Genome Annotation and Open Source Annotation Pipelines Assignment

Assignment Task

You have been provided with annotation data obtained from an assembly of Acinetobacter junii, a bacterial species. Prokka, an automated annotation pipeline has been used to perform this annotation. You have been provided with this output (see ‘Data files for Assessment’ folder) and must generate an R Markdown or R Notebook report based on these outputs. Before beginning this assessment you should download the data files provided.

Genes in this dataset will contain annotations from several databases, including Enzyme Commission (EC) and COG (Clusters of Orthologous Groups) identifiers.

• The GFF (.gff) file contains all of the information about the features annotated. Within this file, you can find each gene that is present in your genome, as well as tRNAs and rRNAs.
• The .txt file contains a summary of the number of features annotated.
• The .tsv file contains a more detailed summary of the features annotated.

1. Using a function in R, create a new folder that you will use for this assignment Using the file.copy() function, move the annotation files into the assignment folder you created. Recall that you can use either the help function or operator in R when determining how to use this function.

2. Import each of the PROKKA annotation data files into R using an appropriate function. 

3. How many coding regions were identified by Prokka? Report the result and include the R code used to count coding regions.

4. In your R Notebook and using ggplot, include a plot summarising the lengths of all CDS regions identified. Include a figure caption and label axes appropriately.

5. In your R Notebook and using ggplot, include a bar plot summarising the numbers of coding regions, rRNAs and tRNAs identified. Include a figure caption and label axes appropriately. Add text labels to each bar on the graph showing the number identified in each category

6. Using the imported data, identify and report how many genes have been annotated with enzyme and COG IDs

7. Data from the MinPath program, which predicts metabolic pathways, has also been made available on Blackboard. MinPath only considers the minimum number of pathways required to explain the set of enzymes in a sample.

8. Import the MinPath data file into R using an appropriate function.

Figure 1. Schematic illustration of the MinPath method reproduced from Ye et al. (see Useful Resources).

9. How many pathways has MinPath predicted in your genome? Report the result in your R Notebook and include the code you used to count pathways.

9. How many pathways were predicted if you counted all possible pathways as being present?

10. Conclude by providing a written summary of the overall annotation including a statement of results obtained in steps 4, 7, 8 and 9 as well as the following:

• The number of hypothetical proteins
• Genome coding density
• Average gene length Has an annotation of A. junii previously been described in the literature? In what way(s) does this differ from the annotation that you’ve reported?

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