Dataset Information

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

ABSTRACT: In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.

SUBMITTER: Wang S

PROVIDER: S-EPMC9581407 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

Wang Siguo S Zhang Qinhu Q He Ying Y Cui Zhen Z Guo Zhenghao Z Han Kyungsook K Huang De-Shuang DS

PLoS computational biology 20221007 10

In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi ...[more]

PMID: 36206320

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Project description:MotivationPromoter-centered chromatin interactions, which include promoter-enhancer (PE) and promoter-promoter (PP) interactions, are important to decipher gene regulation and disease mechanisms. The development of next-generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues/cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions. Most existing computational methods for predicting chromatin interactions are based on in situ Hi-C and can detect chromatin interactions across the entire genome. However, they may not be optimal for predicting promoter-centered chromatin interactions.ResultsWe develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features such as genomic sequence, epigenetic signal, anchor distance, evolutionary features and DNA structural features to predict tissue/cell type-specific PE and PP interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework and demonstrate that the proposed approach outperforms state-of-the-art deep learning methods. Moreover, the proposed approach can achieve comparable prediction performance using predefined biologically relevant tissues/cell types compared to using all tissues/cell types in the pretraining especially for predicting PE interactions. The prediction performance can be further improved by using computationally inferred biologically relevant tissues/cell types in the pretraining, which are defined based on the common genes in the proximity of two anchors in the chromatin interactions.Availability and implementationhttps://github.com/lichen-lab/DeepPHiC.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:In contrast to the situation in mammals, very little is known about the molecular mechanisms regulating early avian ovarian development. This study aimed to investigate the dynamic changes in the histomorphology as well as the genome-wide transcriptome and chromatin accessibility landscapes of the goose ovary during late embryonic and early post-hatching stages. Results from hematoxylin-eosin, periodic acid-Schiff, and anti-CVH immunohistochemical stainings demonstrated that programmed oocyte loss, oocyte nest breakdown and primordial follicle formation, and the primordial-to-secondary follicle transition occur during the periods from embryonic day 15 (E15) to post-hatching day 0 (P0), from P0 to P4, and from P4 to P28, respectively. RNA-seq and ATAC-seq analyses revealed dynamic changes in both the ovarian transcriptome and accessible chromatin landscapes during early ovarian development, exhibiting the most extensive changes during peri-hatching oocyte loss, and moreover, differences were also identified in the genomic distribution of the differential ATAC-seq peaks between different developmental stages, suggesting that chromatin-level regulation of gene expression is facilitated by modulating the accessibility of different functional genomic regions to transcription factors. Motif analysis of developmental stage-selective peak regions identified hundreds of potential cis-regulatory elements that contain binding sites for many transcription factors, including SF1, NR5A2, ESRRβ, NF1, and THRβ, as well as members of the GATA, SMAD, and LHX families, whose expression fluctuated throughout early goose ovarian development. Integrated ATAC-seq and RNA-seq analysis suggested that the number and genomic distribution of the newly appeared and disappeared peaks differed according to developmental stage, and in combination with qRT-PCR validation potentiated the critical actions of the DEGs enriched in cell cycle, MAPK signaling, and FoxO signaling pathways during peri-hatching oocyte loss and those in ligand-receptor interaction, tissue remodeling, lipid metabolism, and Wnt signaling during primordial follicle formation and development. In conclusion, our study provides a framework for understanding the transcriptome and accessible chromatin dynamics during early avian ovarian development and a new avenue to unravel the transcriptional regulatory mechanisms that facilitate the occurrence of relevant molecular events.

Dataset Information

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

Publications

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

Similar Datasets

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