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Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-06-19 DOI: 10.1002/cpbi.62

Cover: In Stansfield et al. (https://doi.org/10.1002/cpbi.76) image shows composite MD plot with significant differentially interacting regions highlighted. Highlighted points display where the differential interactions are occurring in relation to unit genomic distance on the x axis and log2 fold change on the y axis. Points highlighted in yellow are moderately significant, while points highlighted in red are highly significant.

封面:在Stansfield等人(https://doi.org/10.1002/cpbi.76)的图像中,复合MD图突出显示了显著差异相互作用的区域。突出显示的点显示了与x轴上的单位基因组距离和y轴上的log2倍变化有关的差异相互作用。黄色突出显示的点是中等重要的,而红色突出显示的点是非常重要的。
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引用次数: 0
R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets R教程:从多个Hi-C数据集检测差异相互作用的染色质区域
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-05-24 DOI: 10.1002/cpbi.76
John C. Stansfield, Duc Tran, Tin Nguyen, Mikhail G. Dozmorov

The three-dimensional (3D) interactions of chromatin regulate cell-type-specific gene expression, recombination, X-chromosome inactivation, and many other genomic processes. High-throughput chromatin conformation capture (Hi-C) technologies capture the structure of the chromatin on a global scale by measuring all-vs.-all interactions and can provide new insights into genomic regulation. The workflow presented here describes how to analyze and interpret a comparative Hi-C experiment. We describe the process of obtaining Hi-C data from public repositories and give suggestions for pre-processing pipelines for users who intend to analyze their own raw data. We then describe the data normalization and comparative analysis process. We present three protocols describing the use of the multiHiCcompare, diffHic, and FIND R packages, respectively, to perform a comparative analysis of Hi-C experiments. Finally, visualization of the results and downstream interpretation of the differentially interacting regions are discussed. The bulk of this tutorial uses the R programming environment, and the processes described can be performed with most operating systems and a single computer. © 2019 by John Wiley & Sons, Inc.

染色质的三维(3D)相互作用调节细胞类型特异性基因表达、重组、x染色体失活和许多其他基因组过程。高通量染色质构象捕获(Hi-C)技术通过测量all-vs在全球范围内捕获染色质的结构。-所有的相互作用,可以为基因组调控提供新的见解。这里介绍的工作流程描述了如何分析和解释一个比较的Hi-C实验。我们描述了从公共存储库获取Hi-C数据的过程,并为打算分析自己的原始数据的用户提供预处理管道的建议。然后描述了数据归一化和比较分析过程。我们提出了三个协议,分别描述了使用multiHiCcompare、diffic和FIND R包来执行Hi-C实验的比较分析。最后,讨论了结果的可视化和差异相互作用区域的下游解释。本教程的大部分内容使用R编程环境,所描述的过程可以在大多数操作系统和一台计算机上执行。©2019 by John Wiley &儿子,Inc。
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引用次数: 5
Using PconsC4 and PconsFold2 to Predict Protein Structure 利用PconsC4和pconfold2预测蛋白质结构
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-05-07 DOI: 10.1002/cpbi.75
Claudio Bassot, David Menendez Hurtado, Arne Elofsson

In spite of the fact that there has been a significant increase in the number of solved protein structures, structural information is missing for many proteins. Although structural information is codified in the amino acid sequence, computational prediction using only this information is still an unsolved problem. However, one successful method to model a protein's structure starting from the primary sequence is to use contact prediction derived from multiple sequence alignment (MSA). Here we use our contact predictor PconsC4 to generate a list of probable contacts between residues in the primary sequences. These contacts are then used together with the secondary structure prediction as constraints for the CONFOLD folding method. In this way, a 3D protein model can be built starting directly from the primary sequence. © 2019 by John Wiley & Sons, Inc.

