Chromatin folding contributes to the regulation of genomic processes such as gene activity. Existing conformation capture methods characterize genome topology through analysis of pairwise chromatin contacts in populations of cells but cannot discern whether individual interactions occur simultaneously or competitively. Here we present multi-contact 4C (MC-4C), which applies Nanopore sequencing to study multi-way DNA conformations of individual alleles. MC-4C distinguishes cooperative from random and competing interactions and identifies previously missed structures in subpopulations of cells. We show that individual elements of the β-globin superenhancer can aggregate into an enhancer hub that can simultaneously accommodate two genes. Neighboring chromatin domain loops can form rosette-like structures through collision of their CTCF-bound anchors, as seen most prominently in cells lacking the cohesin-unloading factor WAPL. Here, massive collision of CTCF-anchored chromatin loops is believed to reflect 'cohesin traffic jams'. Single-allele topology studies thus help us understand the mechanisms underlying genome folding and functioning.

Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation.Results: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer.Availability and implementation: All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip.Contact: j.deridder@tudelft.nlSupplementary information: Supplementary data are available at Bioinformatics online.

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