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

<ol>
	<li>Morris, K. V., Mattick, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+rise+of+regulatory+RNA.">The rise of regulatory RNA.</a> Nature Reviews Genetics. 15 (6), 423-437 (2014).</li><li>Li, X., Jin, D. S., Eadara, S., Caterina, M. J., Meffert, M. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+by+noncoding+RNAs+of+local+translation,+injury+responses,+and+pain+in+the+peripheral+nervous+system.">Regulation by noncoding RNAs of local translation, injury responses, and pain in the peripheral nervous system.</a> Neurobiology of Pain (Cambridge, Mass.). 13, 100119 (2023).</li><li>Shi, J., Zhou, T., Chen, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+the+expanding+universe+of+small+RNAs.">Exploring the expanding universe of small RNAs.</a> Nature Cell Biology. 24 (4), 415-423 (2022).</li><li>Broughton, J. P., Lovci, M. T., Huang, J. L., Yeo, G. W., Pasquinelli, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pairing+beyond+the+seed+supports+microRNA+targeting+specificity.">Pairing beyond the seed supports microRNA targeting specificity.</a> Molecular Cell. 64 (2), 320-333 (2016).</li><li>Grosswendt, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unambiguous+identification+of+miRNA:target+site+interactions+by+different+types+of+ligation+reactions.">Unambiguous identification of miRNA:target site interactions by different types of ligation reactions.</a> Molecular Cell. 54 (6), 1042-1054 (2014).</li><li>Mills, W. T., Eadara, S., Jaffe, A. E., Meffert, M. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SCRAP:+a+bioinformatic+pipeline+for+the+analysis+of+small+chimeric+RNA-seq+data.">SCRAP: a bioinformatic pipeline for the analysis of small chimeric RNA-seq data.</a> RNA. 29 (1), 1-17 (2023).</li><li>Helwak, A., Kudla, G., Dudnakova, T., Tollervey, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+human+miRNA+interactome+by+CLASH+reveals+frequent+noncanonical+binding.">Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding.</a> Cell. 153 (3), 654-665 (2013).</li><li>Hoefert, J. E., Bjerke, G. A., Wang, D., Yi, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+microRNA-200+family+coordinately+regulates+cell+adhesion+and+proliferation+in+hair+morphogenesis.">The microRNA-200 family coordinately regulates cell adhesion and proliferation in hair morphogenesis.</a> Journal of Cell Biology. 217 (6), 2185-2204 (2018).</li><li>Moore, M. J., Zhang, C., Gantman, E. C., Mele, A., Darnell, J. C., Darnell, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+Argonaute+and+conventional+RNA-binding+protein+interactions+with+RNA+at+single-nucleotide+resolution+using+HITS-CLIP+and+CIMS+analysis.">Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis.</a> Nature Protocols. 9 (2), 263-293 (2014).</li><li>Bjerke, G. A., Yi, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+analysis+of+directly+captured+microRNA+targets+reveals+the+impact+of+microRNAs+on+mammalian+transcriptome.">Integrated analysis of directly captured microRNA targets reveals the impact of microRNAs on mammalian transcriptome.</a> RNA. 26 (3), 306-323 (2020).</li><li>Reuter, J. S., Mathews, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNAstructure:+software+for+RNA+secondary+structure+prediction+and+analysis.">RNAstructure: software for RNA secondary structure prediction and analysis.</a> BMC Bioinformatics. 11 (1), 129 (2010).</li><li>Moore, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRNA-target+chimeras+reveal+miRNA+3&#8242;-end+pairing+as+a+major+determinant+of+Argonaute+target+specificity.">miRNA-target chimeras reveal miRNA 3&#8242;-end pairing as a major determinant of Argonaute target specificity.</a> Nature Communications. 6 (1), 8864 (2015).</li><li>Travis, A. J., Moody, J., Helwak, A., Tollervey, D., Kudla, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hyb:+a+bioinformatics+pipeline+for+the+analysis+of+CLASH+(crosslinking,+ligation+and+sequencing+of+hybrids)+data.">Hyb: a bioinformatics pipeline for the analysis of CLASH (crosslinking, ligation and sequencing of hybrids) data.</a> Methods (San Diego, Calif.). 65 (3), 263-273 (2014).</li></ol>

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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 (&#62;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 &#62;=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