尽管已经解决的蛋白质结构数量显著增加,但许多蛋白质的结构信息仍然缺失。虽然结构信息在氨基酸序列中被编码,但仅使用这些信息进行计算预测仍然是一个未解决的问题。然而,一种从初级序列开始模拟蛋白质结构的成功方法是使用来自多序列比对(MSA)的接触预测。在这里,我们使用我们的接触预测器PconsC4来生成初级序列中残基之间可能接触的列表。然后将这些接触与二级结构预测一起用作conold折叠方法的约束。通过这种方式,可以直接从初级序列开始构建三维蛋白质模型。©2019 by John Wiley &儿子,Inc。
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引用次数: 7
Using EMBL-EBI Services via Web Interface and Programmatically via Web Services 通过Web接口和通过Web服务编程使用EMBL-EBI服务
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-04-30 DOI: 10.1002/cpbi.74
Fábio Madeira, Nandana Madhusoodanan, Joon Lee, Adrian R.N. Tivey, Rodrigo Lopez

The European Bioinformatics Institute (EMBL-EBI) provides access to a wide range of core databases and analysis tools that are of key importance in bioinformatics. As well as providing web interfaces to these resources, web services are available using REST and SOAP protocols that enable programmatic access and allow their integration into other applications and analytical workflows and pipelines. This article describes the various options available to researchers and bioinformaticians who would like to use our resources via the web interface employing RESTful web service clients provided in Perl, Python, and Java, or would like to use Docker containers to integrate the resources into analysis pipelines and workflows. © 2019 by John Wiley & Sons, Inc.

欧洲生物信息学研究所(EMBL-EBI)提供对生物信息学中至关重要的广泛核心数据库和分析工具的访问。除了为这些资源提供web接口外,web服务还可以使用REST和SOAP协议来实现编程访问,并允许将它们集成到其他应用程序和分析工作流和管道中。本文描述了研究人员和生物信息学家的各种选择,他们希望通过使用Perl、Python和Java提供的RESTful web服务客户端通过web界面使用我们的资源,或者希望使用Docker容器将资源集成到分析管道和工作流中。©2019 by John Wiley &儿子,Inc。
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引用次数: 22
Issue Information TOC 发布信息TOC
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-02-25 DOI: 10.1002/cpbi.61
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引用次数: 0
Integrating Bacterial ChIP-seq and RNA-seq Data With SnakeChunks 利用SnakeChunks整合细菌ChIP-seq和RNA-seq数据
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-02-20 DOI: 10.1002/cpbi.72
Claire Rioualen, Lucie Charbonnier-Khamvongsa, Julio Collado-Vides, Jacques van Helden

Next-generation sequencing (NGS) is becoming a routine approach in most domains of the life sciences. To ensure reproducibility of results, there is a crucial need to improve the automation of NGS data processing and enable forthcoming studies relying on big datasets. Although user-friendly interfaces now exist, there remains a strong need for accessible solutions that allow experimental biologists to analyze and explore their results in an autonomous and flexible way. The protocols here describe a modular system that enable a user to compose and fine-tune workflows based on SnakeChunks, a library of rules for the Snakemake workflow engine. They are illustrated using a study combining ChIP-seq and RNA-seq to identify target genes of the global transcription factor FNR in Escherichia coli, which has the advantage that results can be compared with the most up-to-date collection of existing knowledge about transcriptional regulation in this model organism, extracted from the RegulonDB database. © 2019 by John Wiley & Sons, Inc.

新一代测序(NGS)正在成为生命科学大多数领域的常规方法。为了确保结果的可重复性,迫切需要提高NGS数据处理的自动化程度,并使未来的研究依赖于大数据集。虽然用户友好的界面现在已经存在,但仍然迫切需要可访问的解决方案,使实验生物学家能够以自主和灵活的方式分析和探索他们的结果。这里的协议描述了一个模块化系统,使用户能够基于SnakeChunks (Snakemake工作流引擎的规则库)编写和微调工作流。他们使用一项结合ChIP-seq和RNA-seq的研究来鉴定大肠杆菌中全局转录因子FNR的靶基因,其优点是结果可以与从RegulonDB数据库中提取的关于这种模式生物中转录调控的最新现有知识集合进行比较。©2019 by John Wiley &儿子,Inc。
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引用次数: 2
Utilizing iVariantGuide for Variant Assessment of Next-Generation Sequencing 利用iVariantGuide进行下一代测序的变异评估
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-02-12 DOI: 10.1002/cpbi.73
Sophia R. Chaudhry, Michael A. Tainsky

