01Purpose
PathoMap, a simple automated tool for genomic annotation.
Give it a gene and a variant. It returns one row of 104 columns, built from eleven public sources, covering clinical, functional, conservation, pathway, expression and model-organism data. Each value records where it came from.
Tasneem Mohamed · Mohamed Reyad
Seed round · 2026
02Problem
One variant takes eleven databases and a hand-built spreadsheet.
In practice that means chaining VEP, bcftools and gnomAD, reshaping the output in R, and writing a converter for each file format.
03Solution
One command returns the row, including the columns labs build by hand.
104 columns across clinical, functional, conservation, pathway, expression and model-organism data, each labelled with the source that produced it.
04Product
It resolves the variant first, which is where most errors begin.
Four steps, one command, and a run log that records what succeeded, failed or was served from cache.
01
Resolve
Notation to GRCh38 coordinates, checked against ClinVar.
SPDI: chromosome, position, bases.
02
Annotate
Clinical, frequency and protein-level predictions.
VEP: Ensembl's effect predictor.
03
Orthologs
Same gene in other species, residue projected.
Residue: the amino acid position.
04
Report
One table, plus a log of what each source returned.
104
columns, each source-tagged
660
tests, all offline
11
data sources
~1 min
per variant
05Customer discovery
Discovery is in progress: three segments, three things we are testing.
N of 25 interviews done. Themes and quotes go in the appendix as they close, and we are not quoting findings we have not coded.
Genomics cores
Run annotations for other labs.
Site licence before seats.
Rare-disease labs
Need model-organism context for follow-up work.
Cross-species is the first step dropped.
Model-organism groups
Already work this way, slowly.
Reproducibility beats coverage.
06Competition
Cross-species tools exist. They are not batch pipelines.
MARRVEL does the lookup one query at a time. VEP and ANNOVAR batch-annotate thousands of variants, but stay inside the human genome.
Placement follows each tool's documented use, not a benchmark. MARRVEL is the neighbour to benchmark against.
The gap is workflow, not capability: a list of a thousand variants, one table, every value traceable.
07Market
A $150M segment today, heading for $500M by 2030.
Annotation software for research. The larger interpretation market above it is mostly clinical; it is context, and we do not claim it today.
08Business model
Labs pay per seat, cores pay for a licence, teaching is free.
Private deployment for institutions that cannot send data out. The source is proprietary; the method and the operational work are the hard parts to copy.
Labs
Per-seat subscription, billed annually.
$ TBD
Cores
One site licence covering every user at the facility.
$ TBD
Institutions
Private deployment behind their firewall.
$ TBD
09Team
Two builders, and a gap we know about.
We built the pipeline, the data integration, the interface and the deployment. What we do not have in-house is clinical and regulatory experience, or anyone whose job is selling.
Tasneem Mohamed
role line
Mohamed Reyad
role line
Supervisor: Olaitan I. Awe, Ph.D. (ASBCB)
Acknowledgements: Hackbio, ASBCB, Ensembl, NCBI, UCSC
10The ask
Raise amount to reach paying pilots and a published benchmark.
months of runway, three milestones, and we are building this as a company rather than a side project.
Q1
Pilot
TBD
Q2
Benchmark
TBD
Q3
Licences
TBD
Q4
Company
TBD
