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Gene representation in scRNA-seq is correlated with common motifs at the 3' end of transcripts. scRNA-seq中的基因表达与转录物3'端的常见基序相关。
Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-05-15 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1120290
Xinling Li, Greg Gibson, Peng Qiu

One important characteristic of single-cell RNA sequencing (scRNA-seq) data is its high sparsity, where the gene-cell count data matrix contains high proportion of zeros. The sparsity has motivated widespread discussions on dropouts and missing data, as well as imputation algorithms of scRNA-seq analysis. Here, we aim to investigate whether there exist genes that are more prone to be under-detected in scRNA-seq, and if yes, what commonalities those genes may share. From public data sources, we gathered paired bulk RNA-seq and scRNA-seq data from 53 human samples, which were generated in diverse biological contexts. We derived pseudo-bulk gene expression by averaging the scRNA-seq data across cells. Comparisons of the paired bulk and pseudo-bulk gene expression profiles revealed that there indeed exists a collection of genes that are frequently under-detected in scRNA-seq compared to bulk RNA-seq. This result was robust to randomization when unpaired bulk and pseudo-bulk gene expression profiles were compared. We performed motif search to the last 350 bp of the identified genes, and observed an enrichment of poly(T) motif. The poly(T) motif toward the tails of those genes may be able to form hairpin structures with the poly(A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during scRNA-seq library preparation, which is a mechanistic conjecture of why certain genes may be more prone to be under-detected in scRNA-seq.

单细胞RNA测序(scRNA-seq)数据的一个重要特征是其高度稀疏性,其中基因细胞计数数据矩阵包含高比例的零。稀疏性引发了关于辍学和缺失数据以及scRNA-seq分析的插补算法的广泛讨论。在这里,我们的目的是调查是否存在在scRNA-seq中更容易被检测不足的基因,如果存在,这些基因可能有哪些共性。从公共数据来源,我们从53个人类样本中收集了成对的大块RNA-seq和scRNA-seq数据,这些样本是在不同的生物环境中产生的。我们通过对细胞间的scRNA-seq数据进行平均,得出了伪体基因表达。配对大块和伪大块基因表达谱的比较表明,与大块RNA-seq相比,确实存在一组在scRNA-seq中经常检测不足的基因。当比较未配对的大块和伪大块基因表达谱时,该结果对随机化是稳健的。我们对已鉴定基因的最后350bp进行了基序搜索,并观察到poly(T)基序的富集。朝向这些基因尾部的聚(T)基序可能能够与它们的mRNA转录物的聚(A)尾部形成发夹结构,这使得它们的信使核糖核酸转录物在scRNA-seq文库制备过程中很难被捕获,这是为什么某些基因在scRNA-seq中可能更容易被检测不足的机制推测。
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引用次数: 0
Orthogonal analysis of variants in APOE gene using in-silico approaches reveals novel disrupting variants. 采用硅内方法对 APOE 基因变异进行正交分析,发现新的干扰变异。
IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-04-06 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1122559
Chang Li, Ian Hou, Mingjia Ma, Grace Wang, Yongsheng Bai, Xiaoming Liu

Introduction: Alzheimer's disease (AD) is one of the most prominent medical conditions in the world. Understanding the genetic component of the disease can greatly advance our knowledge regarding its progression, treatment and prognosis. Single amino-acid variants (SAVs) in the APOE gene have been widely investigated as a risk factor for AD Studies, including genome-wide association studies, meta-analysis based studies, and in-vivo animal studies, were carried out to investigate the functional importance and pathogenesis potential of APOE SAVs. However, given the high cost of such large-scale or experimental studies, there are only a handful of variants being reported that have definite explanations. The recent development of in-silico analytical approaches, especially large-scale deep learning models, has opened new opportunities for us to probe the structural and functional importance of APOE variants extensively. Method: In this study, we are taking an ensemble approach that simultaneously uses large-scale protein sequence-based models, including Evolutionary Scale Model and AlphaFold, together with a few in-silico functional prediction web services to investigate the known and possibly disease-causing SAVs in APOE and evaluate their likelihood of being functional and structurally disruptive. Results: As a result, using an ensemble approach with little to no prior field-specific knowledge, we reported 5 SAVs in APOE gene to be potentially disruptive, one of which (C112R) was classificed by previous studies as a key risk factor for AD. Discussion: Our study provided a novel framework to analyze and prioritize the functional and structural importance of SAVs for future experimental and functional validation.

