GSE77704 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_Stability]

1. 1

   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 GitHub

   $ Bash example

   # Install cutadapt (example using conda)
   # conda install -c bioconda cutadapt
   
   # Define input and output file names (placeholders)
   # For single-end reads:
   INPUT_FASTQ="input.fastq.gz"
   OUTPUT_FASTQ="trimmed_output.fastq.gz"
   
   # For paired-end reads, uncomment and adjust:
   # INPUT_FASTQ_R1="input_R1.fastq.gz"
   # INPUT_FASTQ_R2="input_R2.fastq.gz"
   # OUTPUT_FASTQ_R1="trimmed_output_R1.fastq.gz"
   # OUTPUT_FASTQ_R2="trimmed_output_R2.fastq.gz"
   
   # Run cutadapt for single-end reads
   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 "${OUTPUT_FASTQ}" \
     "${INPUT_FASTQ}"
   
   # If processing paired-end reads, use the following command instead:
   # 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 "${OUTPUT_FASTQ_R1}" \
   #   -p "${OUTPUT_FASTQ_R2}" \
   #   "${INPUT_FASTQ_R1}" \
   #   "${INPUT_FASTQ_R2}"

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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) v2.5.0 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bowtie2 if not already installed
   # conda install -c bioconda bowtie2
   
   # --- Prepare RepBase18.05 index ---
   # The RepBase18.05 database needs to be obtained (e.g., as a FASTA file).
   # For demonstration, we assume 'RepBase18.05.fasta' is the file containing repetitive elements.
   # This file would typically be derived from RepBase (e.g., from www.girinst.org, which often requires a license).
   
   # Build bowtie2 index for RepBase18.05
   # 'RepBase18.05_index' will be the prefix for the generated index files.
   bowtie2-build RepBase18.05.fasta RepBase18.05_index
   
   # --- Mapping reads ---
   # Assuming 'reads.fastq' are the input single-end reads.
   # Reads are mapped against the RepBase18.05 index.
   bowtie2 -x RepBase18.05_index -U reads.fastq -S repbase_mapped.sam
   
   # Optional: Convert SAM to BAM and sort for downstream analysis
   # samtools view -bS repbase_mapped.sam > repbase_mapped.bam
   # samtools sort repbase_mapped.bam -o repbase_mapped_sorted.bam
   # samtools index repbase_mapped_sorted.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

   $ Bash example

   # Install Bowtie (if not already installed)
   # For example, using conda:
   # conda install -c bioconda bowtie=1.0.0
   
   # Placeholder for Repbase index and input reads
   # Replace 'path/to/repbase_index' with the actual path to your Bowtie index prefix (e.g., 'repbase_index' if files are repbase_index.1.ebwt, etc.)
   # Replace 'path/to/input_reads.fastq' with the actual path to your input FASTQ file
   # Replace 'path/to/output_aligned_reads.sam' with your desired output path
   
   bowtie -S -q -p 16 -e 100 -l 20 path/to/repbase_index path/to/input_reads.fastq > path/to/output_aligned_reads.sam

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4. 4

   Reads not mapped to Repbase sequences were aligned to the hg19 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

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star=2.3.0e
   
   # Placeholder for STAR genome index generation (if not already done)
   # STAR --runMode genomeGenerate --genomeDir hg19_star_index --genomeFastaFiles hg19.fa --sjdbGTFfile hg19.gtf --runThreadN 8
   
   # Align reads to hg19 using STAR
   STAR \
     --genomeDir hg19_star_index \
     --readFilesIn filtered_repbase_reads.fastq \
     --outFileNamePrefix star_alignment_output_ \
     --outSAMtype BAM SortedByCoordinate \
     --outSAMunmapped Within \
     --outFilterMultimapNmax 1 \
     --outFilterMultimapScoreRange 1 \
     --runThreadN 8 # Example: use 8 threads, adjust as needed
   
   # The output will be star_alignment_output_Aligned.sortedByCoord.out.bam

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5. 5

   counts of reads for each gene annotated in gencode v17 were calculated from featureCounts

   featureCounts v2.0.3

   $ Bash example

   # Install Subread (which includes featureCounts)
   # conda install -c bioconda subread
   
   # Define input and output paths
   # Placeholder for Gencode v17 GTF file. You may need to download it from the Gencode archive.
   ANNOTATION_GTF="/path/to/gencode.v17.annotation.gtf"
   # Placeholder for input BAM files (e.g., from alignment step)
   INPUT_BAMS="aligned_reads/*.bam"
   OUTPUT_COUNTS="gene_counts.txt"
   
   # Run featureCounts to calculate gene-level read counts
   # -a: Specify the annotation file (GTF/GFF format)
   # -o: Specify the output file name
   # -F GTF: Specify that the annotation file is in GTF format (default, but good to be explicit)
   # -t exon: Specify feature type to count (e.g., 'exon' for gene-level counts)
   # -g gene_id: Specify attribute type to use for grouping features (e.g., 'gene_id')
   # -s 0: Specify strand specificity (0=unstranded, 1=stranded, 2=reverse stranded). Unstranded is a common default.
   # -T 8: Number of threads to use for parallel processing
   featureCounts \
     -a ${ANNOTATION_GTF} \
     -o ${OUTPUT_COUNTS} \
     -F GTF \
     -t exon \
     -g gene_id \
     -s 0 \
     -T 8 \
     ${INPUT_BAMS}

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Tools Used