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The cohesin complex (SMC1-SMC3-RAD21) shapes chromosomes by DNA loop extrusion, but individual extrusion trajectories were so far unappreciable in vivo. Here, we site-specifically induced dozens of extrusion trajectories anchored at artificial loading sites in living cells. Extruding cohesin transports loading proteins MAU2-NIPBL over megabase DNA distances to blocking CTCF sites that then loop back to the loading sequences, showing that CTCF-CTCF interactions are unnecessary for stabilized contacts between loop extrusion obstacles. When stalled, cohesin can block other extruding cohesin from either direction. Without RAD21, MAU2-NIPBL exclusively accumulate at loading sites, here genome-wide defined as enhancers. SMC1 now also selectively accumulates here, suggesting that cohesin may load modularly on chromatin. Genes inside high cohesin extrusion trajectories are collectively hindered in transcription. This work characterizes the impact, entry and exit sites of individual cohesin loop extrusion trajectories.

In routine diagnostic pathology, cancer biopsies are preserved by formalin-fixed, paraffin-embedding (FFPE) procedures for examination of (intra-) cellular morphology. Such procedures inadvertently induce DNA fragmentation, which compromises sequencing-based analyses of chromosomal rearrangements. Yet, rearrangements drive many types of hematolymphoid malignancies and solid tumors, and their manifestation is instructive for diagnosis, prognosis, and treatment. Here, we present FFPE-targeted locus capture (FFPE-TLC) for targeted sequencing of proximity-ligation products formed in FFPE tissue blocks, and PLIER, a computational framework that allows automated identification and characterization of rearrangements involving selected, clinically relevant, loci. FFPE-TLC, blindly applied to 149 lymphoma and control FFPE samples, identifies the known and previously uncharacterized rearrangement partners. It outperforms fluorescence in situ hybridization (FISH) in sensitivity and specificity, and shows clear advantages over standard capture-NGS methods, finding rearrangements involving repetitive sequences which they typically miss. FFPE-TLC is therefore a powerful clinical diagnostics tool for accurate targeted rearrangement detection in FFPE specimens., Preservation of cancer biopsies by FFPE introduces DNA fragmentation, hindering analysis of rearrangements. Here the authors introduce FFPE Targeted Locus Capture for identification of translocations in preserved samples.

To understand how chromatin domains coordinate gene expression, we dissected select genetic elements organizing topology and transcription around the Prdm14 super enhancer in mouse embryonic stem cells. Taking advantage of allelic polymorphisms, we developed methods to sensitively analyze changes in chromatin topology, gene expression, and protein recruitment. We show that enhancer insulation does not rely strictly on loop formation between its flanking boundaries, that the enhancer activates the Slco5a1 gene beyond its prominent domain boundary, and that it recruits cohesin for loop extrusion. Upon boundary inversion, we find that oppositely oriented CTCF terminates extrusion trajectories but does not stall cohesin, while deleted or mutated CTCF sites allow cohesin to extend its trajectory. Enhancer-mediated gene activation occurs independent of paused loop extrusion near the gene promoter. We expand upon the loop extrusion model to propose that cohesin loading and extrusion trajectories originating at an enhancer contribute to gene activation.

We present the experimental protocol and data analysis toolbox for multi-contact 4C (MC-4C), a new proximity ligation method tailored to study the higher-order chromatin contact patterns of selected genomic sites. Conventional chromatin conformation capture (3C) methods fragment proximity ligation products for efficient analysis of pairwise DNA contacts. By contrast, MC-4C is designed to preserve and collect large concatemers of proximity ligated fragments for long-molecule sequencing on an Oxford Nanopore or Pacific Biosciences platform. Each concatemer of proximity ligation products represents a snapshot topology of a different individual allele, revealing its multi-way chromatin interactions. By inverse PCR with primers specific for a fragment of interest (the viewpoint) and DNA size selection, sequencing is selectively targeted to thousands of different complex interactions containing this viewpoint. A tailored statistical analysis toolbox is able to generate background models and three-way interaction profiles from the same dataset. These profiles can be used to distinguish whether contacts between more than two regulatory sequences are mutually exclusive or, conversely, simultaneously occurring at chromatin hubs. The entire procedure can be completed in 2 w, and requires standard molecular biology and data analysis skills and equipment, plus access to a third-generation sequencing platform.

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.

