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

Wang, Siguo; Zhang, Qinhu; He, Ying; Cui, Zhen; Guo, Zhenghao; Han, Kyungsook; Huang, De-Shuang

doi:10.1371/journal.pcbi.1010572

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…These methodologies provide an alternative approach of in silico prediction of the 3D-conformation of genome based on more accessible features, such as DNA sequences and epigenetic marks. Indeed, several deep-learning methods have demonstrated the potential of using deep learning methods to predict DNA loops [46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Chrombus-XMBD: A Graph Generative Model Predicting 3D-Genome,ab initiofrom Chromatin Features

Zeng

You

Guo

et al. 2023

Preprint

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The landscape of 3D-genome is crucial for transcription regulation. But capturing the dynamics of chromatin conformation is costly and technically challenging. Here we described Chrombus-XMBD, a graph generative model capable of predicting chromatin interactions ab inito based on available chromatin features. Chrombus employes dynamic edge convolution with QKV attention setup, which maps the relevant chromatin features to a learnable embedding space thereby generate genome-wide 3D-contactmap. We validated Chrombus predictions with published databases of topological associated domains (TAD), eQTLs and gene-enhancer interactions. Chrombus outperforms existing algorithms in efficiently predicting long-range chromatin interactions. Chrombus also exhibits strong generalizability across different cell lineage and species. Additionally, the parameter sets of Chrombus inform the biological processes underlying 3D-genome. Our model provides a new perspective towards interpretable AI-modeling of the dynamics of chromatin interactions and better understanding of cis-regulation of gene expression.

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Section: Discussionmentioning

confidence: 99%

Chrombus-XMBD: A Graph Generative Model Predicting 3D-Genome,ab initiofrom Chromatin Features

Zeng

You

Guo

et al. 2023

Preprint

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“…Ultimately, a sigmoid layer is used to predict candidate chromatin loops. These methods have been successful in predicting the presence of loop structures from genome-wide contact maps (Ay et al 2014, Cairns et al 2016, Ardakany et al 2020, Rowley et al 2020, Wang et al 2022. ML-based models have also been used to find high interaction frequencies among genomic loci and detect loops in the contact map.…”

Section: Theoretical and Computational Developmentsmentioning

confidence: 99%

Fundamental insights into the correlation between chromosome configuration and transcription

et al. 2023

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Eukaryotic chromosomes exhibit a hierarchical organization that spans a spectrum of length scales, ranging from sub-regions known as loops, which typically comprise hundreds of base pairs, to much larger chromosome territories that can encompass a few mega base pairs. Chromosome conformation capture experiments that involve high-throughput sequencing methods combined with microscopy techniques have enabled a new understanding of inter- and intra-chromosomal interactions with unprecedented details. This information also provides mechanistic insights on the relationship between genome architecture and gene expression. In this article, we review the recent findings on three-dimensional interactions among chromosomes at the compartment, topologically associating domain (TAD), and loop levels and the impact of these interactions on the transcription process. We also discuss current understanding of various biophysical processes involved in multi-layer structural organization of chromosomes. Then, we discuss the relationships between gene expression and genome structure from perturbative genome-wide association studies. Furthermore, for a better understanding of how chromosome architecture and function are linked, we emphasize the role of epigenetic modifications in the regulation of gene expression. Such an understanding of the relationship between genome architecture and gene expression can provide a new perspective on the range of potential future discoveries and therapeutic research.

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“…With its unique features, our new interpretable method for dictionary learning adds to the growing literature on machine learning approaches that aim to elucidate properties of chromatin interactions [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Interpretable online network dictionary learning for inferring long-range chromatin interactions

Rana,

Peng,

Pan

et al. 2024

PLoS Comput Biol

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Dictionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational biology to tackle ubiquitous clustering problems. The method is favored due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability in terms of real biological data. Additionally, they are not optimized for graph-structured data and hence often fail to handle them in a scalable manner. In order to address these limitations, we propose a novel DL algorithm called online convex network dictionary learning (online cvxNDL). Unlike classical DL algorithms, online cvxNDL is implemented via MF and designed to handle extremely large datasets by virtue of its online nature. Importantly, it enables the interpretation of dictionary elements, which serve as cluster representatives, through convex combinations of real measurements. Moreover, the algorithm can be applied to data with a network structure by incorporating specialized subnetwork sampling techniques. To demonstrate the utility of our approach, we apply cvxNDL on 3D-genome RNAPII ChIA-Drop data with the goal of identifying important long-range interaction patterns (long-range dictionary elements). ChIA-Drop probes higher-order interactions, and produces data in the form of hypergraphs whose nodes represent genomic fragments. The hyperedges represent observed physical contacts. Our hypergraph model analysis has the objective of creating an interpretable dictionary of long-range interaction patterns that accurately represent global chromatin physical contact maps. Through the use of dictionary information, one can also associate the contact maps with RNA transcripts and infer cellular functions. To accomplish the task at hand, we focus on RNAPII-enriched ChIA-Drop data from Drosophila Melanogaster S2 cell lines. Our results offer two key insights. First, we demonstrate that online cvxNDL retains the accuracy of classical DL (MF) methods while simultaneously ensuring unique interpretability and scalability. Second, we identify distinct collections of proximal and distal interaction patterns involving chromatin elements shared by related processes across different chromosomes, as well as patterns unique to specific chromosomes. To associate the dictionary elements with biological properties of the corresponding chromatin regions, we employ Gene Ontology (GO) enrichment analysis and perform multiple RNA coexpression studies.

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

Cited by 6 publications

References 55 publications

Chrombus-XMBD: A Graph Generative Model Predicting 3D-Genome,ab initiofrom Chromatin Features

Chrombus-XMBD: A Graph Generative Model Predicting 3D-Genome,ab initiofrom Chromatin Features

Fundamental insights into the correlation between chromosome configuration and transcription

Interpretable online network dictionary learning for inferring long-range chromatin interactions

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