我们感谢 Meffert 实验室的成员进行有益的讨论，包括 BH Powell 和 WT Mills IV，感谢他们对描述管道安装和实施的重要反馈。这项工作得到了布劳德基金会奖、马里兰州干细胞研究基金启动计划、布劳斯坦疼痛研究与教育基金会以及 NINDS RO1NS103974 和 NIMH 对 MKM RO1MH129292的支持。

注:该协议将从下载和安装使用 SCRAP 分析嵌合 RNA 测序文库所需的软件开始。
1. 安装
<ol><li>在安装 SCRAP 之前，请在要用于分析的计算机上安装依赖项 Git 和 Miniconda。Git 可能已安装。例如，在 Mac OSX 平台上，使用哪个 git 验证这一点，以查看" git "实用程序是否存在并安装在此目录中。检查 Miniconda 是否使用 哪个 conda 安装。如果未返回任何内容，请安装 Miniconda。Miniconda 需要 400 MB 的磁盘空间才能安装。<ol><li>有几种方法可以安装 Miniconda，它们因平台而异。请参阅 Meffert Lab GitHub 存储库 [https://github.com/Meffert-Lab/SCRAP/blob/main/PLATFORM-SETUP.md] 上的 PLATFORM-SETUP markdown 文件，其中包含有关在 Windows、MacOS 和 Ubuntu 上安装的更多说明。对于 Linux 用户，Linux 有自己的默认包管理器 （apt）。在本研究的特定情况下，使用命令 brew install Miniconda 使用现有包管理器 brew 安装 Miniconda 。 注意:"Homebrew"，称为"brew"，是一种开源软件包管理系统，可简化 Apple 操作系统 macOS 上的软件安装。</li>
		<li>如果是首次安装 conda，请为正在使用的特定 shell 运行 conda init 。在此示例中，正在使用的 shell 是 zsh。然后，关闭并重新打开外壳。如果 conda 安装成功，将看到在终端会话中激活 的基本 环境。</li>
	</ol></li>
	<li>下载 SCRAP 源代码并安装其依赖项。<ol><li>获取 SCRAP 源代码的首选方法是使用 Git。通过运行 git clone https://github.com/Meffert-Lab/SCRAP 来访问它以获取源代码的最新副本。</li>
		<li>安装 mamba（一个改进的 conda 包求解器），并使用以下命令将 SCRAP 的所有依赖项从 SCRAP_environment.yml 安装到它自己的 conda 环境中: conda 安装 -n 基础 conda-forge::mamba mamba env create -f SCRAP/SCRAP_environment.yml -n SCRAP</li>
	</ol></li>
	<li>接下来，运行 SCRAP 的参考安装。参考装置中使用的参数将特定于正在分析其 sncRNA-mRNA 相互作用的生物体。 bash SCRAP/bin/Reference_Installation.sh -r full/path/to/SCRAP/ -m 有 -g hg38 -s 人类
	<ol><li>提供 SCRAP 源文件夹的目录以供参考安装。然后，将使用 fasta 和 annotation 文件夹中的文件执行安装步骤。列出不带任何速记的完整路径。以斜杠结尾。</li>
		<li>请参阅 README.md 中的表格，了解正确的 miRbase 物种缩写。最新的参考基因组可以在 https://genome.ucsc.edu/ 或 https://www.ncbi.nlm.nih.gov/data-hub/genome/ 找到。在此示例中，hg38 将用于小鼠 GRCm38 基因组。</li>
		<li>目前纳入注释的物种是 人类、小鼠和 蠕虫。查看 SCRAP 源文件夹中 annotation 目录下对应的 species.annotation.bed 文件。 如果需要使用不同的物种进行分析，请提供遵循相同命名方案 species.annotation.bed 的 annotation.bed 文件。</li>
	</ol></li>
</ol>2. 运行 SCRAP
<ol><li>现在，依赖项和 SCRAP 已安装，请运行脚本 SCRAP.sh bash SCRAP/bin/SCRAP.sh -d full/path/to/CLASH_Human/ -a full/path/to/CLASH_Human/CLASH_Human_Adapters.txt -p no -f yes -r full/path/to/SCRAP/ -m has -g hg38
	<ol><li>列出示例目录的整个路径，不带任何速记。使用与示例名称完全匹配的文件夹名称来格式化示例目录，如 图 1 所示。</li>
		<li>请注意，列出的路径是包含所有示例文件夹的目录的路径，而不是任何单个示例文件夹或示例文件的路径（请参阅步骤 2.1 中的命令行）。</li>
		<li>接下来，列出适配器文件的整个路径。确保适配器文件中的示例名称与前面提到的文件夹名称和文件名匹配（请参阅步骤 2.1 中的命令行）。</li>
