English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Researchers developed chromosome capture methods such as Hi-C to betterunderstand DNA's 3D folding in nuclei. The Hi-C method captures contactfrequencies between DNA segment pairs across the genome. When analyzing Hi-Cdata sets, it is common to group these pairs using standard bioinformaticsmethods (e.g., PCA). Other approaches handle Hi-C data as weighted networks,where connected node represent DNA segments in 3D proximity. In thisrepresentation, one can leverage community detection techniques developed incomplex network theory to group nodes into mesoscale communities containingsimilar connection patterns. While there are several successful attempts toanalyze Hi-C data in this way, it is common to report and study the mosttypical community structure. But in reality, there are often several validcandidates. Therefore, depending on algorithm design, different communitydetection methods focusing on slightly different connectivity features may havediffering views on the ideal node groupings. In fact, even the same communitydetection method may yield different results if using a stochastic algorithm.This ambiguity is fundamental to community detection and shared by most complexnetworks whenever interactions span all scales in the network. This is known ascommunity inconsistency. This paper explores this inconsistency of 3Dcommunities in Hi-C data for all human chromosomes. We base our analysis on twoinconsistency metrics, one local and one global, and quantify the networkscales where the community separation is most variable. For example, we findthat TADs are less reliable than A/B compartments and that nodes with highlyvariable node-community memberships are associated with open chromatin.Overall, our study provides a helpful framework for data-driven researchers andincreases awareness of some inherent challenges when clustering Hi-C data into3D communities.

Exploring 3D community inconsistency in human chromosome contact networks