GSE78508 Processing Pipeline

Publication

Cell reports (2016) — PMID 27068461

Dataset

Enhanced CLIP uncovers IMP protein-RNA targets in human pluripotent stem cells important for cell adhesion and survival [RNA-Seq]

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 (if not already installed)
   # conda install -c bioconda cutadapt
   
   # Define input and output file names (placeholders)
   # For paired-end reads, you would typically run cutadapt on both R1 and R2, 
   # or use the -p option for paired-end trimming.
   # Assuming single-end for this example based on the description.
   INPUT_FASTQ="input.fastq.gz"
   OUTPUT_FASTQ="output.trimmed.fastq.gz"
   
   # Run cutadapt to trim adapters, polyA/T tails, 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 "${OUTPUT_FASTQ}" \
     "${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) v2.5.0 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bowtie2 (if not already installed)
   # conda install -c bioconda bowtie2
   
   # Placeholder for RepBase 18.05 sequences
   # In a real scenario, you would download or generate a FASTA file
   # containing the repetitive elements from RepBase 18.05.
   # For example, from a source like UCSC genome browser's repeat masker track,
   # or directly from RepBase (which might require a license).
   # Let's assume 'repbase_18.05.fasta' is available in the working directory.
   # Example: wget -O repbase_18.05.fasta "http://some_url_to_repbase_18.05.fasta"
   
   # Build bowtie2 index for repetitive elements
   # The index prefix will be 'repbase_18.05_index'
   bowtie2-build repbase_18.05.fasta repbase_18.05_index
   
   # Define input reads (replace with actual file names)
   # Assuming single-end reads for simplicity, adjust for paired-end if necessary.
   INPUT_READS="input_reads.fastq.gz"
   
   # Map reads against the repetitive elements database
   # -x: index prefix
   # -U: unaligned reads (single-end input)
   # -S: output SAM file
   # --very-sensitive-local: a common preset for sensitive mapping, often used for repeat masking
   bowtie2 -x repbase_18.05_index \
           -U "${INPUT_READS}" \
           --very-sensitive-local \
           -S mapped_to_repbase_18.05.sam
   
   # Optional: Convert SAM to BAM and sort for downstream analysis
   # samtools view -bS mapped_to_repbase_18.05.sam | samtools sort -o mapped_to_repbase_18.05.bam
   # samtools index mapped_to_repbase_18.05.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
   
   # Placeholder for the Repbase index prefix (e.g., generated by bowtie-build)
   # Replace 'repbase_index' with the actual path and prefix of your Bowtie index
   REPBASE_INDEX="repbase_index"
   
   # Placeholder for the input reads file (e.g., FASTQ format)
   # Replace 'reads.fastq' with the actual path to your input reads file
   INPUT_READS="reads.fastq"
   
   # Placeholder for the output SAM file
   # Replace 'output.sam' with the desired path for the output SAM file
   OUTPUT_SAM="output.sam"
   
   # Align reads using Bowtie version 1.0.0 with specified parameters
   bowtie -S -q -p 16 -e 100 -l 20 "${REPBASE_INDEX}" "${INPUT_READS}" > "${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 (if not already installed)
   # conda install -c bioconda star
   
   # Placeholder for STAR genome index directory for hg19
   # To generate the index, you would typically run:
   # STAR --runMode genomeGenerate --genomeDir /path/to/STAR_index/hg19 --genomeFastaFiles hg19.fa --sjdbGTFfile genes.gtf --runThreadN <num_threads>
   genome_index_dir="/path/to/STAR_index/hg19"
   
   # Placeholder for input reads (e.g., FASTQ file of reads not mapped to Repbase)
   # This assumes the input reads have already been filtered as described.
   input_reads_fastq="reads_not_mapped_to_repbase.fastq"
   
   # Placeholder for output file prefix
   output_prefix="aligned_to_hg19_"
   
   # Align reads to the hg19 human genome using STAR
   STAR \
     --genomeDir "${genome_index_dir}" \
     --readFilesIn "${input_reads_fastq}" \
     --outFileNamePrefix "${output_prefix}" \
     --outSAMunmapped Within \
     --outFilterMultimapNmax 1 \
     --outFilterMultimapScoreRange 1 \
     --runThreadN 8 # Adjust number of threads as appropriate for your system

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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 featureCounts (part of Subread package)
   # conda install -c bioconda subread
   
   # Download and decompress the GENCODE v17 GTF file (corresponding to Ensembl release 67 / GRCh37)
   # mkdir -p reference
   # wget -O reference/Homo_sapiens.GRCh37.67.gtf.gz ftp://ftp.ensembl.org/pub/release-67/gtf/homo_sapiens/Homo_sapiens.GRCh37.67.gtf.gz
   # gunzip reference/Homo_sapiens.GRCh37.67.gtf.gz
   
   # Placeholder for input BAM files (aligned reads)
   # Replace with actual BAM file paths, e.g., "sample1.bam sample2.bam"
   INPUT_BAM_FILES="<input_aligned_reads_1.bam> <input_aligned_reads_2.bam>"
   
   # Path to the GTF annotation file
   GTF_FILE="reference/Homo_sapiens.GRCh37.67.gtf"
   
   # Output file for gene counts
   OUTPUT_FILE="gene_counts.txt"
   
   # Number of threads to use for parallel processing
   NUM_THREADS=8
   
   # Execute featureCounts
   # -a: Annotation file (GTF/GFF)
   # -o: Output file
   # -F GTF: Specify GTF format for annotation file
   # -t exon: Count features of type 'exon' (common for RNA-seq gene counting)
   # -g gene_id: Group features by 'gene_id' attribute to summarize counts per gene
   # -s 0: Unstranded (0), use -s 1 for forward stranded, -s 2 for reverse stranded
   # -T: Number of threads
   # Add -p if reads are paired-end (e.g., featureCounts ... -p ...)
   featureCounts -a "${GTF_FILE}" -o "${OUTPUT_FILE}" -F GTF -t exon -g gene_id -s 0 -T "${NUM_THREADS}" ${INPUT_BAM_FILES}

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Tools Used