GSE227455 eCLIP Data Processing

eCLIP geo_data_processing 7 steps

Publication

Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data.

RNA (New York, N.Y.) (2025) — PMID 40399037

Dataset

GSE227455

RBP Footprint Grand Challenge: An evaluation of novel computational approaches to RNA-binding protein target prediction from structural data

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

    Processed Using https://github.com/YeoLab/eclip 0.7.0

    eCLIP v0.7.0
  2. 2

    After standard HiSeq demultiplexing, eCLIP libraries with distinct in-line barcodes were demultiplexed using custom scripts, and the random-mer was appended to the read name for later usage.

    eCLIP
  3. 3

    Reads were then adapter trimmed (cutadapt v1.9.dev1) and reads less than 18 bp were discarded

    cutadapt v1.9
  4. 4

    Mapping was then first performed against human elements in RepBase (v18.05) with STAR (v2.4.0i), repeat-mapping reads were segregated for separate analysis, and all others were then mapped against the full human genome (hg19) including a database of splice junctions with STAR (v 2.4.0i) (Dobin et al., 2013).

    STAR v2.4.0i
  5. 5

    Uniquely mapping reads were then run through a custom-built PCR duplicate removal script, removing duplicate reads based on sharing identical Read1 start position, Read2 start position, and random-mer sequence to leave 'Usable' reads.

  6. 6

    eCLIP data sets with multiple in-line barcodes were merged at the usable read stage, and cluster identification was performed on usable reads using CLIPper (Yeo et al., 2009) (available at https://github.com/YeoLab/clipper/releases/tag/1.0) with options –s GRCh38 –o –bonferroni –superlocal–threshold-method binomial–save-pickle

    CLIPper
  7. 7

    data are further downsampled to roughly equal number per replicate

Raw Source Text 
Processed Using https://github.com/YeoLab/eclip 0.7.0
After standard HiSeq demultiplexing, eCLIP libraries with distinct in-line barcodes were demultiplexed using custom scripts, and the random-mer was appended to the read name for later usage. Reads were then adapter trimmed (cutadapt v1.9.dev1) and reads less than 18 bp were discarded
Mapping was then first performed against human elements in RepBase (v18.05) with STAR (v2.4.0i), repeat-mapping reads were segregated for separate analysis, and all others were then mapped against the full human genome (hg19) including a database of splice junctions with STAR (v 2.4.0i) (Dobin et al., 2013). Uniquely mapping reads were then run through a custom-built PCR duplicate removal script, removing duplicate reads based on sharing identical Read1 start position, Read2 start position, and random-mer sequence to leave 'Usable' reads.
eCLIP data sets with multiple in-line barcodes were merged at the usable read stage, and cluster identification was performed on usable reads using CLIPper (Yeo et al., 2009) (available at https://github.com/YeoLab/clipper/releases/tag/1.0) with options –s GRCh38 –o –bonferroni –superlocal–threshold-method binomial–save-pickle
data are further downsampled to roughly equal number per replicate
Assembly: hg38
Supplementary files format and content: wig files represent read covergae for plus and minus strands
Supplementary files format and content: peak files
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