GSE77703 Processing Pipeline

Publication

Nature communications (2016) — PMID 27378374

Dataset

Distinct and shared functions of ALS-associated TDP-43, FUS, and TAF15 revealed by comprehensive multi-system integrative analyses [RNA-Seq_mouse]

1. 1

   Sequencing reads from RNA-seq libraries were first trimmed of polyA tails, adapters, and low quality ends using cutadapt with parameters --match-read-wildcards --times 2 -e 0 -O 5 --quality-cutoff' 6 -m 18 -b TCGTATGCCGTCTTCTGCTTG -b ATCTCGTATGCCGTCTTCTGCTTG -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC -b TGGAATTCTCGGGTGCCAAGG -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT.

   cutadapt v4.1 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   bash
   # Install cutadapt (example using conda)
   # conda install -c bioconda cutadapt
   
   # Define input and output files (placeholders)
   INPUT_READS="input.fastq.gz"
   OUTPUT_READS="output.trimmed.fastq.gz"
   
   # Run cutadapt to trim polyA tails, adapters, and low quality ends
   cutadapt \
     --match-read-wildcards \
     --times 2 \
     -e 0 \
     -O 5 \
     --quality-cutoff 6 \
     -m 18 \
     -b TCGTATGCCGTCTTCTGCTTG \
     -b ATCTCGTATGCCGTCTTCTGCTTG \
     -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC \
     -b TGGAATTCTCGGGTGCCAAGG \
     -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA \
     -b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT \
     -o "${OUTPUT_READS}" \
     "${INPUT_READS}"
   

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2. 2

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

   STAR (Inferred with models/gemini-2.5-flash) v2.7.3a (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star
   
   # Define variables
   # Replace with your actual input FASTQ file
   INPUT_FASTQ="input_reads.fastq.gz"
   # Replace with the path to your STAR index for RepBase18.05
   # This index needs to be pre-built using STAR genomeGenerate command with RepBase18.05 sequences.
   REPBASE_STAR_INDEX_DIR="/path/to/repbase_18_05_star_index"
   # Prefix for output files
   OUTPUT_PREFIX="sample_repbase_aligned"
   # Number of threads to use
   NUM_THREADS=8
   
   # Align reads to the RepBase18.05 repetitive elements database
   STAR \
     --runThreadN ${NUM_THREADS} \
     --genomeDir ${REPBASE_STAR_INDEX_DIR} \
     --readFilesIn ${INPUT_FASTQ} \
     --outFileNamePrefix ${OUTPUT_PREFIX} \
     --outFilterMultimapNmax 100 \
     --outFilterMismatchNmax 10 \
     --outFilterMismatchNoverLmax 0.05 \
     --outFilterScoreMin 10 \
     --outFilterScoreMinOverLread 0.3 \
     --outFilterMatchNoverLread 0.3 \
     --outSAMattributes All \
     --outSAMtype BAM Unsorted \
     --outSAMunmapped Within \
     --outReadsUnmapped Fastx
   

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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
   
   # Align reads using Bowtie
   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 human genome (UCSC assembly) using STAR (Dobin et al., 2013) version 2.3.0e with parameters --outSAMunmapped Within –outFilterMultimapNmax 1 –outFilterMultimapScoreRange 1.

   STAR v2.3.0e GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star=2.3.0e
   
   # Placeholder for STAR genome index directory (mm9 mouse genome, UCSC assembly)
   # Note: The description states "mm9 human genome", which is a contradiction. mm9 is a mouse genome.
   GENOME_DIR="/path/to/mm9_star_index"
   
   # Placeholder for input FASTQ file (reads not mapped to Repbase sequences)
   INPUT_FASTQ="input_reads_filtered_from_repbase.fastq.gz"
   
   # Output directory for STAR alignment results
   OUTPUT_DIR="star_alignment_output"
   
   # Create output directory if it doesn't exist
   mkdir -p "${OUTPUT_DIR}"
   
   # Align reads using STAR with specified parameters
   # --outSAMunmapped Within: Output unmapped reads in the SAM file, but only those that fall within the alignment boundaries.
   # --outFilterMultimapNmax 1: Only output alignments for uniquely mapping reads (i.e., reads that map to at most 1 locus).
   # --outFilterMultimapScoreRange 1: The best alignment score must be at least 1 point better than the second best alignment score.
   STAR --genomeDir "${GENOME_DIR}" \
        --readFilesIn "${INPUT_FASTQ}" \
        --outFileNamePrefix "${OUTPUT_DIR}/" \
        --outSAMunmapped Within \
        --outFilterMultimapNmax 1 \
        --outFilterMultimapScoreRange 1 \
        --runThreadN 8 # Adjust number of threads as needed

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5. 5

   counts of reads for each gene annotated in gencode vM1 were calculated from featureCounts

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

   $ Bash example

   # Install featureCounts (part of the Subread package) if not already installed
   # For example, using conda:
   # conda install -c bioconda subread
   
   # Define input and output files
   INPUT_BAM="aligned_reads.bam" # Placeholder for your input BAM file
   OUTPUT_COUNTS="gene_counts.txt"
   
   # Define the Gencode vM1 annotation GTF file
   # Gencode vM1 is an older mouse annotation release. 
   # You might need to download it from the Gencode archives if not available locally.
   # Example download (adjust URL for specific M1 release if needed):
   # wget -O gencode.vM1.annotation.gtf.gz "ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M1/gencode.vM1.annotation.gtf.gz"
   # gunzip gencode.vM1.annotation.gtf.gz
   GENCODE_GTF="gencode.vM1.annotation.gtf" # Placeholder for the path to your Gencode vM1 GTF file
   
   # Run featureCounts to calculate read counts for each gene
   # -a: Annotation file (GTF)
   # -o: Output file for counts
   # -F GTF: Specify GTF format for annotation
   # -t exon: Count features of type 'exon' (standard for gene-level counts)
   # -g gene_id: Aggregate counts by 'gene_id' attribute in the GTF
   # -T 8: Use 8 threads (adjust as needed for your system)
   # -s 0: Unstranded (0), 1 for forward, 2 for reverse. Adjust if your library is stranded.
   # # -p: Uncomment if your reads are paired-end
   featureCounts -a "${GENCODE_GTF}" -o "${OUTPUT_COUNTS}" -F GTF -t exon -g gene_id -T 8 -s 0 "${INPUT_BAM}"

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Tools Used