		<li>指示样品是否为配对末端，以及是否将进行前 miRNA 和/或 tRNA 的过滤。如果需要，添加用于 rRNA 清洁的过滤器（请参阅步骤 2.1 中的命令行）。 注意:用户可能会也可能不会决定使用这些过滤器，具体取决于样品类型和实验目标。根据实验设计，pre-miRNA、tRNA 和 rRNA 可以消耗真实 sncRNA:靶标 RNA 嵌合体的可用测序深度，用户可以使用过滤器来排除它们。然而，在某些情况下，用户可能希望避免这种过滤（例如，将sncRNA靶标映射到包含线粒体rRNA的线粒体基因组）。</li>
		<li>接下来，列出参考目录的整个路径、miRbase 缩写和参考基因组缩写（请参阅步骤 2.1 中的命令行）。 注意:该脚本可能需要几个小时才能完成，具体取决于数据集大小和所用计算机的 CPU。</li>
	</ol></li>
</ol>3. 峰值调用和注释
<ol><li>完成 SCRAP 运行后，检查输出是否包括 SAMPLE.aligned.unique.bam 文件等文件。这是一个二进制文件，包含靶RNA与用户提供的参考基因组的比对。</li>
	<li>现在通过运行 Peak_Calling.sh来执行峰值呼叫。 bash SCRAP/bin/Peak_Calling.sh -d CLASH_Human/ -a CLASH_Human/CLASH_Human_Adapters.txt -c 3 -l 2 -f no -r SCRAP/ -m has -g hg38 注意:峰值检出是 SCRAP 的一项功能，旨在使研究人员能够轻松评估其嵌合 RNA 文库中最稳健且可重复的小非编码 RNA:靶 RNA 相互作用。例如，此功能可以帮助研究人员识别他们可能想要选择以进行进一步研究的相互作用。下面的步骤3.2.2描述了用户如何设置他们希望用于定义峰调用严格性的标准 - 这包括唯一相互作用或测序读数的数量，这些相互作用必须发生才能被调用峰，以及必须发生这种特定相互作用的文库数量。<ol><li>同样，列出包含示例文件夹和适配器文件的目录的完整路径（请参阅步骤 3.2 中的命令行）。</li>
		<li>接下来，设置要调用峰所需的最小测序读数（请参阅步骤3.2中的命令行）。</li>
		<li>设置必须包含峰才能调用的不同测序文库的最小数量（请参阅步骤 3.2 中的命令行）。 注:3.2.2 和 3.2.3 的值选择将取决于测序样本的性质以及样本数量或样本类型。在这里，一个样本中至少需要 3 个嵌合测序读长才能调用峰，并且该峰必须得到至少 2 个样本的支持。例如，在评估数据集时，如果某个数据集中有许多针对给定条件的测序文库重复，则研究人员可能会决定要求在更多的样本测序文库中存在读段。</li>
		<li>指示同一家族的 sncRNA 是否必须对同一峰做出贡献。例如，由于同一家族的miRNA共享种子序列，因此这些miRNA可以结合共享和重叠的基因靶标集;用户可能希望通过评估这些目标的集体峰值来确定一个系列对这些目标的全面影响（请参阅步骤 3.2 中的命令行）。</li>
		<li>接下来，指示参考目录的完整路径、miRBase 缩写和参考基因组缩写（请参阅步骤 3.2 中的命令行）。</li>
	</ol></li>
	<li>峰值调用完成后，运行峰值注释。 bash SCRAP/bin/Peak_Annotation.sh -p CLASH_Human/peaks.bed -r SCRAP/ -s 人类
	<ol><li>列出从峰值调用中生成的 peaks.bed（或 peaks.family.bed ）文件的完整路径、参考目录的完整路径以及注释所需的物种。</li>
	</ol></li>
</ol>4. 可视化数据
注意:使用 SCRAP 进行分析的所有步骤现已完成。为了可视化数据，建议采用以下几种方法:
<ol><li>将所有需要可视化的 .bam（二进制 SAM 文件）文件合并在一起 （samtools 合并）。</li>
	<li>对生成的合并的 .bam 文件进行排序（samtools 排序）。文件内容逐行排序，以便 samtools 可以编制索引。</li>
	<li>为排序的 .bam 文件（samtools 索引）编制索引。生成 BAI（二进制 samtools 格式索引）文件，以允许在综合基因组学查看器 （IGV） 中进行可视化。</li>
	<li>最后，在 IGV 中打开生成的排序的 .bam 和索引的 .bai 文件。 注意:SncRNA:目标RNA相互作用可以通过多种研究特定方式优先进行随访。一种通用的初始方法是评估最嵌合测序读长支持峰的相互作用。通过从检测到的相互作用11 输入 sncRNA 和靶 RNA 的序列，也可以使用 RNAstructure 包中的 DuplexFold Web Server 来可视化感兴趣的相互作用。对于每个峰，染色体（第一列）和基因组坐标（开始:第一列结束:第二列）可以在峰注释中生成的peaks.bed.species.annotation.txt文件中找到。特别是对于miRNA，虽然可重复和功能性相互作用可能缺乏广泛的种子匹配结合（例如，相互作用可能使用3'补偿性结合），但靶RNA的同源结合基序中种子匹配位点的存在仍然可以被评估为功能上重要的检测到的相互作用的验证特征4,12.辅助数据处理可能包括比较不同生物学条件下峰之间的差异读取覆盖率，以及使用通路分析工具评估受调控基因聚类到通路中的可能性。</li>
</ol>

