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Analysing differential RNA modifications of SG-NEx samples

In this tutorial, we will perform differential RNA modification analysis on the SG-NEx samples. We will use the Nanopore direct RNA-sequencing data of A549 and HepG2 cell lines from the SG-NEx project for this analysis. The A549 cell line was extracted from the lung tissues of a patient with lung cancer. Meanwhile, the HepG2 cell line was extracted from hepatocellular carcinoma from a patient with liver cancer. A Python package known as xPore, will be used to identify differential RNA modification sites in these two cell lines.
Note: This tutorial may take 2 hours to complete(due to the use of large datasets).

Content

Installation

xPore requires Python 3 to run. It can be installed through PyPI using the following command. Note: This command requires pip3 to be installed.

pip install xpore

Alternatively, it can be installed from our GitHub repository.

git clone https://github.com/GoekeLab/xpore.git
cd xpore
python setup.py install
cd ..  # return to the working directory

Data Access and Preparation

xPore requires at least two samples for comparison. For this tutorial, we will use the data from A549_directRNA_replicate6_run1 and HepG2_directRNA_replicate6_run1. The required data include:

  • feature values from both the A549 and HepG2 samples (two json files, one for each sample),
  • indexes of the feature values file (two index files, one for each sample).

Download data for xPore

The data files can be downloaded from the SG-NEx AWS S3 bucket. Please refer to this page for a comprehensive tutorial to access the SG-NEx dataset.

To begin, we will create directories to store the required files for the respective samples.

# create directories for A549 and HepG2 samples
mkdir A549_directRNA_replicate6_run1
mkdir HepG2_directRNA_replicate6_run1

Then, we will access the S3 bucket to download the data into the created directories. There are two methods to download the data.

Method One(Recommended):

Note: The command requires you to have AWS CLI installed.

For A549 sample:

# To download the processed data for A549 sample
aws s3 sync --no-sign-request s3://sg-nex-data/data/processed_data/xpore/SGNex_A549_directRNA_replicate6_run1 ./A549_directRNA_replicate6_run1
# The directory should now contain two files. They are the json and index files.

For HepG2 sample:

# To download the processed data for HepG2 sample
aws s3 sync --no-sign-request s3://sg-nex-data/data/processed_data/xpore/SGNex_HepG2_directRNA_replicate6_run1 ./HepG2_directRNA_replicate6_run1
# The directory should now contain two files. They are the json and index files.

Alternatively, we can use the URL to the S3 bucket to download the processed data.

Method Two:

For A549 sample:

cd A549_directRNA_replicate6_run1 

# To download the json file
wget https://sg-nex-data.s3.ap-southeast-1.amazonaws.com/data/processed_data/xpore/SGNex_A549_directRNA_replicate6_run1/data.json

# To download the index file
wget https://sg-nex-data.s3.ap-southeast-1.amazonaws.com/data/processed_data/xpore/SGNex_A549_directRNA_replicate6_run1/data.index

# Exit the A549 directory to return to our working directory
cd ..

For HepG2 sample:

cd HepG2_directRNA_replicate6_run1 

# To download the json file
wget https://sg-nex-data.s3.ap-southeast-1.amazonaws.com/data/processed_data/xpore/SGNex_HepG2_directRNA_replicate6_run1/data.json	

# To download the index file
wget https://sg-nex-data.s3.ap-southeast-1.amazonaws.com/data/processed_data/xpore/SGNex_HepG2_directRNA_replicate6_run1/data.index

# Exit the HepG2 directory to return to our working directory 
cd ..

Then, we need to prepare a YAML file to specify the experimental design, the data directories, the output directory, and the method options. We can create a .yml file using the command below:

# To create .yml file
cat > config.yml

Next, copy and paste the command below to the terminal!

notes: Pairwise comparison without replicates with default parameter setting.

data:
    Control:  # Condition Name 1
        rep1: ./A549_directRNA_replicate6_run1   # Input directory containing the json and index file
    Treatment:  # Condition Name 2
        rep1: ./HepG2_directRNA_replicate6_run1  # Input directory containing the json and index file
    
out: ./out    # Output directory 

method:
    prefiltering:  # To remove positions that are unlikely to be differentially modified
        method: t-test
        threshold: 0.1

Please press the Enter key followed by the Ctrl and D keys to save the file. This file is now saved as config.yml


Note: If you want xPore to test every genomic/transcriptomic position, you may remove the prefiltering section.
Please refer to xPore documentation for more information on the .yml configuration file.

You may refer to this page for the list of URLs to access the processed data for xPore from different samples.

Running xPore

Now, we are all set to run xPore to identify differential RNA modification.

# Run xPore
# Before running the command, please ensure that you are at the directory where the .yml configuration file is located.
xpore diffmod --config config.yml --n_processes 8

Note: You can reduce the "n_processes" if you have lesser processes available in your machine. However, lowering the "n_processes" may increase the running time. Alternatively, you may use the downsampled dataset (demo.tar.gz file) from the xPore documentation, which can reduce the total running time to less than ten minutes.

xPore returns a table (diffmod.table) consisting of differential RNA modification rates for all tested positions, saved in the specified output directory in the .yml file. Please refer to the xPore documentation for more information regarding the output table.


NOTE: This is a short tutorial to demonstrate the usage of xPore on the SG-NEx data. Please refer to the xPore documentation for the complete workflow on performing differential RNA modification analysis with xPore.


Access to the required files for the complete workflow

If you wish to run the complete workflow of xPore, you can access all the required files (fast5, fastq and bam files) from the SG-NEx S3 bucket.

# Note: Please make sure to replace the "sample_alias" with your sample name
# To download the fast5 file
aws s3 sync --no-sign-request s3://sg-nex-data/data/sequencing_data_ont/fast5/<sample_alias> ./

# To download the fastq file
aws s3 sync --no-sign-request s3://sg-nex-data/data/sequencing_data_ont/fastq/<sample_alias> ./

# To download the bam file
aws s3 sync --no-sign-request s3://sg-nex-data/data/sequencing_data_ont/bam/transcriptome/<sample_alias> ./

Meanwhile, you can also access the genome and transcriptome fasta files and the gtf file (annotation file) using the following command.

# To download the transcriptome fasta file
aws s3 sync --no-sign-request s3://sg-nex-data/data/annotations/transcriptome_fasta ./ --exclude hg38*

# To download the gtf file
aws s3 cp --no-sign-request s3://sg-nex-data/data/annotations/gtf_file/Homo_sapiens.GRCh38.91.gtf ./

Alternatively, you can download the files with their respective URL.

Reference

In this tutorial, we performed differential RNA modification analysis on the SG-NEx dataset using xPore. If you use xPore and the dataset from SG-NEx in your work, please cite the following paper.

Pratanwanich, Ploy Naruemon, et al. "Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore." Nature Biotechnology (2021). doi: https://doi.org/10.1038/s41587-021-00949-w

Chen, Ying, et al. "A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines." bioRxiv (2021). doi: https://doi.org/10.1101/2021.04.21.440736