AI for Genomic Variant Interpretation: AlphaGenome & Functional Evidence
Connect AI predictions with biological evidence to interpret variants and guide the next experiment.
Skills you will gain:
About Workshop:
This workshop develops a practical approach to interpreting regulatory and splice-related variants using AlphaGenome predictions, genomic annotations, and experimental functional evidence. Participants investigate two connected teaching cases—one regulatory variant and one splice-related variant—progressing from record verification to prediction interpretation, evidence assessment, and experimental validation planning.
Aim: To equip participants with practical skills to interpret AlphaGenome predictions, assess functional evidence, and develop reproducible research interpretations with justified next steps.
Workshop Objectives:
- Verify variant identity, genome assembly, alleles, and transcript context.
- Interpret predicted regulatory and splicing effects in relevant tissues.
- Evaluate functional-assay quality and biological relevance.
- Reconcile supporting, conflicting, and missing findings.
- Prepare a structured interpretation and targeted validation plan.
What you will learn?
Day 0 — Foundations, Case Orientation & Environment Readiness (30 Min )
Focus: Establish essential terminology and verify access to workshop materials.
The mentor introduces the two teaching cases and distinguishes sequence variation, predicted molecular effects, experimental observations, and disease causation. Participants review essential variant fields, open the prepared notebook, and inspect a sample prediction plot. The session concludes with a readiness check and setup questions.
🛠️ Hands-on: Explore Your First Variant Record
- Open the supplied notebook and teaching datasets.
- Identify chromosome, position, reference allele, alternate allele, and genome assembly.
- Inspect the variant’s location using a supplied genome-browser link.
- Open a precomputed AlphaGenome output and identify its main components.
🧰 Tools: Google Colab, pandas, genome-browser links.
Deliverable: Annotated variant record and working notebook.
Day 1 — Establish the Context: Variant Verification & Biological Hypotheses
Focus: Determine what each variant could affect before examining AI predictions.
Topics Covered
- Regulatory and splice-related variants: mechanisms and interpretation challenges.
- Genome assemblies, coordinate conventions, and reference-allele verification.
- Transcript selection and strand-aware interpretation.
- Population frequencies and ancestry-representation limitations.
- ClinVar review status and conflicting assertions.
- Regulatory-element context and candidate target genes.
- Developing a testable molecular hypothesis.
- Identifying evidence gaps before prediction analysis.
🛠️ Hands-on: Investigate Two Teaching Variants
- Verify both variant records using supplied annotations and browser views.
- Identify the relevant transcript, splice region, or regulatory element.
- Review population-frequency evidence and database assertions.
- Write one molecular hypothesis and one evidence gap for each variant.
🧰 Tools: Google Colab, Ensembl/UCSC, curated annotation exports.
Day 2 — Interpret AlphaGenome: Molecular Effects & Tissue Relevance
Focus: Translate prediction outputs into specific, qualified biological interpretations.
Topics Covered
- AlphaGenome inputs, prediction modalities, and scoring concepts.
- Comparing reference and alternate sequence predictions.
- Selecting biologically relevant tissue and cell-type tracks.
- Interpreting expression, splice-junction, and chromatin-accessibility changes.
- Effect direction, genomic location, and score context.
- Why prediction scores are not probabilities of pathogenicity.
- Recognising conflicting outputs and model limitations.
- Recording analysis settings and prediction provenance.
🛠️ Hands-on: Compare Predicted Variant Effects
- Visualise reference-versus-alternate outputs using the prepared notebook.
- Examine the predicted regulatory effect and splice-related change.
- Compare relevant and less-relevant tissue contexts.
- Summarise each predicted effect, supporting observations, and remaining uncertainty.
🧰 Tools: AlphaGenome outputs, Google Colab, matplotlib.
Day 3 — Functional Evidence Clinic: Resolve Uncertainty & Plan Validation
Focus: Assess whether experimental findings support the predicted mechanism.
Topics Covered
- Computational predictions versus experimentally observed effects.
- Reporter assays for regulatory questions and RNA assays for splicing questions.
- Controls, replication, dynamic range, and reproducibility.
- Matching assay design to tissue, transcript, and proposed mechanism.
- Avoiding double-counting of related evidence.
- Reconciling agreement, disagreement, and missing findings.
- Brief orientation to ACMG/AMP and ClinGen evidence principles.
- Selecting informative follow-up experiments and reporting limitations.
🛠️ Hands-on: Review Evidence & Design the Next Experiment
- Evaluate each variant’s supplied functional results and assay controls.
- Combine annotations, predictions, and observations in an evidence matrix.
- Identify supported conclusions, conflicting findings, and unresolved questions.
- Complete an interpretation report proposing one follow-up experiment per variant, including essential controls.
🧰 Tools: Evidence-review workbook, Google Colab, reporting template.
Mentor Profile
Fee Plan
Important Dates
21 Nov 2026 Indian Standard Timing 4:30 PM
18 Nov 2026 to 21 Nov 2026 Indian Standard Timing 5:00 PM
Get an e-Certificate of Participation!
Intended For :
- Graduate and postgraduate students: Genetics, biotechnology, bioinformatics, molecular biology, and biomedical sciences.
- PhD scholars: Functional genomics, gene regulation, RNA biology, computational biology, and disease-mechanism research.
- Academicians: Faculty developing genomics teaching, research projects, and interdisciplinary supervision.
- Industry professionals: Genomics analysts, bioinformaticians, and biotechnology or pharmaceutical R&D teams.
Career Supporting Skills
Workshop Outcomes
- Verify and document a research variant consistently.
- Interpret AlphaGenome predictions in genomic and tissue context.
- Distinguish predicted effects from demonstrated disease causation.
- Evaluate whether functional experiments address the proposed mechanism.
- Explain conflicting findings and prioritise missing evidence.
- Produce a structured interpretation with a justified experimental next step.