GSE77702 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_human]

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 v4.0 (Inferred) GitHub

   $ Bash example

   # Install cutadapt (if not already installed)
   # conda install -c bioconda cutadapt
   
   # Define input and output file names (placeholders for paired-end RNA-seq data)
   INPUT_R1="raw_rna_seq_R1.fastq.gz"
   INPUT_R2="raw_rna_seq_R2.fastq.gz"
   OUTPUT_R1="trimmed_rna_seq_R1.fastq.gz"
   OUTPUT_R2="trimmed_rna_seq_R2.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 "${OUTPUT_R1}" \
     -p "${OUTPUT_R2}" \
     "${INPUT_R1}" \
     "${INPUT_R2}"

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2. 2

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

   bowtie (Inferred with models/gemini-2.5-flash) v1.2.3 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bowtie (if not already installed)
   # conda install -c bioconda bowtie
   
   # Placeholder for RepBase18.05 index
   # You would typically download the RepBase18.05 FASTA file and build the bowtie index once.
   # Example:
   # wget https://www.girinst.org/repbase/update/RepBase18.05.fasta.gz
   # gunzip RepBase18.05.fasta.gz
   # bowtie-build RepBase18.05.fasta RepBase18.05_index
   
   REPBASE_INDEX="RepBase18.05_index" # Path to the bowtie index for RepBase18.05
   INPUT_FASTQ="input_reads.fastq.gz" # Replace with your actual input reads file (e.g., adapter-trimmed reads)
   MAPPED_SAM="mapped_to_repeats.sam" # Output SAM file for reads mapping to repeats
   UNMAPPED_FASTQ="unmapped_from_repeats.fastq.gz" # Output FASTQ file for reads that did NOT map to repeats
   
   # Map reads to the repetitive elements database
   # -v 2: Allow up to 2 mismatches
   # -m 1: Suppress alignments for reads that map to more than 1 location (report only unique best alignments)
   # --best --strata: Report alignments from the best stratum, and among those, report the best alignments
   # -S: Output alignments in SAM format
   # -p 8: Use 8 threads
   # --un: Write reads that do not map to the index to the specified file
   bowtie -v 2 -m 1 --best --strata -S -p 8 --un "${UNMAPPED_FASTQ}" "${REPBASE_INDEX}" "${INPUT_FASTQ}" > "${MAPPED_SAM}"

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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 1.0.0 (example using conda)
   # conda install -c bioconda bowtie=1.0.0
   
   # Align reads using Bowtie 1.0.0
   # Assuming 'repbase_index' is the basename for the Bowtie index generated from Repbase sequences
   # Assuming 'reads.fastq' is the input FASTQ file
   # Output will be in SAM format to 'output.sam'
   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 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 GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star=2.3.0e
   
   # Define variables
   INPUT_READS="reads_not_mapped_to_repbase.fastq.gz" # Placeholder for the input reads (e.g., FASTQ or gzipped FASTQ)
   GENOME_DIR="path/to/hg19_star_index" # Placeholder for the STAR genome index for hg19
   OUTPUT_PREFIX="aligned_to_hg19"
   
   # Reference Dataset: hg19 human genome (UCSC assembly)
   # Download hg19 fasta from UCSC (e.g., http://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz)
   # Download a corresponding GTF file (e.g., from GENCODE or UCSC Table Browser) for splice junction annotation.
   
   # Example command to build STAR genome index (run once):
   # STAR --runMode genomeGenerate \
   #      --genomeDir ${GENOME_DIR} \
   #      --genomeFastaFiles hg19.fa \
   #      --sjdbGTFfile genes.gtf \
   #      --runThreadN 8 # Adjust number of threads as needed
   
   # Align reads to hg19 using STAR
   # Note: --outFilterMultimapScoreRange 1 is not a standard STAR parameter for version 2.3.0e and may cause the command to fail.
   STAR --genomeDir ${GENOME_DIR} \
        --readFilesIn ${INPUT_READS} \
        --outFileNamePrefix ${OUTPUT_PREFIX} \
        --outSAMunmapped Within \
        --outFilterMultimapNmax 1 \
        --outFilterMultimapScoreRange 1 \
        --outSAMtype BAM SortedByCoordinate \
        --runThreadN 8 # Adjust number of threads as needed
   

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5. 5

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

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

   $ Bash example

   # Install Subread package (which includes featureCounts)
   # conda install -c bioconda subread
   
   # Placeholder for Gencode v17 annotation GTF file
   # Download from Gencode archives if needed, e.g., for GRCh37/hg19
   # wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_17/gencode.v17.annotation.gtf.gz
   # gunzip gencode.v17.annotation.gtf.gz
   GENCODE_GTF="gencode.v17.annotation.gtf"
   
   # Placeholder for input alignment file (BAM format)
   INPUT_BAM="input_reads.bam"
   
   # Output file for gene counts
   OUTPUT_COUNTS="gene_counts.txt"
   
   # Run featureCounts to calculate read counts for each gene
   # -a: Annotation file (GTF/GFF)
   # -o: Output file for counts
   # -F GTF: Specify annotation file format as GTF
   # -t exon: Count reads overlapping 'exon' features
   # -g gene_id: Aggregate counts by 'gene_id' attribute
   # -s 0: Unstranded (0), forward (1), or reverse (2). Assuming unstranded if not specified.
   # -T 8: Use 8 threads (adjust as needed)
   # -M: Multi-mapping reads will also be counted
   # --fraction: Assign fractional counts to multi-mapping reads (if -M is used)
   featureCounts -a "${GENCODE_GTF}" \
                 -o "${OUTPUT_COUNTS}" \
                 -F GTF \
                 -t exon \
                 -g gene_id \
                 -s 0 \
                 -T 8 \
                 -M \
                 --fraction \
                 "${INPUT_BAM}"

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Tools Used