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Mutation Accumulation Data Aggregation: Mutation Accumulation Analyses Of Saccharomyces cerevisiae

The data for this project came from Dr. Scott Lujan of the NIH, under principle investigator Dr. Thomas Kunkel. Dr. Lujan performs a significant amount of post-processing analysis on the lab’s mutation accumulation data for deriving biological significance from the raw mutation counts. Currently, his analysis is all contained within a set of excel graphs, but the volume of data, including some ~1,000,000 variant calls, is stretching the limits of this format.

The original motivation behind this project was to create a database and interface that would be faster than excel and enable all members of the lab to quickly access various characteristics of the accumulation data. This would primarily be a page that performs and outputs analysis for a selected yeast strain. We would need to create analytical graphs based on Dr. Lujan’s work, aggregating data according to strain, mutation type, location, and various genome features. To make it more versatile, there would be options provided for changing mutation subsets and genome features. We would also need to perform various statistics and display them in a format that would be informative and easy to navigate

Contributors

Kyler Anderson

Manas Dhanuka

Nikita Tomar

Shrishtee Kandoi

Italo Duran

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  • Python 81.1%
  • Jupyter Notebook 17.9%
  • Shell 1.0%