scholarly journals SuperTAD: robust detection of hierarchical topologically associated domains with optimized structural information

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Wei Zhang ◽  
Meng Bo Wang ◽  
Shuai Cheng Li
Keyword(s):  
Structural Information ◽  
Current Method ◽  
Structural Proteins ◽  
Robust Detection ◽  
Topologically Associating Domains ◽  
Topologically Associated Domains ◽  
Significant Enrichment ◽  
High Consistency ◽  
Public Datasets ◽  
State Of Art

AbstractTopologically associating domains (TADs) are the organizational units of chromosome structures. TADs can contain TADs, thus forming a hierarchy. TAD hierarchies can be inferred from Hi-C data through coding trees. However, the current method for computing coding trees is not optimal. In this paper, we propose optimal algorithms for this computation. In comparison with seven state-of-art methods using two public datasets, from GM12878 and IMR90 cells, SuperTAD shows a significant enrichment of structural proteins around detected boundaries and histone modifications within TADs and displays a high consistency between various resolutions of identical Hi-C matrices.

scholarly journals An Attention-Based Graph Neural Network for Heterogeneous Structural Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4132-4139
Author(s):  
Huiting Hong ◽  
Hantao Guo ◽  
Yucheng Lin ◽  
Xiaoqing Yang ◽  
Zang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Information ◽  
Representation Learning ◽  
Graph Representation ◽  
Heterogeneous Information ◽  
Domain Experts ◽  
Proposed Model ◽  
Meta Path ◽  
Low Dimensional ◽  
Public Datasets

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions.

scholarly journals Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancers

2021 ◽  
Author(s):  
Xiang Liu ◽  
Bo Zhao ◽  
Timothy Shaw ◽  
Brooke Fridley ◽  
Derek Duckett ◽  
...  
Keyword(s):  
Computational Method ◽  
Differential Analysis ◽  
Internal Dynamics ◽  
Functional Interpretation ◽  
Disease Mechanisms ◽  
Structural Differences ◽  
Different Types ◽  
Public Datasets ◽  
Regulatory Properties ◽  
State Of Art

Super enhancers (SEs) are broad enhancer domains usually containing multiple constitute enhancers with significantly elevated activities. The constitute enhancers work together through chromatin looping to build up distinct regulatory properties of SEs. Aberrant SE activities, which are critical to understand disease mechanisms, could be raised by the alterations of one or more of their constitute enhancers. However, the state-of-art binary strategy in calling differential SEs only relies on overall activity changes, neglecting the local differences of constitute enhancers within SEs. We propose a computational method to identify differential SEs by accounting for the combinatorial effects of constitute enhancers weighted with their activities and locations (internal dynamics). In addition to overall changes, our method finds four novel types of differential SEs pointing to the structural differences within SEs. When applied to public datasets for six cancer cells, we demonstrate that different types of differential SEs complement each other with distinct sets of gene targets and varied degrees of regulatory impacts. More importantly, we found that some cell-specific genes are linked to SE structural differences specifically, suggesting improved sensitivity by our methods in identifying and interpreting differential SEs. Such improvements further lead to increased discernment of cell identifies.

scholarly journals The SARS-CoV-2 Spike Glycoprotein as a Drug and Vaccine Target: Structural Insights into Its Complexes with ACE2 and Antibodies

Cells ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 2343
Author(s):  
Anastassios C. Papageorgiou ◽  
Imran Mohsin
Keyword(s):  
Structural Biology ◽  
Viral Entry ◽  
Structural Information ◽  
Current Knowledge ◽  
Intervention Strategies ◽  
Structural Proteins ◽  
Envelope Membrane ◽  
Spike Glycoprotein ◽  
Angiotensin Converting Enzyme 2 ◽  
Structural Insights

