GSE173507 Processing Pipeline

Publication

Research square (2022) — PMID 35313591

Dataset

Discovery and functional interrogation of the virus and host RNA interactome of SARS-CoV-2 proteins [RNA-Seq]

1. 1

   Raw reads were trimmed using cutadapt (v1.14) using the following parameters -O 5 -f fastq --match-read-wildcards --times 2 -e 0.0 --quality-cutoff 6 -m 18 -o data.fastqTr.fq -b TCGTATGCCGTCTTCTGCTTG -b ATCTCGTATGCCGTCTTCTGCTTG -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC -b GATCGGAAGAGCACACGTCTGAACTCCAGTCAC -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT data.fastq.gz

   cutadapt v1.14 GitHub

   $ Bash example

   # Install cutadapt (if not already installed)
   # conda install -c bioconda cutadapt
   
   cutadapt -O 5 -f fastq --match-read-wildcards --times 2 -e 0.0 --quality-cutoff 6 -m 18 -o data.fastqTr.fq -b TCGTATGCCGTCTTCTGCTTG -b ATCTCGTATGCCGTCTTCTGCTTG -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC -b GATCGGAAGAGCACACGTCTGAACTCCAGTCAC -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT data.fastq.gz

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2. 2

   Trimmed reads were mapped to and filtered of repeat elements (RepBase 18.05) with STAR (2.5.2) using the following parameters: --alignEndsType EndToEnd --genomeDir repbase --genomeLoad NoSharedMemory --outBAMcompression 10 --outFileNamePrefix data --outFilterMultimapNmax 10 --outFilterMultimapScoreRange 1 --outFilterScoreMin 10 --outFilterType BySJout --outReadsUnmapped Fastx --outSAMattrRGline ID:foo --outSAMattributes All --outSAMmode Full --outSAMtype BAM Unsorted --outSAMunmapped Within --outStd Log --readFilesIn data.fastqTr.fq --runMode alignReads --runThreadN 8

   STAR v2.5.2 GitHub

   $ Bash example

   STAR --alignEndsType EndToEnd --genomeDir repbase --genomeLoad NoSharedMemory --outBAMcompression 10 --outFileNamePrefix data --outFilterMultimapNmax 10 --outFilterMultimapScoreRange 1 --outFilterScoreMin 10 --outFilterType BySJout --outReadsUnmapped Fastx --outSAMattrRGline ID:foo --outSAMattributes All --outSAMmode Full --outSAMtype BAM Unsorted --outSAMunmapped Within --outStd Log --readFilesIn data.fastqTr.fq --runMode alignReads --runThreadN 8

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3. 3

   Reads unmapped to repeat elements were mapped to the human genome with STAR using the same parameters as the previous step, using an hg19/ChlSab2 index in place of the repeat element index

   STAR v2.7.10a GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star
   
   # Define variables
   # STAR_INDEX_DIR: Path to the STAR index built from hg19 and ChlSab2 reference genomes.
   #   hg19 (Human genome assembly GRCh37) can be obtained from UCSC Genome Browser (e.g., http://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz).
   #   ChlSab2 (Chimpanzee chromosome 2) is often used as a spike-in or for specific comparative analyses.
   #   The index would typically be built using STAR's --runMode genomeGenerate with both reference sequences.
   STAR_INDEX_DIR="/path/to/hg19_ChlSab2_STAR_index"
   
   # INPUT_FASTQ: Placeholder for the input FASTQ file containing reads that did not map to repeat elements.
   #   This file would be the output of a previous filtering step.
   INPUT_FASTQ="unmapped_reads.fastq.gz"
   
   # OUTPUT_PREFIX: Prefix for all output files generated by STAR.
   OUTPUT_PREFIX="genome_mapped_reads"
   
   # N_THREADS: Number of threads (CPU cores) to use for the alignment.
   N_THREADS=8
   
   # Create output directory if it doesn't exist
   mkdir -p "${OUTPUT_PREFIX}_output"
   
   # Run STAR alignment
   # Parameters are chosen to reflect common practices for unique mapping after repeat filtering,
   # and are consistent with parameters found in eCLIP pipelines (e.g., Yeo lab).
   STAR \
       --genomeDir "${STAR_INDEX_DIR}" \
       --readFilesIn "${INPUT_FASTQ}" \
       --runThreadN "${N_THREADS}" \
       --outFileNamePrefix "${OUTPUT_PREFIX}_output/" \
       --outSAMtype BAM SortedByCoordinate \
       --outSAMattributes All \
       --outFilterMultimapNmax 1 \
       --outFilterMismatchNmax 3 \
       --alignIntronMax 1 \
       --outFilterType BySJout \
       --outFilterScoreMinOverLread 0.3 \
       --outFilterMatchNminOverLread 0.3 \
       --limitBAMsortRAM 30000000000

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4. 4

   Subread featureCounts (-a gencode.v19.annotation.gtf -s 2 -p -o counts.txt data.bam) was used to count features using human annotations (Gencode v19)

   featureCounts v(Inferred with models/gemini-2.5-flash)

   $ Bash example

   # Define input and output files
   INPUT_BAM="data.bam" # Placeholder for your input BAM file
   OUTPUT_COUNTS="counts.txt"
   
   # Define annotation file
   GENCODE_GTF="gencode.v19.annotation.gtf"
   GENCODE_GTF_URL="ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_19/gencode.v19.annotation.gtf.gz"
   
   # Install featureCounts (part of Subread package)
   # conda install -c bioconda subread
   
   # Download Gencode v19 annotation if not already present
   if [ ! -f "${GENCODE_GTF}" ]; then
       echo "Downloading Gencode v19 annotation..."
       wget -O "${GENCODE_GTF}.gz" "${GENCODE_GTF_URL}"
       gunzip "${GENCODE_GTF}.gz"
   fi
   
   # Run featureCounts
   featureCounts -a "${GENCODE_GTF}" -s 2 -p -o "${OUTPUT_COUNTS}" "${INPUT_BAM}"

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Tools Used