Dataset Information

HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets.

ABSTRACT: HiChIP and PLAC-Seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by the protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-Seq data. Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-Seq data. We applied HPRep to multiple real datasets and demonstrate that HPRep outperforms existing reproducibility measures developed for Hi-C data. Specifically, we applied HPRep to H3K4me3 PLAC-Seq data from mouse embryonic stem cells and mouse brain tissues as well as H3K27ac HiChIP data from human lymphoblastoid cell line GM12878 and leukemia cell line K562, showing that HPRep can more clearly separate among pseudo-replicates, real replicates, and non-replicates. Furthermore, in an H3K4me3 PLAC-Seq dataset consisting of 11 samples from four human brain cell types, HPRep demonstrated the expected clustering of data that could not be achieved by existing methods developed for Hi-C data, highlighting the need for a reproducibility metric tailored to HiChIP and PLAC-Seq data.

SUBMITTER: Rosen JD

PROVIDER: S-EPMC8929028 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets.

Rosen Jonathan D JD Yang Yuchen Y Abnousi Armen A Chen Jiawen J Song Michael M Jones Ian R IR Shen Yin Y Hu Ming M Li Yun Y

Current issues in molecular biology 20210917 2

HiChIP and PLAC-Seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by the protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-Seq data. Here, we present HPRep, a stratified and w ...[more]

PMID: 34563051

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Project description:The application of toxicogenomics as a predictive tool for chemical risk assessment has been under evaluation by the toxicology community for more than a decade. However, toxicogenomics predominately remains a tool for investigative research rather than for regulatory risk assessment. In this study, we aimed to determine whether the current generation of microarray technology in combination with an in vitro experimental design was capable of generating robust, reproducible data of sufficient quality to show promise as a tool for regulatory risk assessment. To this end, we designed a prospective collaborative study to determine the level of inter- and intra-laboratory reproducibility between three independent laboratories. All test sites adopted the same protocols for all aspects of the toxicogenomic experiment including cell culture, chemical exposure, RNA extraction, microarray data generation and analysis. As a case study, the genotoxic carcinogen B[a]P and the human hepatocyte cell line HepG2 were used to generate three comparable toxicogenomic data sets. High levels of technical reproducibility were demonstrated using a widely employed gene expression microarray platform. While differences at the global transcriptome level were still observed between the test sites, a common subset of B[a]P responsive genes was identified across all test sites which included both genes previously reported in the literature as B[a]P responsive in addition to the same most highly up and down regulated genes. Pending further analysis, these preliminary data show promise that the current generation of microarray technology in combination with a standard in vitro experimental design can produce robust data that can be reproducibly generated in independent laboratories. Future work will need to determine whether in vitro model(s) can be not only reproducible but also predictive for a range of toxic chemicals with different mechanisms of action. Such an approach may potentially be part of future in vitro testing regimes for regulatory risk assessment. The aim of this study was to evaluate the inter- and intra-laboratory reproducibility of toxicogenomics data for toxicity testing in a regulatory setting. 3 Test Centres performed the same experimental protocol treating HepG2 cells with 3 mM Benzo[a]pyrene (B[a]P) (in 0.5% DMSO) for 24 hours prior to RNA extraction and microarray analysis. Three biological replicates of B[a]P and DMSO treatments were analysed by microarray analysis in each Test Centre. Two Test Centres analysed one sample in triplicate to determine technical reproducibility of the chosen array platform. One Centre analysed two samples in duplicate. 24 samples in total were used in this study

Dataset Information

HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets.

Publications

HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets.

Similar Datasets

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