Molecular genetic testing provides the capability for personalized prediction, diagnosis, and pharmacological treatments of disease and disorders. Variant assessment of next-generation sequencing (NGS) is a crucial component of genetic testing for clinicians to counsel patients on risk and management. The iVariantGuide application is a dynamic Web-based application made for the tertiary analysis of NGS. Along with variant assessment, iVariantGuide provides a unique interactive pathway impact analysis of genetic variants, as well as a unique Gene Ontology (GO) analysis. Here we provide a step-by-step guide on how to utilize iVariantGuide, employing a publicly available NGS dataset consisting of a cohort of germline DNAs from high-risk serous ovarian cancer (OVCA) patients. The application will be used to exhibit the ease in filtering down to a set of compelling novel variants and their impact on biological pathways and GO terms. © 2019 by John Wiley & Sons, Inc.

分子基因检测为疾病和失调的个性化预测、诊断和药理学治疗提供了能力。下一代测序(NGS)的变异评估是临床医生就风险和管理向患者提供咨询的基因检测的重要组成部分。iVariantGuide应用程序是一个动态的基于web的应用程序,用于NGS的三级分析。除了变体评估,iVariantGuide还提供了独特的遗传变异交互途径影响分析,以及独特的基因本体(GO)分析。在这里,我们提供了一个关于如何使用iVariantGuide的逐步指南,采用了一个公开可用的NGS数据集,包括来自高风险浆液性卵巢癌(OVCA)患者的种系dna队列。该应用程序将用于展示过滤到一组引人注目的新变体及其对生物途径和氧化石墨烯术语的影响的便利性。©2019 by John Wiley &儿子,Inc。
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引用次数: 2
Visualizing Post-Translational Modifications in Protein Interaction Networks Using PTMOracle 利用PTMOracle可视化蛋白质相互作用网络中的翻译后修饰
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2019-01-17 DOI: 10.1002/cpbi.71
Aidan P. Tay, Angelita Liang, Marc R. Wilkins, Chi Nam Ignatius Pang

Post-translational modifications (PTMs) of proteins act as key regulators of protein activity, including the regulation of protein-protein interactions (PPIs). However, exploring functional links between PTMs and PPIs can be difficult. PTMOracle is a Cytoscape app that facilitates the co-visualization and co-analysis of PTMs in the context of PPI networks. PTMOracle also allows extensive data to be integrated and co-analyzed, allowing the role of domains, motifs, and disordered regions to be considered. Here, we describe several PTMOracle protocols investigating complex PTM-associated relationships and their role in PPIs. This is assisted by OraclePainter for coloring proteins by the modifications present and visualizing these in the context of networks, by OracleTools for cross-matching PTMs with sequence feature for all nodes in the network, and by OracleResults for exploring specific proteins and visualizing their PTMs in the context of protein sequences. This unit aims to demonstrate how PTMOracle can be used to systematically explore network visualizations and generate testable hypotheses regarding the functional role of PTMs in PPIs, and how the results can be analyzed to better understand the regulatory role of PTMs in PPIs. © 2019 by John Wiley & Sons, Inc.

蛋白质的翻译后修饰(PTMs)是蛋白质活性的关键调节因子,包括蛋白质-蛋白质相互作用(PPIs)的调节。然而,探索ptm和ppi之间的功能联系可能很困难。PTMOracle是一个Cytoscape应用程序,可以在PPI网络的背景下促进ptm的共同可视化和共同分析。PTMOracle还允许对大量数据进行集成和共同分析,允许考虑域、基序和无序区域的作用。在这里,我们描述了几个PTMOracle协议,研究复杂的ptm相关关系及其在ppi中的作用。通过OraclePainter, OraclePainter可以在网络环境中通过修改为蛋白质着色并将其可视化;通过OracleTools, OracleTools可以将网络中所有节点的ptm与序列特征交叉匹配;通过OracleResults, OraclePainter可以探索特定的蛋白质,并在蛋白质序列的环境中可视化它们的ptm。本单元旨在演示如何使用PTMOracle系统地探索网络可视化,并生成关于PTMs在ppi中的功能作用的可测试假设,以及如何分析结果以更好地理解PTMs在ppi中的调节作用。©2019 by John Wiley &儿子,Inc。
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引用次数: 2
Discovering Transcriptional Regulatory Elements From Run-On and Sequencing Data Using the Web-Based dREG Gateway 使用基于web的dREG网关从运行和测序数据中发现转录调控元件
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-12-27 DOI: 10.1002/cpbi.70
Tinyi Chu, Zhong Wang, Shao-Pei Chou, Charles G. Danko

