Full Tutorial for Runing the Pipeline
Welcome to the Full Tutorial for the bulk RNA-seq quantification pipeline! This tutorial provides comprehensive documentation and step-by-step guidance for this pipeline. and it is intended for users who are new to this field. From this tutorial, you will find detailed instructions to the rationale, inputs, outputs and key parameters for each step. If you want to quickly run this pipeline on your own data, please refer to the Quick Tutorial.
Before you get started
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Ensure this pipeline is set up on your system.
This pipeline, including its tools, scripts, and databases, can be set up and maintained in a single conda environment. We have set up this pipeline in the conda environment below, with four pre-built genome assemblies: hg38, hg19, mm39 and mm10. You can activate it using the following commands:
module load conda3/202402 # conda version: 24.1.2 conda activate /research_jude/rgs01_jude/groups/yu3grp/projects/software_JY/yu3grp/conda_env/bulkRNAseq_2025 # change it to your conda environment accordingly- If you can access to this environment but require a different reference genome assembly, please refer to the Database Preparation tutorial.
- To set up your own conda environment for this pipeline, please refer to the Pipeline Setup tutorial. It typically takes ~3 hours to complete.
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Ensure you have input data available
This pipeline accepts both FASTQ files and pre-computated alignment (BAM/SAM) files. We always encourage you to start walking through this pipeline with your own data. However, if you do not have your own input data to start with, you can use the test data provided with this pipeline (see the table below):
- For those who can access to the pre-built conda environment, the test data is available at:
/research_jude/rgs01_jude/groups/yu3grp/projects/software_JY/yu3grp/conda_env/bulkRNAseq_2025/pipeline/testdata. - For other users, please download them from Zenodo.
Format Library Type Phred Encoding Species Number of Reads Preparation sample1 FASTQ Paired-end Phred+33 human 19,410,373 * 2 Downsampled from real data sample2 FASTQ Single-end Phred+33 mouse 16,005,450 Downsampled from real data sample3 FASTQ, multiple lanes Paired-end Phred+33 human 19,410,373 * 2 Split by lane from sample1 sample4 FASTQ, multiple lanes Single-end Phred+33 mouse 16,005,450 Split by lane from sample2 sample5 BAM/SAM (to genome) Paired-end Phred+33 human 13,856,075 * 2 Downsampled from real data sample6 BAM/SAM (to transcriptome) Single-end Phred+33 mouse 13,533,162 Downsampled from real data - For those who can access to the pre-built conda environment, the test data is available at: