Dataset Information

Reference panel-guided super-resolution inference of Hi-C data.

ABSTRACT:

Motivation

Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types.

Results

Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains.

Availability and implementation

https://github.com/BlanchetteLab/RefHiC.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC10311349 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Reference panel-guided super-resolution inference of Hi-C data.

Zhang Yanlin Y Blanchette Mathieu M

Bioinformatics (Oxford, England) 20230601 39 Suppl 1

<h4>Motivation</h4>Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance ...[more]

PMID: 37387127

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Dataset Information

Reference panel-guided super-resolution inference of Hi-C data.

Motivation

Results

Availability and implementation

Publications

Reference panel-guided super-resolution inference of Hi-C data.

Similar Datasets

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