We present the protocol and data analysis toolbox for Multi-Contact 4C (MC-4C), a new proximity ligation method tailored to study the higher-order chromatin contact patterns of selected genomic sites. Conventional chromosome conformation capture (3C) methods fragment proximity ligation products for efficient analysis of pairwise DNA contacts. In contrast, MC-4C is designed to preserve and collect large concatemers of proximity ligated fragments for long molecule sequencing on Oxford Nanopore or Pacific Biosciences platforms, thus allowing study of multi-way chromatin interactions. By inverse PCR with primers specific for a fragment of interest (the viewpoint) and DNA size selection, sequencing is selectively targeted to thousands of different complex interactions containing this viewpoint. A tailored statistical analysis toolbox employing data-intrinsic background models then discerns whether contacts between more than two regulatory sequences are mutually exclusive or, conversely, simultaneously happening at chromatin hubs. The entire procedure can be completed in two weeks and requires access to a third generation sequencing platform.

Chromosomal translocations with immunoglobin (IG) loci are the classic drivers in a large subset of B-cell lymphomas. Detection of these translocations is important for confirmation of diagnosis and for prognosis and therapy decisions. Currently, molecular diagnosis of translocations in lymphomas is not addressed well by next-generation sequencing (NGS). The standard method for detection of translocations is fluorescence in situ hybridization (FISH), which is labor intensive and can be difficult to interpret. There is a need for a robust technology that can be standardized. Targeted Locus Capture (TLC) selectively enriches and sequences entire genes based on the crosslinking of physically proximal sequences, and thereby enables complete sequencing of genes of interest, including detection of large structural variants. Because the technology is based on the crosslinking and fragmenting of DNA, it has particular advantages in the analysis of formalin-fixed, paraffin-embedded (FFPE) samples in which DNA is inherently crosslinked and fragmented. In order to validate the FFPE-TLC technology as a novel approach for translocation detection in lymphoma samples, we have developed a panel assay containing genes with frequent translocations (MYC, BCL2, BCL6, IG loci). With this assay we have analyzed >140 lymphoma and control FFPE samples of variable input amounts and qualities that had previously been analyzed with FISH, and a subset also with standard targeted NGS. Good concordance with FISH results was observed for both translocation-positive and -negative samples. In 10 cases for which FFPE-TLC analysis resulted in a different finding than FISH, discordance could be explained by higher sensitivity of FFPE-TLC or by inconclusive FISH results. In a specific case, FFPE-TLC detected a small-distance rearrangement on chromosome 3 that caused a BCL6 fusion but led to insufficient and therefore undetectable break-apart with FISH. Secondly, the FFPE-TLC approach was tested on a set of 19 B-cell lymphoma FFPE samples that had previously been analyzed using standard targeted NGS and FISH and was enriched for discordant results between these methods. FFPE-TLC-based NGS enables more robust translocation calling as the detection relies on broad sequencing coverage across the translocation partner rather than on breakpoint sequences only. In 3 cases, FFPE-TLC could proof false negative calls in standard targeted NGS due to breakpoints located in regions difficult to capture or to sequence. In 1 case, standard targeted NGS had made a false positive call on a breakpoint sequence that was shown to be caused by a small insertion rather than a genuine translocation. This study shows that FFPE-TLC promises to be a robust alternative for FISH analysis and standard targeted NGS procedures in lymphoma diagnostics and in other cancers with frequent structural variants. The FFPE-TLC approach enables a single, DNA-based NGS test detecting both small mutations and translocations. Citation Format: Amin Allahyar, Mark Pieterse, Joost Swennenhuis, Tjitske Los-de Vries, Mehmet Yilmaz, Roos Leguit, Ruud Meijers, Nathalie Hijmering, Daphne de Jong, Bauke Ylstra, Robert van der Geize, Joost Vermaat, Arjen Cleven, Tom van Wezel, Arjan Diepstra, Leon van Kempen, Karima Hajo, Harma Feitsma, Marieke Simonis, Max van Min, Erik Splinter, Wouter de Laat. Robust detection of translocations in lymphoma FFPE samples using Targeted Locus Capture-based sequencing [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-45.

