GSE90650 Processing Pipeline

Publication

Nature structural & molecular biology (2017) — PMID 28846091

Dataset

The LncRNA NEAT1 Nests RNA Binding Proteins and the Microprocessor to Globally Enhance Pri-miRNA Processing

1. 1

   For each CLIP-seq read, the first 4nt index sequence was extracted and the 3'-adaptor sequence CTCGTATGCCGTCTTCTGCTTG was removed with program cutadapt (version 1.8.3).

   cutadapt v1.8.3 GitHub

   $ Bash example

   # Install cutadapt (version 1.8.3 is quite old, consider a newer version if possible)
   # conda install -c bioconda cutadapt=1.8.3
   
   # Remove the 3'-adaptor sequence
   cutadapt -a CTCGTATGCCGTCTTCTGCTTG -o trimmed_reads.fastq.gz reads.fastq.gz

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2. 2

   The reads with at least 18nt were mapped to the human genome (hg19) by Bowtie (version 1.1.1) with parameters '-l25 -n2 -k1 -m10 -e 200 --best --strata'.

   Bowtie v1.1.1 GitHub

   $ Bash example

   # Install Bowtie (if not already installed)
   # conda install -c bioconda bowtie
   
   # Ensure the hg19 index is built or available. 
   # If not, you would build it using bowtie-build:
   # bowtie-build <path_to_hg19.fa> hg19
   
   # Align reads to hg19
   bowtie -l 25 -n 2 -k 1 -m 10 -e 200 --best --strata hg19 input_reads.fastq > output_aligned_reads.sam

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3. 3

   For each small RNA-seq read, the firsst 5nt NNNTC was extracted and 3' adaptor sequence 'NNCTCGTATGCCGTCTTCTGCTTG' was removed with program cutadapt (version 1.8.3).

   cutadapt v1.8.3 GitHub

   $ Bash example

   # Install cutadapt if not already installed
   # conda install -c bioconda cutadapt=1.8.3
   
   # Example usage:
   # Assuming input.fastq.gz is the raw small RNA-seq reads
   # and output.fastq.gz will be the trimmed reads.
   cutadapt -g NNNTC \
            -a NNCTCGTATGCCGTCTTCTGCTTG \
            -o output.fastq.gz \
            input.fastq.gz

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4. 4

   The target insert sequences were required to be at least 16 nt.

   fastp (Inferred with models/gemini-2.5-flash) v0.20.0 GitHub

   $ Bash example

   # Install fastp if not already installed
   # conda install -c bioconda fastp
   
   # Filter sequences to be at least 16 nt long
   # Assuming single-end input for simplicity. For paired-end, use --in1, --out1, --in2, --out2.
   fastp --in1 input.fastq.gz --out1 output.fastq.gz --length_required 16

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5. 5

   The filtered reads were mapped to the genome genome (hg19) by Bowtie (version 1.1.1) with parameters '-n 0 -e 80 -l 18 -a -m 5 --best --strata'.

   Bowtie v1.1.1 GitHub

   $ Bash example

   # Install Bowtie (if not already installed)
   # conda install -c bioconda bowtie=1.1.1
   
   # Placeholder for Bowtie index for hg19. This index needs to be built prior to alignment.
   # Example: bowtie-build <hg19.fa> hg19_index
   hg19_index="/path/to/bowtie_indexes/hg19"
   
   # Placeholder for input filtered reads file
   filtered_reads="filtered_reads.fastq"
   
   # Placeholder for output SAM file
   output_sam="mapped_reads.sam"
   
   bowtie -n 0 -e 80 -l 18 -a -m 5 --best --strata "${hg19_index}" "${filtered_reads}" > "${output_sam}"

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6. 6

   The mapped reads were used to generate the genomic coverage profiles in bigWig format with programs genomeCoverageBed (bedtools version 2.24) and samtools (version 0.1.19).

   samtools v0.1.19 GitHub

   $ Bash example

   # Install bedtools (version 2.24) and samtools (version 0.1.19) if not already installed
   # conda install -c bioconda bedtools=2.24 samtools=0.1.19
   
   # Install UCSC tools (bedGraphToBigWig) if not already installed
   # wget https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig -O bedGraphToBigWig
   # chmod +x bedGraphToBigWig
   
   # Define input and output files
   INPUT_BAM="input.sorted.bam" # Placeholder: Assuming input is a sorted and indexed BAM file
   OUTPUT_BEDGRAPH="output.bedgraph"
   OUTPUT_BIGWIG="output.bigWig"
   CHROM_SIZES="hg38.chrom.sizes" # Placeholder: Path to chromosome sizes file (e.g., from UCSC goldenPath)
   
   # Example of how to get chrom.sizes for hg38 (if not already available)
   # fetchChromSizes hg38 > hg38.chrom.sizes
   
   # Generate bedGraph from BAM using genomeCoverageBed
   # samtools 0.1.19 might have been used for sorting/indexing the BAM prior to this step.
   genomeCoverageBed -ibam "$INPUT_BAM" -bg -g "$CHROM_SIZES" > "$OUTPUT_BEDGRAPH"
   
   # Convert bedGraph to bigWig
   # bedGraphToBigWig is a UCSC tool, commonly used for this conversion.
   # Its use is inferred as bigWig format is the specified output.
   bedGraphToBigWig "$OUTPUT_BEDGRAPH" "$CHROM_SIZES" "$OUTPUT_BIGWIG"

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