体内 RNA:使用 SCRAP 生物信息学管道的 RNA 相互作用

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作者没有什么可透露的。

本关于使用 SCRAP 管道分析 sncRNA:靶标 RNA 相互作用的协议旨在帮助正在进入计算分析的研究人员。本教程的完成预计将指导具有入门级或更高计算经验的研究人员完成安装和使用该管道及其应用以分析从嵌合 RNA 测序文库获得的数据所需的步骤。完成此协议的关键步骤包括正确的参考安装和运行 SCRAP，这可能非常耗时，并且可能是错误的根源，尤其是在使用 Anaconda 安装依赖项或键入命令行参数时?...

小的非编码 RNA 因其在不同过程中协调基因套件表达（如分化和发育、信号处理和疾病）中的转录后作用而受到高度研究 1,2,3。准确测定基因调控小非编码 RNA （sncRNA） 的靶转录本（包括 microRNA （miRNA）的能力对于基础和翻译水平的 RNA 生物学研究都很重要。利用miRNA种子序列与其潜在靶标之间预期互补性的生物信息学算法已经常用于预测miRNA:靶标RNA相互作用。虽然这些生物信息学算法已经成功，但它们也可能同时存在假阳性和假阴性结果，正如在其他地方所回顾的那样 4,5,6。最近，已经设计并实施了几种生化方法，这些方法允许通过体内交联和随后的连接步骤掺入将 sncRNA 物理连接到其靶标以形成单个嵌合 RNA 4,5,7,8,9,10 .随后从嵌合RNA制备测序文库，可以通过对测序数据的计算处理来评估sncRNA:靶RNA的相互作用。本视频提供了安装和使用称为小型嵌合 RNA 分析流程 （SCRAP） 的计算管道的教程，该流程旨在对嵌合 RNA 测序文库6 中的 sncRNA:靶标 RNA 相互作用进行稳健且可重复的分析。
本教程的目标是通过降低分析通过提供 sncRNA:靶 RNA 相互作用的嵌合分子读数的生化方法生成的数据的障碍，帮助研究人员避免过度依赖纯粹的预测性生物信息学算法。本教程提供了实用步骤和技巧，以指导入门级计算科学家使用管道 SCRAP，该管道是为分析嵌合 RNA 测序数据而开发的，这些数据可以由几种现有的生化方案生成，包括交联、连接和杂交种测序 （CLASH） 和内源性 Argonaute 结合 RNA 的共价连接 - 交联和免疫沉淀 （CLEAR-CLIP）7,9。
与其他计算管道相比，使用 SCRAP 为嵌合 RNA 测序数据的分析提供了几个优势6.一个突出的优点是，与通常依赖自定义和/或不支持的脚本进行管道步骤的替代管道相比，它具有广泛的注释，并将标注纳入管道中支持良好且定期更新的生物信息学脚本。此功能为 SCRAP 提供了稳定性，使研究人员更值得熟悉管道并将其使用纳入他们的工作流程。SCRAP也被证明在调用sncRNA:靶RNA相互作用的峰方面优于替代管道，并具有跨平台功能，详见先前的出版物6。
在本教程结束时，用户将能够 （i） 了解 SCRAP 的平台要求并安装 SCRAP 管道，（ii） 安装参考基因组并设置 SCRAP 的命令行参数，以及 （iii） 了解峰值调用标准并执行峰值调用和峰值注释。
本视频将详细描述研究 RNA 生物学的研究人员如何安装并优化使用计算管道 SCRAP，以分析嵌合 RNA 测序数据中的 sncRNA 与靶 RNA（如信使 RNA）的相互作用，这些数据是通过讨论的测序文库制备生化方法之一获得的。
SCRAP 是一个命令行实用程序。通常，按照以下指南，用户需要 （i） 下载并安装 SCRAP （https://github.com/Meffert-Lab/SCRAP），（ii） 安装参考基因组并运行 SCRAP，以及 （iii） 执行峰值检出和注释。
