Analysis code for cell-type-specific RNA-Seq dataset, which of the manuscript titled "Cell-Type-Specific Transcriptomics Identifies Neddylation as a Novel Therapeutic Target in Multiple Sclerosis" (Brain, awaa421, https://doi.org/10.1093/brain/awaa421 [Epub ahead of print])
Total RNA was purified from FACS-sorted CD4+, CD8+ T cells and CD14+ monocytes from the blood of multiple sclerosis patients. Sequencing libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit for Illumina and NEBNext® rRNA Depletion Kit (Human/Mouse/Rat). Therefore, all options software used here are adapted for stranded RNA-seq reads using dUTP method.
BASH scripts are script for running of parallel jobs in the UCSF Wynton cluster (https://ucsf-hpc.github.io/wynton/).
1. Adaptor trimming and low quality sequence: BBDuk of BBTools v38.05 [Wynton-cluster/rawQC-trimming-mapping-counting.sh]
3. Mapping to reference genome: STAR aligner v2.6.0c [Wynton-cluster/rawQC-trimming-mapping-counting.sh]
- reference genome: GRCh38.p12 with Gencode annotation (release 28)
- R v3.5.1 and Bioconductor v3.7
- WGCNA v1.64.1 (R package)
- BAM files of different cell subset from same subject were merged [Wynton-cluster/VarCall_2_mergeBAM.sh]
- Variant calling procedure is adapted from GATK Best Practices for variant calling on RNAseq [Wynton-cluster/VarCall_1_filter-calling.sh] (https://software.broadinstitute.org/gatk/best-practices/workflow?id=11164)
- Created by Kicheol Kim, PhD (August 13, 2020 updated)
- Baranzini Lab. (https://github.com/baranzini-lab), Department of Neurology, UCSF