Transcription is a chromatin mark that can be used effectively to identify the location of active enhancers and promoters, collectively known as transcriptional regulatory elements (TREs). We recently introduced dREG, a tool for the identification of TREs using run-on and sequencing (RO-seq) assays, including global run-on and sequencing (GRO-seq), precision run-on and sequencing (PRO-seq), and chromatin run-on and sequencing (ChRO-seq). In this protocol, we present step-by-step instructions for running dREG on an arbitrary run-on and sequencing dataset. Users provide dREG with bigWig files (in which each read is represented by a single base) representing the location of RNA polymerase in a cell or tissue sample of interest, and dREG returns a list of genomic regions that are predicted to be active TREs. Finally, we demonstrate the use of dREG regions in discovering transcription factors controlling response to a stimulus and predicting their target genes. Together, this protocol provides detailed instructions for running dREG on arbitrary run-on and sequencing data. © 2018 by John Wiley & Sons, Inc.

转录是一种染色质标记,可以有效地用于识别活性增强子和启动子的位置,这些增强子和启动子统称为转录调控元件(transcriptional regulatory elements, TREs)。我们最近介绍了dREG,这是一种使用运行和测序(RO-seq)检测来鉴定TREs的工具,包括全局运行和测序(GRO-seq),精确运行和测序(PRO-seq)和染色质运行和测序(cr -seq)。在本协议中,我们提供了在任意运行和排序数据集上运行dREG的分步说明。用户向dREG提供bigWig文件(其中每个读取都由单个碱基表示),表示感兴趣的细胞或组织样本中RNA聚合酶的位置,dREG返回预测为活跃TREs的基因组区域列表。最后,我们展示了dREG区域在发现控制刺激反应的转录因子和预测其靶基因中的应用。总之,该协议提供了在任意运行和测序数据上运行dREG的详细说明。©2018 by John Wiley &儿子,Inc。
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引用次数: 22
Using OmicsNet for Network Integration and 3D Visualization 使用OmicsNet进行网络集成和三维可视化
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-12-17 DOI: 10.1002/cpbi.69
Guangyan Zhou, Jianguo Xia

OmicsNet is a novel web-based tool for creating and visualizing complex biological networks in 3D space. By coupling a comprehensive knowledgebase with the powerful WebGL technology, OmicsNet allows researchers to intuitively explore molecular interactions and regulatory relationships among genes, transcription factors, microRNAs, and metabolites. OmicsNet fills an important gap by facilitating multi-omics integration and systems biology. This article contains three basic protocols covering the key features of OmicsNet, including how to create biological networks from a single or multiple list(s) of molecules, how to integrate or enrich different types of networks, and how to navigate the 3D visualization system to obtain biological insights. The OmicsNet web server is freely available at https://www.omicsnet.ca. © 2018 by John Wiley & Sons, Inc.

OmicsNet是一个新颖的基于网络的工具,用于在3D空间中创建和可视化复杂的生物网络。通过将全面的知识库与强大的WebGL技术相结合,OmicsNet允许研究人员直观地探索基因、转录因子、microrna和代谢物之间的分子相互作用和调节关系。OmicsNet通过促进多组学集成和系统生物学填补了一个重要的空白。本文包含三个基本协议,涵盖OmicsNet的关键特性,包括如何从单个或多个分子列表创建生物网络,如何集成或丰富不同类型的网络,以及如何导航3D可视化系统以获得生物学见解。OmicsNet web服务器可在https://www.omicsnet.ca免费获得。©2018 by John Wiley &儿子,Inc。
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引用次数: 39
期刊
Current protocols in bioinformatics
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