This project analyzes the distribution of CpG sites across the genomes of two Bat SARS-like coronaviruses, ZXC21 and ZC45 (2015-2017), using a sliding window approach.

This project analyzes the distribution of CpG sites across the genomes of two Bat SARS-like coronaviruses, ZXC21 and ZC45 (2015-2017), using a sliding window approach. Key findings include:<br>- CpG Counts : Both viruses exhibit regions with elevated CpG densities, suggesting potential functional significance.- Observed/Expected Ratios : Most regions show CpG depletion (O/E ratio &lt; 1), consistent with evolutionary pressures observed in coronaviruses.- Comparison : The results for ZXC21 and ZC45 are nearly identical, indicating similar CpG dynamics and evolutionary strategies.<br> Dataset Includes:- Tab-delimited files (`ZXC21_sliding_window_cpg_analysis.txt`, `ZC45_sliding_window_cpg_analysis.txt`) containing CpG counts and O/E ratios for each sliding window.- Visualizations: - Bar charts showing CpG counts across sliding windows for both viruses. - Line plots illustrating observed/expected (O/E) ratios for both viruses.- Python scripts used for performing the sliding window analysis and generating plots.- Input genome files (`MG772933.gb`, `MG772934.gb`).<br> Methods:The analysis was performed using the following steps:1. Divided the genomes into fixed-size windows (100 bp) with a step size of 50 bp.2. Calculated CpG counts and observed/expected (O/E) ratios for each window.3. Generated visualizations using Python libraries like Pandas and Matplotlib.<br> Implications:This work contributes to understanding the genomic architecture of Bat SARS-like coronaviruses and their evolutionary strategies. The findings align with broader trends observed in coronaviruses, highlighting shared mechanisms of CpG dynamics.<br>Published on Figshare: 10.6084/m9.figshare.28705895