简介阿尔茨海默病(AD)是世界上最常见的疾病之一。了解阿尔茨海默病的遗传因素可极大地促进我们对该病的进展、治疗和预后的了解。APOE 基因中的单氨基酸变异体(SAVs)作为 AD 的风险因素受到了广泛的研究。然而,由于此类大规模研究或实验研究的成本较高,目前报道的变异中只有少数几个能给出明确的解释。近来,体内分析方法的发展,尤其是大规模深度学习模型的发展,为我们广泛探究APOE变异体的结构和功能重要性提供了新的机遇。研究方法在本研究中,我们采用了一种集合方法,同时使用基于大规模蛋白质序列的模型(包括进化尺度模型和 AlphaFold)以及几种内部功能预测网络服务来研究 APOE 中已知的和可能致病的 SAV,并评估它们在功能和结构上具有破坏性的可能性。结果:结果:在几乎没有特定领域知识的情况下,我们使用集合方法报告了 APOE 基因中的 5 个 SAVs 可能具有破坏性,其中一个 SAVs(C112R)被先前的研究归类为 AD 的关键风险因素。讨论我们的研究为今后的实验和功能验证提供了一个新的框架,用于分析 SAVs 的功能和结构重要性并确定其优先次序。
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引用次数: 0
Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets 生物数据集改进MDS嵌入的正交离群点检测与维数估计
Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-03-29 DOI: 10.1101/2023.02.13.528380
Wanxin Li, Jules Mirone, Ashok Prasad, Nina Miolane, Carine Legrand, K. D. Duc
Conventional dimensionality reduction methods like Multidimensional Scaling (MDS) are sensitive to the presence of orthogonal outliers, leading to significant defects in the embedding. We introduce a robust MDS method, called DeCOr-MDS (Detection and Correction of Orthogonal outliers using MDS), based on the geometry and statistics of simplices formed by data points, that allows to detect orthogonal outliers and subsequently reduce dimensionality. We validate our methods using synthetic datasets, and further show how it can be applied to a variety of large real biological datasets, including cancer image cell data and human microbiome project data.
传统的降维方法如多维尺度(MDS)对正交离群点的存在很敏感,导致嵌入存在明显缺陷。我们介绍了一种鲁棒的MDS方法,称为decoro -MDS(使用MDS检测和校正正交异常值),该方法基于由数据点构成的简单体的几何和统计,允许检测正交异常值并随后降低维数。我们使用合成数据集验证了我们的方法,并进一步展示了如何将其应用于各种大型真实生物数据集,包括癌症图像细胞数据和人类微生物组项目数据。
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引用次数: 1
The thorny complexities of visualization research for clinical settings: A case study from genomics. 临床环境可视化研究的棘手难题:基因组学案例研究。
IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-03-29 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1112649
Emilia Ståhlbom, Jesper Molin, Anders Ynnerman, Claes Lundström

In this perspective article we discuss a certain type of research on visualization for bioinformatics data, namely, methods targeting clinical use. We argue that in this subarea additional complex challenges come into play, particularly so in genomics. We here describe four such challenge areas, elicited from a domain characterization effort in clinical genomics. We also list opportunities for visualization research to address clinical challenges in genomics that were uncovered in the case study. The findings are shown to have parallels with experiences from the diagnostic imaging domain.

