检索结果-内蒙古大学图书馆

HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

Nature Communications 2021年第1期12卷 3366页

作者： Sahin, Merve Wong, Wilfred Zhan, Yingqian Van Deynze, Kinsey Koche, Richard Leslie, Christina S. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY United States Tri-Institutional Training Program in Computational Biology and Medicine New York NY United States Center for Epigenetics Research Memorial Sloan Kettering Cancer Center New York NY United States Bioinformatics Program University of California at San Diego San Diego CA United States

Recent genome-wide chromosome conformation capture assays such as Hi-C and HiChIP have vastly expanded the resolution and throughput with which we can study 3D genomic architecture and function. Here, we present HiC-DC+, a software tool for Hi-C/HiChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools and includes topologically associating domain (TAD) and A/B compartment callers. We found that HiC-DC+ can more accurately identify enhancer-promoter interactions in H3K27ac HiChIP, as validated by CRISPRi-FlowFISH experiments, compared to existing methods. Differential HiC-DC+ analyses of published HiChIP and Hi-C data sets in settings of cellular differentiation and cohesin perturbation systematically and quantitatively recovers biological findings, including enhancer hubs, TAD aggregation, and the relationship between promoter-enhancer loop dynamics and gene expression changes. HiC-DC+ therefore provides a principled statistical analysis tool to empower genome-wide studies of 3D chromatin architecture and function. © 2021, The Author(s).

关键词： Chromosomes Software Statistical methods

来源：评论

学校读者我要写书评

暂无评论

Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

引用

Plant Communications 2024年第8期5卷 1-17页

作者： Xiaotong Liu Daisuke Igarashi Rachel A.Hillmer Thomas Stoddard You Lu Kenichi Tsuda Chad L.Myers Fumiaki Katagiri Department of Plant and Microbial Biology University of Minnesota-Twin CitiesSt PaulMN 55108USA Department of Computer Science and Engineering University of Minnesota-Twin CitiesMinneapolisMN 55455USA Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin CitiesMinneapolisMN 55455USA Institute for Innovation Ajinomoto Co.Inc.KawasakiJapan State Key Laboratory of Agricultural Microbiology Hubei Hongshan LaboratoryHubei Key Lab of Plant PathologyCollege of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China Present address:225 S 6th St MinneapolisMN 55402USA Present address:Biotalys Inc.2520 Meridian Parkway Suite 480DurhamNC 27713USA Present address:Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMI 48109USA

Rapid plant immune responses in the appropriate cells are needed for effective defense against *** transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is *** reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in ***-peak time-course patterns are prevalent among thousands of upregulated *** implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each *** decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated *** demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to ***,the peak decomposition has extracted spatial information from the time-course *** echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered *** of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor *** discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

关键词： Arabidopsis Pseudomonas syringae plant immune response multi-compartment model WRKY transcription factors resilient immune signaling network

来源：评论

学校读者我要写书评

暂无评论

Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets

arXiv

引用

arXiv 2024年

作者： Landa, Boris Kluger, Yuval Ma, Rong Department of Electrical Engineering Yale University United States Program in Applied Mathematics Yale University United States Department of Biostatistics Harvard University United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University United States Department of Pathology Yale University School of Medicine United States

Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis. In numerous applications, it is necessary to align and jointly embed multiple datasets from different studies or experimental conditions. Such datasets may share underlying structures of interest but exhibit individual distortions, resulting in misaligned embeddings using traditional techniques. In this work, we propose Entropic Optimal Transport (EOT) eigenmaps, a principled approach for aligning and jointly embedding a pair of datasets with theoretical guarantees. Our approach leverages the leading singular vectors of the EOT plan matrix between two datasets to extract their shared underlying structure and align the datasets accordingly in a common embedding space. We interpret our approach as an inter-data variant of the classical Laplacian eigenmaps and diffusion maps embeddings, showing that it enjoys many favorable analogous properties. We then analyze a data-generative model where two observed high-dimensional datasets share latent variables on a common low-dimensional manifold, but each dataset is subject to data-specific translation, scaling, nuisance structures, and noise. We show that in a high-dimensional asymptotic regime, the EOT plan recovers the shared manifold structure by approximating a kernel function evaluated at the locations of the latent variables. Subsequently, we provide a geometric interpretation of our embedding by relating it to the eigenfunctions of population-level operators encoding the density and geometry of the shared manifold. Finally, we showcase the performance of our approach for data integration and embedding through simulations and analyses of real-world biological data, demonstrating its advantages over alternative methods in challenging scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Origin of Overstretching Transitions in Single-Stranded Nucleic Acids

