SegPore

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Nanopore raw signal segmentation and RNA modification estimation.

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## SegPore: Raw Signal Segmentation for Estimating RNA Modification from Nanopore Direct RNA Sequencing Data

[![Read the manuscript](https://img.shields.io/badge/eLife-Manuscript-green)](https://elifesciences.org/articles/104618)
[![Release](https://img.shields.io/github/v/release/guangzhaocs/SegPore?include_prereleases)](https://github.com/guangzhaocs/SegPore/releases)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://raw.githubusercontent.com/guangzhaocs/SegPore/main/LICENSE)
[![GitHub Stars](https://img.shields.io/github/stars/guangzhaocs/SegPore.svg?style=social)](https://github.com/guangzhaocs/SegPore/stargazers)

## SegPore Workflow

More details on [SegPore tutorials](docs/).

### Environment setup
```
git clone https://github.com/guangzhaocs/SegPore.git
cd SegPore
conda env create -f environment.yml
conda activate segpore_env
pip3 install git+https://github.com/EGA-archive/ont2cram
# install Guppy
```

### Download demo data

Here we use the WT demo data of [xPore](https://xpore.readthedocs.io/en/latest/index.html).
```
cd SegPore
wget https://zenodo.org/record/5162402/files/demo.tar.gz
tar -xvf demo.tar.gz
cd scripts
sh 0_data_proc.sh
```

### Step 1: Basecalling, mapping and preprocessing
```
sh 1_basecalling.sh
sh 1_nanopolish.sh
```

### Step 2: Hierarchical hidden Markov model (HHMM) for signal segmentation
2.1 Firstly, prepare the input of HHMM.
```
sh 2_hhmm_prepare.sh
```
2.2 Next, run HHMM on CUDA:
```
sh 2_hhmm_GPU.sh
```
2.3 Finally, generate the final output:
```
sh 2_hhmm_post_proc.sh
```
### Step 3: Alignment of signal segments with reference sequence
```
sh 3_alignment.sh
```

### Step 4: GMM to update 5mer parameter table
Fix the mean of the first component of GMM.
```
sh 4_gmm.sh
```
Use the results of GMM to update the 5mer parameter table and iteratively run Step 3 and Step 4.

## Cite SegPore

Guangzhao Cheng, Aki Vehtari, Lu Cheng (2026) **Raw signal segmentation for estimating RNA modification from Nanopore direct RNA sequencing data** *eLife* **14**:RP104618
    
```
@article {10.7554/eLife.104618,
article_type = {journal},
title = {Raw signal segmentation for estimating RNA modification from Nanopore direct RNA sequencing data},
author = {Cheng, Guangzhao and Vehtari, Aki and Cheng, Lu},
editor = {Altemose, Nicolas and Moses, Alan M},
volume = 14,
year = 2026,
month = {mar},
pub_date = {2026-03-02},
pages = {RP104618},
citation = {eLife 2026;14:RP104618},
doi = {10.7554/eLife.104618},
url = {https://doi.org/10.7554/eLife.104618},
keywords = {Nanopore, RNA modification, raw signal, alignment, segmentation},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}

```

## Reference
1. Pratanwanich, P.N., Yao, F., Chen, Y. et al. Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat Biotechnol 39, 1394–1402 (2021). https://doi.org/10.1038/s41587-021-00949-w
2. Zhong, ZD., Xie, YY., Chen, HX. et al. Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing. Nat Commun 14, 1906 (2023). https://doi.org/10.1038/s41467-023-37596-5

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