Rnaseq Pipeline From nf-core

Rnaseq pipeline provides meaningful programs to analyse RNA sequencing data obtained from organisms with a reference genome and annotation. This analysis includes various steps including trimming of FASTQ files, contamination removal, alignment, counting, quality control and normalization of sequenced reads, and, in most cases, differential expression (DE) analysis across conditions. This pipeline is supported by nf-core

1. Trimming

• cat Merge re-sequenced FastQ files
• FastQC Read QC
• UMI-tools UMI extraction
• Trim galore Adapter and quality trimming

2. Contamination Removal

• BBSplit Removal of genome contaminants
• SortMeRNA Removal of ribosomal RNA

3. Alignment and Quantification

• STAR using Salmon
• STAR via RSEM
• HiSAT2 NO QUANTIFICATION
• SAMtools Sort and index alignments
• UMI-tools UMI-based deduplication
• picard MarkDuplicates Duplicate read marking
• StringTie Transcript assembly and quantification
• bedGraphToBigWig Create bigWig coverage files

4. Quality Control and Normalization

• RSeQC An RNA-seq quality control package
• QualiMap Evaluating sequencing alignment data
• dupRadar A package for the assessment of PCR artifacts in RNA-Seq data
• Preseq A package for predicting and estimating the complexity of a genomic sequencing library

5. Differential Expression Analysis

• DEseq2 A package for estimating differential gene expression based on the negative binomial distribution

6. Sum up

• MultiQC Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks

Installation

Rnaseq is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures and it also uses Docker/Singularity containers making installation trivial and results highly reproducible. This guide covers the installation and configuration for Ubuntu.

Nextflow

a. Make sure that Java v8+ is installed

java -version

b. Install Nextflow

curl -fsSL get.nextflow.io | bash

c. Move the file to a directory accessible by your $PATH variable

sudo mv nextflow /usr/local/bin/

Docker

For more information, visit Docker website

a. Update the apt package index, and install the latest version of Docker Engine

sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io

b. List the versions available in your repo

apt-cache madison docker-ce

c. Install a specific version

sudo apt-get install docker-ce=<VERSION_STRING> docker-ce-cli=<VERSION_STRING> containerd.io

d. Verify that Docker is installed correctly by running the hello-world image

sudo docker run hello-world

e. Enable Docker permissions

sudo chmod 666 /var/run/docker.sock

nf-core

a. Install nf-core tools

sudo pip3 install nf-core

b. List all nf-core pipelines and show available updates

nf-core list

Generate Samplesheet Format

There are two methods to create the samplesheet that is used as input to rnaseq pipeline:

1. From fetchngs pipeline. It can be found as samplesheet.csv. Nevertheless, the absolute path of fastq_1 and fastq_2 columns must be modified so that the rnaseq pipeline can read the FastQC.

2. An executable Python script called fastq_dir_to_samplesheet.py has been provided if you would like to auto-create an input samplesheet based on a directory containing FastQ files before you run the pipeline (requires Python 3 installed locally)

a. Download python script

wget -L https://raw.githubusercontent.com/nf-core/rnaseq/master/bin/fastq_dir_to_samplesheet.py

b. Use as follows

./fastq_dir_to_samplesheet.py <FASTQ_DIR> samplesheet.csv --strandedness reverse

Reference Genome Options

One of the first choice for retrieving the most common reference genomes of diverse organisms is by means of AWS iGenomes, stored in AWS S3. To obtain human genome as well as its annotation, this repository contains a script aws-igenomes.sh that can synchronize AWS-iGenomes and download these files

curl -fsSL https://ewels.github.io/AWS-iGenomes/aws-igenomes.sh > aws-igenomes.sh

Path To Genome Indexes

If you manually provide the genome indexes, it is important to keep in mind that they must be in the following path

<{output}>/genome/index/<{idx}>

where output is the folder name containing the results and idx the indexes such as rsem, hisat2 and salmon

Usage

To perform an RNA-seq data analysis, the script scr/rnaseq.sh was implemented to systematically prepare, validate, and generate the results of pipeline

bash rnaseq.sh -c file.csv -r genome.fa -a genes.gtf -b star_rsem -o results -d rRNA-paths.txt -p 30 -m 230 -x n

Arguments

Mandatory

• -c: Samplesheet file containing information about the samples in the experiment. An example is available in data

• -r: Reference genome (FASTA)

• -a: Genome annotation (GTF)

• -b: Specifies the alignment algorithm to use. Available options are: star_salmon, star_rsem or hisat2

• -o: The output directory where the results will be saved

Optional

• -d: Text file containing paths to fasta files (one per line) that will be used to create the database for SortMeRNA. An example is available in data

• -e: Specifies the pseudo aligner to use. Available option is: salmon

• -p: CPUs

• -m: Max memory to be used

• -x: This execution is a resume of a previous run or it is a new run. The options are: y or n

• -t: A custom configuration file to be used in the pipeline. An example is shown in data folder

• -i: Create or not a new Genome index. If not specified, it will be created based on the type of aligner supplied

Running In The Background

The Nextflow -bg flag launches Nextflow in the background or alternatively, you can use screen/tmux or similar tool to create a detached session which you can log back into at a later time

Result

The script will create a local directory based on the given output name showing the following folders:

• output_name: Contains the results of RNA-seq analysis

• work: Contains the main pipeline workflows

• 2022-01-31_15:46:17.COMMAND: Contains the commands used for the actual launch. File name contains the date (%y%m%d) and the time (%H%M%S) when the command was last run. Thus, if it is resumed, it will be overwritten

Bug Reports

Please report bugs through the GitHub issues system