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Identification of Key Residues in Proteins Through Centrality Analysis and Flexibility Prediction with RINspector 利用RINspector进行中心性分析和柔韧性预测的蛋白质关键残基鉴定
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-11-29 DOI: 10.1002/cpbi.66
Guillaume Brysbaert, Théo Mauri, Jérôme de Ruyck, Marc F. Lensink

Protein structures inherently contain information that can be used to decipher their functions, but the exploitation of this knowledge is not trivial. We recently developed an app for the Cytoscape network visualization and analysis program, called RINspector, the goal of which is to integrate two different approaches that identify key residues in a protein structure or complex. The first approach consists of calculating centralities on a residue interaction network (RIN) generated from the three-dimensional structure; the second consists of predicting backbone flexibility and needs only the primary sequence. The identified residues are highly correlated with functional relevance and constitute a good set of targets for mutagenesis experiments. Here we present a protocol that details in a step-by-step fashion how to create a RIN from a structure and then calculate centralities and predict flexibilities. We also discuss how to understand and use the results of the analyses. © 2018 by John Wiley & Sons, Inc.

蛋白质结构本身包含着可以用来破译其功能的信息,但对这些知识的利用并非微不足道。我们最近为Cytoscape网络可视化和分析程序开发了一个应用程序,叫做RINspector,它的目标是整合两种不同的方法来识别蛋白质结构或复合体中的关键残基。第一种方法是计算由三维结构生成的残馀相互作用网络(RIN)的中心性;第二种方法是预测主干的灵活性,只需要主序列。所鉴定的残基具有高度的功能相关性,为诱变实验提供了良好的靶点。在这里,我们提出了一个协议,详细介绍了如何一步一步地从一个结构中创建一个RIN,然后计算中心性并预测灵活性。我们还讨论了如何理解和使用分析结果。©2018 by John Wiley &儿子,Inc。
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引用次数: 15
Using the sORFs.Org Database 使用sorf。组织数据库
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-11-28 DOI: 10.1002/cpbi.68
Volodimir Olexiouk, Gerben Menschaert

Ribosome profiling involves sequencing of approximately 30-base-long stretches of ribosome-protected mRNA. The technique enables genome-wide mapping of RNA undergoing active translation. Numerous small open reading frames have been identified by using ribosome profiling, leading researchers to question the assumed non-functional character of sORFs and to the identification of various important sORF translation products. sORFs.org (https://www.sorfs.org) is a public repository of small open reading frames identified by ribosome profiling in a database of over 3 million sORFs across 78 datasets from six species. sORFs.org is a multi-omics endeavor providing tools and metrics to assess the coding potential of the delineated sORFs. A pipeline is also in place to systematically rescan public mass spectrometry datasets to acquire new experimental evidence for sORF-encoded polypeptides. sORFs.org provides two distinct query interfaces, export functionality, and various visualization tools to enable inspection of the available information. © 2018 by John Wiley & Sons, Inc.

核糖体分析包括对大约30个碱基长的核糖体保护的mRNA进行测序。该技术能够对正在进行主动翻译的RNA进行全基因组定位。通过使用核糖体分析已经鉴定出许多小的开放阅读框,这使得研究人员质疑sORF的假定非功能特征,并鉴定出各种重要的sORF翻译产物。sORFs.org (https://www.sorfs.org)是一个小型开放阅读框的公共存储库,通过对来自6个物种的78个数据集的300多万个sorf的核糖体分析识别。sORFs.org是一个多组学的努力,提供工具和指标来评估所描述的sorf的编码潜力。系统地重新扫描公共质谱数据集,以获得sorf编码多肽的新实验证据。org提供了两个不同的查询接口、导出功能和各种可视化工具,以支持对可用信息的检查。©2018 by John Wiley &儿子,Inc。
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引用次数: 10
Predicting Genes in Single Genomes with AUGUSTUS 利用AUGUSTUS预测单基因组基因
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-11-22 DOI: 10.1002/cpbi.57
Katharina J. Hoff, Mario Stanke

AUGUSTUS is a tool for finding protein-coding genes and their exon-intron structure in genomic sequences. It does not necessarily require additional experimental input, as it can be applied in so-called ab initio mode. However, extrinsic evidence from various sources such as transcriptome sequencing or the annotations of closely related genomes can be integrated in order to improve the accuracy and completeness of the annotation. AUGUSTUS can be applied to single genomes, or simultaneously to several aligned genomes. Here, we describe steps required for training AUGUSTUS for the annotation of individual genomes and the steps to do the actual structural annotation. Further, we describe the generation and integration of evidence from various sources of extrinsic evidence. © 2018 by John Wiley & Sons, Inc.

