RNA-Seq Analysis

Introduction

This repository utilizes Nextflow to create a reproducable bioinformatics pipeline for RNA sequencing analysis using STAR, RSEM and/or Salmon with gene counts and extensive quality control.

The pipeline takes FASTQ files as input, performs initial MD5 checks, quality control (QC) checks, trimming, and alignment, and produces a gene expression matrix, QC reports, and gene set enrichment data.

Pipeline Process

• 1a. Get FASTQ data from Dropbox and move it to the HPC (get_dropbox_data-<PI_NAME>.nf)
• 1b. Check the MD5 Checksum values of the transferred data (check_md5.py)
• 2a. Update Wormbase GeneIDs based on Version Number (e.g.,WS289) (utility/wormbase_download.sh, create_star_rsem_index.nf, create_salmon_index.nf)
• 2b. Get genome/transcript data from Wormbase for alignment (utility/wormbase_download.sh)
• 3a. Create STAR and rsem indexes (create_star_rsem_index.nf)
• 3b. Create Salmon index file (create_salmon_index.nf)
  • NOTE Salmon process is currently used for testing and validation only
• 4a. Execute Quality Control on FASTQ Data (rnaseq-rsem-<PI_NAME>.nf)
• 4b. Align FASTQ data to the Worm Genome
• 4c. Quantify the Gene Expression
• 4d. Summarize results for further analysis
• 4e. Aggregate the QC Reports for simplified review
• 5a. Execute DEBrowser to perform Differential Expression Analysis (utility/start_debrowser.sh)
• 5b. Trim Data as needed
• 5c. Produce heatmap visualizations
• 6a. Execute Wormcat Batch (wormcat_batch.nf)

Note: when running use nextflow run PIPLINE.nf -bg -N [email protected], which will run in the background and email when the process terminates (success or failure)

Pipeline Outputs

• MD5 Checksum Report
• FAST QC Reports
• Multi QC Report
• Isoform Quantification
• Gene Quantification
• DESeq2 heatmap visualizations
• Wormcat annotations and visualization of gene set enrichment data

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Process flow diagrams