nf-core RNASeq Tutorial

This tutorial demonstrates how to run nf-core's RNAseq pipeline on Code Ocean. nf-core/rnaseq is a bioinformatics pipeline that can be used to analyse RNA sequencing data obtained from organisms with a reference genome and annotation. It takes a samplesheet and FASTQ files as input, performs quality control (QC), trimming and (pseudo-)alignment, and produces a gene expression matrix and extensive QC report.

Table of Contents

1. Prerequisites

   1. Example Sequencing Reads

   2. hg38 Reference Sequence

   3. hg38 Annotation

2. Create the Pipeline

3. Attach Data Assets to Pipeline

4. Configure Sample Sheet

5. Configuring App Panel

6. Reproducible Run

7. Results

Prerequisites

First, create an Internal Data Asset of the sequencing reads. This Data Asset can be imported from the public S3 bucket with the following bucket name and path:

We shall use the following Data Asset to demonstrate.

Example Sequencing Reads

Bucket Name: codeocean-public-data

Path: example_datasets/Normox

hg38 Reference Sequence

Bucket Name: codeocean-public-data

Path: genomes/hg38/Reference/

hg38 Annotation

Bucket Name: codeocean-public-data

Path: genomes/hg38_Annotation

Create the Pipeline

• From the Sidebar, create a new Pipeline by Import from nf-core

• Search for rnaseq and v3.14.0

• Click on Import to import the pipeline into your deployment.

Once the pipeline has been imported you'll be greeted with its README file

Attach Data Assets to the Pipeline

Click on Manage Data Assets

Search and Attach the following 3 Data Assets:

1. Normox-Sequencing

2. Gencode v42 Basic Annotation

3. hg38 Reference Sequence

Configure the Sample Sheet

Edit the sample sheet at /pipeline/assets/samplesheet.csv to specify the sample names, location of read 1 and read 2 (if paired end), and strandedness. The strandedness refers to the library preparation and will be automatically inferred if set to auto. Must be one of unstranded, forward, reverse or auto. Rows with the same sample identifier are considered technical replicates and merged automatically.

sample,fastq_1,fastq_2,strandednesscontrol_REP1,../data/Reads/SRR2049547/SRR2049547_1.fastq.gz,../data/Reads/SRR2049547/SRR2049547_2.fastq.gz,autocontrol_REP2,../data/Reads/SRR2049548/SRR2049548_1.fastq.gz,../data/Reads/SRR2049548/SRR2049548_2.fastq.gz,autocontrol_REP3,../data/Reads/SRR2049549/SRR2049549_1.fastq.gz,../data/Reads/SRR2049549/SRR2049549_2.fastq.gz,autotreatment_REP1,../data/Reads/SRR2049550/SRR2049550_1.fastq.gz,../data/Reads/SRR2049550/SRR2049550_2.fastq.gz,autotreatment_REP2,../data/Reads/SRR2049551/SRR2049551_1.fastq.gz,../data/Reads/SRR2049551/SRR2049551_2.fastq.gz,autotreatment_REP3,../data/Reads/SRR2049552/SRR2049552_1.fastq.gz,../data/Reads/SRR2049552/SRR2049552_2.fastq.gz,auto

Configure the App Panel

In the App Panel, update the Input,Fasta and Gtf parameters according to the location of your data.

Delete the value of the Igenomes Base parameter as those resources are not used in this tutorial. See iGenomes for instructions on how to use iGenomes resources.

Reproducible Run

Click on Run or Run with Parameters

Results

The Results are available in the Pipeline Timeline and a Data Asset can be created for downstream processing.