FlowCyto
Web Application - developed by DIMP

FLOWCYTO

Blood Cell-type identification tool

Input a panel of gene markers and get a ranked, scored, probabilistic prediction of the cell populations present — powered by CellMarker 2.0 DataBase and PanglaoDB.

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FlowCyto - BLOOD-cell version

What is FlowCyto?

FlowCyto is a bioinformatics web tool designed to identify cell populations from gene expression profiles. Given a list of gene symbols in HGNC format, the predictor cross-references them against thousands of gene–cell relationships extracted from the CellMarker and PanglaoDB database.

Each candidate cell type receives a cumulative weighted score based on how many queried genes are known markers — and with what relevance. Results are ranked and expressed as probability percentages.

Every query is logged per user, building a personal prediction history over time. FlowCyto bridges the gap between raw flow cytometry data and reliable cell-type annotation.

Gene queries
DB
CellMarker source
%
Probability ranking
<1s
Response time
FlowCyto - BLOOD-cell version

What need does it solve?

Manual bottleneck

Identifying cell types from gene panels requires specialist knowledge and hours of cross-referencing scattered literature.

No quantitative rank

Existing tools rarely provide confidence scores or probability metrics, leaving researchers without a reliable decision framework.

Fragmented databases

CellMarker is comprehensive but not directly queryable via a user-friendly interface — FlowCyto makes it accessible in seconds.

FlowCyto - BLOOD-cell version

How it works

01
Input genes
Enter HGNC gene symbols separated by commas. No limit on query size.
02
DB matching
Genes are cross-referenced against thousands of weighted cell–marker associations.
03
Score calc
Cumulative scores are normalised into probability percentages per cell type.
04
Ranked output
Results returned as a ranked table and stored in your prediction history.
FlowCyto - BLOOD-cell version

Meet the creators: The DINP Team

FlowCyto was built by four bioinformatics students from Universitat Pompeu Fabra (UPF), Barcelona, as part of an academic project. Our background spans wet-lab biology, database engineering, and web development.

D
Diego Vicente
Backend & Project design
Main developer of the user authentication flow. Contributor to the project database design and the backend logic.
diego.vicente01@estudiant.upf.edu
I
Itxaso Alonso
Database curation & Schema design
Led the CellMarker data recopilation and relational schema design. Expert in biological data curation at scale.
itxaso.alonso01@estudiant.upf.edu
N
Nahia Urra
Backend, Frontend & Full Stack
Responsible for Flask application architecture, API design, and UI development. Integrated backend logic with the frontend interface end-to-end.
nahia.urra01@estudiant.upf.edu
P
Pau Villén
SQL & Backend
Expert in SQL optimisation and backend logic. Responsible for creating the database and developing the prediction scoring system and user profile.
pau.villenvidal01@estudiant.upf.edu

Ready to identify
your cells?

Run your first gene query and get your results in less than second!

FlowCyto  ·  DINP Team  ·  UPF  ·  2025  ·  DINPteam@flowcyto.com