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of the Coronavirus disease (COVID-19) pandemic, has so far resulted in more than 1.1 M deaths and 40 M cases worldwide with no confirmed remedy yet available. Since the first outbreak in Wuhan, China in December 2019, researchers across the globe have been in a race to develop therapies and vaccines against the disease. SARS-CoV-2, similar to other previously identified Coronaviridae family members, encodes several structural proteins, such as spike, envelope, membrane, and nucleocapsid, that are responsible for host penetration, binding, recycling, and pathogenesis. Structural biology has been a key player in understanding the viral infection mechanism and in developing intervention strategies against the new coronavirus. The spike glycoprotein has drawn considerable attention as a means to block viral entry owing to its interactions with the human angiotensin-converting enzyme 2 (ACE2), which acts as a receptor. Here, we review the current knowledge of SARS-CoV-2 and its interactions with ACE2 and antibodies. Structural information of SARS-CoV-2 spike glycoprotein and its complexes with ACE2 and antibodies can provide key input for the development of therapies and vaccines against the new coronavirus.

scholarly journals Evolution of Composting Process in Maize Biomass Revealed by Analytical Pyrolysis (Py-GC/MS) and Pyrolysis Compound Specific Isotope Analysis (Py-CSIA)

2021 ◽  
Vol 11 (15) ◽  
pp. 6684
Author(s):  
Layla M. San-Emeterio ◽  
Rafael López-Núñez ◽  
Francisco J. González-Vila ◽  
José A. González-Pérez
Keyword(s):  
Structural Information ◽  
Isotope Analysis ◽  
Gas Chromatography Mass Spectrometry ◽  
Analytical Pyrolysis ◽  
Chain Molecules ◽  
Composting Process ◽  
Compound Specific Isotope Analysis ◽  
Significant Enrichment ◽  
Quantitative Results ◽  
Over Time

An innovative approach based on the combination of analytical pyrolysis coupled with gas chromatography-mass spectrometry (Py-GC/MS) with compound-specific isotope analysis (Py-CSIA) is used to study the composting process of maize biomass. This multidisciplinary approach aims to elucidate the decomposition rate of the main biogenic materials (lignin, cellulose, proteins, lipids, and waxes) responses to the composting process. According to Py-GC/MS data/structural composition, a noticeable and significant decrease during the first stage of the composting process of carbohydrates and aromatic compounds is found, followed by a gradual increase of all compounds till the end of the experiment. This trend, along with an increase of fatty acids methyl-ester at the first composting stage, sustains the microbial activity and its stabilization over time. Py-CSIA data showed a significant enrichment in 13C in all identified compounds over time, supporting the semi-quantitative results and the decomposition of initial biomass throughout the composting process. This trend is also perceptible in lignin moieties, long-chain aliphatic structures, and isoprenoids, as highly recalcitrant compounds, presumably due to depolymerization and carbon translocation of side-chain molecules during the composting process. Compound-specific isotope values showed a good correlation with the bulk isotope data, and this served as validation of the technique. However, bulk values showed higher heterogeneity because those represent an average of all organic compounds in the sample. By combining isotopic and structural information using Py-GC/MS and Py-CSIA, we are able to provide further information and a more detailed approach to the study of the decomposition process of biomass by considering the diverse dynamics of the main biogenic compounds.

scholarly journals SpectralTAD: an R package for defining a hierarchy of Topologically Associated Domains using spectral clustering

2019 ◽  
Author(s):  
Kellen G. Cresswell ◽  
John C. Stansfield ◽  
Mikhail G. Dozmorov
Keyword(s):  
Spectral Clustering ◽  
3D Structure ◽  
Real Life ◽  
Depth Resolution ◽  
Building Blocks ◽  
R Package ◽  
Sequencing Depth ◽  
Ctcf Binding ◽  
Robust Detection ◽  
Topologically Associated Domains

