Variant Visualizer

Developed by:

• Dylan Lu dylanlu@ucsb.edu
• Juliana Acosta-Uribe acostauribe@ucsb.edu

The Variant Visualizer is a dash-based web application designed to visualize and compare allelic counts in selected genes across multiple cohorts.
We have used it to plot the allelic counts of variant in neurodegeneration associated genes in the TANGL¹ and ReDLat² cohorts.
You can see it in action here

You can follow the following steps to plot your own data with the variant visualizer

Contents

• Introduction
• Generating the gene files
• Installation
• Running the App
• Usage
• Troubleshooting
• References

Introduction

This application reads variant data from tab-delimited files named after genes (e.g., PSEN1, MAPT). Each file contains columns for exon number, amino-acid position (AA), variant identifier, and case/control counts, including subgroup counts (e.g., AD, EOD, FTLD, AAO<65, Healthy). It clusters nearby variants within the same exon, renders vertical lines and scatter points to represent counts, and draws exon-range bars with a legend.

Generating the gene files

Each gene file must be tab-delimited with no file extension. Here's an example of the content inside a file (e.g., ANXA11:NM_001157):

Gene.refGene	variant	AA	exon	all.Hom_A1	all.Het	ad.Hom_A1	ad.Het	ftd.Hom_A1	ftd.Het	aao.Hom_A1	aao.Het	healthy.Hom_A1	healthy.Het
ANXA11	G503R	503	15	0	3	0	3	0	0	0	0	0	0
ANXA11	S486L	486	14	0	4	0	3	0	0	0	0	0	0
ANXA11	I457V	457	14	0	15	0	7	0	6	0	0	0	0

These gene-named files can be generated using extract_variants.py:

# Extract tangl variants
python ./data_preprocessing/extract_variants.py \
  --input data/tangl/tangl_id.hg38_multianno.annotated-variant-counts.tsv \
  --isoforms '{
      "ANXA11": "NM_001157",
      "APOE":  "NM_000041",
      "APP":   "NM_000484",
      "CHMP2B":"NM_014043",
      "CSF1R": "NM_005211",
      "DNAJC5":"NM_025219",
      "FUS":   "NM_001170634",
      "GRN":   "NM_002087",
      "LRRK2": "NM_198578",
      "MAPT":  "NM_005910",
      "NOTCH3":"NM_000435",
      "PSEN1": "NM_000021",
      "PSEN2": "NM_000447",
      "RELN":  "NM_005045",
      "SOD1":  "NM_000454",
      "SQSTM1":"NM_003900",
      "TARDBP":"NM_007375",
      "TBK1":  "NM_013254",
      "TREM2": "NM_018965",
      "VCP":   "NM_007126"
  }' \
  --output-dir data/tangl

Installation

1. Clone this repository:

   git clone https://github.com/ThePickleGawd/variant-visualizer.git
   cd variant-visualizer

2. Create a virtual environment and install dependencies:

   python -m venv .venv
   source .venv/bin/activate
   pip install -r requirements.txt

   You need to have Python ≥ 3.11 installed. If you do python --version inside that environment and it’s < 3.11, follow the troubleshooting guide at the end.

Running the App

Run the following command from the terminal

# Deploy/Run locally
gunicorn app:server --bind 0.0.0.0:8050

By default, the app runs on http://127.0.0.1:8050 You can change the port number if 8050 is being used, or if you want to re-lauch the app, you can reset the 8050 port by doing lsof -i :8050

Usage

1. Select a cohort
2. Choose a category
3. Choose a gene from the dropdown
4. View the generated plot below

You will have to edit the app.py file to match your own cohort and categories

---

Troubleshooting

Use the correct python version for your virtual environment

• Install Python 3.11

For example, with Homebrew on an Intel Mac:

brew install python@3.11

This will give you a python3.11 executable (typically at /usr/local/opt/python@3.11/libexec/bin/python3).
You can search for the path by typing in the terminal which python3

• Recreate your venv using that binary:

rm -rf .venv
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip    # optional but recommended
pip install -r requirements.txt

• Verify

python --version   # Python 3.11.x
pip list           # shows your project’s dependencies

References

If you use this app to visualize your data, please cite us:

• Dylan Lu, & Juliana Acosta-Uribe. (2025). TauConsortium/variant-visualizer (v1.0.0). Zenodo.

If you use any of the TANGL or ReDLat data, please cite the corresponding paper:

1. TANGL: Acosta-Uribe, J., Aguillón, D., Cochran, J. N., Giraldo, M., Madrigal, L., Killingsworth, B. W., ... & Kosik, K. S. (2022). A neurodegenerative disease landscape of rare mutations in Colombia due to founder effects. Genome Medicine, 14(1), 27.
2. ReDLat: Acosta-Uribe, J., Piña-Escudero, S. D., Cochran, J. N., Taylor, J. W., Castruita, P. A., Jonson, C., ... Kosik, K. S. & Yokoyama, J. S. (2024). Genetic Contributions to Alzheimer’s Disease and Frontotemporal Dementia in Admixed Latin American Populations. medRxiv.