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RNA-Seq data analysis: whole transcriptome, stranded, dUTP method

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Full transcript RNA-Seq data analysis

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" (submitted to Brain)

RNA-Seq library

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.

Analysis workflow

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]

2. QC of fastq file: FastQC v0.11.7 [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)

4. Gene and transcript counting: RSEM v1.3.1 [Wynton-cluster/rawQC-trimming-mapping-counting.sh]

5. Statistical analysis using R and Bioconductor [R]

  • R v3.5.1 and Bioconductor v3.7

6. Weighted gene co-expression network analysis (WGCNA) [R/WGCNA_MS-HC.R]

  • WGCNA v1.64.1 (R package)

7. Variant calling: GATK4, vcftools, bcftools

8. Cis-eQTL analysis: FastQTL v2.0 [Wynton-cluster/ and R/]


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