AbstractThe three-dimensional (3D) structure of the genome plays a crucial role in regulating gene expression. Chromatin conformation capture technologies (Hi-C) have revealed that the genome is organized in a hierarchy of topologically associated domains (TADs), the fundamental building blocks of the genome. Identifying such hierarchical structures is a critical step in understanding regulatory interactions within the genome. Existing tools for TAD calling frequently require tunable parameters, are sensitive to biases such as sequencing depth, resolution, and sparsity of Hi-C data, and are computationally inefficient. Furthermore, the choice of TAD callers within the R/Bioconductor ecosystem is limited. To address these challenges, we frame the problem of TAD detection in a spectral clustering framework. Our SpectralTAD R package has automatic parameter selection, robust to sequencing depth, resolution and sparsity of Hi-C data, and detects hierarchical, biologically relevant TAD structure. Using simulated and real-life Hi-C data, we show that SpectralTAD outperforms rGMAP and TopDom, two state-of-the-art R-based TAD callers. TAD boundaries that are shared among multiple levels of the hierarchy were more enriched in relevant genomic annotations, e.g., CTCF binding sites, suggesting their higher biological importance. In contrast, boundaries of primary TADs, defined as TADs which cannot be split into sub-TADs, were found to be less enriched in genomic annotations, suggesting their more dynamic role in genome regulation. In summary, we present a simple, fast, and user-friendly R package for robust detection of TAD hierarchies supported by biological evidence. SpectralTAD is available on https://github.com/dozmorovlab/SpectralTAD and Bioconductor (submitted).

scholarly journals Plausible fast & economic detection of SARS-CoV infection using paper-based isoelectric focusing: a hypothesis

2020 ◽  
Author(s):  
Arindam Kushagra
Keyword(s):  
Isoelectric Focusing ◽  
Current Method ◽  
Structural Proteins ◽  
Infected Person ◽  
E Proteins ◽  
Severe Respiratory Distress ◽  
M Proteins ◽  
Isoelectric Points ◽  
Fast Pace ◽  
To Come

In the wake of current pandemic of COVID-19, there has arisen an urgent need to come up with novel ways to detect the infected person with SARS-CoV-2 at an extremely fast pace so that the person is immediately quarantined to prevent further transmission of the virus to other susceptible individuals. Such quick detection measures would allow the timely mitigation of the dreadful disease which has claimed close to 100,000 lives worldwide. In this article, a very fast and cheap way to detect the presence of SARS-CoV in the biopsied tissue has been proposed. The current method discussed here would drastically reduce the time of detection of an infected person to less than ~ 6 hours in addition to the economic benefit of using paper-based isoelectric focusing. The infecting coronavirus that causes severe respiratory distress contains four major types of structural proteins: spike (S) proteins, membrane (M) proteins, envelope (E) proteins & nucleocapsid (N) proteins. Peculiar isoelectric points of these four structural proteins have been proposed to detect the SARS-CoV infection. This work would be of immense interest to the scientists, researchers as well as health professionals who are trying to mitigate the spread of the disease as well as to cure it at the same time.

Comprehensive analysis of LDHAP5 pseudogene expression and potential pathogenesis in ovarian serous cystadenocarcinoma

2020 ◽  
Author(s):  
Shitong Lin ◽  
Yifan Meng ◽  
Canhui Cao ◽  
Ping Wu ◽  
Peipei Gao ◽  
...  
Keyword(s):  
Kegg Pathway ◽  
Cervical Squamous Cell Carcinoma ◽  
Potential Functions ◽  
Cancer Prognosis ◽  
Akt Signaling Pathway ◽  
Poor Overall Survival ◽  
Serous Cystadenocarcinoma ◽  
Kegg Pathway Analysis ◽  
Significant Enrichment ◽  
Public Datasets

