Welcome to GammaHelix. This is still a Minimum Viable Product (MVP). We are currently building the secure pipeline for the AlphaGenome model. While the deep-learning integration is actively in progress, our UI, user routing, and documentation systems are fully live. Feel free to explore the user journey and let us know what you think!

Architecture Powered by AlphaGenome

Genomic interpretation,
quantified.

A specialized interface designed to translate complex 1Mb deep-learning outputs into clear, quantitative biological insights. Evaluate variations across multiple tissues in seconds.

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GammaHelix Interface Preview

Built for modern research workflows

GENOMICS INST.
BIO·TECH LABS
COMP·BIO INC
TARGET PHARMA

1,048,576

Base Pair Context

50+

Tissue Modalities

100%

Local API Privacy

0

Python Required

Focus on the biology, not the boilerplate.

Extracting meaning from predictive models traditionally requires custom Python scripting, matrix flattening, and manual data parsing. We handle the code infrastructure so you don't have to, bringing advanced computational power directly to your fingertips.

Stop depending on raw numerical outputs. GammaHelix minimizes your analysis time by instantly translating complex tensors into clear, actionable metrics. Visually compare the exact delta between your reference and mutated sequences with zero friction.

Target Modality Pancreas (ATAC-seq)
Accessibility Change -62.40%
Peak Status Lost (-1)
Confidence High

Who is GammaHelix for?

Bioinformaticians

Standardize your lab's prediction workflows. Generate reproducible, high-quality PDF reports for variants of unknown significance without writing a single line of Matplotlib.

Clinical Geneticists

Rapidly assess whether a newly discovered patient mutation disrupts critical regulatory elements (enhancers/promoters) in tissue-specific contexts.

Computational Biologists

Navigate 1Mb context windows dynamically. Pan upstream and downstream to visually identify long-range structural impacts caused by a single point mutation.

Platform Architecture

Built specifically to bridge the gap between predictive deep learning architectures and practical research outputs.

Full-Context Modeling

Sequences are automatically standardized and padded to 1Mb windows, ensuring distal enhancer-promoter elements and 3D folding contexts are computed.

Multi-Modal Inference

Simultaneously evaluate impacts. The platform automatically prioritizes ATAC-seq, DNAse, or RNA-seq prediction tracks based on ontology availability.

Dynamic Navigation

The interactive viewport lets you zoom out from 1.2kb details to 50kb regional views and pan across the sequence without waiting for server recalculations.

PDF Reporting Engine

Export your findings instantly. Generate clean, printable PDF reports containing visualizations, impact metrics, and query metadata for lab notebooks.

Frequently Asked Questions

Is my sequence data stored or logged?
No. GammaHelix operates as a secure gateway. Your input sequences and API keys are maintained in session-only browser storage and passed directly to the model endpoint. We do not log, store, or train on user genomic data.
How are "Peak Status" metrics calculated?
The platform utilizes SciPy's find_peaks algorithm on the backend. It scans both the reference and mutated prediction arrays, identifying significant signal spikes, and mathematically compares the counts to definitively label binding sites as Lost, Gained, or Stable.
Do I need to pad sequences to 1Mb manually?
No. You can paste sequences of any length (e.g., a 1.2kb promoter region). GammaHelix automatically handles the algorithmic padding up to the required 1,048,576 base pair context window required by the underlying model.
Can I run multiple tissues simultaneously?
Yes. You can select an unlimited number of target ontologies (tissues/cell lines) per query. GammaHelix processes them sequentially and provides a ranked table sorted by the absolute maximum impact score.

The Team

Just a researcher, an idea, and AI.

GammaHelix began the moment I came across Google's AlphaGenome — a genomic prediction model that genuinely amazed me. Realizing that accessing its full potential required writing code, the idea was immediate: build an interface that any researcher could use, no programming knowledge required. As development progressed, new ideas naturally emerged — tools for sorting and understanding transcriptomics data, workflows for exploring smaller bioinformatics models. These are directions I intend to build toward, while the core focus remains: giving researchers every possible way to interact with AlphaGenome. The honest truth is that I didn't have all the answers when I started. The learning happened through the building. Working alongside AI models like Gemini and Claude helped me bridge my life-sciences background with software development, turning an idea into a platform, What i rather belive is Work, Build & Learn Growth will follo-up.

Srirang Gaddamwar
Srirang Gaddamwar