Tertiary (Manual) Analysis¶

Tertiary analysis in AIVA provides a workspace for focused, per-sample variant interpretation. The table view displays your variant data with full filtering, sorting, and column customization, while AIVA Chat is available as a floating bubble in the bottom right that expands into a chat panel scoped to the selected sample.

Accessing Tertiary Analysis¶

1. Navigate to the Samples page.
2. Select a sample from the sample list and click the Analyze button.
3. The tertiary analysis view opens with the table view displaying the sample's variant data.
4. Click the AIVA Chat bubble in the bottom right to open the AI assistant.

Table View¶

The table view provides the full data exploration experience:

• All columns are available via the column chooser, including VCF fields, INFO subfields, and Small Variant Annotation / Structural Variant Annotation columns.
• Filtering lets you narrow the variant list using text, numeric, date, and multi-select filters.
• Sorting by any column to prioritize variants of interest.
• Variant flagging directly in the table to mark variants as Pathogenic, VUS, Benign, etc.
• Threaded comments on individual variants for discussion and documentation.
• ACMG classification accessible from the variant row for evidence-based assessment. See ACMG Classification.

AIVA Chat¶

The floating chat bubble expands into an AI assistant connected to the selected sample's data:

• AIVA can query the sample's data directly using the Genomic Data Query tool.
• Ask questions about specific variants visible in the table, or request summaries across the entire dataset.
• Use Variant Annotation for real-time ClinVar, gnomAD, and prediction score lookups.
• Search literature with the Biomedical Literature tool and the web for evidence.
• Generate plots and run statistical tests with the Code Interpreter.

Typical Tertiary Analysis Workflow¶

Step 1: Initial filtering¶

Apply filters to reduce the variant list to candidates of interest:

1. Filter FILTER column to PASS to exclude low-quality calls.
2. Filter by allele frequency (e.g., gnomAD AF < 0.01) to focus on rare variants.
3. Filter by consequence type (e.g., missense, frameshift, stop gained) to focus on functional variants.

Step 2: Review and flag¶

Scroll through the filtered variants and flag those requiring attention:

1. Review each variant's annotation columns (Gene, Consequence, SIFT, PolyPhen, ClinVar).
2. Flag variants using the appropriate category (Primary, Secondary, etc.).
3. Add comments to document your initial impressions.

Step 3: AI-assisted deep dive¶

For each flagged variant, use AIVA to gather additional evidence:

1. Ask AIVA to look up ClinVar and gnomAD data for the variant.
2. Request a literature search for the gene and associated conditions.
3. Query the knowledge graph for drug-target interactions if relevant.
4. Check for related clinical trials.

Step 4: Formal classification¶

For variants requiring a formal assessment, open the ACMG classifier and apply criteria based on the evidence gathered.

Step 5: Export¶

Export your flagged and classified variants for inclusion in clinical reports or team review.

Tips¶

• Filter aggressively first: Reduce the variant list to a manageable size before starting detailed review. Most samples contain thousands of variants, but only a fraction are clinically relevant.
• Use AIVA for repetitive lookups: Instead of manually checking ClinVar or gnomAD for each variant, ask AIVA to batch-query or look up individual variants as you encounter them.
• Document as you go: Add comments and flags during review rather than after. This creates a real-time record of your interpretation process.