UMI-ATAC-seq

UMI-ATAC-seq improves quantification of chromatin accessibility by integrating unique molecular identifiers (UMIs) into ATAC-seq to distinguish true Tn5 transposase insertion events from PCR duplicates and enhance transcription factor footprint detection.


Key Features:

  • Improved Quantification: UMIs are incorporated to distinguish true biological Tn5 transposase insertion events from PCR-induced duplicates, enabling more accurate chromatin accessibility quantification.
  • Enhanced Footprinting Sensitivity: UMI-ATAC-seq rescues approximately 20% of reads that would be discarded by standard duplicate removal and yields over 50% more identified transcription factor footprints.
  • Bias Reduction: The approach mitigates the bias toward highly accessible chromatin regions introduced by traditional duplicate removal based on mapping coordinates.
  • Analytic Pipeline: The pipeline performs sequencing adapter removal, UMI extraction from FASTQ read1 files, ME sequence removal, and UMI-based PCR duplicate removal.

Scientific Applications:

  • Chromatin accessibility mapping: Provides more accurate quantification of accessible chromatin regions for genome-wide accessibility profiling.
  • Transcription factor footprinting: Increases sensitivity and recovery of transcription factor binding site footprints through improved duplicate resolution.
  • Nucleosome-free region identification: Enhances detection of nucleosome-free regions by recovering insertion events obscured by PCR duplicates.
  • Epigenetics and cellular differentiation studies: Facilitates analysis of epigenetic modifications and accessibility changes during cellular differentiation.
  • Disease-associated chromatin changes: Supports investigation of chromatin structure alterations linked to disease by improving signal fidelity.

Methodology:

Computational processing includes sequencing adapter removal, UMI extraction from FASTQ read1 files, ME sequence removal, and UMI-based PCR duplicate removal.

Topics

Details

Programming Languages:
Python
Added:
1/18/2021
Last Updated:
3/6/2021

Operations

Publications

Zhu T, Liao K, Zhou R, Xia C, Xie W. ATAC-seq with unique molecular identifiers improves quantification and footprinting. Unknown Journal. 2020. doi:10.1101/2020.10.22.351478.