在这篇视角文章中,我们讨论了生物信息学数据可视化的某类研究,即针对临床使用的方法。我们认为,在这一子领域会出现更多复杂的挑战,尤其是在基因组学领域。我们在此描述了临床基因组学领域特征描述工作中出现的四个挑战领域。我们还列举了案例研究中发现的可视化研究机会,以应对基因组学中的临床挑战。这些发现与影像诊断领域的经验有相似之处。
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引用次数: 0
Joint visualization of seasonal influenza serology and phylogeny to inform vaccine composition. 季节性流感血清学和系统发育的联合可视化,为疫苗成分提供信息。
Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-03-22 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1069487
Jover Lee, James Hadfield, Allison Black, Thomas R Sibley, Richard A Neher, Trevor Bedford, John Huddleston

Seasonal influenza vaccines must be updated regularly to account for mutations that allow influenza viruses to escape our existing immunity. A successful vaccine should represent the genetic diversity of recently circulating viruses and induce antibodies that effectively prevent infection by those recent viruses. Thus, linking the genetic composition of circulating viruses and the serological experimental results measuring antibody efficacy is crucial to the vaccine design decision. Historically, genetic and serological data have been presented separately in the form of static visualizations of phylogenetic trees and tabular serological results to identify vaccine candidates. To simplify this decision-making process, we have created an interactive tool for visualizing serological data that has been integrated into Nextstrain's real-time phylogenetic visualization framework, Auspice. We show how the combined interactive visualizations may be used by decision makers to explore the relationships between complex data sets for both prospective vaccine virus selection and retrospectively exploring the performance of vaccine viruses.

季节性流感疫苗必须定期更新,以应对使流感病毒逃脱我们现有免疫力的突变。一种成功的疫苗应该代表最近传播的病毒的基因多样性,并诱导抗体,有效防止这些最近的病毒感染。因此,将循环病毒的基因组成与测量抗体效力的血清学实验结果联系起来,对疫苗设计决策至关重要。历史上,遗传和血清学数据以系统发育树的静态可视化和血清学结果表的形式分别呈现,以确定候选疫苗。为了简化这一决策过程,我们创建了一个交互式工具,用于可视化血清学数据,该工具已集成到Nextstrain的实时系统发育可视化框架Auspice中。我们展示了决策者如何使用组合的交互式可视化来探索前瞻性疫苗病毒选择和回顾性疫苗病毒性能的复杂数据集之间的关系。
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引用次数: 1
Immersive and interactive visualization of 3D spatio-temporal data using a space time hypercube: Application to cell division and morphogenesis analysis. 利用时空超立方体实现三维时空数据的沉浸式交互可视化:应用于细胞分裂和形态发生分析。
IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-03-08 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.998991
Gwendal Fouché, Ferran Argelaguet, Emmanuel Faure, Charles Kervrann

The analysis of multidimensional time-varying datasets faces challenges, notably regarding the representation of the data and the visualization of temporal variations. We propose an extension of the well-known Space-Time Cube (STC) visualization technique in order to visualize time-varying 3D spatial data, taking advantage of the interaction capabilities of Virtual Reality (VR). First, we propose the Space-Time Hypercube (STH) as an abstraction for 3D temporal data, extended from the STC concept. Second, through the example of embryo development imaging dataset, we detail the construction and visualization of a STC based on a user-driven projection of the spatial and temporal information. This projection yields a 3D STC visualization, which can also encode additional numerical and categorical data. Additionally, we propose a set of tools allowing the user to filter and manipulate the 3D STC which benefits the visualization, exploration and interaction possibilities offered by VR. Finally, we evaluated the proposed visualization method in the context of 3D temporal cell imaging data analysis, through a user study (n = 5) reporting the feedback from five biologists. These domain experts also accompanied the application design as consultants, providing insights on how the STC visualization could be used for the exploration of complex 3D temporal morphogenesis data.

