
Types of work can include
● Writing code for data analysis (R, Python, shell, MySQL)
● Writing code for graphical analysis interfases (R)
Are you interested in being part of this type of project please contact Tammi Vesth (email@example.com)