引用

Physical Review Letters 2013年第18期111卷 188302-188302页

作者： Zackary N. Scholl Mahir Rabbi David Lee Laura Manson Hanna S-Gracz Piotr E. Marszalek Program in Computational Biology and Bioinformatics Duke University Durham North Carolina 27708 USA Department of Mechanical Engineering and Materials Science Center for Biologically Inspired Materials and Material Systems Duke University Durham North Carolina 27708 USA Department of Molecular and Structural Biochemistry North Carolina State University Raleigh North Carolina 27708 USA

We combined single-molecule force spectroscopy with nuclear magnetic resonance measurements and molecular mechanics simulations to examine overstretching transitions in single-stranded nucleic acids. In single-stranded DNA and single-stranded RNA there is a low-force transition that involves unwinding of the helical structure, along with base unstacking. We determined that the high-force transition that occurs in polydeoxyadenylic acid single-stranded DNA is caused by the cooperative forced flipping of the dihedral angle formed between four atoms, O5’-C5’-C4’-C3’ (γ torsion), in the nucleic acid backbone within the canonical B-type helix. The γ torsion also flips under force in A-type helices, where the helix is shorter and wider as compared to the B-type helix, but this transition is less cooperative than in the B type and does not generate a high-force plateau in the force spectrums of A-type helices. We find that a similar high-force transition can be induced in polyadenylic acid single-stranded RNA by urea, presumably due to disrupting the intramolecular hydrogen bonding in the backbone. We hypothesize that a pronounced high-force transition observed for B-type helices of double stranded DNA also involves a cooperative flip of the γ torsion. These observations suggest new fundamental relationships between the canonical structures of single-and double-stranded DNA and the mechanism of their molecular elasticity.

关键词： NUCLEAR magnetic resonance spectroscopy SINGLE-stranded DNA NUCLEOTIDE sequence DEOXYRIBOSE ATOMIC force microscopy

来源：评论

学校读者我要写书评

暂无评论

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

arXiv

引用

arXiv 2018年

作者： Allam, Ahmed Nagy, Mate Thoma, George Krauthammer, Michael Department of Quantitative Biomedicine University of Zurich Biomedical Informatics Group University Hospital of Zurich Program in Computational Biology and Bioinformatics Yale University Department of Pathology Yale School of Medicine Lister Hill National Center for Biomedical Communications National Library of Medicine

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1) explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2) to examine the additive value of patients’ hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95% CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Rapid Sampling of Hydrogen Bond Networks for computational Protein Design (vol 14, pg 2751, 2018)

引用

JOURNAL OF CHEMICAL THEORY AND COMPUTATION 2018年第10期14卷 5434-5434页

作者： Maguire, Jack B. Boyken, Scott E. Baker, David Kuhlman, Brian [†] Program in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 United States [‡] Department of Biochemistry University of Washington Seattle Washington 98195 United States [§] Institute for Protein Design University of Washington Seattle Washington 98195 United States [∥] Howard Hughes Medical Institute University of Washington Seattle Washington 98195 United States [⊥] Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 United States [#] Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 United States

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

关键词： Interfaces Peptides and proteins Monomers Noncovalent interactions Computer simulations

来源：评论

学校读者我要写书评

暂无评论

Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease

引用

Quantitative biology 2021年第2期9卷 216-227页

作者： Mo Li Xue Zeng Chentian Jin Sheng Chih Jin Weilai Dong Martina Brueckner Richard Lifton Qiongshi Lu Hongyu Zhao Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Department of Molecular Cellular&Developmental BiologyYale UniversityCT 06510USA Department of Genetics Washington University School of MedicineSt LouisMO 63110USA Department of Pediatrics Yale UniversityNew HavenCT 06510USA Laboratory of Human Genetics and Genomics Rockefeller UniversityNew YorkNY 10065USA Department of Biostatistics and Medical Informatics University of Wisconsin-MadisonMadisonWl 53792USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Background:Whole-exome sequencing(WES)studies have identified multiple genes enriched for de novo mutations(DNMs)in congenital heart disease(CHD)***,risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each ***:In this manuscript,we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted *** integrative modeling of multiple types of genetic variants,gene-level annotations,and reference data from large population cohorts,our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs ***:Applied to WES data of 2,645 CHD proband-parent trios,our method identified 15 significant genes,half of which are novel,leading to new insights into the genetic bases of ***:These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.

