Dataset Information

Computational identification of cell-specific variable regions in ChIP-seq data.

ABSTRACT: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate ChIP-seq variability, we developed a reproducibility score and a method that identifies cell-specific variable regions in ChIP-seq data by integrating replicated ChIP-seq experiments for multiple protein targets on a particular cell type. Using our method, we found variable regions in human cell lines K562, GM12878, HepG2, MCF-7 and in mouse embryonic stem cells (mESCs). These variable-occupancy target regions (VOTs) are CG dinucleotide rich, and show enrichment at promoters and R-loops. They overlap significantly with HOT regions, but are not blacklisted regions producing non-specific binding ChIP-seq peaks. Furthermore, in mESCs, VOTs are conserved among placental species suggesting that they could have a function important for this taxon. Our method can be useful to point to such regions along the genome in a given cell type of interest, to improve the downstream interpretative analysis before follow-up experiments.

SUBMITTER: Andreani T

PROVIDER: S-EPMC7229859 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Computational identification of cell-specific variable regions in ChIP-seq data.

Andreani Tommaso T Albrecht Steffen S Fontaine Jean-Fred JF Andrade-Navarro Miguel A MA

Nucleic acids research 20200501 9

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate ChIP-seq variability, we developed a repro ...[more]

PMID: 32187374

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Project description:Chromatin immunoprecipitation and sequencing (ChIP-seq) has been widely used to map DNA-binding proteins, histone proteins and their modifications. ChIP-seq data contains redundant reads termed duplicates, referring to those mapping to the same genomic location and strand. There are two main sources of duplicates: polymerase chain reaction (PCR) duplicates and natural duplicates. Unlike natural duplicates that represent true signals from sequencing of independent DNA templates, PCR duplicates are artifacts originating from sequencing of identical copies amplified from the same DNA template. In analysis, duplicates are removed from peak calling and signal quantification. Nevertheless, a significant portion of the duplicates is believed to represent true signals. Obviously, removing all duplicates will underestimate the signal level in peaks and impact the identification of signal changes across samples. Therefore, an in-depth evaluation of the impact from duplicate removal is needed. Using eight public ChIP-seq datasets from three narrow-peak and two broad-peak marks, we tried to understand the distribution of duplicates in the genome, the extent by which duplicate removal impacts peak calling and signal estimation, and the factors associated with duplicate level in peaks. The three PCR-free histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq data had about 40% duplicates and 97% of them were within peaks. For the other datasets generated with PCR amplification of ChIP DNA, as expected, the narrow-peak marks have a much higher proportion of duplicates than the broad-peak marks. We found that duplicates are enriched in peaks and largely represent true signals, more conspicuous in those with high confidence. Furthermore, duplicate level in peaks is strongly correlated with the target enrichment level estimated using nonredundant reads, which provides the basis to properly allocate duplicates between noise and signal. Our analysis supports the feasibility of retaining the portion of signal duplicates into downstream analysis, thus alleviating the limitation of complete deduplication.

Dataset Information

Computational identification of cell-specific variable regions in ChIP-seq data.

Publications

Computational identification of cell-specific variable regions in ChIP-seq data.

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