AlphaGenome vs Enformer vs Sei: Which DNA Variant Effect Predictor Should You Use?

Why Variant Effect Prediction Matters

Over 90% of disease-associated variants identified by genome-wide association studies (GWAS) fall in non-coding regions of the genome. Understanding how these variants alter gene regulation is one of the central challenges in modern genomics. Variant effect predictors use deep learning to estimate how a DNA sequence change affects transcription, splicing, and chromatin state, bridging the gap between statistical association and molecular mechanism.

Three models now dominate this space: AlphaGenome, Enformer, and Sei. Each takes a fundamentally different approach to the problem, and choosing the right one depends on your research question, throughput requirements, and the type of functional readout you need.

AlphaGenome Overview

Released by DeepMind in 2026, AlphaGenome is a multi-task genomic foundation model that jointly predicts gene expression levels, mRNA splicing patterns, chromatin accessibility, and transcription factor binding from raw DNA sequence. It processes input windows of up to one megabase, captures distal regulatory elements, and outputs tissue-specific predictions across dozens of human cell types. AlphaGenome achieves state-of-the-art performance on variant effect benchmarks published in Nature, outperforming prior models on both expression and splicing prediction tasks.

Enformer Overview

Enformer, also from DeepMind (2021), was the first transformer-based model to capture long-range genomic interactions up to 100 kilobases from a target gene. It predicts gene expression and chromatin tracks (CAGE, DNase, histone marks) at 128-bp resolution. Enformer significantly improved on its predecessor Basenji2 by using self-attention to model enhancer-promoter communication over long distances, making it particularly useful for studying distal regulatory elements.

Sei Overview

Sei, developed by the Expecto team at Princeton, takes a classification-based approach to regulatory genomics. Rather than predicting continuous expression values, Sei assigns DNA sequences to 40 interpretable regulatory sequence classes (e.g., strong enhancer, CTCF-bound insulator, polycomb-repressed). It also predicts over 21,000 chromatin profiles. This makes Sei especially useful for categorizing the regulatory function of a genomic region rather than quantifying its precise expression impact.

Key Differences

Genomic Coverage and Resolution

• AlphaGenome: Up to 1 Mb input window, base-pair resolution variant scoring, multi-task output (expression, splicing, chromatin, TF binding).
• Enformer: 200 kb input window, 128-bp bin resolution for output tracks. Excellent for long-range regulatory context but lower output granularity.
• Sei: 4 kb input window. Classifies sequences into regulatory categories rather than predicting continuous values. Limited long-range context.

Tissue Specificity

• AlphaGenome: Predicts effects across multiple tissues simultaneously. Tissue-resolved expression fold-changes enable pharmacogenomic and disease-context analysis.
• Enformer: Predicts tissue-specific CAGE and chromatin tracks, but requires post-processing to derive variant effect scores per tissue.
• Sei: Sequence class assignments are not tissue-specific by default. Chromatin profile predictions span many cell types but require additional interpretation.

Speed and Compute

• AlphaGenome: GPU-required. Processes variants in seconds via API, but heavier compute per prediction than Sei.
• Enformer: GPU-required. Similar compute footprint to AlphaGenome. Long input windows increase memory demands.
• Sei: Can run on CPU for small batches due to shorter input windows. Fastest of the three for classification tasks.

When to Use Each Tool

Choose AlphaGenome When:

• You need quantitative, tissue-specific expression and splicing effect predictions
• You are interpreting non-coding GWAS hits or somatic mutations in cancer
• You want a single model that covers expression, splicing, and chromatin effects
• You need state-of-the-art accuracy on variant effect benchmarks

Choose Enformer When:

• Your analysis focuses on long-range enhancer-promoter interactions
• You need high-resolution chromatin track predictions (CAGE, DNase, histone marks)
• You are studying regulatory landscapes rather than scoring individual variants
• You have existing pipelines built around Enformer output formats

Choose Sei When:

• You want to classify a genomic region into a regulatory category (enhancer, insulator, repressed)
• You need fast, lightweight inference without GPU infrastructure
• Your workflow requires interpretable sequence class labels rather than continuous predictions
• You are annotating large sets of regulatory elements for downstream filtering

How to Use AlphaGenome via SciRouter API

SciRouter provides AlphaGenome as a hosted endpoint with GPU acceleration. Submit variant coordinates and receive tissue-specific predictions without any infrastructure setup.

import requests

API_KEY = "sk-sci-your-api-key"
BASE = "https://api.scirouter.ai/v1"
headers = {"Authorization": f"Bearer {API_KEY}"}

# Score variants for expression and splicing effects
response = requests.post(f"{BASE}/genomics/alphagenome",
    headers=headers,
    json={
        "variants": [
            {"chromosome": "chr17", "position": 41245466,
             "ref": "A", "alt": "G", "gene": "BRCA1"},
            {"chromosome": "chr7", "position": 140753336,
             "ref": "A", "alt": "T", "gene": "BRAF"}
        ],
        "genome_build": "GRCh38",
        "tissues": ["liver", "brain", "blood"]
    })

result = response.json()["data"]
for v in result["variants"]:
    expr = v["predictions"]["expression_effect"]
    print(f"{v['variant']['gene']}: {expr['direction']} "
          f"(log2FC={expr['log2_fold_change']:.2f}, "
          f"confidence={expr['confidence']:.2f})")
    print(f"  Severity: {v['overall_assessment']['severity']}")

Conclusion

AlphaGenome, Enformer, and Sei each solve different facets of the variant effect prediction problem. AlphaGenome offers the most comprehensive, tissue-resolved predictions and leads on current benchmarks. Enformer remains the best choice for studying long-range regulatory architecture. Sei provides fast, interpretable regulatory classification with minimal compute.

For most genomics researchers working on variant interpretation, AlphaGenome via SciRouter offers the strongest combination of accuracy, tissue specificity, and ease of use. Sign up for a free API key to start scoring variants in minutes with no GPU setup required.