AUGUSTUS是一种在基因组序列中寻找蛋白质编码基因及其外显子-内含子结构的工具。它不一定需要额外的实验输入,因为它可以应用于所谓的从头算模式。然而,可以整合来自各种来源的外部证据,如转录组测序或密切相关基因组的注释,以提高注释的准确性和完整性。AUGUSTUS可以应用于单个基因组,也可以同时应用于多个对齐的基因组。在这里,我们描述了训练AUGUSTUS进行个体基因组注释所需的步骤,以及进行实际结构注释的步骤。此外,我们还描述了来自各种外部证据来源的证据的产生和整合。©2018 by John Wiley &儿子,Inc。
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引用次数: 177
Issue Information TOC 问题信息TOC
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-11-21 DOI: 10.1002/cpbi.60
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引用次数: 0
Good Citizenship Made Easy: A Step-by-Step Guide to Submitting RNA-Seq Data to NCBI 良好的公民身份很容易:向NCBI提交RNA-Seq数据的分步指南
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-11-13 DOI: 10.1002/cpbi.67
Wiebke Feindt, Sara J. Oppenheim, Robert DeSalle, Shaadi Mehr

The analysis of transcriptome data from non-model organisms contributes to our understanding of diverse aspects of evolutionary biology, including developmental processes, speciation, adaptation, and extinction. Underlying this diversity is one shared feature, the generation of enormous amounts of sequence data. Data availability requirements in most journals oblige researchers to make their raw transcriptome data publicly available, and the databases housed at the National Center for Biotechnology Information (NCBI) are a popular choice for data deposition. Unfortunately, the successful submission of raw sequences to the Sequence Read Archive (SRA) and transcriptome assemblies to the Transcriptome Shotgun Assembly (TSA) can be challenging for novice users, significantly delaying data availability and publication. Here we present two comprehensive protocols for submitting RNA-Seq data to NCBI databases, accompanied by an easy-to-use website that facilitates the timely submission of data by researchers of any experience level. © 2018 by John Wiley & Sons, Inc.

对非模式生物转录组数据的分析有助于我们理解进化生物学的各个方面,包括发育过程、物种形成、适应和灭绝。这种多样性的基础是一个共同的特征,即产生大量的序列数据。大多数期刊的数据可用性要求迫使研究人员公开他们的原始转录组数据,而位于国家生物技术信息中心(NCBI)的数据库是数据存储的热门选择。不幸的是,成功地将原始序列提交到序列读取档案(SRA)和转录组组装到转录组霰弹枪组装(TSA)对于新手用户来说可能是具有挑战性的,这大大延迟了数据的可用性和发布。在这里,我们提出了两种将RNA-Seq数据提交到NCBI数据库的综合方案,并附有一个易于使用的网站,方便任何经验水平的研究人员及时提交数据。©2018 by John Wiley &儿子,Inc。
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引用次数: 1
Pre-Processing MALDI/TOF Mass Spectra by Using Geena 2 利用Geena 2对MALDI/TOF质谱进行预处理
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-11-13 DOI: 10.1002/cpbi.59
P. Romano, A. Profumo, A. Facchiano

Geena 2 is a tool for filtering, averaging, and aligning MALDI/TOF mass spectra, designed to assist scientists in the analysis of high volumes of data and support them for comparative studies. Three web interfaces are available with different levels of complexity. In this manuscript, we explain how to use Geena 2 with these three interfaces to perform analyses of one's own data. Two support protocols showing how to check the example input file and how to create an input file with own data are also presented. © 2018 by John Wiley & Sons, Inc.

