GSE228444 Processing Pipeline

Publication

Cell systems (2025) — PMID 40054464

Dataset

Integrated proteomic and multi-mic characterizations of the synapse reveal RNA processing factor and ubiquitin ligases associated with neurodevelopme…

1. 1

   Following sequencing, raw reads were aligned to GRCh38 and analyzed following a previously published pipeline (Nostrand et al, 2016)

   STAR (Inferred with models/gemini-2.5-flash) v2.5.2b GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Placeholder for STAR genome index creation (run once)
   # This step prepares the GRCh38 genome for alignment.
   # Replace /path/to/ with actual paths to your genome files.
   # STAR --runMode genomeGenerate \
   #      --genomeDir /path/to/GRCh38_STAR_index \
   #      --genomeFastaFiles /path/to/GRCh38.fasta \
   #      --sjdbGTFfile /path/to/GRCh38.gtf \
   #      --runThreadN 8
   
   # Align raw reads to GRCh38 using STAR, following parameters typical for eCLIP.
   # Replace 'read1.fastq.gz', 'read2.fastq.gz' with your actual input files.
   # Replace '/path/to/GRCh38_STAR_index' with the path to your STAR genome index.
   # Output BAM file will be 'aligned_reads/sample_Aligned.sortedByCoord.out.bam'.
   
   mkdir -p aligned_reads
   
   STAR \
     --runThreadN 8 \
     --genomeDir /path/to/GRCh38_STAR_index \
     --readFilesIn read1.fastq.gz read2.fastq.gz \
     --readFilesCommand zcat \
     --outFileNamePrefix aligned_reads/sample_ \
     --outSAMtype BAM SortedByCoordinate \
     --outSAMattributes All \
     --outFilterMultimapNmax 20 \
     --alignSJDBoverhangMin 8 \
     --alignIntronMin 20 \
     --alignIntronMax 1000000 \
     --alignMatesGapMax 1000000 \
     --outFilterMismatchNmax 999 \
     --outFilterMismatchNoverLmax 0.05 \
     --seedSearchStartLmax 30 \
     --winAnchorMultimapNmax 50 \
     --outFilterScoreMinOverLread 0 \
     --outFilterMatchNminOverLread 0 \
     --limitBAMsortRAM 60000000000
   
   # Note: The full Nostrand et al, 2016 eCLIP pipeline (as implemented in yeolab/eclip) 
   # includes additional steps for adapter trimming, duplicate removal, peak calling 
   # (e.g., CLIPper), IDR, and downstream analysis beyond just alignment.

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2. 2

   Consistent with the ENCODE standard 65, reads aligning to artifact-enriched or repetitive genomic regions were removed

   samtools (Inferred with models/gemini-2.5-flash) v1.10 GitHub

   $ Bash example

   # Install samtools if not already installed
   # conda install -c bioconda samtools
   
   # Download ENCODE hg38 blacklist (example, use appropriate assembly for your data)
   # wget -O hg38-blacklist.v2.bed.gz https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz
   # gunzip hg38-blacklist.v2.bed.gz
   
   # Define input and output files
   INPUT_BAM="aligned_reads.bam" # Placeholder for input aligned BAM file
   OUTPUT_BAM="filtered_reads.bam"
   BLACKLIST_BED="hg38-blacklist.v2.bed" # Path to the ENCODE hg38 blacklist BED file
   
   # Filter reads aligning to blacklisted regions
   # Reads overlapping the blacklist are discarded, and non-overlapping reads are written to OUTPUT_BAM
   samtools view -L "${BLACKLIST_BED}" "${INPUT_BAM}" -U "${OUTPUT_BAM}" -b > /dev/null

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3. 3

   Reproducible and significant peaks of aligned reads were defined as IDR cutoff of 0.01, P ≤ 0.001, and fold enrichment ≥8.

   IDR v0.01 GitHub

   $ Bash example

   # Install IDR Python package (if not already installed)
   # pip install idr
   
   # Clone the yeolab/merge_peaks repository (if not already cloned)
   # git clone https://github.com/yeolab/merge_peaks.git
   # cd merge_peaks
   
   # Example usage of merge_peaks.py for IDR analysis
   # Input peak files (e.g., from MACS2) for two replicates.
   # These files are expected to be in a format like narrowPeak, containing P-values and fold enrichment.
   # Replace 'rep1_peaks.narrowPeak' and 'rep2_peaks.narrowPeak' with actual file paths.
   # Replace 'reproducible_peaks' with your desired output prefix.
   
   python merge_peaks.py \
       --peak_files rep1_peaks.narrowPeak rep2_peaks.narrowPeak \
       --idr_threshold 0.01 \
       --p_value_threshold 0.001 \
       --fold_enrichment_threshold 8 \
       --output_prefix reproducible_peaks

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4. 4

   Genic regions of eCLIP peaks were annotated based on overlap with GENCODE v26 transcripts following the priority order consistent with the previous study

   GENCODE v2.27.1 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install dependencies (if not already installed, recommended in a conda environment):
   # conda create -n eclip_annotation python=3.7 pybedtools gffutils bedtools
   # conda activate eclip_annotation
   
   # Download the GENCODE v26 annotation GTF file
   GENCODE_GTF_URL="https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_26/gencode.v26.annotation.gtf.gz"
   GENCODE_GTF="gencode.v26.annotation.gtf"
   wget -O "${GENCODE_GTF}.gz" "${GENCODE_GTF_URL}"
   gunzip -f "${GENCODE_GTF}.gz"
   
   # Placeholder for eCLIP peaks file (replace with your actual input BED file)
   # Example: eCLIP_peaks.bed
   # For demonstration, create a dummy peaks file:
   echo -e "chr1\t1000\t2000\tpeak1\t100\t+\nchr1\t3000\t4000\tpeak2\t100\t-" > eCLIP_peaks.bed
   
   # The annotation with priority order is typically handled by a custom Python script
   # from the Yeo lab's eCLIP pipeline, which leverages pybedtools (a Python wrapper for bedtools)
   # and gffutils to parse GTF and perform intersections with specific prioritization logic
   # (e.g., exon > UTR > intron > intergenic).
   
   # To use the exact script from the Yeo lab's eCLIP pipeline, you would first download it:
   # wget https://raw.githubusercontent.com/yeolab/eclip/master/tools/annotate_peaks_with_genes/annotate_peaks_with_genes.py
   # chmod +x annotate_peaks_with_genes.py
   
   # Execute the annotation script
   python annotate_peaks_with_genes.py \
     --peaks eCLIP_peaks.bed \
     --annotation "${GENCODE_GTF}" \
     --output annotated_eCLIP_peaks.bed

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