scholarly journals MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector): a statistical framework for identifying long-range chromatin interactions from multiple cell types

2020 ◽  
Author(s):  
Weifang Liu ◽  
Armen Abnousi ◽  
Qian Zhang ◽  
Yun Li ◽  
Ming Hu ◽  
...  
Keyword(s):  
Long Range ◽  
Spatial Organization ◽  
Critical Role ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Cell Type ◽  
Statistical Framework ◽  
Chromatin Interactions ◽  
The Status ◽  
Multiple Cell

AbstractChromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple cell types. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other cell types. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting cell-type-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-cell-type analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple cell types.

scholarly journals Systematic evaluation of chromosome conformation capture assays

2020 ◽  
Author(s):  
Betul Akgol Oksuz ◽  
Liyan Yang ◽  
Sameer Abraham ◽  
Sergey V. Venev ◽  
Nils Krietenstein ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Cell Types ◽  
Chromosome Conformation Capture ◽  
Chromatin Interaction ◽  
Systematic Evaluation ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Experimental Parameters ◽  
Genome Features ◽  
Multiple Cell

AbstractChromosome conformation capture (3C)-based assays are used to map chromatin interactions genome-wide. Quantitative analyses of chromatin interaction maps can lead to insights into the spatial organization of chromosomes and the mechanisms by which they fold. A number of protocols such as in situ Hi-C and Micro-C are now widely used and these differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of experimental parameters of 3C-based protocols. We find that different protocols capture different 3D genome features with different efficiencies. First, the use of cross-linkers such as DSG in addition to formaldehyde improves signal-to-noise allowing detection of thousands of additional loops and strengthens the compartment signal. Second, fragmenting chromatin to the level of nucleosomes using MNase allows detection of more loops. On the other hand, protocols that generate larger multi-kb fragments produce stronger compartmentalization signals. We confirmed our results for multiple cell types and cell cycle stages. We find that cell type-specific quantitative differences in chromosome folding are not detected or underestimated by some protocols. Based on these insights we developed Hi-C 3.0, a single protocol that can be used to both efficiently detect chromatin loops and to quantify compartmentalization. Finally, this study produced ultra-deeply sequenced reference interaction maps using conventional Hi-C, Micro-C and Hi-C 3.0 for commonly used cell lines in the 4D Nucleome Project.

scholarly journals MAPS: model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments

2018 ◽  
Author(s):  
Ivan Juric ◽  
Miao Yu ◽  
Armen Abnousi ◽  
Ramya Raviram ◽  
Rongxin Fang ◽  
...  
Keyword(s):  
Long Range ◽  
Cell Types ◽  
Computational Method ◽  
Superior Performance ◽  
Model Based ◽  
Chromatin Interactions ◽  
Enhanced Resolution ◽  
Multiple Cell ◽  
Systematic Biases ◽  
Model Based Analysis

AbstractHi-C and chromatin immunoprecipitation (ChIP) have been combined to identify long-range chromatin interactions genome-wide at reduced cost and enhanced resolution, but extracting the information from the resulting datasets has been challenging. Here we describe a computational method, MAPS, Model-based Analysis of PLAC-seq and HiChIP, to process the data from such experiments and identify long-range chromatin interactions. MAPS adopts a zero-truncated Poisson regression framework to explicitly remove systematic biases in the PLAC-seq and HiChIP datasets, and then uses the normalized chromatin contact frequencies to identify significant chromatin interactions anchored at genomic regions bound by the protein of interest. MAPS shows superior performance over existing software tools in analysis of chromatin interactions centered on cohesin, CTCF and H3K4me3 associated regions in multiple cell types. MAPS is freely available at https://github.com/ijuric/MAPS.

scholarly journals Improving chromatin-interaction prediction using single-cell open-chromatin profile and making insight about the cis-regulatory landscape of the human brain

2020 ◽  
Author(s):  
Neetesh Pandey ◽  
Omkar Chandra ◽  
Shreya Mishra ◽  
Vibhor Kumar
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Long Range ◽  
Target Genes ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Range Interaction ◽  
Chromatin Interactions ◽  
A Cell