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.Author SummaryCancer is caused by disrupted activity of several pathways. Therefore, outcome predictors analyze patient’s expression profiles from perspective of gene groups collected from interactomes (e.g. protein interaction networks). These Network based Outcome Predictors (NOPs) hold potential to facilitate identification of dysregulated pathways and delivering improved prognosis. Nonetheless, recent studies revealed that compared to classical models, neither performance nor consistency can be improved using NOPs.We argue that NOPs can only perform well under guidance of suitable networks. The commonly used networks may miss associations specially for under-studied genes. Additionally, these networks are often generic with low resemblance to perturbations that arise in cancer.To address this issue, we exploit ~4100 samples and infer a disease specific network called SyNet linking synergistic gene pairs that collectively show predictivity beyond individual performance of genes.Using identical datasets, we show that a NOP yields superior performance merely by considering groups of genes in SyNet. Further, NOP performance severely reduces if SyNet nodes are shuffled, confirming relevance of SyNet links.Due to simplicity of our approach, this framework can be used for any phenotype of interest. Our findings represent the value of network-based models and crucial role of interactome in their performance.

Semi-supervised clustering tries to surpass the limits of unsupervised clustering using extra information contained in occasional labeled data points. However, providing such labeled samples is not always possible or easy in real world applications. A weaker, yet still very useful option is providing constraints on the unlabeled training samples, which is the focus of the Constrained Semi-Supervised (CSS) clustering. On the other hand, online learning has gained considerable amount of interests in real world problems with massive sample size or streaming behavior, as lack of memory and computational resources seriously restrict the application of the offline and batch methods. However, the existing algorithms for online CSS clustering problem either assumed that the entire dataset is available and added constraints incrementally or considered chunks of constrained data points and applied an offline CSS clustering algorithm. Thus, none of them can be categorized as a genuine online CSS clustering algorithm. In this paper, we propose CS2GS, an online CSS clustering algorithm. CS2GS is constructed by modifying the online learning process of Semi-Supervised Growing Self-Organizing Map, and converting it to an iterative constrained metric learning problem that can be solved using the Bregman׳s iterative projections. The proposed CS2GS is studied via a series of thorough tests using synthetic and real data including selections from UCI datasets and FEP – a recent bilingual corpus used for sentence aligning stage of machine translation. Experimental results show the effectiveness of CS2GS in online CSS clustering, and prove that indeed, the limits of the system accuracy may be pushed higher using unlabeled samples.

In this paper, we introduce an incremental version of recently proposed constrained Linear Discriminant Analysis (LDA). In addition of application in constrained LDA problems, our algorithm which we call Online Discriminative Component Analysis (ODCA) is usable in standard incremental LDA problems. ODCA incrementally computes the solution of LDA with the time complexity lower than most incremental algorithm for LDA while keeps the accuracy of final result as close as possible to offline version. This is done using a special formulation for the scatter matrix updating along with Eigen-space calculation. By exploiting such formulation, the proposed algorithm made capable of updating the solution where a data point added or removed from the problem. It is also usable in problems where its data points have concept drift property. To show efficiency of proposed algorithm, its speed is compared to other existing incremental algorithms as order of complexity. In addition, the classification accuracy of our approach is experimentally compared to other algorithms.

In Linear Discriminant Analysis (LDA), it is assumed that each class has a Gaussian distribution. This assumption rarely holds in the real world problems. However, by removing this assumption, the problem become intractable and cannot be solved in analytic form. Quite recently, a group of evolutionary algorithms is introduced to solve this problem. These algorithms used a combination of fisher criterion and fuzzy membership function as their fitness function. It is widely acknowledged that computing the fitness function in an evolutionary algorithm needs to be very fast. Unfortunately, calculating fisher criterion for each chromosome in iterations of an evolutionary algorithm has a high computational cost. Furthermore it is known that the fuzzy membership function has an assumption of Gaussian distribution, thus using it as a fitness function will have same assumption issue that LDA had previously. In this paper, we suggest a new fisher criterion to incorporate in fitness function and show that it is theoretically faster than previous introduced criterion. In addition we theoretically prove the equality of proposed criterion. Next, in order to eliminate the Gaussian assumption, we offer a substitution for fuzzy membership fitness function which does not have Gaussian assumption. Moreover, the superior speed introduced fitness function theoretically investigated. At last, in order to confirm the effectiveness of proposed fitness functions, comprehensive experiments using twelve UCI repository dataset and two real world problems in face and object recognition is performed and the results is compared in both speed and accuracy.