GSE86227 Processing Pipeline

Publication

Neuron (2016) — PMID 27773581

Dataset

HNRNPA2B1 regulates alternative RNA processing in the nervous system and accumulates in granules in ALS IPSC-derived motor neurons [hnRNPA2B1_small_r…

1. 1

   Sequencing reads from small RNA-seq libraries were first trimmed adapters using cutadapt

   cutadapt vNot specified, assuming a recent stable version (e.g., 4.x) GitHub

   $ Bash example

   # Install cutadapt (choose one method)
   # conda install -c bioconda cutadapt
   # pip install cutadapt
   
   # Define input and output files
   INPUT_FASTQ="small_rna_seq_reads.fastq.gz" # Placeholder for your input small RNA-seq FASTQ file
   OUTPUT_FASTQ="small_rna_seq_reads_trimmed.fastq.gz" # Placeholder for your output trimmed FASTQ file
   
   # Define the 3' adapter sequence for small RNA-seq.
   # This is a common Illumina TruSeq Small RNA 3' adapter. 
   # Verify the exact adapter sequence used in your library preparation kit.
   ADAPTER_SEQUENCE="TGGAATTCTCGGGTGCCAAGGAACTCCAG"
   
   # Define minimum length for trimmed reads (common for small RNA-seq, e.g., 18-20 bp)
   MIN_LENGTH=18
   
   # Number of CPU threads to use for parallel processing
   NUM_THREADS=4
   
   # Execute cutadapt to trim adapters
   # -a: Specifies the 3' adapter sequence to be removed
   # --minimum-length: Discards reads shorter than this length after trimming
   # --discard-untrimmed: Discards reads that do not contain the adapter sequence
   # -o: Specifies the output file for trimmed reads
   # -j: Specifies the number of CPU threads to use
   cutadapt \
     -a "${ADAPTER_SEQUENCE}" \
     --minimum-length "${MIN_LENGTH}" \
     --discard-untrimmed \
     -o "${OUTPUT_FASTQ}" \
     -j "${NUM_THREADS}" \
     "${INPUT_FASTQ}"
   

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2. 2

   Reads were then mapped against a database of repetitive elements derived from RepBase18.05.

   bowtie2 (Inferred with models/gemini-2.5-flash) v2.4.5 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bowtie2 (example using conda)
   # conda install -c bioconda bowtie2
   
   # Install samtools (example using conda)
   # conda install -c bioconda samtools
   
   # --- Reference Data Preparation ---
   # Download RepBase18.05 (Note: RepBase is a commercial database. 
   # For this example, we assume a FASTA file 'RepBase18.05.fasta' is available.
   # You would typically obtain this from a licensed source or a derived public subset.)
   # Example: wget -O RepBase18.05.fasta "URL_TO_REPBASE_FASTA"
   
   # Build Bowtie2 index for RepBase18.05
   bowtie2-build RepBase18.05.fasta RepBase18.05_index
   
   # --- Mapping Reads ---
   # Define input read files (replace with actual paths to your FASTQ files)
   READS_R1="your_reads_R1.fastq.gz"
   READS_R2="your_reads_R2.fastq.gz"
   OUTPUT_BAM="mapped_to_repeats.bam"
   THREADS=8 # Number of threads to use for bowtie2
   
   # Map reads against the RepBase index
   bowtie2 --very-sensitive-local -p ${THREADS} \
           -x RepBase18.05_index \
           -1 ${READS_R1} \
           -2 ${READS_R2} \
           | samtools view -bS - \
           | samtools sort -o ${OUTPUT_BAM} -
   

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3. 3

   Bowtie version 1.0.0 with parameters -S -q -p 16 -e 100 -l 20 was used to align reads against an index generated from Repbase sequences (Langmead et al., 2009).

   Bowtie v1.0.0 GitHub

   $ Bash example

   # Install Bowtie (if not already installed)
   # conda install -c bioconda bowtie=1.0.0
   
   # Align reads using Bowtie
   # Assuming 'repbase_index' is the prefix for the Repbase index files
   # Assuming 'reads.fastq' is the input FASTQ file
   # Assuming 'output.sam' is the desired output SAM file
   bowtie -S -q -p 16 -e 100 -l 20 repbase_index reads.fastq > output.sam

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4. 4

   Reads not mapped to Repbase sequences were aligned to the mm9 or hg19 genome (UCSC assembly) using bowtie version 1.1.1 with parameters -p 8 -k 1 -m 10 -l 25 --best --chunkmbs 128

   Bowtie v1.1.1 GitHub

   $ Bash example

   # Install Bowtie (if not already installed)
   # conda install -c bioconda bowtie=1.1.1
   
   # Placeholder for Bowtie index (replace with actual path to mm9 or hg19 index)
   # The index would typically be built once using 'bowtie-build'
   # For example: bowtie-build <path_to_mm9_fasta> mm9_index
   GENOME_INDEX="mm9_index" # Or "hg19_index" depending on the specific genome used
   
   # Placeholder for input reads file (reads not mapped to Repbase sequences)
   INPUT_READS="unmapped_to_repbase_reads.fastq"
   
   # Placeholder for output SAM file
   OUTPUT_SAM="aligned_to_genome.sam"
   
   # Align reads to the mm9 or hg19 genome
   bowtie -p 8 -k 1 -m 10 -l 25 --best --chunkmbs 128 "${GENOME_INDEX}" "${INPUT_READS}" > "${OUTPUT_SAM}"

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5. 5

   counts of reads for each gene annotated in gencode vM3 were calculated from featureCounts for mouse and v19 for human

   featureCounts v2.0.3 (Inferred with models/gemini-2.5-flash)

   $ Bash example

   # Install featureCounts (part of Subread package)
   # conda install -c bioconda subread
   
   # Command for mouse (gencode vM3)
   # Replace input_mouse_1.bam input_mouse_2.bam with your actual mouse BAM file(s)
   # Replace /path/to/gencode.vM3.annotation.gtf with the actual path to the Gencode vM3 GTF file
   featureCounts -a /path/to/gencode.vM3.annotation.gtf \
                 -o mouse_gene_counts.txt \
                 -F GTF \
                 -t exon \
                 -g gene_id \
                 -s 0 \
                 -T 8 \
                 input_mouse_1.bam input_mouse_2.bam
   
   # Command for human (gencode v19)
   # Replace input_human_1.bam input_human_2.bam with your actual human BAM file(s)
   # Replace /path/to/gencode.v19.annotation.gtf with the actual path to the Gencode v19 GTF file
   featureCounts -a /path/to/gencode.v19.annotation.gtf \
                 -o human_gene_counts.txt \
                 -F GTF \
                 -t exon \
                 -g gene_id \
                 -s 0 \
                 -T 8 \
                 input_human_1.bam input_human_2.bam

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