Ensembl COVID-19

Ensembl COVID-19 provides an integrated resource for curation, annotation, and comparative analysis of SARS-CoV-2 genomic data to support variant identification, epidemiological tracking, and assessment of effects on vaccines and therapeutics.

Key Features:

• Ensembl Gene Set: A SARS-CoV-2-specific gene set annotated on the reference SARS-CoV-2 assembly.
• Multiple Variant Sets: Incorporation of multiple variant sets, including novel variation calls, for tracking viral strain evolution and spread.
• Integrated Annotations: Annotations from several external resources integrated into the reference SARS-CoV-2 assembly.
• Adapted Annotation Methods: Genome annotation methods adapted to model ribosomal slippage in coronaviruses.
• High Confidence Variants: Stringent filtering applied to identify high-confidence variants.
• Comparative Genomics Pipelines: Ensembl comparative genomics pipelines applied to viruses to compare SARS-CoV-2 with other coronaviruses.
• Ensembl Variant Effect Predictor Integration: Integration with Ensembl Variant Effect Predictor to assess potential impacts of variants.
• Regular Updates: Content is regularly updated to incorporate new sequence and variant data.

Scientific Applications:

• Epidemiological Tracking: Use of genome sequencing data to track virus transmission patterns.
• Variant Identification: Identification of emerging variants and novel variation calls for monitoring viral evolution.
• Vaccine and Therapeutic Impact Assessment: Assessment of how genetic changes might influence vaccine efficacy and therapeutic effectiveness.
• Comparative Genomics: Comparative analysis of SARS-CoV-2 and other coronaviruses to study evolution and viral biology.
• Functional Variant Effect Prediction: Prediction of potential functional effects of variants via integration with VEP.

Methodology:

Curation and annotation of SARS-CoV-2 genomic data; integration of multiple variant sets and external annotations into the reference assembly; adaptation of genome annotation methods to model ribosomal slippage; application of stringent variant filtering; use of Ensembl comparative genomics pipelines; and integration with Ensembl Variant Effect Predictor.