scholarly journals TADBD: a sensitive and fast method for detection of typologically associated domain boundaries

BioTechniques ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 18-25
Author(s):  
Hongqiang Lyu ◽  
Lin Li ◽  
Zhifang Wu ◽  
Tian Wang ◽  
Jiguang Zheng ◽  
...  
Keyword(s):  
R Package ◽  
Computational Method ◽  
Genome Architecture ◽  
Fast Method ◽  
3D Genome ◽  
Multi Scale ◽  
Comparison Results ◽  
Contact Matrix ◽  
Domain Boundaries ◽  
Template Size

A topologically associated domain (TAD) is a self-interacting genomic block. Detection of TAD boundaries on Hi-C contact matrix is one of the most important issues in the analysis of 3D genome architecture at TAD level. Here, we present TAD boundary detection (TADBD), a sensitive and fast computational method for detection of TAD boundaries on Hi-C contact matrix. This method implements a Haar-based algorithm by considering Haar diagonal template, acceleration via a compact integrogram, multi-scale aggregation at template size and statistical filtering. In most cases, comparison results from simulated and experimental data show that TADBD outperforms the other five methods. In addition, a new R package for TADBD is freely available online.

scholarly journals HiChIP: Efficient and sensitive analysis of protein-directed genome architecture

2016 ◽  
Author(s):  
Maxwell R. Mumbach ◽  
Adam J. Rubin ◽  
Ryan A. Flynn ◽  
Chao Dai ◽  
Paul A. Khavari ◽  
...  
Keyword(s):  
Biomedical Applications ◽  
Genome Structure ◽  
Genome Architecture ◽  
Gene Control ◽  
Chromatin Conformation ◽  
Sensitive Analysis ◽  
3D Genome ◽  
Multi Scale ◽  
3D Genome Structure

AbstractGenome conformation is central to gene control but challenging to interrogate. Here we present HiChIP, a protein-centric chromatin conformation method. HiChIP improves the yield of conformation-informative reads by over 10-fold and lowers input requirement over 100-fold relative to ChIA-PET. HiChIP of cohesin reveals multi-scale genome architecture with greater signal to background than in situ Hi-C. Thus, HiChIP adds to the toolbox of 3D genome structure and regulation for diverse biomedical applications.

The 3D Genome Structure of Single Cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tianming Zhou ◽  
Ruochi Zhang ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Spatial Organization ◽  
Method Development ◽  
Single Cells ◽  
Genome Structure ◽  
Computational Method ◽  
Publication Date ◽  
3D Genome ◽  
Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Organizational principles of 3D genome architecture

2018 ◽  
Vol 19 (12) ◽  
pp. 789-800 ◽  
Author(s):  
M. Jordan Rowley ◽  
Victor G. Corces
Keyword(s):  
Genome Architecture ◽  
3D Genome ◽  
Organizational Principles

scholarly journals Hierarchical dinucleotide distribution in genome along evolution and its effect on chromatin packing

2021 ◽  
Vol 4 (8) ◽  
pp. e202101028
Author(s):  
Zhicheng Cai ◽  
Yueying He ◽  
Sirui Liu ◽  
Yue Xue ◽  
Hui Quan ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Chromatin Structure ◽  
Distribution Patterns ◽  
Length Scales ◽  
Multi Scale ◽  
Topologically Associating Domain ◽  
Domain Boundaries ◽  
Chromatin Packing ◽  
The Difference ◽  
Genomic Length

Dinucleotide densities and their distribution patterns vary significantly among species. Previous studies revealed that CpG is susceptible to methylation, enriched at topologically associating domain boundaries and its distribution along the genome correlates with chromatin compartmentalization. However, the multi-scale organizations of CpG in the linear genome, their role in chromatin organization, and how they change along the evolution are only partially understood. By comparing the CpG distribution at different genomic length scales, we quantify the difference between the CpG distributions of different species and evaluate how the hierarchical uneven CpG distribution appears in evolution. The clustering of species based on the CpG distribution is consistent with the phylogenetic tree. Interestingly, we found the CpG distribution and chromatin structure to be correlated in many different length scales, especially for mammals and avians, consistent with the mosaic CpG distribution in the genomes of these species.