541 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 

Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA

Whole transcriptome sequencing by mRNA-Seq is now used extensively to perform global gene expression, mutation, allele-specific expression and other genome-wide analyses. mRNA-Seq even opens the gate for gene expression analysis of non-sequenced genomes. mRNA-Seq offers high sensitivity, a large dynamic range and allows measurement of transcript copy numbers in a sample. Illumina&#x2019;s genome analyzer performs sequencing of a large number (&gt; 107) of relatively short sequence reads (&lt; 150 bp).The &quot;paired end&quot; approach, wherein a single long read is sequenced at both its ends, allows for tracking alternate splice junctions, insertions and deletions, and is useful for de novo transcriptome assembly.
One of the major challenges faced by researchers is a limited amount of starting material. For example, in experiments where cells are harvested by laser micro-dissection, available starting total RNA may measure in nanograms. Preparation of mRNA-Seq libraries from such samples have been described1, 2 but involves significant PCR amplification that may introduce bias. Other RNA-Seq library construction procedures with minimal PCR amplification have been published3, 4 but require microgram amounts of starting total RNA.
Here we describe a protocol for the Illumina Genome Analyzer II platform for mRNA-Seq sequencing for library preparation that avoids significant PCR amplification and requires only 10 nanograms of total RNA. While this protocol has been described previously and validated for single-end sequencing5, where it was shown to produce directional libraries without introducing significant amplification bias, here we validate it further for use as a paired end protocol. We selectively amplify polyadenylated messenger RNAs from starting total RNA using the T7 based Eberwine linear amplification method, coined &quot;T7LA&quot; (T7 linear amplification). The amplified poly-A mRNAs are fragmented, reverse transcribed and adapter ligated to produce the final sequencing library. For both single read and paired end runs, sequences are mapped to the human transcriptome6 and normalized so that data from multiple runs can be compared. We report the gene expression measurement in units of transcripts per million (TPM), which is a superior measure to RPKM when comparing samples7.

Here we describe a method for preparation of both single read and paired end Illumina mRNA-Seq sequencing libraries for gene expression analysis based on T7 linear RNA amplification. This protocol requires only 10 nanograms of starting total RNA and generates highly consistent libraries representing whole transcripts.

单次读取和配对的最终从10微克总RNA的mRNA - Seq的Illumina的图书馆

Whole transcriptome sequencing by mRNA-Seq is now used extensively to perform global gene expression, mutation, allele-specific expression and other ...

科研

生物学

In vitro Transcription and Capping of Gaussia Luciferase mRNA Followed by HeLa Cell Transfection

In vitro transcription is the synthesis of RNA transcripts by RNA polymerase from a linear DNA template containing the corresponding promoter sequence (T7, T3, SP6) and the gene to be transcribed (Figure 1A). A typical transcription reaction consists of the template DNA, RNA polymerase, ribonucleotide triphosphates, RNase inhibitor and buffer containing Mg2+ ions.
Large amounts of high quality RNA are often required for a variety of applications. Use of in vitro transcription has been reported for RNA structure and function studies such as splicing1, RNAi experiments in mammalian cells2, antisense RNA amplification by the "Eberwine method"3, microarray analysis4 and for RNA vaccine studies5. The technique can also be used for producing radiolabeled and dye labeled probes6. Warren, et al. recently reported reprogramming of human cells by transfection with in vitro transcribed capped RNA7. The T7 High Yield RNA Synthesis Kit from New England Biolabs has been designed to synthesize up to 180 &mu;g RNA per 20 &mu;l reaction. RNA of length up to 10kb has been successfully transcribed using this kit. Linearized plasmid DNA, PCR products and synthetic DNA oligonucleotides can be used as templates for transcription as long as they have the T7 promoter sequence upstream of the gene to be transcribed.
Addition of a 5' end cap structure to the RNA is an important process in eukaryotes. It is essential for RNA stability8, efficient translation9, nuclear transport10 and splicing11. The process involves addition of a 7-methylguanosine cap at the 5' triphosphate end of the RNA. RNA capping can be carried out post-transcriptionally using capping enzymes or co-transcriptionally using cap analogs. In the enzymatic method, the mRNA is capped using the Vaccinia virus capping enzyme12,13. The enzyme adds on a 7-methylguanosine cap at the 5' end of the RNA using GTP and S-adenosyl methionine as donors (cap 0 structure). Both methods yield functionally active capped RNA suitable for transfection or other applications14 such as generating viral genomic RNA for reverse-genetic systems15 and crystallographic studies of cap binding proteins such as eIF4E16.
In the method described below, the T7 High Yield RNA Synthesis Kit from NEB is used to synthesize capped and uncapped RNA transcripts of Gaussia luciferase (GLuc) and Cypridina luciferase (CLuc). A portion of the uncapped GLuc RNA is capped using the Vaccinia Capping System (NEB). A linearized plasmid containing the GLuc or CLuc gene and T7 promoter is used as the template DNA. The transcribed RNA is transfected into HeLa cells and cell culture supernatants are assayed for luciferase activity. Capped CLuc RNA is used as the internal control to normalize GLuc expression.

