CGB

CGB analyzes and visualizes large-scale SARS-CoV-2 genomic datasets to investigate viral evolution, mutation patterns, and transmission dynamics.

Key Features:

• Large-Scale Genome Data Integration: Manages and analyzes over one million high-quality SARS-CoV-2 genome sequences together with transmission-related metadata.
• Efficient Phylogenetic Visualization: Implements a node-picking rendering strategy to visualize annotated evolutionary trees of SARS-CoV-2 genomes.
• Genomic Data Filtering: Enables filtering of pre-analyzed genomic data based on user-defined criteria to investigate transmission and evolutionary patterns.
• Evolutionary Signal Detection: Identifies genomic regions exhibiting accelerated evolution and signatures of ongoing positive selection.
• Conserved Genomic Region Identification: Detects genomic regions conserved in SARS-CoV-2 but absent in other coronaviruses, suggesting potential functional elements.
• Lineage Nomenclature System: Introduces a binary nomenclature scheme for classification of internal SARS-CoV-2 lineages.

Scientific Applications:

• Genomic Epidemiology of SARS-CoV-2: Tracks viral mutations, lineage diversification, and transmission patterns during the COVID-19 pandemic.
• Viral Evolution Analysis: Investigates evolutionary dynamics and selective pressures acting on SARS-CoV-2 genomes.

Methodology:

CGB integrates large-scale SARS-CoV-2 genomic sequences and associated metadata, constructs annotated evolutionary trees, and applies node-picking rendering and evolutionary analyses to detect mutation patterns and selection signals.