Alphagenome Single Variant Analysis

Interpretation Guide

This guide covers biological interpretation, signal patterns, motif analysis, and the pre-report reasoning checklist. Read this before writing any report.

Signal Patterns Reference

Use this table to identify mechanisms from model outputs.

Expression & Regulatory

Some example mechanisms (non-exhaustive, use your reasoning ability):

Splicing

Splicing Interpretation Rules

• Specific mechanisms only: Do NOT write "likely exon skipping or intron retention" if data allows you to distinguish. Use the junction coordinates.

• Proxy signals: If Expression Loss (-0.999) >>> Splicing Disruption (0.99), the primary mechanism is likely transcriptional (promoter/enhancer loss), not splicing. Exception: clearly aberrant new splice junction.

• Complex outcome: If multiple new cryptic sites compete with canonical site, describe as "complex outcome of new candidate sites."

Tissue and Location Verification

• Verify Tissue Lineage: Both top hits and disease-relevant tissues are valuable. Do NOT ignore unexpected top hits (e.g., "Mesenchymal Stem Cell" for an erythroid gene), as they may reveal regulatory potential. However, you MUST also search for and include the expected disease-relevant tissues (e.g., K562/Erythroblast) to ensure the report directly addresses the clinical context.

• Verify Tissue Relevance:

  • Match Disease to Organ System: If the disease is "Cardiomyopathy", you MUST use "heart", "atrium", "ventricle", or "cardiomyocyte".

  • Avoid Generic Proxies: Do NOT use "Smooth Muscle Cell" for Heart (Cardiac Muscle != Smooth Muscle). Do NOT use "Fibroblast" for Brain.

  • Search Strategy: If a specific cell type query (e.g., "cardiomyocyte") yields 0 hits, search for the organ (e.g., "heart", "cardiac"). While you should report unexpected top hits in other tissues, ensure you also include the relevant organ/tissue to address the clinical context.

• Verify Location (GTF Overlap): Do NOT guess "Promoter" vs "5' UTR". Check the GTF coordinates. A variant at -30 might be in the 5' UTR if the TSS is upstream. Use lookup_gene_info.py --coord='chr:pos' or bedtools intersect to confirm.

Score Interpretation

Raw Score vs Quantile Score

• Raw score: Effect magnitude, scorer-specific scale. Use for comparing tissues WITHIN the same scorer only.

• Quantile score: Percentile rank vs common variants. Use to assess significance. Saturates at ±0.999990.

• See variant-scoring-info section in alphagenome-api.md for mathematical details.

Magnitude Rules

[!NOTE] Disclaimer: The interpretation of raw scores depends heavily on the specific modality and assay type. The thresholds below are general rules of thumb, primarily derived from RNA-seq data, and should not be treated as absolute rules. Always validate quantitative scores by visually inspecting the plotted tracks.

• < 0.1: Typically indicates No Significant Effect or background noise, even if the quantile score is high.

• 0.1 - 0.5: Often represents a subtle or weak effect. Report as a potential subtle change.

• 0.5 - 1.0: Suggests a moderate effect (roughly corresponding to a 1.4-2x fold change in RNA-seq).

• > 1.0: Strong effect (generally >2x fold change for RNA-seq).

• RNA-seq raw_score ≈ log2 fold-change: -4 ≈ 16-fold reduction, -1 ≈ 2-fold reduction, -0.35 ≈ 1.27-fold reduction.

• NO percentage interpretation: Raw scores are NOT percentages. Do not interpret "0.09" as "9%".

• Relative Magnitude: Use relative comparisons (e.g., "low magnitude compared to TERT's 1.5") or qualitative terms (e.g., "marginal effect").

• Always cite the raw score in reports (e.g., "Raw Score -1.89").

High Quantile + Low Raw Score

This is the most common trap. A quantile of 0.99+ with |raw_score| < 0.1 is effectively NO MOLECULAR EFFECT. This occurs in low-expression genes where variance stabilization inflates quantiles. Report as "No Significant Effect" and explain the statistical artifact.

Negative Results & Scientific Integrity

[!CRITICAL] Most variants are Benign. Do not "stretch" to find a mechanism where none exists.

• Value of Negative Results: Reporting "AlphaGenome predicts NO molecular effect" is a valuable scientific finding.

• Strict Anti-Speculation: Do not invent mechanisms (e.g., "cryptic splice site", "enhancer disruption") unless explicitly shown by the model. If distinct evidence is missing, use broad terms like "predicted splicing disruption".

• Occupancy ≠ Disruption: A variant landing in an active enhancer (e.g., H3K27ac peak present) or promoter (e.g. before gene TSS) does not imply disruption. If REF and ALT tracks are identical and scores are near zero, the variant has no predicted effect. Do not claim disruption based on location alone.

• Model Limitations: AlphaGenome is a DNA-to-molecular phenotype model but has limited scope of modeled molecular phenotypes. For example, it does NOT model:

  • Post-Transcriptional RNA Biology: Beyond splicing (e.g., RNA secondary structure folding, macromolecular assembly, transport).

  • miRNA Processing: Due to low abundance or short length of transcripts.

  • Protein Coding Effects: Missense, nonsense, or frameshift mutations affecting protein structure or function.

  • Developmental Specificity: Effects restricted to specific developmental timeframes not represented in the training data.