In vitro Transcription and Capping of Gaussia Luciferase mRNA Followed by HeLa Cell Transfection

This method describes high yield in vitro synthesis of both capped and uncapped mRNA from a linearized plasmid containing the Gaussia luciferase (GLuc) gene. The RNA is purified and a fraction of the uncapped RNA is enzymatically capped using the Vaccinia virus capping enzyme. In the final step, the mRNA is transfected into HeLa cells and cell culture supernatants are assayed for luciferase activity.

在体外转录和Gaussia荧光素酶基因的上限由HeLa细胞转染

In vitro transcription is the synthesis of RNA transcripts by RNA polymerase from a linear DNA template containing the corresponding promoter sequence ...

Substrate Generation for Endonucleases of CRISPR/Cas Systems

The interaction of viruses and their prokaryotic hosts shaped the evolution of bacterial and archaeal life. Prokaryotes developed several strategies to evade viral attacks that include restriction modification, abortive infection and CRISPR/Cas systems. These adaptive immune systems found in many Bacteria and most Archaea consist of clustered regularly interspaced short palindromic repeat (CRISPR) sequences and a number of CRISPR associated (Cas) genes (Fig. 1) 1-3. Different sets of Cas proteins and repeats define at least three major divergent types of CRISPR/Cas systems 4. The universal proteins Cas1 and Cas2 are proposed to be involved in the uptake of viral DNA that will generate a new spacer element between two repeats at the 5' terminus of an extending CRISPR cluster 5. The entire cluster is transcribed into a precursor-crRNA containing all spacer and repeat sequences and is subsequently processed by an enzyme of the diverse Cas6 family into smaller crRNAs 6-8. These crRNAs consist of the spacer sequence flanked by a 5' terminal (8 nucleotides) and a 3' terminal tag derived from the repeat sequence 9. A repeated infection of the virus can now be blocked as the new crRNA will be directed by a Cas protein complex (Cascade) to the viral DNA and identify it as such via base complementarity10. Finally, for CRISPR/Cas type 1 systems, the nuclease Cas3 will destroy the detected invader DNA 11,12 .
These processes define CRISPR/Cas as an adaptive immune system of prokaryotes and opened a fascinating research field for the study of the involved Cas proteins. The function of many Cas proteins is still elusive and the causes for the apparent diversity of the CRISPR/Cas systems remain to be illuminated. Potential activities of most Cas proteins were predicted via detailed computational analyses. A major fraction of Cas proteins are either shown or proposed to function as endonucleases 4.
Here, we present methods to generate crRNAs and precursor-cRNAs for the study of Cas endoribonucleases. Different endonuclease assays require either short repeat sequences that can directly be synthesized as RNA oligonucleotides or longer crRNA and pre-crRNA sequences that are generated via in vitro T7 RNA polymerase run-off transcription. This methodology allows the incorporation of radioactive nucleotides for the generation of internally labeled endonuclease substrates and the creation of synthetic or mutant crRNAs. Cas6 endonuclease activity is utilized to mature pre-crRNAs into crRNAs with 5'-hydroxyl and a 2',3'-cyclic phosphate termini.

CRISPR/Cas systems mediate adaptive immunity in Bacteria and Archaea. Many Cas proteins are proposed to act as endoribonucleases acting on crRNA precursors of varying length. Here we illustrate three different approaches to generate pre-crRNA substrates for the biochemical analysis of Cas endonuclease activity.

CRISPR / CAS系统内切酶的底物生成

The  interaction of viruses and their prokaryotic hosts shaped the evolution of  bacterial and archaeal life. Prokaryotes developed several strategies to ...

