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