对多维时变数据集的分析面临着挑战,尤其是在数据表示和时变可视化方面。我们利用虚拟现实(VR)的交互能力,对著名的时空立方体(STC)可视化技术进行了扩展,以实现时变三维空间数据的可视化。首先,我们提出了时空超立方体(STH)作为三维时空数据的抽象概念,它是从 STC 概念延伸而来的。其次,我们以胚胎发育成像数据集为例,详细介绍了基于用户驱动的时空信息投影的时空超立方体的构建和可视化。这种投影产生了三维 STC 可视化,它还可以编码额外的数字和分类数据。此外,我们还提出了一套工具,允许用户过滤和操作三维 STC,这有利于虚拟现实技术提供的可视化、探索和互动可能性。最后,我们在三维时空细胞成像数据分析的背景下,通过用户研究(n = 5)评估了所提出的可视化方法,报告了五位生物学家的反馈意见。这些领域专家还作为顾问参与了应用设计,就如何利用 STC 可视化技术探索复杂的三维时态形态发生数据提供了见解。
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引用次数: 0
Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters. 从蛋白质家族水平探索微生物功能生物多样性--从元基因组序列读数到注释蛋白质群。
IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-03-03 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1157956
Fotis A Baltoumas, Evangelos Karatzas, David Paez-Espino, Nefeli K Venetsianou, Eleni Aplakidou, Anastasis Oulas, Robert D Finn, Sergey Ovchinnikov, Evangelos Pafilis, Nikos C Kyrpides, Georgios A Pavlopoulos

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

元基因组学使人们能够获得自然微生物群落的基因库。元基因组枪式测序已成为研究和分类各种环境中微生物的首选方法。为此,人们开发了多种方法来处理和分析从原始读数到最终产品(如预测的蛋白质序列或家族)的序列数据。在本文中,我们将对这些方法进行全面回顾,以简化处理过程,并讨论可供选择的方法,以便在蛋白质家族水平上探索生物多样性。我们提供了分析工具的详细信息,并对其可扩展性及其优缺点进行了评论。最后,我们报告了可用的数据存储库,并推荐了与系统发育分布、结构预测和元数据富集有关的蛋白质家族注释的各种方法。
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引用次数: 0
Before and after AlphaFold2: An overview of protein structure prediction. AlphaFold2 前后:蛋白质结构预测概述。
IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-02-28 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1120370
Letícia M F Bertoline, Angélica N Lima, Jose E Krieger, Samantha K Teixeira

Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

蛋白质的三维结构与其功能直接相关,确定蛋白质的三维结构对于理解生物过程、解决人类健康和生命科学问题至关重要。尽管随着时间的推移不断有新的蛋白质结构通过实验获得,但在 Uniprot 中的蛋白质序列数量与已解析三级结构的蛋白质序列数量之间仍存在很大差距。在这种情况下,出现了通过基于模板或自由建模的方法预测蛋白质结构的研究。在过去几年中,不同的方法被结合起来以克服各自的局限性,直到 AlphaFold2 的出现,它证明了以前所未有的规模高精度预测蛋白质结构是可能的。尽管 AlphaFold2 目前在该领域颇具影响力,但它也有局限性。最近,基于蛋白质语言模型的新方法有望彻底改变蛋白质结构生物学,使人们能够仅从蛋白质序列的进化模式中发现蛋白质的结构和功能。尽管这些方法达不到 AlphaFold2 的精确度,但它们已经克服了 AlphaFold2 的一些局限性,能够从元基因组数据库中高精度预测 2 亿多个蛋白质。在这篇小型综述中,我们将概述 AlphaFold2 出现前后蛋白质结构预测领域取得的突破。
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引用次数: 0
Enhancer/gene relationships: Need for more reliable genome-wide reference sets. 增强子/基因关系:需要更可靠的全基因组参考集。
IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-02-24 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1092853
Tristan Hoellinger, Camille Mestre, Hugues Aschard, Wilfried Le Goff, Sylvain Foissac, Thomas Faraut, Sarah Djebali

Differences in cells' functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC. Since 1) and 3) are costly, the current strategy is to develop functional link methods and to use data from 1) and 3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of enhancer-gene links, TargetFinder, Average-Rank, and the ABC model, using the three latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI, and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three methods performed best on the three references. CRiFF and CRISPRi reference sets are likely more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.

