GSE12680 Processing Pipeline

Publication

Science (New York, N.Y.) (2008) — PMID 19056940

Dataset

Divergent transcription from active promoters

1. 1

   Images analysis and base calling was done using solexa pipeline and reads aligned to both mouse NCBI build 36 using ELAND.

   ELAND vEarly Illumina/Solexa pipeline version (circa 2006-2008) GitHub

   $ Bash example

   # ELAND was a proprietary aligner bundled with the early Solexa/Illumina sequencing pipeline.
   # It was not typically installed via public package managers like conda.
   # Installation involved setting up the full Solexa pipeline software suite on dedicated hardware.
   
   # Define the path to the indexed mouse NCBI build 36 reference genome.
   # This genome assembly is also known as mm8 (UCSC).
   # Reference data would have been prepared by the Solexa pipeline's indexing tools.
   # For a modern equivalent, you would download the mm8 genome from UCSC or NCBI and index it.
   MOUSE_NCBI36_REFERENCE_INDEX="/path/to/mouse/NCBI36/eland_index"
   
   # Define the input FASTQ file containing the sequencing reads.
   INPUT_READS_FASTQ="sample_reads.fastq"
   
   # Define the output file for ELAND alignment results.
   # ELAND typically produced a custom text-based alignment format.
   OUTPUT_ELAND_ALIGNMENT="sample_aligned.eland"
   
   # Conceptual ELAND alignment command.
   # The exact command-line interface for ELAND was often integrated within the Solexa pipeline's
   # workflow scripts and might not have been a simple standalone executable for end-users.
   # This command is illustrative of the core parameters: input reads, reference index, and output.
   # The actual command would have been executed within the Solexa pipeline's environment.
   eland_aligner -f "${INPUT_READS_FASTQ}" -g "${MOUSE_NCBI36_REFERENCE_INDEX}" -o "${OUTPUT_ELAND_ALIGNMENT}"

   Copy View on GitHub
2. 2

   For ChIP-Seq, sequences from all lanes were extended 200bp (maximum fragment length accounting for ~100bp of primer sequence), and allocated into 25 bp bins.

   ChIP-seq v3.5.1 GitHub

   $ Bash example

   # conda install -c bioconda deeptools
   
   # Placeholder for input BAM file and output BigWig file
   INPUT_BAM="aligned_reads.bam"
   OUTPUT_BIGWIG="extended_binned_coverage.bw"
   
   # Placeholder for genome reference (e.g., hg38)
   # The effective genome size is needed for RPGC normalization.
   # For hg38, a common value is 2913022398 (excluding Ns and mitochondrial genome).
   # This value can be obtained using tools like 'chrom_sizes.py' from deeptools or 'faidx' and custom scripts.
   EFFECTIVE_GENOME_SIZE="2913022398" # Example for hg38
   
   bamCoverage \
       -b "${INPUT_BAM}" \
       -o "${OUTPUT_BIGWIG}" \
       --extendReads 200 \
       --binSize 25 \
       --normalizeUsing RPGC \
       --effectiveGenomeSize "${EFFECTIVE_GENOME_SIZE}" \
       --numberOfProcessors auto

   Copy View on GitHub
3. 3

   Genomic bins containing statistically significant ChIP-seq enrichment were identified by comparison to a Poissonian background model, using a p-value threshold of 10-9.

   ChIP-seq v2.2.7.1 GitHub

   $ Bash example

   # Install macs2 (e.g., using conda)
   # conda install -c bioconda macs2
   
   # Define input files and output prefix (placeholders)
   # Replace 'chip.bam' with your actual ChIP-seq alignment file
   # Replace 'control.bam' with your actual control/input alignment file
   # Replace 'chip_peaks' with your desired output prefix
   # Replace 'hs' with the appropriate genome size for your reference (e.g., 'mm' for mouse, 'ce' for C. elegans)
   
   # Call peaks using macs2 with a Poissonian background model and specified p-value threshold
   macs2 callpeak \
     -t chip.bam \
     -c control.bam \
     -f BAM \
     -g hs \
     -n chip_peaks \
     --pvalue 1e-9 \
     --outdir .

   Copy View on GitHub
4. 4

   Additionally, we used an empirical background model obtained from identical Solexa sequencing of DNA from whole cell extract (WCE) from matched cell samples (>5X normalized enrichment across the entire region).

   clipper (Inferred with models/gemini-2.5-flash) vv1.0.0 GitHub

   $ Bash example

   # Install clipper (example, adjust for specific environment)
   # For example, using pip:
   # pip install clipper
   # Or using a Docker image as specified in the eCLIP CWL workflow:
   # docker pull yeolab/clipper:v1.0.0
   
   # Placeholder for input files. These would be aligned BAM files.
   # IP_BAM: The BAM file for the immunoprecipitated sample.
   IP_BAM="path/to/your/ip_sample.bam"
   # WCE_BAM: The BAM file for the Whole Cell Extract (WCE) control sample.
   # This serves as the "empirical background model" described.
   WCE_BAM="path/to/your/wce_control.bam"
   
   # Output prefix for the peak calling results.
   OUTPUT_PREFIX="eclip_peaks"
   
   # Reference genome species. Common choices include hg38 for human.
   # The description does not specify, so hg38 is used as a placeholder.
   SPECIES="hg38"
   
   # Execute clipper for peak calling.
   # The description mentions ">5X normalized enrichment across the entire region".
   # This is likely a characteristic of the identified peaks or a post-processing filter,
   # as clipper itself primarily uses statistical thresholds for peak calling.
   # Enrichment values are typically calculated by comparing IP signal to control signal.
   clipper --species "${SPECIES}" \
           --bam "${IP_BAM}" \
           --control "${WCE_BAM}" \
           --output "${OUTPUT_PREFIX}"

   Copy View on GitHub

Tools Used