Isolation of Small Noncoding RNAs from Human Serum

The analysis of RNA and its expression is a common feature in many laboratories. Of significance is the emergence of small RNAs like microRNAs, which are found in mammalian cells. These small RNAs are potent gene regulators controlling vital pathways such as growth, development and death and much interest has been directed at their expression in bodily fluids. This is due to their dysregulation in human diseases such as cancer and their potential application as serum biomarkers. However, the analysis of miRNA expression in serum may be problematic. In most cases the amount of serum is limiting and serum contains low amounts of total RNA, of which small RNAs only constitute 0.4-0.5%1. Thus the isolation of sufficient amounts of quality RNA from serum is a major challenge to researchers today. In this technical paper, we demonstrate a method which uses only 400 &#181;l of human serum to obtain sufficient RNA for either DNA arrays or qPCR analysis. The advantages of this method are its simplicity and ability to yield high quality RNA. It requires no specialized columns for purification of small RNAs and utilizes general reagents and hardware found in common laboratories. Our method utilizes a Phase Lock Gel to eliminate phenol contamination while at the same time yielding high quality RNA. We also introduce an additional step to further remove all contaminants during the isolation step. This protocol is very effective in isolating yields of total RNA of up to 100 ng/&#181;l from serum but can also be adapted for other biological tissues.

This protocol describes a method for extracting small RNAs from human serum. We have used this method to isolate microRNAs from cancer serum for use in DNA arrays and also singleplex quantitative PCR. The protocol utilizes phenol and guanidinium thiocyanate reagents with modifications to yield high quality RNA.

小非编码RNA从人血清中分离

The analysis of RNA and its expression is a common feature in many laboratories. Of significance is the emergence of small RNAs like microRNAs, which are ...

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

的16S核糖体RNA基因的扩增子的新一代测序

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

The last decades have witnessed the explosion of scientific interest around gene expression control mechanisms at the RNA level. This branch of molecular biology has been greatly fueled by the discovery of noncoding RNAs as major players in post-transcriptional regulation. Such a revolutionary perspective has been accompanied and triggered by the development of powerful technologies for profiling short RNAs expression, both at the high-throughput level (genome-wide identification) or as single-candidate analysis (steady state accumulation of specific species). Although several state-of-art strategies are currently available for dosing or visualizing such fleeing molecules, Northern Blot assay remains the eligible approach in molecular biology for immediate and accurate evaluation of RNA expression. It represents a first step toward the application of more sophisticated, costly technologies and, in many cases, remains a preferential method to easily gain insights into RNA biology. Here we overview an efficient protocol (Enhanced Northern Blot) for detecting weakly expressed microRNAs (or other small regulatory RNA species) from Drosophila melanogaster whole embryos, manually dissected larval/adult tissues or in vitro cultured cells. A very limited amount of RNA is required and the use of material from flow cytometry-isolated cells can be also envisaged.

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

The aim of this publication is to visualize and discuss the operative steps of an Enhanced Northern Blot protocol on RNA extracted from Drosophila melanogaster embryos, cells, and tissues. This protocol is particularly useful for the efficient detection of small RNA species.

增强Northern印迹检测小RNA种类的<em&gt;果蝇</em

The last decades have witnessed the explosion of scientific interest around gene expression control mechanisms at the RNA level. This branch of molecular ...

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide resolution. This technique captures miRNAs by attaching 3&#8217; and 5&#8217; oligonucleotide adapters to miRNA molecules and allows de novo miRNA discovery. Coupling with powerful next-generation sequencing platforms, miR-Seq has been instrumental in the study of miRNA biology. However, significant biases introduced by oligonucleotide ligation steps have prevented miR-Seq from being employed as an accurate quantitation tool. Previous studies demonstrate that biases in current miR-Seq methods often lead to inaccurate miRNA quantification with errors up to 1,000-fold for some miRNAs1,2. To resolve these biases imparted by RNA ligation, we have developed a small RNA ligation method that results in ligation efficiencies of over 95% for both 3&#8217; and 5&#8242; ligation steps. Benchmarking this improved library construction method using equimolar or differentially mixed synthetic miRNAs, consistently yields reads numbers with less than two-fold deviation from the expected value. Furthermore, this high-efficiency miR-Seq method permits accurate genome-wide miRNA profiling from in vivo total RNA samples2.

MicroRNAs (miRNAs) are a widely conserved class of regulatory molecules. Here we describe a miRNA cloning method that relies upon two potent ligation steps followed by high-throughput sequencing. Our method permits accurate genome-wide quantitation of miRNAs.

