GSE51741 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

1. 1

   Illumina Casava1.8.2 software used for basecalling.

   Illumina Casava v1.8.2

   $ Bash example

   # Illumina Casava 1.8.2 is proprietary software run on Illumina sequencing instruments
   # for basecalling and demultiplexing. This step is performed by the sequencer itself.
   # No user-executable command-line tool is typically run for Casava basecalling post-sequencing.
   # The output of this step would be raw sequencing data in FASTQ format, which serves as input for downstream bioinformatics analysis.

   Copy
2. 2

   Demultiplexing based on index sequences

   bcl2fastq (Inferred with models/gemini-2.5-flash) v2.20.0.422 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bcl2fastq (example using conda)
   # conda install -c bioconda bcl2fastq2
   
   # Define input and output directories and sample sheet
   RUN_FOLDER_DIR="/path/to/illumina/run/directory" # Directory containing BCL files
   OUTPUT_FASTQ_DIR="/path/to/output/fastq/directory"
   SAMPLE_SHEET="/path/to/sample_sheet.csv" # This file defines samples and their index sequences
   
   # Execute bcl2fastq for demultiplexing based on index sequences
   bcl2fastq --runfolder-dir "${RUN_FOLDER_DIR}" \
             --output-dir "${OUTPUT_FASTQ_DIR}" \
             --sample-sheet "${SAMPLE_SHEET}" \
             --no-lane-splitting \
             --barcode-mismatches 1 \
             --minimum-trimmed-read-length 8 \
             --mask-short-adapter-reads 8 \
             --ignore-missing-bcl \
             --ignore-missing-stats \
             --ignore-missing-positions

   Copy View on GitHub
3. 3

   Sequenced reads were mapped to Refseq RNA sequences using bowtie v0.12.7 with parameters -q -e 100 -m 10 --best --strata

   Bowtie v0.12.7 GitHub

   $ Bash example

   # Install Bowtie v0.12.7 if not already installed
   # conda install -c bioconda bowtie=0.12.7
   
   # Placeholder for Refseq RNA index. This index needs to be built beforehand
   # using bowtie-build from your Refseq RNA FASTA file (e.g., refseq_rna.fasta).
   # Example: bowtie-build refseq_rna.fasta refseq_rna_index
   
   # Placeholder for input sequenced reads (e.g., a FASTQ file)
   # Placeholder for output SAM file
   
   # Execute Bowtie mapping
   bowtie -q -e 100 -m 10 --best --strata refseq_rna_index reads.fastq > output.sam

   Copy View on GitHub
4. 4

   Count reads for each genes

   featureCounts (Inferred with models/gemini-2.5-flash) v2.0.1 GitHub

   $ Bash example

   # Install Subread (which includes featureCounts)
   # conda install -c bioconda subread
   
   # Define input and output files
   # Replace 'aligned_reads.bam' with your actual aligned BAM file(s)
   # Replace 'Homo_sapiens.GRCh38.109.gtf' with your specific gene annotation GTF file
   INPUT_BAM="aligned_reads.bam"
   GENE_ANNOTATION_GTF="Homo_sapiens.GRCh38.109.gtf" # Example: Ensembl GRCh38 release 109
   OUTPUT_COUNTS="gene_counts.txt"
   
   # Execute featureCounts to count reads per gene
   # -a: Annotation file
   # -o: Output file
   # -F GTF: Specify annotation file format
   # -t exon: Feature type to count (e.g., 'exon')
   # -g gene_id: Attribute type to group features (e.g., 'gene_id')
   # -s 2: Strandedness (0=unstranded, 1=forward, 2=reverse). eCLIP often uses reverse stranded.
   # -T 8: Number of threads
   featureCounts \
     -a "${GENE_ANNOTATION_GTF}" \
     -o "${OUTPUT_COUNTS}" \
     -F GTF \
     -t exon \
     -g gene_id \
     -s 2 \
     -T 8 \
     "${INPUT_BAM}"

   Copy View on GitHub