关键词： rare variants gene-level association test congenital heart disease de novo mutation

来源：评论

学校读者我要写书评

暂无评论

SARS-CoV-2 surveillance of wastewater in small rural communities identifies lack of vaccine coverage as influence of omicron outbreak

引用

Water Research 2025年 283卷 123818页

作者： Solana Narum Thibault Stalder Benjamin Ridenhour Erik R. Coats Bioinformatics and Computational Biology Graduate Program (BCB) University of Idaho 875 Perimeter Drive MS 1103 Moscow ID 83844 United States Institute for Modeling Collaboration and Innovation (IMCI) University of Idaho 875 Perimeter Drive MS 1122 Moscow ID 83844 United States Department of Biological Sciences University of Idaho 875 Perimeter Drive MS 3051 Moscow ID 83844 United States Institute for Interdisciplinary Data Sciences (IIDS) University of Idaho 875 Perimeter Drive MS 3051 Moscow ID 83844 United States Department of Mathematics and Statistical Science University of Idaho 875 Perimeter Drive MS 1103 Moscow ID 83844 United States Department of Civil and Environmental Engineering University of Idaho 875 Perimeter Drive MS 1022 Moscow ID 83844 United States

Wastewater-based epidemiology (WBE) can provide critical early warnings to aid public health, which can be particularly beneficial in rural communities with limited access to health care. Spikes of SARS-CoV-2 RNA concentration in wastewater have been used to represent infections in a community, but wastewater holds a wealth of information that has not been explored yet. The objectives of this research were to expand the use of WBE to 1) determine the dynamic of SARS-CoV-2 variants in rural communities, and 2) evaluate the relationship between community vaccination status and the outbreak of a variant. We quantified the concentration of SARS-CoV-2 RNA, as well as specific mutations that are consistent with Delta and Omicron in influent raw wastewater samples collected from wastewater treatment facilities (WWTFs) for five populations with <1000 residents and one larger population in Latah County, ID. A binomial generalized linear model using the percent of the population with protection against Omicron from the initial vaccines and the booster shot was able to predict the probability of an uptick in Omicron concentration in wastewater with an accuracy of 0.96. Evaluation of vaccination data indicate that the spike in Omicron infections in December 2021 in the studied towns was linked to low levels of population protection from the initial shots of the COVID-19 vaccine against Omicron infection and limited uptake of booster shots in these communities. Despite difficulties with applying WBE in rural regions, this study shows that beyond evaluating spikes of viral infections, WBE can be used to evaluate the effect of a population’s vaccine coverage on SARS-CoV-2 variant dynamics.

关键词： COVID-19 Delta Modeling Omicron Population coverage Prediction

来源：评论

学校读者我要写书评

暂无评论

Network Analysis as a Grand Unifier in Biomedical Data Science

引用

Biomedical Data Science 1000年第1期1卷 153-180页

作者： Patrick McGillivray Declan Clarke William Meyerson Jing Zhang Donghoon Lee Mengting Gu Sushant Kumar Holly Zhou Mark Gerstein 1Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA email: mark@*** 2Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA 3Department of Computer Science Yale University New Haven Connecticut 06520 USA

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

关键词： network analysis molecular interaction systems biology cross-disciplinary research network medicine

来源：评论

学校读者我要写书评

暂无评论

A Flow Artist for High-Dimensional Cellular Data

A Flow Artist for High-Dimensional Cellular Data

引用

IEEE Workshop on Machine Learning for Signal Processing

作者： Kincaid MacDonald Dhananjay Bhaskar Guy Thampakkul Nhi Nguyen Joia Zhang Michael Perlmutter Ian Adelstein Smita Krishnaswamy Department of Mathematics Yale University Department of Genetics Yale School of Medicine Department of Computer Science Yale University Department of Mathematics Pomona College Applied Mathematics Program Yale University Department of Statistics University of Washington Department of Mathematics Boise State University Computational Biology and Bioinformatics Program Yale University

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：