Abstract Background: We aimed to identify differentially expressed pseudogenes and explore their potential functions in four types of common gynecological malignancies (e.g., cervical squamous cell carcinoma, ovarian serous cystadenocarcinoma, uterine corpus endometrial carcinoma, and uterine carcinosarcoma) using bioinformatics technology. Materials & methods: We identified up-regulated and down-regulated pseudogenes and built a pseudogene-miRNA-mRNA regulatory network through public datasets to explore their potential functions in carcinogenesis and cancer prognosis. Results: Among the 63 up-regulated pseudogenes identified, LDHAP5 demonstrated the greatest potential as a candidate pseudogene due to its significant association with poor overall survival in ovarian serous cystadenocarcinoma. KEGG pathway analysis revealed that LDHAP5 showed significant enrichment in MicroRNAs in cancer, Pathway in cancer and PI3K-AKT signaling pathway. Further analysis revealed that EGFR was the potential target mRNA of LDHAP5, which may play an important role in ovarian serous cystadenocarcinoma. Conclusions: LDHAP5 was associated with the occurrence and prognosis of ovarian serous cystadenocarcinoma, and thus shows potential as a novel therapeutic target against such cancer.

A scale-aware YOLO model for pedestrian detection

2020 ◽  
Author(s):  
Xingyi Yang ◽  
Yonghu Wang ◽  
Robert Laganiere
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Real Time ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Small Scale ◽  
Robust Detection ◽  
Public Datasets ◽  
Traditional Approaches ◽  
New Framework

<div>Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection, it remains very challenging to leverage this method for real-time pedestrian detection. In this paper, we propose a new framework called SA YOLOv3, a scale-aware You Only Look Once framework which improves YOLOv3 in improving pedestrian detection of small scale pedestrian instances in a real-time manner.</div><div>Our network introduces two sub-networks which detect pedestrians of different scales. Outputs from the sub-networks are then combined to generate robust detection results.</div><div>Experimental results show that the proposed SA YOLOv3 framework outperforms the results of YOLOv3 on public datasets and run at an average of 11 fps on a GPU.</div>

A scale-aware YOLO model for pedestrian detection

2020 ◽  
Author(s):  
Xingyi Yang ◽  
Yong Wang ◽  
Robert Laganiere
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Real Time ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Small Scale ◽  
Robust Detection ◽  
Public Datasets ◽  
Traditional Approaches ◽  
New Framework

<div>Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection, it remains very challenging to leverage this method for real-time pedestrian detection. In this paper, we propose a new framework called SA YOLOv3, a scale-aware You Only Look Once framework which improves YOLOv3 in improving pedestrian detection of small scale pedestrian instances in a real-time manner.</div><div>Our network introduces two sub-networks which detect pedestrians of different scales. Outputs from the sub-networks are then combined to generate robust detection results.</div><div>Experimental results show that the proposed SA YOLOv3 framework outperforms the results of YOLOv3 on public datasets and run at an average of 11 fps on a GPU.</div>

Secondary structure of caveolins: a mini review

2019 ◽  
Vol 47 (5) ◽  
pp. 1489-1498 ◽  
Author(s):  
Kyle T. Root ◽  
Jeffrey A. Julien ◽  
Kerney Jebrell Glover
Keyword(s):  
Signal Transduction ◽  
Plasma Membrane ◽  
Secondary Structure ◽  
Structural Information ◽  
Structural Data ◽  
Integral Membrane Proteins ◽  
Structural Proteins ◽  
Disease States ◽  
Loop Conformation ◽  
Cytoplasmic Side

Abstract Caveolae are 50–100 nm invaginations found within the plasma membrane of cells. Caveolae are involved in many processes that are essential for homeostasis, most notably endocytosis, mechano-protection, and signal transduction. Within these invaginations, the most important proteins are caveolins, which in addition to participating in the aforementioned processes are structural proteins responsible for caveolae biogenesis. When caveolin is misregulated or mutated, many disease states can arise which include muscular dystrophy, cancers, and heart disease. Unlike most integral membrane proteins, caveolin does not have a transmembrane orientation; instead, it is postulated to adopt an unusual topography where both the N- and C-termini lie on the cytoplasmic side of the membrane, and the hydrophobic span adopts an intramembrane loop conformation. While knowledge concerning the biology of caveolin has progressed apace, fundamental structural information has proven more difficult to obtain. In this mini-review, we curate as well as critically assess the structural data that have been obtained on caveolins to date in order to build a robust and compelling model of the caveolin secondary structure.

Sign in / Sign up

Export Citation Format

Share Document