有关此过程中计算步骤的更多详细信息，请参见 https://github.com/Meffert-Lab/SCRAP。本文将提供设置和背景信息，使具有入门级计算技能的研究人员能够在嵌合 RNA 测序文库数据集上安装、优化和使用 SCRAP。

<table><tbody><tr><td>Genomes</td><td>UCSC Genome browser</td><td>N/A</td><td>https://genome.ucsc.edu/ or https://www.ncbi.nlm.nih.gov/data-hub/genome/</td></tr><tr><td>Linux</td><td>Linux</td><td>Ubuntu 20.04 or 22.04 LTS recommended</td><td></td></tr><tr><td>Mac</td><td>Apple</td><td>Mac OSX (&gt;11)</td><td></td></tr><tr><td>Platform setup</td><td>GitHub</td><td>N/A</td><td>https://github.com/Meffert-Lab/SCRAP/blob/main/PLATFORM-SETUP.md]</td></tr><tr><td>SCRAP pipeline</td><td>GitHub</td><td>N/A</td><td>https://github.com/Meffert-Lab/SCRAP</td></tr><tr><td>Unix shell</td><td>Unix operating system</td><td>bash &gt;=5.0</td><td></td></tr><tr><td>Unix shell</td><td>Unix operating system</td><td>zsh (5.9 recommended)</td><td></td></tr><tr><td>Windows</td><td>Windows</td><td>WSL Ubuntu 20.04 or 22.04 LTS</td><td></td></tr></tbody></table>

computational analysis tutorial for chimeric small noncoding rna: target rna sequencing libraries

近年来，通过生化方法，通过使用交联和连接，通过形成嵌合 RNA 和随后的测序文库来捕获 sncRNA:靶标 RNA 相互作用，从而推进了对小非编码 RNA （sncRNA） 等小非编码 RNA （sncRNA） 与其靶 RNA 的 体内 基因调控相互作用的理解。虽然来自嵌合RNA测序的数据集提供了全基因组的输入，并且比miRNA预测软件的模糊性要少得多，但将这些数据提炼成有意义和可操作的信息需要额外的分析，并且可能会劝阻缺乏计算背景的研究人员。本报告提供了一个教程，以支持入门级计算生物学家安装和应用最新的开源软件工具:小嵌合 RNA 分析管道 （SCRAP）。提供了平台要求、更新以及管道步骤和关键用户输入变量操作的说明。减少生物学家从嵌合RNA测序方法中获得见解的障碍，有可能在多种生物学背景下对调节性sncRNA:靶RNA相互作用进行基于发现的研究。

在使用 CLEAR-CLIP9 制备的先前发表的测序数据集上，由 SCRAP 的修改版本（SCRAP 版本 2.0，实现了 rRNA 过滤的修改）检测到的 sncRNA:靶 RNA 的结果如 图 2 和 表 1 所示。用户可以欣赏到在SCRAP中通过峰检出分离高置信度相互作用后发生的与内含子区域的相对部分miRNA相互作用的减少。使用 SCRAP 进行分析的其他数据也可在本管道6 的初?...

