2021 systemic sclerosis PBMC and skin punch biopsy RNA-Seq

Preprint: RNA-Seq analysis identifies novel roles for the primary cilia gene SPAG17 and the SOX9 locus non-coding RNAs in systemic sclerosis

Paper: RNA-Seq analysis identifies alterations of the primary cilia gene SPAG17 and SOX9 locus non-coding RNAs in systemic sclerosis

Downloadable data: FigShare Project

Project description

This project used samples collected from controls without autoimmune disease and inviduals with rheumatologist-confirmed systemic sclerosis (SSc). Biological samples included isolated peripheral blood mononuclear cells (PBMCS) and skin punch biopsies stabilized to reduce RNA degradation.

We used Takara / Clontech stranded, ribosomal depletion, total RNA library preprations. We sequenced samples with Illumina paired-end sequencing.

Code description

This repository contains the code used to process data and to generate statistical analysis / paper figures.

Beware all ye who enter here: this project was started before we switched to Snakemake for workflow management. This analysis is instead coordinated using Make. Analyses for highly parallelizable operations were performed on a cluster at Washington University using PBS scripts. Other operations were performed on a local lab server. Much of the figure generation and analysis was performed using a local laptop. This all means that the overall workflow is less easy to follow and reimplement than a Snakemake workflow, but more reproducible than having no workflow management at all.

Software Requirements

Software - general

• R
• Python
• Perl
• salmon
• featureCounts
• RNA-STAR
• pigz

Annotation and related data

• GRCh38 human genome
• GRCh38 GTF file
• Additional contigs for FASTA file (see preprint or published paper for details; not strictly necessary)
• Additional contig SAF file (see featureCounts documentation)
• Gene start, end, strand SAF file for feature counts (export from Biomart)
• Salmon index
• RNA-STAR index of modified FASTA file

Software - R packages

• broom
• cowplot
• data.table
• DESeq2
• doMC (depending on platform)
• doSNOW (depending on platform)
• dynamicTreeCut
• ggplotify
• ggrepel
• grid
• here
• knitr
• leafcutter
• optparse
• reshape2
• sleuth
• sva
• tidyverse
• UpSetR
• wasabi
• WGCNA

Recreating analysis

• Clone repository to local environment.

• Install necessary softare, associated packages, and human genome annotation files to your local environment. and packages to your computing environment.

• If you want to include the additional microbial genomes to your FASTA file as we did (detailed in manuscript), concatenate them onto the GRCh38 human reference.

• Generate all necessary (RNA-STAR and salmon) indexes.

• Setup the environmental variables file so the paths work on your system.

• Run PBS scripts 1-8 to do basic alignments and QC. If you aren't running in a PBS / Torque high-performance computing cluster these files could be ported to shell file loops for local execution on a single system.

• Run Makefiles for salmon and leafcutter analysis.

• Knit the R Markdown analysis files in order.

Critically important note All command-line analyses should be run from the top-level of the repository, i.e. "make -f makefiles/run_salmon.make" to ensure proper use of relative directories. R markdown files use the "here" package and can be knitted as is.

Recreating analysis - shortcut method (recommended)

The most important parts of this analysis are performed using R Markdown. If you want to perform the analyses (DESeq2, WGCNA, etc) without running the alignments and such, you can do the R Markdown portion of the analysis. The most critical files will be downloaded automatically from FigShare by running the first script.