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HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test.

ABSTRACT: Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40 kb bin) but is invalid at high resolution (e.g., 5 or 10 kb bins) because spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field-based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT)-based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line, GM12878, and mouse embryonic stem cells (mESCs). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal-to-noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks in both GM12878 and mESCs. HiC-ACT, effectively a summary statistics-based approach, is computationally efficient (∼6 min and ∼2 GB memory to process 25,000 pairwise interactions).

SUBMITTER: Lagler TM

PROVIDER: S-EPMC7895999 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Chromatin conformation capture techniques have evolved rapidly over the last few years and have provided new insights into genome organization at an unprecedented resolution. Analysis of Hi-C data is complex and computationally intensive involving multiple tasks and requiring robust quality assessment. This has led to the development of several tools and methods for processing Hi-C data. However, most of the existing tools do not cover all aspects of the analysis and only offer few quality assessment options. Additionally, availability of a multitude of tools makes scientists wonder how these tools and associated parameters can be optimally used, and how potential discrepancies can be interpreted and resolved. Most importantly, investigators need to be ensured that slight changes in parameters and/or methods do not affect the conclusions of their studies. RESULTS:To address these issues (compare, explore and reproduce), we introduce HiC-bench, a configurable computational platform for comprehensive and reproducible analysis of Hi-C sequencing data. HiC-bench performs all common Hi-C analysis tasks, such as alignment, filtering, contact matrix generation and normalization, identification of topological domains, scoring and annotation of specific interactions using both published tools and our own. We have also embedded various tasks that perform quality assessment and visualization. HiC-bench is implemented as a data flow platform with an emphasis on analysis reproducibility. Additionally, the user can readily perform parameter exploration and comparison of different tools in a combinatorial manner that takes into account all desired parameter settings in each pipeline task. This unique feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple tool/parameter choices in each step of the analysis. To demonstrate the usefulness of our platform, we performed a comprehensive benchmark of existing and new TAD callers exploring different matrix correction methods, parameter settings and sequencing depths. Users can extend our pipeline by adding more tools as they become available. CONCLUSIONS:HiC-bench consists an easy-to-use and extensible platform for comprehensive analysis of Hi-C datasets. We expect that it will facilitate current analyses and help scientists formulate and test new hypotheses in the field of three-dimensional genome organization.

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HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test.

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