小RNA分子的高效结扎的微小RNA定量高通量测序

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide ...

TChIP-Seq: Cell-Type-Specific Epigenome Profiling

Epigenetic regulation plays central roles in gene expression. Since histone modification was discovered in the 1960s, its physiological and pathological functions have been extensively studied. Indeed, the advent of next-generation deep sequencing and chromatin immunoprecipitation (ChIP) via specific histone modification antibodies has revolutionized our view of epigenetic regulation across the genome. Conversely, tissues typically consist of diverse cell types, and their complex mixture poses analytic challenges to investigating the epigenome in a particular cell type. To address the cell type-specific chromatin state in a genome-wide manner, we recently developed tandem chromatin immunoprecipitation sequencing (tChIP-Seq), which is based on the selective purification of chromatin by tagged core histone proteins from cell types of interest, followed by ChIP-Seq. The goal of this protocol is the introduction of best practices of tChIP-Seq. This technique provides a versatile tool for tissue-specific epigenome investigation in diverse histone modifications and model organisms.

We describe a step-by-step protocol for tandem chromatin immunoprecipitation sequencing (tChIP-Seq) that enables the analysis of cell-type-specific genome-wide histone modification.

tchip-seq: 细胞专用表观体分析

Epigenetic regulation plays central roles in gene expression. Since histone modification was discovered in the 1960s, its physiological and pathological ...

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

The development of a complex multicellular organism is governed by distinct cell types that have different transcriptional profiles. To identify transcriptional regulatory networks that govern developmental processes it is necessary to measure the spatial and temporal gene expression profiles of these individual cell types. Therefore, insight into the spatio-temporal control of gene expression is essential to gain understanding of how biological and developmental processes are regulated. Here, we describe a laser-capture microdissection (LCM) method to isolate small number of cells from three barley embryo organs over a time-course during germination followed by transcript profiling. The method consists of tissue fixation, tissue processing, paraffin embedding, sectioning, LCM and RNA extraction followed by real-time PCR or RNA-seq. This method has enabled us to obtain spatial and temporal profiles of seed organ transcriptomes from varying numbers of cells (tens to hundreds), providing much greater tissue-specificity than typical bulk-tissue analyses. From these data we were able to define and compare transcriptional regulatory networks as well as predict candidate regulatory transcription factors for individual tissues. The method should be applicable to other plant tissues with minimal optimization.

Presented here is a protocol for laser-capture microdissection (LCM) of plant tissues. LCM is a microscopic technique for isolating areas of tissue in a contamination-free manner. The procedure includes tissue fixation, paraffin embedding, sectioning, LCM and RNA extraction. RNA is used in the downstream tissue-specific, temporally resolved analysis of transcriptomes.

激光捕获微分块RNA-测序空间和时间组织特异性基因表达分析

The development of a complex multicellular organism is governed by distinct cell types that have different transcriptional profiles. To identify ...

Nuclei Isolation from Adult Mouse Kidney for Single-Nucleus RNA-Sequencing

The kidneys regulate diverse biological processes such as water, electrolyte, and acid-base homeostasis. Physiological functions of the kidney are executed by multiple cell types arranged in a complex architecture across the corticomedullary axis of the organ. Recent advances in single-cell transcriptomics have accelerated the understanding of cell type-specific gene expression in renal physiology and disease. However, enzyme-based tissue dissociation protocols, which are frequently utilized for single-cell RNA-sequencing (scRNA-seq), require mostly fresh (non-archived) tissue, introduce transcriptional stress responses, and favor the selection of abundant cell types of the kidney cortex resulting in an underrepresentation of cells of the medulla.
Here, we present a protocol that avoids these problems. The protocol is based on nuclei isolation at 4 &#176;C from frozen kidney tissue. Nuclei are isolated from a central piece of the mouse kidney comprised of the cortex, outer medulla, and inner medulla. This reduces the overrepresentation of cortical cells typical for whole-kidney samples for the benefit of medullary cells such that data will represent the entire corticomedullary axis at sufficient abundance. The protocol is simple, rapid, and adaptable and provides a step towards the standardization of single-nuclei transcriptomics in kidney research.

Here, we present a protocol to isolate high-quality nuclei from frozen mouse kidneys that improve the representation of medullary kidney cell types and avoids the gene expression artifacts from enzymatic tissue dissociation.