细胞功能的差异源于包括增强子在内的调控元件的不同活性。增强子是顺式调控元件,通过转录因子与启动子合作,在细胞核的三维空间中通过物理方式靠近启动子,激活一个或多个基因的表达。越来越多的证据表明,与常见疾病相关的基因变异富集在与这些疾病相关的细胞类型中活跃的增强子中。识别与基因相关的增强子,以及反过来说,在不同细胞类型中由每个增强子激活的基因集(即所谓的增强子/基因或 E/G 关系),有助于理解人类疾病的遗传机制。在全基因组范围内鉴定细胞类型中的 E/G 关系有三种广泛的方法:1)基因链接方法或 eQTL;2)基于一维功能数据(如开放染色质、组蛋白标记或基因表达)的功能链接方法;3)基于三维数据(如 HiC)的空间链接方法。由于 1) 和 3) 的成本较高,目前的策略是开发功能链接方法,并将 1) 和 3) 的数据作为评估这些方法的参考。然而,迄今为止,对于最佳的功能链接方法仍未达成共识,而且方法比较仍然很少。在这里,我们使用该领域的三个最新基准:一个结合了三维数据和 eQTL 数据的基准(称为 BENGI),以及两个遗传筛选基准(称为 CRiFF 和 CRiSPRi),比较了 TargetFinder、Average-Rank 和 ABC 模型这三种最新的增强子-基因链接识别方法的相对性能。总的来说,这三种方法在三个参考文献中的表现都不是最好的。CRiFF 和 CRISPRi 参考集可能更可靠,但 CRiFF 不是全基因组的,而 CRiFF 和 CRISPRi 大部分是 K562 癌细胞系的数据。BENGI 参考集是全基因组的,但可能包含许多假阳性。因此,这项研究需要新的可靠的全基因组 E/G 参考数据,而不是新的功能联系 E/G 鉴定方法。
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引用次数: 0
Virulence network of interacting domains of influenza a and mouse proteins. 甲型流感与小鼠蛋白相互作用结构域的病毒网络。
IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-02-17 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1123993
Teng Ann Ng, Shamima Rashid, Chee Keong Kwoh

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.

有几个数据库提供病毒-宿主蛋白相互作用的信息。虽然大多数数据库都提供了病毒-宿主蛋白相互作用对的整理记录,但缺乏有关毒株特异性毒力因子或相关蛋白结构域的信息。由于需要筛选大量文献(包括艾滋病毒和登革热等主要病毒的文献),一些数据库提供的流感病毒株信息并不完整。目前还没有一个数据库提供完整的、针对甲型流感病毒群的特定毒株蛋白质-蛋白质相互作用记录。在本文中,我们介绍了甲型流感病毒(IAV)与小鼠宿主蛋白质之间的全面的预测域-域相互作用(DDI)网络,通过考虑毒力信息(致死剂量),可以对疾病因素进行系统研究。根据先前发表的小鼠感染 IAV 致命剂量研究数据集,我们构建了一个以小鼠和病毒蛋白质结构域为节点、带有加权边的相互作用结构域网络。用域相互作用统计势能(DISPOT)对边缘进行评分,以显示推定的DDI。该毒力网络可通过网络浏览器轻松浏览,并显著显示相关毒力信息(LD50 值)。该网络通过提供菌株特异性毒力水平和相互作用的蛋白质结构域,将有助于甲型流感疾病模型的建立。它还可能有助于采用计算方法,揭示通过病毒和宿主蛋白之间的蛋白结构域相互作用介导的流感感染机制。可在 https://iav-ppi.onrender.com/home 上查阅。
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引用次数: 0
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