Reproduction of Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects

This repository contains code, data, and figures for reproducing the results presented in:

Citation Information
Chen, Y., Hu, R., Li, K., Zhang, Y., Fu, L., Zhang, J., & Si, T. (2023). Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects. ACS Synthetic Biology, 12(5), 1461–1473. https://doi.org/10.1021/acssynbio.2c00662

Overview

This project attempts to reproduce a subset of the main analyses and figures of the paper. It includes:

• Overview of the paper we are trying to replicate and the results that we obtained (chen_et_al-2023.MD)
• Preprocessed and raw data from the paper in (data/)
• Scripts used to process the raw data, analyze the data, and plot the figures for our replication attempt(analysis_pipeline/)
• Replicated figures (figures/)
• PDF versions of the RMD processing and analysis code showing what the output should look like. (analysis_pipeline/)

🗂️ Repository Structure

.
├── analysis_pipeline
│   ├── data_analysis.pdf
│   ├── data_analysis.RMD
│   ├── data_processing.pdf
│   └── data_processing.RMD
├── chen_et_al-2023.MD
├── data
│   ├── processed
│   │   ├── combinatorial_mutant_data.csv
│   │   └── single_mutant_data.csv
│   └── raw
│       ├── sb2c00662_si_001.xlsx
│       └── sb2c00662_si_002.xlsx
├── data_analysis_flowchart.png
├── figures
│   ├── fig_1b
│   │   ├── fig_1b_1.png
│   │   ├── fig_1b_2.png
│   │   └── fig_1b_3.png
│   ├── fig_2a.png
│   ├── fig_2b_heatmap.png
│   ├── fig_2b_lower.png
│   ├── fig_3a.png
│   ├── fig_3b.png
│   ├── fig_3d.png
│   └── fig_3e.png
└── README.MD

📦 Requirements

This project was written in R and was run in Rstudio Version 2024.12.1+563 (2024.12.1+563). To install Rstudio, follow the instructions for your given operating system: https://posit.co/download/rstudio-desktop/

It requires several R libraries and these are described at the beginning of the analysis_pipeline/data_processing.RMD and analysis_pipeline/data_analysis.RMD scripts.

Required Libaries (and Versions Used)

Library	Version
scales	1.3.0
openxlsx	4.2.8
pbapply	1.7.2
readxl	1.4.3
lubridate	1.9.4
forcats	1.0.0
stringr	1.5.1
dplyr	1.1.4
purrr	1.0.4
readr	2.1.5
tibble	3.2.1
ggplot2	3.5.1
tidyverse	2.0.0
tidyr	1.3.1
ggpmisc	0.6.1
ggpp	0.5.8.1
ggbeeswarm	0.7.2

How to Reproduce

1. Clone this repository:

   git clone https://github.com/RGodin-ISU/BCB546-Final_Project.git
   cd BCB546-Final_Project

2. Install dependencies (see above).

3. Run the data processing and data analysis scripts in the analysis_pipeline/ folder. Examples of the output are provided as PDFs in the folder:

   1. First, run analysis_pipeline/data_processing.RMD to process the raw data for analysis. The output processed data files are stored in data/processed.
   2. Second, run analysis_pipeline/data_analysis.RMD. The output figures are printed to the terminal but also saved to figures/ for analysis.

📁 Data

• Raw data: See data/raw/. These are the original datasets as described in the paper.
  • sb2c00662_si_001.xlsx contains activity data for single point mutations.
  • sb2c00662_si_002.xlsx contains activity data for the combinatorial (multi-site) mutations.
• Processed data: See data/processed/. These are derived from the raw data and used in the analysis scripts for figure generation.

Data Analysis Pipeline

📊 Figures

The main figures replicated from the paper are saved in figures/. Each figure is labeled corresponding to the figure number in the paper, e.g., fig_1b.png.

📝 Notes

• While the overall results are consistent, there are slight differences, likely due to unavailable details. These are described in more detail in chen_et_al-2023.MD.