Jaewon Oh - Week 2 and 3

So there still hasn't been any updates on the shortage of ALK data on neuroblastoma. So instead of moving forward with this project, I focused on the TP53 project. As I mentioned in my previous post, I have been learning how to use the coding language "R" in order to manage and analyse large sets of data. The data I have been looking at contains functional assessments of mutations at specific codons and their resulting amino acid change. I was looking into whether the mutations resulted in a gain or loss of function in the TP53 gene and was aiming to eventually compare these results to the selection intensity data my lab had provided for me. However, before I did the final step, Dr. Cannataro had sent me another paper to look at. (End of week 2)

Now when week 3 began, I began reading another p53 paper. This paper, however, focused on RFS values. RFS values, short for relative fitness scores, represents the survival rate of different p53 variants when they were cultured together. Data on these RFS values were found in the supplementary information section. I then proceeded to compare these RFS values with the data provided by my lab (selection intensity, frequency of mutation, and proportion of mutations in specific tumors). There is not significant overlap that occurs between these two data sets, but when there is, there are positive correlations between the values. But there is a problem. With the lack of data, some values may repeat themselves. For example, a data set may include multiple RFS values for the same codon number and mutation, but because the other data set only contains one selection intensity, Rstudio (platform for "R"), will input the same value for each data point. This is an issue that will have to be addressed next week.

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Peddie EXP Summer 2018

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