### MCB112: Biological Data Analysis (Fall 2018)

• June: Installs kallisto with conda. Simulation is properly wrapped in a function, so it can be re-run with different conditions. Reads kallisto output with Pandas, and uses Pandas to analyze the difference between kallisto results and the true abundance parameters of the simulation. In part 5, explores the effect of circularity and of different amounts of overlap.
• Michael: Simulation is neatly modularized into functions. Uses shell commands to view and numpy.loadtxt to load kallisto output for analysis. In part 5, tests the effect of circularity.
• Sean: Installs kallisto by building from source. Simulation is lazily controlled by global variables in the notebook, so I have to rerun the whole notebook if I change my simulation conditions in part 5. Reads kallisto output with basic Python and with unix command line calls. Includes using matplotlib to make some simple graphs, comparing kallisto results to the true TPM parameters and to Moriarty's result.