GSE51685 Processing Pipeline

Publication

Proceedings of the National Academy of Sciences of the United States of America (2013) — PMID 24170860

Dataset

Targeted degradation of sense and antisense C9orf72 RNA foci as therapy for amyotrophic lateral sclerosis and frontotemporal dementia (strand specifi…

1. 1

   Illumina Casava1.8.2 software used for basecalling.

   Illumina Casava v1.8.2 GitHub

   $ Bash example

   # Illumina Casava 1.8.2 is an integrated software suite on Illumina sequencing instruments
   # responsible for basecalling and initial data processing, including demultiplexing and
   # generating FASTQ files. This is typically an instrument-level process, not a user-executable
   # command-line tool. The command below is a conceptual representation of this process.
   
   illumina_casava_basecall_and_demultiplex \
       --run-folder /path/to/illumina/run/folder \
       --output-directory /path/to/output/fastq \
       --version 1.8.2

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2. 2

   Adapters trimmed using Cutadapt v1.2.1.

   cutadapt v1.2.1 GitHub

   $ Bash example

   # Install cutadapt (if not already installed)
   # conda install -c bioconda cutadapt
   
   # Define input and output file paths
   READ1_INPUT="input_R1.fastq.gz"
   READ2_INPUT="input_R2.fastq.gz"
   READ1_TRIMMED="trimmed_R1.fastq.gz"
   READ2_TRIMMED="trimmed_R2.fastq.gz"
   
   # Define adapter sequences (replace with actual sequences if known)
   # Common Illumina 3' adapters:
   # For Read 1 (forward): AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
   # For Read 2 (reverse complement of Read 1 adapter): AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
   ADAPTER_R1="AGATCGGAAGAGCACACGTCTGAACTCCAGTCA" # Example Illumina universal adapter for Read 1
   ADAPTER_R2="AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT" # Example Illumina universal adapter for Read 2
   
   # Run Cutadapt for paired-end reads
   cutadapt \
     -a "${ADAPTER_R1}" \
     -A "${ADAPTER_R2}" \
     --minimum-length 20 \
     -o "${READ1_TRIMMED}" \
     -p "${READ2_TRIMMED}" \
     "${READ1_INPUT}" \
     "${READ2_INPUT}"
   
   # For single-end reads, use:
   # cutadapt \
   #   -a "${ADAPTER_R1}" \
   #   --minimum-length 20 \
   #   -o "${READ1_TRIMMED}" \
   #   "${READ1_INPUT}"

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3. 3

   Command: cutadapt -O 5 -a GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG

   cutadapt v4.x GitHub

   $ Bash example

   # Install cutadapt (if not already installed)
   # conda install -c bioconda cutadapt
   
   # Example usage of cutadapt for adapter trimming
   # Replace 'input.fastq.gz' with your actual input FASTQ file
   # Replace 'output.fastq.gz' with your desired output FASTQ file name
   cutadapt -O 5 -a GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG -o output.fastq.gz input.fastq.gz

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4. 4

   Trimmed reads mapped to mouse genome (mm9) using the GSNAP aligner (part of GMAP package, version 2012-07-20).

   GSNAP v2012-07-20 GitHub

   $ Bash example

   # Install GSNAP (part of GMAP package) if not already available
   # conda install -c bioconda gmap
   
   # Define variables for input, output, and reference genome
   INPUT_READS="trimmed_reads.fastq.gz" # Placeholder for your trimmed reads file
   OUTPUT_SAM="mapped_reads.sam"       # Desired output SAM file name
   GENOME_DIR="/path/to/gmap_indexes"  # Directory where GSNAP genome indexes are stored
   GENOME_NAME="mm9"                   # Name of the indexed mouse genome (mm9)
   NUM_THREADS=8                       # Number of CPU threads to use for alignment
   
   # Note: Ensure the mm9 genome has been indexed for GSNAP using gmap_build prior to this step.
   # Example (if index doesn't exist):
   # gmap_build -D "${GENOME_DIR}" -d "${GENOME_NAME}" /path/to/mm9.fasta
   
   # Execute GSNAP to map trimmed reads to the mm9 mouse genome
   gsnap -D "${GENOME_DIR}" -d "${GENOME_NAME}" \
         -A sam \
         -o "${OUTPUT_SAM}" \
         -N 1 \
         -t "${NUM_THREADS}" \
         "${INPUT_READS}"

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5. 5

   Command: gsnap -Q -n 10 Â --quality-protocol="sanger" -t 4 -N 1 -A sam -B 5 -s mm9 -d mm9

   GSNAP vNot specified

   $ Bash example

   # Install GSNAP (example using conda)
   # conda install -c bioconda gsnap
   
   # Define reference genome (mm9)
   # This assumes the mm9 genome has been indexed for GSNAP.
   # Example indexing command (replace paths as needed):
   # gsnap_build -D /path/to/genome_indices -d mm9 /path/to/mm9.fa
   
   # Execute GSNAP command
   gsnap -Q -n 10 --quality-protocol="sanger" -t 4 -N 1 -A sam -B 5 -s mm9 -d mm9

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6. 6

   Mapped reads assigned to coding regions of Ensembl mouse genes

   Ensembl v2.0.6

   $ Bash example

   # conda install -c bioconda subread
   
   # Define variables
   # Placeholder for Ensembl mouse GTF (e.g., Ensembl release 110, GRCm39 assembly)
   GTF_FILE="Mus_musculus.GRCm39.110.gtf"
   INPUT_BAM="aligned_reads.bam" # Replace with your actual aligned BAM file
   OUTPUT_COUNTS="mouse_ensembl_cds_counts.txt"
   NUM_THREADS=8 # Adjust based on available resources
   
   # Example: Download Ensembl GTF if not already present
   # wget -P . ftp://ftp.ensembl.org/pub/release-110/gtf/mus_musculus/Mus_musculus.GRCm39.110.gtf.gz
   # gunzip Mus_musculus.GRCm39.110.gtf.gz
   
   # Assign mapped reads to coding regions (CDS) of Ensembl mouse genes
   featureCounts -a "${GTF_FILE}" \
                 -o "${OUTPUT_COUNTS}" \
                 -F GTF \
                 -t CDS \
                 -g gene_id \
                 -T "${NUM_THREADS}" \
                 "${INPUT_BAM}"

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