Watch this Scientific Journal Video about A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data at JoVE.com

作者聚焦:用于分析高通量测序数据中嵌合非编码 RNA-靶 RNA 相互作用的计算管道 

在这里，我们提出了一个协议，展示了生物信息学管道的安装和使用，以分析用于研究 体内 RNA:RNA相互作用的嵌合RNA测序数据。

嵌合小非编码RNA的计算分析教程:靶标RNA测序文库

An understanding of the in vivo gene regulatory interactions of small noncoding RNAs (sncRNAs), such as microRNAs (miRNAs), with their target RNAs has ...

该协议涵盖了使用计算管道的安装和实际步骤，该计算管道旨在分析来自嵌合非编码RNA-靶RNA相互作用的高通量测序数据。形成嵌合RNA的分子策略是一个相对较新的发展，与以前的生物色谱预测工具相比，它在明确理解全基因组非编码RNA-靶标RNA相互作用景观方面具有优势。嵌合RNA的高通量测序产生的测序数据可能会给该策略的新采用者带来分析挑战。我们建立了一个开源的跨平台计算管道，该管道经过高度注释，允许入门级用户分析其嵌合非编码RNA-靶标RNA测序实验的数据。在未来的实验中，Mefford实验室计划对我们正在进行的哺乳动物神经系统转录后调控研究产生的数据集进行计算分析，并在我们的GitHub上维护更新的报废。

computational-analysis-tutorial-for-chimeric-small-noncoding-rna

Research

JoVE Journal

Biology

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

552 次观看.  Johns Hopkins University School of Medicine. 该协议涵盖了使用计算管道的安装和实际步骤，该计算管道旨在分析来自嵌合非编码RNA-靶RNA相互作用的高通量测序数据。形成嵌合RNA的分子策略是一个相对较新的发展，与以前的生物色谱预测工具相比，它在明确理解全基因组非编码RNA-靶标RNA相互作用景观方面具有优势。嵌合RNA的高通量测序产生的测序数据可能会给该策略的新采用者带来分析挑战。我们建立了一个开?...

视频: 作者聚焦:用于分析高通量测序数据中嵌合非编码 RNA-靶 RNA 相互作用的计算管道 

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

利用RNA测序来检测新剪接变异体在相关耐药<em&gt;体外</em&gt;癌症模型

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

科研

癌症研究

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

转录因子结合、转录、翻译和营业额的实时分析在细胞激活过程中显示全球事件

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

遗传学

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

人骨髓间充质干细胞细胞外微 rna 的分离、测序和分析

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in disease states and provide therapeutic targets and biomarkers. Here, we detail how to accurately quantify microRNAs. Briefly, this method describes isolating microRNAs, ligating them to adaptors suitable for high-throughput sequencing, amplifying the final products, and preparing a sample library. Then, we explain how to align the obtained sequencing reads to microRNA hairpins, and quantify, normalize, and calculate their differential expression. Versatile and robust, this combined experimental workflow and bioinformatic analysis enables users to begin with tissue extraction and finish with microRNA quantification.

Here, we describe a step-by-step strategy for isolating small RNAs, enriching for microRNAs, and preparing samples for high-throughput sequencing. We then describe how to process sequence reads and align them to microRNAs, using open source tools.

用于隔离和测序微RNA并使用开源工具分析微RNA的完整管道

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in ...

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

使用RNA测序识别循环RNA

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as plant microRNAs (miRNAs) carry a 2&#39;-O-methyl (2&#39;-OMe) modification at their 3&#39;&#160;terminal nucleotide. This modification adds another difficulty as it inhibits 3&#39;&#160;adapter ligation. We previously demonstrated that modified versions of the &#39;TruSeq (TS)&#39;&#160;protocol have less bias and an improved detection of 2&#39;-OMe RNAs. Here we describe in detail protocol &#39;TS5&#39;, which showed the best overall performance. TS5 can be followed either using homemade reagents or reagents from the TS kit, with equal performance.

