About LYFE Sciences

Built by a genome analyst, for everyone

LYFE Sciences began with a familiar problem: gathering the evidence for a single variant meant moving between too many databases. Project HERA grew from that workflow.

LYFE Sciences Project: HERA Since 2023
01

How it started

LYFE Sciences began as a personal project in 2023 to simplify genetic variant interpretation in day-to-day work. As a genome analyst, I frequently encountered the challenge of quickly accessing comprehensive and reliable genetic variant information.

To address that, I built LYFE Sciences: a workspace that brings variant annotations, reference records and interpretation evidence together.

02

What I'm trying to do

My main goal is to streamline variant analysis by reducing the need to navigate between multiple resources or pay for third-party applications.

I continue to refine LYFE Sciences as interpretation guidance, data sources and analysis workflows evolve.

03

How it works

LYFE Sciences is designed around the real workflows of genomic professionals. Rather than requiring users to juggle ClinVar, OncoKB, JAX-CKB, COSMIC and other databases independently, Project HERA aggregates, normalises and presents that data in a unified interface.

Each report identifies the sources and evidence captured for that interpretation. Source coverage and update timing may vary.

Development is guided by practical variant-analysis workflows and changes to the underlying data resources.
Try a variant query
04

The next step: Project HERA

Project HERA is the third iteration of LYFE Sciences: a variant-interpretation pipeline that brings together evidence retrieval, normalisation, framework selection, criterion assessment and structured reporting.

It is designed around the idea that reliable AI needs controlled inputs, explicit evidence mapping, framework-aware decision logic, and outputs that are easy to audit and operationalise.

The pipeline combines automated evidence retrieval with structured criterion assessment, prioritises ClinGen and gene-specific specifications, and produces structured review artifacts for downstream systems.

01
Retrieval
02
Normalisation
03
Framework selection
04
Criterion assessment
05
Structured output

Project HERA is designed to retain criterion-level rationales and source references alongside each classification, so the underlying assessment can be reviewed.

HERA is an AI system, and it can make mistakes. Traceability is the point of the architecture above — every criterion carries its sources so a classification can be checked rather than taken. Review the evidence before relying on any call.
05

Why it matters

By sharing LYFE Sciences, I hope to make genetic analysis less overwhelming, and help others like myself improve their work.

Faster variant interpretation means analysts spend less time hunting for information and more time on the judgment the job actually needs.

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