AbstractSingle-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell-type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin-interactions despite the fact that long-range interaction among genomic sites plays a significant role in gene regulation. Here, we propose a method that predicts both local and long-range interactions among genomic sites using single-cell open chromatin profiles. Using our method’s better sensitivity, we could predict almost 0.7 million interactions among genomic sites across 7 cell-types in the human brain. The chromatin-interactions estimated in the human brain revealed surprising but useful insight about target genes of human-accelerated-elements and disease-associated mutations.

scholarly journals HiChIP and Hi-C Protocol Optimized for Primary Murine T Cells

2021 ◽  
Vol 4 (3) ◽  
pp. 49
Author(s):  
Tomas Zelenka ◽  
Charalampos Spilianakis
Keyword(s):  
T Cells ◽  
Long Range ◽  
Three Dimensional ◽  
Cell Types ◽  
Range Interaction ◽  
Chromatin Interactions ◽  
Optimized Protocol ◽  
Long Range Interaction ◽  
Chromatin Compactness ◽  
Detailed Protocol

The functional implications of the three-dimensional genome organization are becoming increasingly recognized. The Hi-C and HiChIP research approaches belong among the most popular choices for probing long-range chromatin interactions. A few methodical protocols have been published so far, yet their reproducibility and efficiency may vary. Most importantly, the high frequency of the dangling ends may dramatically affect the number of usable reads mapped to valid interaction pairs. Additionally, more obstacles arise from the chromatin compactness of certain investigated cell types, such as primary T cells, which due to their small and compact nuclei, impede limitations for their use in various genomic approaches. Here we systematically optimized all the major steps of the HiChIP protocol in T cells. As a result, we reduced the number of dangling ends to nearly zero and increased the proportion of long-range interaction pairs. Moreover, using three different mouse genotypes and multiple biological replicates, we demonstrated the high reproducibility of the optimized protocol. Although our primary goal was to optimize HiChIP, we also successfully applied the optimized steps to Hi-C, given their significant protocol overlap. Overall, we describe the rationale behind every optimization step, followed by a detailed protocol for both HiChIP and Hi-C experiments.

scholarly journals Functional annotation of human long noncoding RNAs using chromatin conformation data

2021 ◽  
Author(s):  
Saumya Agrawal ◽  
Tanvir Alam ◽  
Masaru Koido ◽  
Ivan V. Kulakovskiy ◽  
Jessica Severin ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Rna Binding ◽  
Functional Characterization ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Spatial Proximity ◽  
Chromatin Conformation ◽  
Cell Type ◽  
Cell Type Specific ◽  
Rna Domains

AbstractTranscription of the human genome yields mostly long non-coding RNAs (lncRNAs). Systematic functional annotation of lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. Currently, 95% of human lncRNAs have no functional characterization. Using chromatin conformation and Cap Analysis of Gene Expression (CAGE) data in 18 human cell types, we systematically located genomic regions in spatial proximity to lncRNA genes and identified functional clusters of interacting protein-coding genes, lncRNAs and enhancers. Using these clusters we provide a cell type-specific functional annotation for 7,651 out of 14,198 (53.88%) lncRNAs. LncRNAs tend to have specialized roles in the cell type in which it is first expressed, and to incorporate more general functions as its expression is acquired by multiple cell types during evolution. By analyzing RNA-binding protein and RNA-chromatin interaction data in the context of the spatial genomic interaction map, we explored mechanisms by which these lncRNAs can act.

Prenet: Predictive network from ATAC-SEQ data

2020 ◽  
Vol 18 (01) ◽  
pp. 2040003 ◽  
Author(s):  
Nazmus Salehin ◽  
Patrick P. L. Tam ◽  
Pierre Osteil
Keyword(s):  
Regulatory Network ◽  
State Of The Art ◽  
Cell Types ◽  
Cell Type ◽  
Regulatory Pathways ◽  
Novel Approach ◽  
Multiple Cell ◽  
Bona Fide ◽  
Gene Regulatory ◽  
Accessible Chromatin

Assays for transposase-accessible chromatin sequencing (ATAC-seq) provides an innovative approach to study chromatin status in multiple cell types. Moreover, it is also possible to efficiently extract differentially accessible chromatin (DACs) regions by using state-of-the-art algorithms (e.g. DESeq2) to predict gene activity in specific samples. Furthermore, it has recently been shown that small dips in sequencing peaks can be attributed to the binding of transcription factors. These dips, also known as footprints, can be used to identify trans-regulating interactions leading to gene expression. Current protocols used to identify footprints (e.g. pyDNAse and HINT-ATAC) have shown limitations resulting in the discovery of many false positive footprints. We generated a novel approach to identify genuine footprints within any given ATAC-seq dataset. Herein, we developed a new pipeline embedding DACs together with bona fide footprints resulting in the generation of a Predictive gene regulatory Network (PreNet) simply from ATAC-seq data. We further demonstrated that PreNet can be used to unveil meaningful molecular regulatory pathways in a given cell type.

