Mapping the semi-nested community structure of 3D chromosome contact networks

Bernenko, Dolores; Lee, Sang Hoon; Stenberg, Per; Lizana, Ludvig

doi:10.1371/journal.pcbi.1011185

Cited by 3 publications

(2 citation statements)

References 46 publications

(66 reference statements)

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Section: Transforming Hi-c Data Into a Weighted Networkmentioning

confidence: 99%

“…We use the same Hi-C intra-chromosomal contact map as our previous series of studies [10,11] (human cell line GM12878 (B-lymphoblastoid) [3,18]). Also, as before [10,11], we use the MAPQG0 data set at the 100 kilobase-pair (kb) resolution and normalize the interaction map with the Knight-Ruiz (KR) matrix balancing [19]. As a result, we treat each 100 kb chromatin locus as the minimal unit, or 'node' , and the normalized interaction weights between nodes i and j as weighted edges, using network science terminology [5].…”

Section: Transforming Hi-c Data Into a Weighted Networkmentioning

confidence: 99%

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Exploring 3D community inconsistency in human chromosome contact networks

Bernenko,

Lee,

Lizana

2023

J. Phys. Complex.

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Researchers have developed chromosome capture methods such as Hi-C to better understand DNA’s 3D folding in nuclei. The Hi-C method captures contact frequencies between DNA segment pairs across the genome. When analyzing Hi-C data sets, it is common to group these pairs using standard bioinformatics methods (e.g., PCA). Other approaches handle Hi-C data as weighted networks, where connected node pairs represent DNA segments in 3D proximity. In this representation, one can leverage community detection techniques developed in complex network theory to group nodes into mesoscale communities containing nodes with similar connection patterns. While there are several successful attempts to analyze Hi-C data in this way, it is common to report and study the most typical community structure. But in reality, there are often several valid candidates. Therefore, depending on algorithm design, diﬀerent community detection methods focusing on slightly diﬀerent connectivity features may have diﬀering views on the ideal node groupings. In fact, even the same community detection method may yield diﬀerent results if using a stochastic algorithm. This ambiguity is fundamental to community detection and shared by most complex networks whenever interactions span all scales in the network. This is known as community inconsistency. This paper explores this inconsistency of 3D communities in Hi-C data for all human chromosomes. We base our analysis on two inconsistency metrics, one local and one global, and quantify the network scales where the community separation is most variable.For example, we ﬁnd that TADs are less reliable than A/B compartments and that nodes with highly variable node-community memberships are associated with open chromatin. Overall, our study provides a helpful framework for data-driven researchers and increases awareness of some inherent challenges when clustering Hi-C data into 3D communities.

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Section: Transforming Hi-c Data Into a Weighted Networkmentioning

confidence: 99%

Section: Transforming Hi-c Data Into a Weighted Networkmentioning

confidence: 99%

Exploring 3D community inconsistency in human chromosome contact networks

Bernenko,

Lee,

Lizana

2023

J. Phys. Complex.

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Enhancer-Insulator Pairing Reveals Heterogeneous Dynamics in Long-Distance 3D Gene Regulation

Hedström,

Metzler,

Lizana

2024

PRX Life

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Cells regulate fates and complex body plans using spatiotemporal signaling cascades that alter gene expression. Short DNA sequences, known as enhancers (50–1500 base pairs), help coordinate these cascades by attracting regulatory proteins that enhance the transcription by binding to distal gene promoters. In humans, there are hundreds of thousands of enhancers dispersed across the genome, which poses a challenging coordination task to prevent unintended gene activation. To mitigate this problem, the genome contains insulator elements that block enhancer-promoter interactions. However, there is an open problem with how the insulation works, especially as enhancer-insulator pairs may be separated by millions of base pairs. Based on recent empirical data from Hi-C experiments, this paper proposes a new mechanism that challenges the common paradigm that rests on specific insulator-insulator interactions. Instead, this paper introduces a stochastic looping model where insulators bind weakly to chromatin rather than other insulators. After calibrating the model to experimental data, we use simulations to study the broad distribution of hitting times between an enhancer and a promoter when insulators are present. We find parameter regimes with large differences between average and most probable hitting times. This makes it difficult to assign a typical timescale and hints at highly defocused regulation times. We also map our computational model onto a resetting problem that allows us to derive several analytical results. Besides offering new insights into enhancer-insulator interactions, our paper advances the understanding of gene regulatory networks and causal connections between genome folding and gene activation. Published by the American Physical Society 2024

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Overlapping community detection in weighted networks via hierarchical clustering

Prokop,

Dráždilová,

Platoš

2024

PLoS ONE

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In real-world networks, community structures often appear as tightly connected clusters of nodes, with recent studies suggesting a hierarchical organization where larger groups subdivide into smaller ones across different levels. This hierarchical structure is particularly complex in trade networks, where actors typically belong to multiple communities due to diverse business relationships and contracts. To address this complexity, we present a novel algorithm for detecting hierarchical structures of overlapping communities in weighted networks, focusing on the interdependency between internal and external quality metrics for evaluating the detected communities. The proposed Graph Hierarchical Agglomerative Clustering (GHAC) approach utilizes maximal cliques as the basis units for hierarchical clustering. The algorithm measures dissimilarities between clusters using the minimal closed trail distance (CT−distance) and the size of maximal cliques within overlaps, capturing the density and connectivity of nodes. Through extensive experiments on synthetic networks with known ground truth, we demonstrate that the adjusted Silhouette index is the most reliable internal metric for determining the optimal cut in the dendrogram. Experimental results indicate that the GHAC method is competitive with widely used community detection techniques, particularly in networks with highly overlapping communities. The method effectively reveals the hierarchical structure of communities in weighted networks, as demonstrated by its application to the OECD weighted trade network, which describes the balanced trade value of bilateral trade relations.

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Mapping the semi-nested community structure of 3D chromosome contact networks

Cited by 3 publications

References 46 publications

Exploring 3D community inconsistency in human chromosome contact networks

Exploring 3D community inconsistency in human chromosome contact networks

Enhancer-Insulator Pairing Reveals Heterogeneous Dynamics in Long-Distance 3D Gene Regulation

Overlapping community detection in weighted networks via hierarchical clustering

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