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

SegPore Workflow

More details on SegPore tutorials.

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.

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.

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