scholarly journals A Comparative Study of Supervised Machine Learning Algorithms for the Prediction of Long-Range Chromatin Interactions

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 985 ◽  
Author(s):  
Thomas Vanhaeren ◽  
Federico Divina ◽  
Miguel García-Torres ◽  
Francisco Gómez-Vela ◽  
Wim Vanhoof ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transcription Factors ◽  
Long Range ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Supervised Machine Learning ◽  
Chromatin Interaction ◽  
Gradient Boosting ◽  
Chromatin Interactions

The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction maps provided by Chromatin Conformation Capture-based techniques, which have greatly improved in recent years. Since these procedures are experimentally laborious and expensive, in silico prediction has emerged as an alternative strategy to generate virtual maps in cell types and conditions for which experimental data of chromatin interactions is not available. Several methods have been based on predictive models trained on one-dimensional (1D) sequencing features, yielding promising results. However, different approaches vary both in the way they model chromatin interactions and in the machine learning-based strategy they rely on, making it challenging to carry out performance comparison of existing methods. In this study, we use publicly available 1D sequencing signals to model cohesin-mediated chromatin interactions in two human cell lines and evaluate the prediction performance of six popular machine learning algorithms: decision trees, random forests, gradient boosting, support vector machines, multi-layer perceptron and deep learning. Our approach accurately predicts long-range interactions and reveals that gradient boosting significantly outperforms the other five methods, yielding accuracies of about 95%. We show that chromatin features in close genomic proximity to the anchors cover most of the predictive information, as has been previously reported. Moreover, we demonstrate that gradient boosting models trained with different subsets of chromatin features, unlike the other methods tested, are able to produce accurate predictions. In this regard, and besides architectural proteins, transcription factors are shown to be highly informative. Our study provides a framework for the systematic prediction of long-range chromatin interactions, identifies gradient boosting as the best suited algorithm for this task and highlights cell-type specific binding of transcription factors at the anchors as important determinants of chromatin wiring mediated by cohesin.

scholarly journals Complex Cell Type-Specific Roles of Autophagy in Liver Fibrosis and Cirrhosis

Pathogens ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 225
Author(s):  
Tzu-Min Hung ◽  
Chih-Chiang Hsiao ◽  
Chih-Wen Lin ◽  
Po-Huang Lee
Keyword(s):  
Liver Fibrosis ◽  
Cell Types ◽  
Degradation Pathway ◽  
Cellular Tissue ◽  
Future Research ◽  
Specific Cell ◽  
Cell Type ◽  
Stage Disease ◽  
Multiple Cell ◽  
End Stage

The lysosomal degradation pathway, or autophagy, plays a fundamental role in cellular, tissue, and organismal homeostasis. A correlation between dysregulated autophagy and liver fibrosis (including end-stage disease, cirrhosis) is well-established. However, both the up and downregulation of autophagy have been implicated in fibrogenesis. For example, the inhibition of autophagy in hepatocytes and macrophages can enhance liver fibrosis, whereas autophagic activity in hepatic stellate cells and reactive ductular cells is permissive towards fibrogenesis. In this review, the contributions of specific cell types to liver fibrosis as well as the mechanisms underlying the effects of autophagy are summarized. In view of the functional effects of multiple cell types on the complex process of hepatic fibrogenesis, integrated approaches that consider the role of autophagy in each liver cell type should be a focus of future research.

scholarly journals A FLEX Switch Targets Channelrhodopsin-2 to Multiple Cell Types for Imaging and Long-Range Circuit Mapping

2008 ◽  
Vol 28 (28) ◽  
pp. 7025-7030 ◽  
Author(s):  
D. Atasoy ◽  
Y. Aponte ◽  
H. H. Su ◽  
S. M. Sternson
Keyword(s):  
Long Range ◽  
Cell Types ◽  
Multiple Cell
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