Geena 2是一个过滤、平均和对准MALDI/TOF质谱的工具,旨在帮助科学家分析大量数据并支持他们进行比较研究。有三种不同复杂程度的web界面可用。在本文中,我们将解释如何使用带有这三个接口的Geena 2对自己的数据执行分析。还介绍了两个支持协议,说明如何检查示例输入文件以及如何使用自己的数据创建输入文件。©2018 by John Wiley &儿子,Inc。
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引用次数: 1
Investigation of RNA-RNA Interactions Using the RISE Database 利用RISE数据库研究RNA-RNA相互作用
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-11-08 DOI: 10.1002/cpbi.58
Yanyan Ju, Jing Gong, Yucheng T. Yang, Qiangfeng Cliff Zhang

RNA-RNA interactions (RRIs) are essential to understanding the regulatory mechanisms of RNAs. Mapping RRIs in vivo in a transcriptome-wide manner remained challenging until the recent development of several sequencing-based technologies. However, RRIs generated from large-scale studies had not been systematically collected and analyzed before. This article introduces RISE, a database of the RNA Interactome from Sequencing Experiments. RISE provides a comprehensive collection of RRIs in human, mouse, and yeast, derived from transcriptome-wide sequencing experiments, as well as targeted sequencing studies and other public databases/datasets. To facilitate better understanding of the biological roles of these RRIs, RISE also offers rich functional annotations involving RNAs, and an interactive interface to explore the analysis results. Here, we provide a brief description of the RISE website and a step-by-step protocol for using RISE to study RRIs. © 2018 by John Wiley & Sons, Inc.

RNA-RNA相互作用(RRIs)对于理解rna的调控机制至关重要。在最近几种基于测序的技术发展之前,以转录组范围的方式在体内绘制RRIs仍然具有挑战性。然而,大规模研究产生的RRIs,在此之前并没有被系统地收集和分析。本文介绍了基于测序实验的RNA相互作用组数据库RISE。RISE提供了人类,小鼠和酵母中RRIs的全面收集,这些RRIs来自转录组测序实验,以及靶向测序研究和其他公共数据库/数据集。为了更好地理解这些RRIs的生物学作用,RISE还提供了丰富的涉及rna的功能注释,以及一个交互式界面来探索分析结果。在这里,我们提供了RISE网站的简要描述和使用RISE研究RRIs的分步协议。©2018 by John Wiley &儿子,Inc。
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引用次数: 0
Using geneid to Identify Genes 利用基因识别基因
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-10-17 DOI: 10.1002/cpbi.56
Tyler Alioto, Enrique Blanco, Genís Parra, Roderic Guigó

This unit describes the usage of geneid, an efficient gene-finding program that allows for the analysis of large genomic sequences, including whole mammalian chromosomes. These sequences can be partially annotated, and geneid can be used to refine this initial annotation. Training geneid is relatively easy, and parameter configurations exist for a number of eukaryotic species. geneid produces output in a variety of standard formats. The results, thus, can be processed by a variety of software tools, including visualization programs. geneid software is in the public domain, and is undergoing constant development. It is easy to install and use. Exhaustive benchmark evaluations show that geneid compares favorably with other existing gene-finding tools. © 2018 by John Wiley & Sons, Inc.

本单元介绍了geneid的用法,这是一种高效的基因发现程序,可以分析大型基因组序列,包括整个哺乳动物染色体。可以对这些序列进行部分注释,并且可以使用geneid来改进这个初始注释。训练基因是相对容易的,参数配置存在于许多真核生物物种。Geneid产生各种标准格式的输出。因此,结果可以通过各种软件工具进行处理,包括可视化程序。Geneid软件是在公共领域,并正在不断发展。它易于安装和使用。详尽的基准评估表明,geneid与其他现有的基因发现工具相比具有优势。©2018 by John Wiley &儿子,Inc。
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引用次数: 53
Improved RNA-seq Workflows Using CyVerse Cyberinfrastructure 使用CyVerse网络基础设施改进RNA-seq工作流程
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-08-31 DOI: 10.1002/cpbi.53
Kapeel M. Chougule, Liya Wang, Joshua C. Stein, Xiaofei Wang, Upendra Kumar Devisetty, Robert R. Klein, Doreen Ware

