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RepeatLab

Open In Colab DOI:10.1038/xyz123

Long-read sequencing data analysis for repeat expansion diseases diagnosis based on Google Colab.

Features

  • Optimized especially for targeted nanopore sequencing data. (Repeat size estimation is available for all long-read sequencing data.)
  • Using the Google Colab platform requires a personal Google account.
  • Using Linux CLI requires GPU for basecalling. (Google Colab offers GPU.)
  • Input : POD5, FAST5, or FASTQ files
  • Output : (1) Sequencing QC, (2) Repeat size estimation, (3) Repeat structure, (4) Methylation profiling

Try RepeatLab on Google Colab

RepeatLab

Try RepeatLab by Linux CLI

1. Installation

Installation takes about 1–3 minutes.

$ git clone https://github.com/ChangLabSNU/RepeatLab.git
$ cd RepeatLab
$ conda env create -f environment.yml
$ conda activate repeatlab

2. Pre-requisites preparation

$ cd RepeatLab/pre-requisites
$ snakemake --cores all

3. Input

Write down belows in config.yml.

  • Raw data directory path
  • Sample name and target gene

4. Run RepeatLab

$ cd ../RepeatLab
$ snakemake --cores 1

Test run

Test run is available with NA03697 DNA nanopore sequencing data in test-data/.
It takes about 5–10 minutes for test run.

1. Test run on Google Colab

Download the test data to your own Google Drive and follow the instructions for Google Colab-based RepeatLab.

2. Test run by Linux CLI

Since the input data information is already written in config.yml, just follow the instructions above.
You can find the report file at analyses/NA03697_test-DMPK/report.html after test run.

Troubleshooting

If you encounter any errors using RepeatLab, please report the trouble issues at Issues.

Citing RepeatLab

Han, Y., Jang, J. H., & Chang, H. (2026). Targeted long-read sequencing for high-resolution repeat profiling in myotonic dystrophy type 1. Experimental & Molecular Medicine, 1-13.

@article{han2026targeted,
  title={Targeted long-read sequencing for high-resolution repeat profiling in myotonic dystrophy type 1},
  author={Han, Yoojung and Jang, Ja-Hyun and Chang, Hyeshik},
  journal={Experimental \& Molecular Medicine},
  pages={1--13},
  year={2026},
  publisher={Nature Publishing Group UK London}
}