scholarly journals CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

2015 ◽  
Author(s):  
Jonathan Cairns ◽  
Paula Freire-Pritchett ◽  
Steven W. Wingett ◽  
Csilla Várnai ◽  
Andrew Dimond ◽  
...  

ABSTRACTCapture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions are captured, such as in Promoter CHi-C. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.

2019 ◽  
Author(s):  
Jonathan Cairns ◽  
William R. Orchard ◽  
Valeriya Malysheva ◽  
Mikhail Spivakov

AbstractSummaryCapture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. ChiScdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalisation and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression. Chicdiff is implemented as an R package that is publicly available at https://github.com/RegulatoryGenomicsGroup/chicdiff.


2019 ◽  
Vol 35 (22) ◽  
pp. 4764-4766 ◽  
Author(s):  
Jonathan Cairns ◽  
William R Orchard ◽  
Valeriya Malysheva ◽  
Mikhail Spivakov

Abstract Summary Capture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. Chicdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalization and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression. Availability and implementation Chicdiff is implemented as an R package that is publicly available at https://github.com/RegulatoryGenomicsGroup/chicdiff. Supplementary information Supplementary data are available at Bioinformatics online.


2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Sven D. Schrinner ◽  
Rebecca Serra Mari ◽  
Jana Ebler ◽  
Mikko Rautiainen ◽  
Lancelot Seillier ◽  
...  

Abstract Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. Polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present WhatsHap polyphase, a novel two-stage approach that addresses these challenges by (i) clustering reads and (ii) threading the haplotypes through the clusters. Our method outperforms the state-of-the-art in terms of phasing quality. Using a real tetraploid potato dataset, we demonstrate how to assemble local genomic regions of interest at the haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap.


2018 ◽  
Author(s):  
Yousra Ben Zouari ◽  
Anne M Molitor ◽  
Natalia Sikorska ◽  
Vera Pancaldi ◽  
Tom Sexton

AbstractCapture Hi-C (CHi-C) is a new technique for assessing genome organization, based on chromosome conformation capture coupled to oligonucleotide capture of regions of interest such as gene promoters. Chromatin loop detection is challenging, since existing Hi-C/4C-like analyses, which make different assumptions about the technical biases presented, are often unsuitable. We describe a new approach, ChiCMaxima, which uses local maxima combined with a background model to detect DNA looping interactions, integrating information from biological replicates. ChiCMaxima shows more stringency and robustness compared to previously developed tools. The tool includes a GUI browser for flexible visualization of CHi-C profiles alongside epigenomic tracks.


Author(s):  
Erik Paul ◽  
Holger Herzog ◽  
Sören Jansen ◽  
Christian Hobert ◽  
Eckhard Langer

Abstract This paper presents an effective device-level failure analysis (FA) method which uses a high-resolution low-kV Scanning Electron Microscope (SEM) in combination with an integrated state-of-the-art nanomanipulator to locate and characterize single defects in failing CMOS devices. The presented case studies utilize several FA-techniques in combination with SEM-based nanoprobing for nanometer node technologies and demonstrate how these methods are used to investigate the root cause of IC device failures. The methodology represents a highly-efficient physical failure analysis flow for 28nm and larger technology nodes.


BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sergio Gabarre ◽  
Frank Vernaillen ◽  
Pieter Baatsen ◽  
Katlijn Vints ◽  
Christopher Cawthorne ◽  
...  

Abstract Background Array tomography (AT) is a high-resolution imaging method to resolve fine details at the organelle level and has the advantage that it can provide 3D volumes to show the tissue context. AT can be carried out in a correlative way, combing light and electron microscopy (LM, EM) techniques. However, the correlation between modalities can be a challenge and delineating specific regions of interest in consecutive sections can be time-consuming. Integrated light and electron microscopes (iLEMs) offer the possibility to provide well-correlated images and may pose an ideal solution for correlative AT. Here, we report a workflow to automate navigation between regions of interest. Results We use a targeted approach that allows imaging specific tissue features, like organelles, cell processes, and nuclei at different scales to enable fast, directly correlated in situ AT using an integrated light and electron microscope (iLEM-AT). Our workflow is based on the detection of section boundaries on an initial transmitted light acquisition that serves as a reference space to compensate for changes in shape between sections, and we apply a stepwise refinement of localizations as the magnification increases from LM to EM. With minimal user interaction, this enables autonomous and speedy acquisition of regions containing cells and cellular organelles of interest correlated across different magnifications for LM and EM modalities, providing a more efficient way to obtain 3D images. We provide a proof of concept of our approach and the developed software tools using both Golgi neuronal impregnation staining and fluorescently labeled protein condensates in cells. Conclusions Our method facilitates tracing and reconstructing cellular structures over multiple sections, is targeted at high resolution ILEMs, and can be integrated into existing devices, both commercial and custom-built systems.


Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.


Author(s):  
J.M. Murray ◽  
P. Pfeffer ◽  
R. Seifert ◽  
A. Hermann ◽  
J. Handke ◽  
...  

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that includes state-of-the-art deep learning models for atherosclerotic plaque segmentation. Approach and Results: Vesseg is a containerized, extensible, open-source, and user-oriented tool. It includes 2 models, trained and tested on 1089 hematoxylin-eosin stained mouse model atherosclerotic brachiocephalic artery sections. The models were compared to 3 human raters. Vesseg can be accessed at https://vesseg .online or downloaded. The models show mean Soerensen-Dice scores of 0.91±0.15 for plaque and 0.97±0.08 for lumen pixels. The mean accuracy is 0.98±0.05. Vesseg is already in active use, generating time savings of >10 minutes per slide. Conclusions: Vesseg brings state-of-the-art deep learning methods to atherosclerosis research, providing drastic time savings, while allowing for continuous improvement of models and the underlying pipeline.


Sign in / Sign up

Export Citation Format

Share Document