GSE206674 eCLIP Data Processing

eCLIP geo_data_processing 10 steps

Publication

Seryl-tRNA synthetase promotes translational readthrough by mRNA binding and involvement of the selenocysteine incorporation machinery.

Nucleic acids research (2023) — PMID 37739431

Dataset

GSE206674

SerRS mediates translational readthrough of through engagement of the selenocysteine incorporation machinery

Warning: Pipeline descriptions and code snippets may be inferred or AI-generated. Use them only as a starting point to guide analysis, and validate before use.

Processing Steps

Generate Jupyter Notebook
  1. 1

    Data was processed using the eCLIP pipeline and available at: http://github.com/yeolab/eclip

    eCLIP
  2. 2

    Unique Molecular Identifiers (UMIs) were extracted from raw sequencing reads with umi_tools extract

    UMI-tools
  3. 3

    Post-umi-extracted reads were trimmed for adapter sequences and barcode sequences (eCLIP samples) using cutadapt.

    cutadapt
  4. 4

    Trimmed reads were mapped against RepBase with STAR to remove reads mapping to repetitive sequences (--outFilterMultimapNmax 30 --alignEndsType EndToEnd --outFilterMultimapScoreRange 1 --outSAMmode Full --outFilterType BySJout --outSAMtype BAM Unsorted --outFilterScoreMin 10 --outReadsUnmapped Fastx --outSAMattributes All)

    STAR
  5. 5

    Remaining reads were mapped to the appropriate genome build (hg19) using STAR aligner (--outFilterMultimapNmax 1 --alignEndsType EndToEnd --outFilterMultimapScoreRange 1 --outSAMmode Full --outFilterType BySJout --outSAMtype BAM Unsorted --outFilterScoreMin 10 --outReadsUnmapped Fastx --outSAMattributes All)

    STAR
  6. 6

    Uniquely mapped reads were removed of PCR duplicates with umi_tools

    UMI-tools
  7. 7

    Peak clusters were identified with CLIPper, available at: https://github.com/YeoLab/clipper

    CLIPper
  8. 8

    Clusters enriched over corresponding size-matched input (SMInput) were identified using a custom Perl script, available in the main eCLIP repository as: overlap_peakfi_with_bam.pl

    eCLIP
  9. 9

    Overlapping enriched clusters (peaks) were merged with a custom perl script, available in the main eCLIP repository as: compress_l2foldenrpeakfi_for_replicate_overlapping_bedformat.pl

    eCLIP
  10. 10

    Peaks that overlapped common exclusion/non-specific binding regions (available at: https://www.encodeproject.org/references/ENCSR922QVQ/) were removed with BEDtools

    bedtools
Raw Source Text 
Data was processed using the eCLIP pipeline and available at: http://github.com/yeolab/eclip
Unique Molecular Identifiers (UMIs) were extracted from raw sequencing reads with umi_tools extract
Post-umi-extracted reads were trimmed for adapter sequences and barcode sequences (eCLIP samples) using cutadapt.
Trimmed reads were mapped against RepBase with STAR to remove reads mapping to repetitive sequences (--outFilterMultimapNmax 30 --alignEndsType EndToEnd --outFilterMultimapScoreRange 1 --outSAMmode Full --outFilterType BySJout --outSAMtype BAM Unsorted --outFilterScoreMin 10 --outReadsUnmapped Fastx --outSAMattributes All)
Remaining reads were mapped to the appropriate genome build (hg19) using STAR aligner (--outFilterMultimapNmax 1 --alignEndsType EndToEnd --outFilterMultimapScoreRange 1 --outSAMmode Full --outFilterType BySJout --outSAMtype BAM Unsorted --outFilterScoreMin 10 --outReadsUnmapped Fastx --outSAMattributes All)
Uniquely mapped reads were removed of PCR duplicates with umi_tools
Peak clusters were identified with CLIPper, available at: https://github.com/YeoLab/clipper
Clusters enriched over corresponding size-matched input (SMInput) were identified using a custom Perl script, available in the main eCLIP repository as: overlap_peakfi_with_bam.pl
Overlapping enriched clusters (peaks) were merged with a custom perl script, available in the main eCLIP repository as: compress_l2foldenrpeakfi_for_replicate_overlapping_bedformat.pl
Peaks that overlapped common exclusion/non-specific binding regions (available at: https://www.encodeproject.org/references/ENCSR922QVQ/) were removed with BEDtools
Assembly: hg19
Supplementary files format and content: bigwigs contain RPM-normalized read densities of uniquely-mapped reads
Supplementary files format and content: BED files contain CLIPper peak clusters. Columns 4 and 5 describe the -log10(p-value) and log2(fold) enrichment IP over corresponding SMInput.
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