SARS-CoV-2 CoCoPUTs

SARS-CoV-2 CoCoPUTs assembles codon usage data, codon-level mutation data, and secondary structure data for SARS-CoV-2 variants to derive trends in viral evolution.

Key Features:

• Codon usage analysis: Calculates codon usage, codon pair usage, dinucleotides, junction dinucleotides, GC content, effective number of codons (ENCs), and effective number of codon pairs (ENCPs) for coding sequences (CDSs).
• Mutation analysis: Tracks synonymous and nonsynonymous mutations at the codon level across SARS-CoV-2 variants.
• Secondary structure prediction: Performs whole-genome secondary RNA structure prediction for each variant using LinearPartition software and modified selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) data.
• Synonymous mutation-deprived structure analysis: Generates a synonymous mutation-deprived set of variant sequences and repeats secondary structure calculations to compare ensemble free energy.
• Temporal variant proportion integration: Pairs resulting statistics with variant proportion data over time to analyze evolutionary trends.
• Sequence curation: Implements strategies to reduce datasets of millions of sequences to one sequence per Pango lineage variant.

Scientific Applications:

• Viral evolution tracking: Monitors changes in codon usage, mutation rates, and GC content over multiyear periods to identify evolutionary trends.
• Mutation rate analysis: Quantifies the ratio of nonsynonymous to synonymous mutations and their relative accumulation over time.
• RNA structure validation: Validates predicted whole-genome secondary structures, including virus-activated inhibitor of translation (VAIT) stem loops, across multiple variants.
• Synonymous mutation functional assessment: Investigates the role of synonymous mutations in viral RNA free energy and stability.

Methodology:

Data are assembled from GISAID and NCBI Virus (GenBank), with sequence curation to one sequence per Pango lineage variant. Coding sequence analyses include calculation of codon usage, codon pair usage, dinucleotides, junction dinucleotides, mutations, GC content, ENCs, and ENCPs. Whole-genome secondary RNA structure prediction uses LinearPartition with modified SHAPE data. A synonymous mutation-deprived sequence set is generated and reanalyzed for secondary structure.