<b>Sliding Window CpG Analysis of Civet SARS-CoV (2003-2004, AY304486.1)</b>

This project analyzes the distribution of CpG sites across the genome of Civet SARS-CoV (2003-2004, AY304486.1) using a sliding window approach. Key findings include:CpG Counts : The sliding window analysis reveals regions with elevated CpG densities, suggesting potential functional significance.- Observed/Expected Ratios : Most regions exhibit CpG depletion (O/E ratio &lt; 1), consistent with evolutionary pressures observed in coronaviruses.<br>&gt; Dataset Includes:- A tab-delimited file (`Civet_SARS_CoV_sliding_window_cpg_analysis.txt`) containing CpG counts and O/E ratios for each sliding window.- Visualizations: - Bar chart showing CpG counts across windows. - Line plot illustrating O/E ratios.- Python scripts used for performing the sliding window analysis and generating plots.- Input genome file (`AY304486.gb`).<br>&gt; Methods:The analysis was performed using the following steps:1. Divided the genome 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>&gt; Implications:This work contributes to understanding the genomic architecture of Civet SARS-CoV and its evolutionary strategies. The findings align with broader trends observed in coronaviruses, highlighting shared mechanisms of CpG dynamics.DOI: 10.6084/m9.figshare.28705613