GSE78960 Processing Pipeline

Publication

Genome medicine (2016) — PMID 27655340

Dataset

Modeling the Neuropathology of Tuberous Sclerosis with Human Stem Cells Reveals a Role for Inflammation and Angiogenic Growth Factors [Treatment]

1. 1

   Linker tags were removed from RNA sequencing and ribosome profiling reads by the FASTX Toolkit, v0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit/)

   RNA-seq v0.0.13 GitHub

   $ Bash example

   bash
   # Install FASTX Toolkit (if not already installed)
   # conda install -c bioconda fastx_toolkit
   
   # Define input and output file names
   INPUT_FASTQ="input_reads.fastq"
   OUTPUT_FASTQ="reads_linker_removed.fastq"
   
   # Placeholder for the linker tag sequence. This needs to be replaced with the actual linker sequence.
   # Example: ADAPTER_SEQUENCE="AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC"
   LINKER_SEQUENCE="YOUR_LINKER_TAG_SEQUENCE"
   
   # Remove linker tags using fastx_clipper
   # -a: adapter sequence to clip
   # -i: input FASTQ file
   # -o: output FASTQ file
   # -Q 33: specify quality score format (Phred+33, common for Illumina)
   fastx_clipper -a "${LINKER_SEQUENCE}" -i "${INPUT_FASTQ}" -o "${OUTPUT_FASTQ}" -Q 33
   

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2. 2

   All reads that mapped to rRNAs, tRNAs or mitochondrial rRNAs were removed, and the remaining reads were mapped to RefSeq (v38) by TopHat v2.0.13.

   TopHat v2.0.13 GitHub

   $ Bash example

   # Create a directory for reference data
   mkdir -p ref_data
   
   # Download GRCh38.p13 genome (RefSeq assembly GCF_000001405.39)
   # wget -P ref_data https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/397/GRCh38.p13/GCF_000001405.39_GRCh38.p13_genomic.fna.gz
   # gunzip ref_data/GCF_000001405.39_GRCh38.p13_genomic.fna.gz
   # mv ref_data/GCF_000001405.39_GRCh38.p13_genomic.fna ref_data/GRCh38.p13_genomic.fna
   
   # Build Bowtie2 index for GRCh38.p13 (TopHat v2 uses Bowtie2 by default)
   # bowtie2-build ref_data/GRCh38.p13_genomic.fna ref_data/grch38_refseq_index
   
   # Download RefSeq GTF for GRCh38.p13
   # wget -P ref_data https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/397/GRCh38.p13/GCF_000001405.39_GRCh38.p13_genomic.gtf.gz
   # gunzip ref_data/GCF_000001405.39_GRCh38.p13_genomic.gtf.gz
   # mv ref_data/GCF_000001405.39_GRCh38.p13_genomic.gtf ref_data/grch38_refseq.gtf
   
   # Installation of TopHat
   # conda install -c bioconda tophat=2.0.13
   
   # Define input and output
   INPUT_READS="filtered_reads.fastq" # Placeholder for reads after removing rRNA/tRNA/mito
   OUTPUT_DIR="tophat_output"
   GENOME_INDEX_PREFIX="ref_data/grch38_refseq_index"
   GTF_FILE="ref_data/grch38_refseq.gtf"
   
   # Create output directory
   mkdir -p "${OUTPUT_DIR}"
   
   # Execute TopHat
   tophat -o "${OUTPUT_DIR}" \
          -G "${GTF_FILE}" \
          "${GENOME_INDEX_PREFIX}" \
          "${INPUT_READS}"

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3. 3

   Finally all read counts that mapped uniquely to genes were extracted for expression analysis with the help of samtools, v1.1.

   samtools v1.1 GitHub

   $ Bash example

   # Install samtools (if not already installed)
   # conda install -c bioconda samtools=1.1
   
   # Extract read counts per reference sequence (chromosome/contig).
   # This command provides counts for mapped reads, unmapped reads, and number of bases per reference.
   # While samtools idxstats counts all mapped reads per reference, the description "mapped uniquely to genes"
   # implies further filtering or interpretation that might be handled by downstream tools or custom scripts
   # not explicitly mentioned here. For true gene-level unique counts, tools like featureCounts or htseq-count
   # are typically used on a BAM file that has been filtered for unique alignments (e.g., using samtools view -F 0x100 -F 0x4).
   # Replace 'input.bam' with your actual alignment file.
   # Replace 'output_read_counts.txt' with your desired output file name.
   # A placeholder reference genome (e.g., hg38) is assumed for context, though not directly used by idxstats.
   samtools idxstats input.bam > output_read_counts.txt

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Tools Used