Snakemake Workflow to Analyze RNA-seq data from Covid-19-infected Samples

The repository contains a Snakemake workflow to analyze data from SARS-CoV-2 infected samples from humans. The workflow analyzes bulk RNA-seq data from a study by Katsura et al. (https://www.cell.com/cell-stem-cell/pdf/S1934-5909(20)30499-9.pdf). Katsura et al. collected bulk RNA-seq from a sample of SARS-CoV-2 infected human lung cells (alveolar type 2 or AT2 cells) 48 hours post inoculation alongwith RNA-seq from a control (uninfected) sample. Each sample had 3 replicates. The data was deposited by the authors at the gene expression omnibus (GEO) database (GSE152586) and can be accessed at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152586.

Note: The current version of the pipeline has only the Data Download, Raw Data Quality Check, Adapter Trimming, Trimmed Data Quality Check, Index reference genomes and Map trimmed reads sections complete. Work is still in progress for other sections (quantification and differential gene expression) of the pipeline

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. License
5. Contact
6. Acknowledgements

About The Project

In this project, I have implemented a Snakemake workflow to analyze bulk RNA-seq data from SARS CoV-2 infected samples. The intent is have the pipeline automated end-to-end to allow the user to run the analysis without any interruptions. This workflow outlines a very basic approach to analyze RNA-seq data and can be modified by any user to add more rules and convert into a more sophisticated workflow. On an added note, I have not included steps for filtering the data for ribosomal RNAs. I assume that mapping the reads to human reference genome and then collecting the data only for mapped reads and assembling the reads into transcripts should result in high confidence mRNA expression data.

For this work, I have used the human and SARS Cov-2 reference genomes available at NCBI.

• Human : https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.39_GRCh38.p13/GCF_000001405.39_GRCh38.p13_genomic.fna.gz
• SAR-CoV-2: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/009/858/895/GCF_009858895.2_ASM985889v3/GCF_009858895.2_ASM985889v3_genomic.fna.gz

Built With

• Python and Snakemake

Getting Started

You will need a Linux machine (Ubuntu 18.04 or similar) to run this workflow. I have run my analysis on a Ubuntu 18.04 instance using the Amazon Web Services (AWS) Elastic Cloud Compute (EC2) services (https://aws.amazon.com/ec2/?ec2-whats-new.sort-by=item.additionalFields.postDateTime&ec2-whats-new.sort-order=desc). If you already have a good configuration Linux machine (e.g., 12 cores, 32 GB memory, ~1 TB diskspace), you should be good to go. Otherwise, if you have a similar configuration Windows machine and have Windows 10 installed, then you can install windows subsystem linux (WSL) to run the analysis (https://docs.microsoft.com/en-us/learn/modules/get-started-with-windows-subsystem-for-linux/).

Prerequisites

Ubuntu 18.04 or similar machine with at least 30 GB memory and 1 TB diskspace. Must have BASH installed.

Installation

Install from scratch

1. Clone the repo

   git clone  https://github.com/sambitmishra0628/RNA-Seq-1
   

2. Install miniconda

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
   chmod +x Miniconda3-latest-Linux-x86_64.sh
   ./Miniconda3-latest-Linux-x86_64.sh
   

   Follow the instructions to complete the installation of miniconda.

3. Create a conda environment

   conda create -n rna_seq_env python=3.9
   conda activate rna_seq_env
   

4. Install mamba

   conda install -c conda-forge mamba
   

5. Install snakemake

   mamba install -c conda-forge -c bioconda snakemake
   

Install using environment file

You can use the environment file provided in this repository to create a similar environment as in the above steps. Follow steps 1 and 2 as given above and then use the following command.

conda create -n rna_seq_env -f ngs_env.yml

Usage

1. Activate the conda environment (if not already activated)

   conda activate rna_seq_env
   

2. Clone the repository

   git clone https://github.com/sambitmishra0628/RNA-Seq-1
   

3. Change the DATADIR in the RNA-Seq-1/config.yml to point to a location in your machine

4. Do a dry run of Snakemake

   snakemake -n -p --use-conda --configfile config.yml --cores 4
   

   Note that I have set the number of cores to 4. You can increase that number depending on the number of cores available in your machine minus 1

5. Run the workflow

   snakemake -p --use-conda --configfile config.yml --cores 4
   

6. Run the following command to view the dag

   snakemake --dag | dot -Tpng > dag.png
   

7. Tip. If you notice that the workflow is running all over again after a certain rule/rules failed despite the files from previously executed rules being present, then it is likely due to a time stamp issue. Follow the following steps:

• Under rule all, comment out the rules that fail in the Snakefile
• Run the command snakemake --touch -p --use-conda --configfile config.yml --cores 4

This will reset all the time stamps incase a changed time stamp is the issue. Then do a dry run and you should see only the rules for which the files are missing shown to be executed.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Sambit Mishra - sambitmishra0628@gmail.com

Project Link: https://github.com/sambitmishra0628/RNA-Seq-1

Acknowledgements