We present a detailed small RNA library reparation protocol with less bias than standard methods and an increased sensitivity for 2&#39;-O-methyl RNAs. This protocol can be followed using homemade reagents to save cost or using kits for convenience.

改进小RNA-seq:减少偏差，更好地检测2'-O-甲基RNA

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as ...

使用 AQRNA-seq 分析 tRNA 动力学

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate quantification of the pool of small RNAs. The AQRNA-seq library preparation procedure described here involves the use of custom-designed sequencing linkers and a step for reducing methylation RNA modifications that block reverse transcription processivity, which results in an increased yield of full-length cDNAs. In addition, a detailed implementation of the accompanying bioinformatics pipeline is presented. This demonstration of AQRNA-seq was conducted through a quantitative analysis of the 45 tRNAs in Mycobacterium bovis BCG harvested on 5 selected days across a 20-day time course of nutrient deprivation and 6 days of resuscitation. Ongoing efforts to improve the efficiency and rigor of AQRNA-seq will also be discussed here. This includes exploring methods to obviate gel purification for mitigating primer dimer issues after PCR amplification and to increase the proportion of full-length reads to enable more accurate read mapping. Future enhancements to AQRNA-seq will be focused on facilitating automation and high-throughput implementation of this technology for quantifying all small RNA species in cell and tissue samples from diverse organisms.

作者聚焦:AQRNA-seq 在绘制小 RNA 和解开蛋白质翻译机制中的作用

Absolute quantification RNA sequencing (AQRNA-seq) is a technology developed to quantify the landscape of all small RNAs in biological mixtures. Here, both the library preparation and data processing steps of AQRNA-seq are demonstrated, quantifying changes in the transfer RNA (tRNA) pool in Mycobacterium bovis BCG during starvation-induced dormancy.

用于定量小 RNA 的 AQRNA-seq

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate ...

Genome-wide Analysis of HDAC Inhibitor-mediated Modulation of microRNAs and mRNAs in B Cells Induced to Undergo Class-switch DNA Recombination and Plasma Cell Differentiation

Antibody responses are accomplished through several critical B cell-intrinsic processes, including somatic hypermutation (SHM), class-switch DNA recombination (CSR), and plasma cell differentiation. In recent years, epigenetic modifications or factors, such as histone deacetylation and microRNAs (miRNAs), have been shown to interact with B-cell genetic programs to shape antibody responses, while the dysfunction of epigenetic factors has been found to lead to autoantibody responses. Analyzing genome-wide miRNA and mRNA expression in B cells in response to epigenetic modulators is important for understanding the epigenetic regulation of B-cell function and antibody response. Here, we demonstrate a protocol for inducing B cells to undergo CSR and plasma cell differentiation, treating these B cells with histone deacetylase (HDAC) inhibitors (HDIs), and analyzing mRNA and microRNA expression. In this protocol, we directly analyze complementary DNA (cDNA) sequences using next-generation mRNA sequencing (mRNA-seq) and miRNA-seq technologies, mapping of the sequencing reads to the genome, and quantitative reverse transcription (qRT)-PCR. With these approaches, we have defined that, in B cells induced to undergo CSR and plasma cell differentiation, HDI, an epigenetic regulator, selectively modulates miRNA and mRNA expression and alters CSR and plasma cell differentiation.

Epigenetic factors can interact with genetic programs to modulate gene expression and regulate B cell function. By combining in vitro B-cell stimulation, qRT-PCR, and high-throughput microRNA-sequence and mRNA-sequence approaches, we can analyze the epigenetic modulation of miRNA and gene expression in B cells.

嵌合小非编码RNA的计算分析教程:靶标RNA测序文库

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利用RNA测序来检测新剪接变异体在相关耐药

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使用RNA测序识别循环RNA

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用于定量小 RNA 的 AQRNA-seq

用于定量小 RNA 的 AQRNA-seq

用于定量小 RNA 的 AQRNA-seq

HDAC 抑制剂介导的 B 细胞 rna 和基因的基因组分析--诱导进行类开关 DNA 重组和血浆细胞分化