RNA-seq is a vital method for understanding gene structure and expression patterns. Typical RNA-seq analysis protocols use sequencing reads of length 50 to 150 nucleotides for alignment to the reference genome and assembly of transcripts. The resultant transcripts are quantified and used for differential expression and visualization. Existing tools and protocols for RNA-seq are vast and diverse; given their differences in performance, it is critical to select an analysis protocol that is scalable, accurate, and easy to use. Tuxedo, a popular alignment-based protocol for RNA-seq analysis, has been updated with HISAT2, StringTie, StringTie-merge, and Ballgown, and the updated protocol outperforms its predecessor. Similarly, new pseudo-alignment-based protocols like Kallisto and Sleuth reduce runtime and improve performance. However, these tools are challenging for researchers lacking command-line experience. Here, we describe two new RNA-seq analysis protocols, in which all tools are deployed on CyVerse Cyberinfrastructure with user-friendly graphical user interfaces, and validate their performance using plant RNA-seq data. © 2018 by John Wiley & Sons, Inc.

RNA-seq是了解基因结构和表达模式的重要方法。典型的RNA-seq分析方案使用长度为50至150个核苷酸的测序reads与参考基因组对齐并组装转录本。结果转录本被量化并用于差异表达和可视化。现有的RNA-seq工具和协议种类繁多;考虑到它们在性能上的差异,选择一个可扩展、准确且易于使用的分析协议是至关重要的。Tuxedo是一种流行的基于比对的RNA-seq分析协议,它已经更新了HISAT2、StringTie、StringTie-merge和Ballgown,更新后的协议优于其前身。类似地,新的基于伪对齐的协议(如Kallisto和Sleuth)减少了运行时间并提高了性能。然而,这些工具对于缺乏命令行经验的研究人员来说是具有挑战性的。在这里,我们描述了两种新的RNA-seq分析协议,其中所有工具都部署在CyVerse网络基础设施上,具有用户友好的图形用户界面,并使用植物RNA-seq数据验证其性能。©2018 by John Wiley &儿子,Inc。
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引用次数: 3
Installing, Maintaining, and Using a Local Copy of BLAST for Compute Cluster or Workstation Use 为计算集群或工作站安装、维护和使用BLAST的本地副本
Q1 Biochemistry, Genetics and Molecular Biology Pub Date : 2018-08-31 DOI: 10.1002/cpbi.54
Istvan Ladunga

The Basic Local Alignment Search Tool (BLAST) is the first resource to computationally characterize a novel amino acid or nucleic acid sequence. BLAST plays important roles in genomics, transcriptomics, and protein science. For numerous academic and commercial researchers, neither BLAST Web servers nor cloud resources satisfy the requirements of high-throughput comparative genomic pipelines or company policies. For such users, this unit describes how to install BLAST locally, either on a standalone workstation, or preferably on a compute cluster. We provide practical guidance for the planning and the installation under the LINUX, Windows, and Mac OS X operating systems. We propose strategies for downloading existing and generating new sequence databases in BLAST format. © 2018 by John Wiley & Sons, Inc.

基本局部比对搜索工具(BLAST)是计算表征一个新的氨基酸或核酸序列的第一个资源。BLAST在基因组学、转录组学和蛋白质科学中发挥着重要作用。对于许多学术和商业研究人员来说,BLAST Web服务器和云资源都不能满足高通量比较基因组管道或公司政策的要求。对于这样的用户,本单元描述了如何在本地安装BLAST,或者在独立工作站上,或者最好在计算集群上。我们为LINUX、Windows和Mac OS X操作系统下的规划和安装提供了实用指导。我们提出了以BLAST格式下载现有序列数据库和生成新序列数据库的策略。©2018 by John Wiley &儿子,Inc。
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引用次数: 2
期刊
Current protocols in bioinformatics
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