  • Environmental or Dynamic Contexts: Effects triggered by specific external stimuli or dynamic cell states not captured by the static biosample profiles.

ISM (In-Silico Mutagenesis) Interpretation

Reading SeqLogo Plots

• Tall letters at variant position: Mutation directly disrupts an important motif.

• Positive height = activating when present; Negative = repressive.

• REF vs ALT: Strong REF letter → ALT disrupts binding site. Strong ALT letter → ALT creates new binding site.

Motif Identification Rules

Use your base knowledge about sequences (semantic knowledge) to identify motifs when available. For well-known canonical sequences, you can identify directly:

For less obvious sequences: use your knowledge of TF motifs, and if no confident match is found, report the consensus sequence with "resembles" or "potential" qualifier (e.g., "Disrupted motif resembling GATA consensus").

Reverse Complement

ALWAYS check the reverse complement. The model sees double-stranded DNA, so the motif may be on the minus strand (e.g., AATAAA ↔ TTTATT).

Negative ISM

If the ISM plot shows only tiny bars (<0.1 height) or random noise, report "No specific motif disruption identified." Do not force a match.

Model Limitations

AlphaGenome predicts transcription and splicing from DNA sequence. It does NOT model:

• miRNA processing (low abundance / short length)

• RNA secondary structure (e.g., 3' UTR selenocysteine insertion sequences, snRNA stem-loops)

• Protein folding or stability (missense effects)

• Catalytic activity (variant may produce stable but non-functional RNA)

• Developmental timing or stress-response contexts

Rules:

• If the gene is a non-coding RNA (snRNA, tRNA, rRNA) and you see High Quantile + Low Raw Score: report "No Significant Effect" and state the structural limitation.

• If the known mechanism is protein stability: state that benign regulatory scores do not rule out protein-level pathogenicity.

• If the known mechanism is enzymatic/catalytic: do not rule out pathogenicity from neutral expression scores.

Ontology Resolution Best Practices

The resolve_ontology_terms script implements smart matching. Key principles:

1. Specificity first: Prefer "Naive thymus-derived CD4-positive..." over generic "T-cell".

2. No silent fallbacks: Better to return [NOT FOUND] than silently map "Kupffer cell" → "Liver".

3. You bridge disease → tissue: The script matches terms, not diseases. You must identify that "Multiple Sclerosis" → Oligodendrocyte, T-cell, Brain, then query those terms.

4. Abbreviation handling: Common abbreviations (lv → left ventricle, huvec → human umbilical vein endothelial) are built in.

5. Conservation of specificity: A match is rejected if any substantive query token is missing from the target. Only generic stopwords ("human", "tissue", "sample") can be safely dropped.

6. Partial match flagging: If a partial match or fallback is necessary, it must be explicitly flagged as [PARTIAL MATCH] in the output.

Tissue selection for reports:

• Match disease to organ system: "Cardiomyopathy" → heart, atrium, ventricle, cardiomyocyte.

• Avoid generic proxies: Do NOT use "Smooth Muscle Cell" for heart (cardiac ≠ smooth), "Fibroblast" for brain.

• Search strategy: If specific query (e.g., "cardiomyocyte") yields 0 hits, search for the organ ("heart", "cardiac").

Pre-Report Reasoning Checklist

Complete this BEFORE writing the report. Ground every interpretation in specific visual observations from the plots.

[!CAUTION] Do not pursue only the "obvious" answer (nearest gene, literature-known tissue). Focus on where the model shows the strongest signal. Ensure you have examined ALL significant scores, not just a convenient "top 5".

1. Cross-Tissue & Cross-Modality Patterns

• [ ] Analyze tissue specificity: Determine if the effect is universal or tissue-restricted, and explain why.

• [ ] Check modality agreement: Verify if different modalities agree within each tissue (concordant = strong evidence; discordant = complex regulation or potential model limitation).

• [ ] Identify largest effect: Pinpoint which tissue shows the largest effect and assess its biological plausibility.

2. Hypothesis Validation

• [ ] Cite supporting plots: Explicitly state which plots support or refute the proposed molecular mechanism.

• [ ] Cite supporting scores: Explicitly state which variant scores support or refute the proposed molecular mechanism.

3. ISM SeqLogo

• [ ] Identify motif disruptions: Detail the motif disruptions evident from REF vs ALT differences.

• [ ] Check consistency: Determine if disruptions are consistent across tissues or tissue-specific.

• [ ] Explain magnitude differences: Use ISM plots to explain magnitude differences between tissues.

4. Synthesis

• [ ] Define primary molecular mechanism: Trace the chain of events: E.g. motif disruption → chromatin effect → transcriptional consequence → potential disease link.

• [ ] Avoid speculation: If scores are low and plots are flat, report "No Significant Effect" instead of forcing a story.

• [ ] Address original query: Ensure the report directly answers the user's initial question.

5. Report Readiness

• [ ] Write a narrative: Focus on telling a biological story, not just dumping data.

• [ ] Verify references: Ensure all plot files are present and correctly referenced.

• [ ] Embed all views: Include ISM plots, Detail Views, and Whole-Gene Views where applicable.

• [ ] Confirm evidence: Verify that allegations of splicing have the required splicing-related plots showing effects, expression changes have RNA-seq differences, and regulatory effects have DNASE/ChIP peak changes.