Gencode_microRNA-seq

microRNA-seq workflow utilizing Cutadapt and STAR to generate a Sample-Gene read count matrix

Authors: Ben Jordan ([email protected]) and Komal Jain ([email protected])

I. Description

Major steps in the workflow include:

1. Trimming of adapters and low-quality reads using Cutadapt and retention of reads with a minimum length of 15 nt
2. Generating QC reports using FastQC and aggregating results using MultiQC
3. Aligning trimmed reads to GRCh38 human reference genome (https://ftp.ensembl.org/pub/release-109/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna_sm.toplevel.fa.gz)) using STAR, and quantifying microRNA reads according to GENCODE V29 (ENCFF470CZH.gtf, https://www.encodeproject.org/files/ENCFF470CZH/) genome annotation file downloaded from Gencode
4. Using the trimmed reads from Step 1, mapping to the miRBase gtf by extracting the hairpin/stem-loop sequences from hsa.gff3 file downloaded from here (https://www.mirbase.org/ftp.shtml). The gff3 file was converted to gtf using gffread software, then a custom script was used to bring the gtf file to the format expected by STAR mapper
5. Merging reads-count tables of all samples for both miRBase and Encode gtf resulting in two count matrices

II. Dependencies

• Snakemake

Docker Containers

• Cutadapt
• FastQC
• MultiQC
• STAR
• R

III. Input

• merged fastq files stored in directory: merged_fastq
• adapters.fa with adapter sequences
• reference genome sequence and annotation files
• microRNA annotation file
• config.json with updated file locations

IV. Output

• Trimmed reads in directory: trimmed/
• QC reports of pre-trimmed reads in direcotry: pretrim_qc/
• QC reports of post-trimmed reads in direcotry: posttrim_qc/
• STAR index in directory: star_index/
• STAR alignent results and statistics reports in directory : star_align/
• merged reads-count table: reads_count/reads_count.csv

V. Running the workflow

1. Update config.json file with the following:

   • reference fasta
   • reference annotation
   • adapters fasta file (optional)
   • miRNA annotations (optional)

2. Run singularity

Example: running on CCAD2 cluster

module load slurm
module load singularity
module load conda

conda activate snakemake

mkdir -p singularity_cache

snakemake \
    --use-singularity \
    --singularity-prefix singularity_cache \
    --keep-going \
    --local-cores $SLURM_CPUS_PER_TASK \
    --jobs 10 \
    --slurm \
    --max-jobs-per-second 1 \
    --max-status-checks-per-second 0.01 \
    --latency-wait 120 all

VI. Working directory structure

.
├── adapters.fa
├── log
│   └── log files
├── merged_fastq
│   └── {sample}.fastq.gz
├── merge.R
├── posttrim_qc
│   ├── posttrim_qc_multiqc_report.html
│   ├── {sample}.trim_fastqc.html
│   └── {sample}.trim_fastqc.zip
├── pretrim_qc
│   ├── pretrim_qc_multiqc_report.html
│   ├── {sample}_fastqc.html
│   └── {sample}_fastqc.zip
├── reads_count
│   └── reads_count.csv
├── sample_names.txt
├── Snakefile
├── star_align
│   ├── log
│   │   ├── {sample}Log.final.out
│   │   └── star_align_multiqc_report.html
│   └── {sample}
│       ├── {sample}Aligned.sortedByCoord.out.bam
│       ├── {sample}Log.final.out
│       ├── {sample}ReadsPerGene.out.tab
│       └── other star output files
├── star_index
│   ├── ENCFF628BVT.gtf
│   ├── gencode.v24.primary_assembly.annotation-tRNAs-ERCC_phiX.gtf
│   └── other index files
└── trimmed
    ├── {sample}_too_long.fastq.gz
    ├── {sample}_too_short.fastq.gz
    ├── {sample}.trim.fastq.gz
    └── {sample}.trim.log