For this assessment, I have processed DNA and RNA sequencing data using the CLCbio Genomics Workbench, and now have a list of RNA variants derived f

For this assessment, I have processed DNA and RNA sequencing data using the CLCbio Genomics Workbench, and now have a list of RNA variants derived from a Melanoma cell model. I'm able to export this list into an Excel table which is attached below. The column headings of this table and what they mean are explained in the second lecture of this week.

We would like you to download the file of RNA variants and:

1) Filter out any so-called dbSNPS. These are mutations that have been previously described as 'polymorphisms', so we remove these.

2) You will see from the sequencing read columns that some genes have only a low frequency of mutant reads (less than 20%). We also remove these low penetrant reads as possible errors.

Therefore, to make your final list, only take a subset of the non-synonymous mutations, without dbSNPs, which have 20% or higher in the mRNA mutant read frequency.This list of gene IDs can be uploaded into DAVID.

Tools such as DAVID (https://david.ncifcrf.gov/summary.jsp) can be used to classify genes into functional pathways.

Assessment:

a. Take the list of mutant genes provided and use this DAVID tool to find one pathway that interests you containing several mutated genes. Upload this image to the Discussion board and describe the pathway (200 words).

b. Would you expect to see these pathways mutated in a tumour based on your knowledge of the hallmarks of cancer and did any pathways surprise you, including those that you did or did not chose to focus on in part "a" above. (200-250 words)?

c. Lastly, you can identify a 'fs' or frameshift mutation in your list which has a high number of mutant reads? These fs mutations can be found in the amino acid change column. An example annotation would include the fs tags such as this: [His195fs]. These fs mutations lead invariably to premature stop codons as well as truly 'new', never seen before protein sequences. For a 'vaccine', the frameshift mutations are the most compelling, as they basically use an alternating reading frame that makes long stretches of protein sequence not seen before. Focusing on the top 100 genes with highest RNA mutations % frequency, comment on the number of genes with frameshift mutations compared to the non-synonymous mutations that result in single amino acid changes-specifically, are there very many fs mutations or not and what might this mean for frameshift mutation vaccine usage? (200-250 words)