Timeline:

• Week one: Preparation of the data (Matepairs, Illumina) for mapping, plus evaluation of the Matepair library quality. Choose suitable mapping programs. Writing scripts in python to change the Matepair read format into the format needed by the mapper. Learning of utilizing clusters like Soroban and Allegro in order to increase mapping speed.
• Week two: Mapping with chosen mappers, using several different parameters. Mapping quality comparison and benchmarking. Choose the best mapping result. Learning about the “Sam” output format.
• Week three: Development of a consensus sequence for the annotation. Start of consensus sequence comparison between Cavia aperea and Cavia porcellus. Usage of the Cavia apperea annotation as a template to spot genetic differences between both Guineapig species.
• Week four and week five: Alteration of the Cavia porcellus annotation pattern as much as necessary within the process to get an accurate annotation for Cavia aperea. Finding of additional CPG islands that will later be used to find the unique methylation pattern in Cavia aperea specimen.
• Week six: Verification and further refinement of the annotation. Testing of the annotation dataset.
• Weeks seven and eight: Writing the Bachelor Thesis while obtaining more results from server runs.

Goal:

Presentation of the annotated Genome for Cavia aperea that consists of annotated genes, including promoter regions and CPG islands. The genome will be used to detect the methylation pattern in several male wild Guinea pigs.

Comments

For computing the consensus sequence of the mapped reads you can use existing SeqAn tools after mapping.