3D genome architecture from populations to single cells

2015 ◽  
Vol 31 ◽  
pp. 36-41 ◽  
Author(s):  
Mayra Furlan-Magaril ◽  
Csilla Várnai ◽  
Takashi Nagano ◽  
Peter Fraser
Keyword(s):  
Single Cells ◽  
Genome Architecture ◽  
3D Genome

scholarly journals An integrative approach for fine-mapping chromatin interactions

2019 ◽  
Vol 36 (6) ◽  
pp. 1704-1711
Author(s):  
Artur Jaroszewicz ◽  
Jason Ernst
Keyword(s):  
Gene Regulation ◽  
High Resolution ◽  
Biological Significance ◽  
Computational Method ◽  
Supplementary Information ◽  
Integrative Approach ◽  
Genome Architecture ◽  
Open Chromatin ◽  
Chromatin Interactions ◽  
Genome Wide

Abstract Motivation Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. Results To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible. Availability and implementation χ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics

2019 ◽  
Vol 18 (6) ◽  
Author(s):  
Oliver M. Crook ◽  
Laurent Gatto ◽  
Paul D. W. Kirk
Keyword(s):  
Variable Selection ◽  
Dirichlet Process ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Fast Method ◽  
Model Based Clustering ◽  
Pan Cancer

Abstract The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs competitively with the current state-of-the-art, while also offering computational benefits. We apply our approach to reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in order to perform a pan-cancer proteomic characterisation of 5157 tumour samples. We have implemented our approach, together with the original SUGS algorithm, in an open-source R package named sugsvarsel, which accelerates analysis by performing intensive computations in C++ and provides automated parallel processing. The R package is freely available from: https://github.com/ococrook/sugsvarsel

scholarly journals Combining fluorescence imaging with Hi-C to study 3D genome architecture of the same single cell

2018 ◽  
Vol 13 (5) ◽  
pp. 1034-1061 ◽  
Author(s):  
David Lando ◽  
Srinjan Basu ◽  
Tim J Stevens ◽  
Andy Riddell ◽  
Kai J Wohlfahrt ◽  
...  
Keyword(s):  
Single Cell ◽  
Fluorescence Imaging ◽  
Genome Architecture ◽  
3D Genome

scholarly journals Fast horizon detection in maritime images using region-of-interest

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Chi Yoon Jeong ◽  
Hyun S Yang ◽  
KyeongDeok Moon
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Region Of Interest ◽  
Least Square ◽  
Fast Method ◽  
Multi Scale ◽  
Art Methods ◽  
Horizon Line ◽  
Horizon Detection ◽  
Interest Detection

In this article, we propose a fast method for detecting the horizon line in maritime scenarios by combining a multi-scale approach and region-of-interest detection. Recently, several methods that adopt a multi-scale approach have been proposed, because edge detection at a single is insufficient to detect all edges of various sizes. However, these methods suffer from high processing times, requiring tens of seconds to complete horizon detection. Moreover, the resolution of images captured from cameras mounted on vessels is increasing, which reduces processing speed. Using the region-of-interest is an efficient way of reducing the amount of processing information required. Thus, we explore a way to efficiently use the region-of-interest for horizon detection. The proposed method first detects the region-of-interest using a property of maritime scenes and then multi-scale edge detection is performed for edge extraction at each scale. The results are then combined to produce a single edge map. Then, Hough transform and a least-square method are sequentially used to estimate the horizon line accurately. We compared the performance of the proposed method with state-of-the-art methods using two publicly available databases, namely, Singapore Marine Dataset and buoy dataset. Experimental results show that the proposed method for region-of-interest detection reduces the processing time of horizon detection, and the accuracy with which the proposed method can identify the horizon is superior to that of state-of-the-art methods.

scholarly journals STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

2020 ◽  
Author(s):  
Massimo Andreatta ◽  
Santiago J Carmona
Keyword(s):  
Single Cell ◽  
Distance Measure ◽  
Source Code ◽  
Cell Types ◽  
R Package ◽  
Computational Method ◽  
Biological Variability ◽  
Rna Seq ◽  
Batch Effects ◽  
Guide Trees

Abstract Summary STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. Availability and implementation Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

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