Name: identification of alternative splicing and polyadenylation in rna-seq data
Uploaded: 2021-06-24T13:00:00
Description: Watch this Scientific Journal Video about Identification of Alternative Splicing and Polyadenylation in RNA-seq Data at JoVE.com

这项研究得到了澳大利亚研究委员会（ARC）未来奖学金（FT16010043）和澳大利亚国立大学期货计划的支持。

1. 安装分析中使用的工具和 R 包
<ol><li>Conda 是一个流行且灵活的包管理器，允许在所有平台上方便地安装包及其依赖项。使用“Anaconda”（conda包管理器）安装“conda”，可用于安装分析所需的工具/包。</li>
	<li>根据 https://www.anaconda.com/products/individual#Downloads 的系统要求下载“Anaconda”，并按照图形安装程序中的提示进行安装。通过在 Linux 命令行中键入以下内容，使用 'conda...

<ol>
	<li>Katz, Y., Wang, E. T., Airoldi, E. M., Burge, C. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+and+design+of+RNA+sequencing+experiments+for+identifying+isoform+regulation.">Analysis and design of RNA sequencing experiments for identifying isoform regulation.</a> Nature Methods. 7 (12), 1009-1015 (2010).</li><li>Wang, Y., 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=Mechanism+of+alternative+splicing+and+its+regulation.">Mechanism of alternative splicing and its regulation.</a> Biomedical Reports. 3 (2), 152-158 (2015).</li><li>Mehmood, A., 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=Systematic+evaluation+of+differential+splicing+tools+for+RNA-seq+studies.">Systematic evaluation of differential splicing tools for RNA-seq studies.</a> Briefings in Bioinformatics. 21 (6), 2052-2065 (2020).</li><li>Movassat, M., 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=Coupling+between+alternative+polyadenylation+and+alternative+splicing+is+limited+to+terminal+introns.">Coupling between alternative polyadenylation and alternative splicing is limited to terminal introns.</a> RNA Biology. 13 (7), 646-655 (2016).</li><li>Tian, B., Manley, J. 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E., 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=limma+powers+differential+expression+analyses+for+RNA-sequencing+and+microarray+studies.">limma powers differential expression analyses for RNA-sequencing and microarray studies.</a> Nucleic Acids Research. 43 (7), 47 (2014).</li><li>Shen, 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=rMATS:+Robust+and+flexible+detection+of+differential+alternative+splicing+from+replicate+RNA-Seq+data.">rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data.</a> Proceedings of the National Academy of Sciences. 111 (51), 5593-5601 (2014).</li><li>Mehmood, A., 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=Systematic+evaluation+of+differential+splicing+tools+for+RNA-seq+studies.">Systematic evaluation of differential splicing tools for RNA-seq studies.</a> Briefings in bioinformatics. 21 (6), 2052-2065 (2020).</li><li>Kanitz, A., 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=Comparative+assessment+of+methods+for+the+computational+inference+of+transcript+isoform+abundance+from+RNA-seq+data.">Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data.</a> Genome biology. 16 (1), 1-26 (2015).</li><li>Love, M. 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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=edgeR:+a+Bioconductor+package+for+differential+expression+analysis+of+digital+gene+expression+data.">edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.</a> Bioinformatics. 26 (1), 139-140 (2010).</li><li>Robinson, M. D., Oshlack, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+scaling+normalization+method+for+differential+expression+analysis+of+RNA-seq+data.">A scaling normalization method for differential expression analysis of RNA-seq data.</a> Genome Biology. 11 (3), 25 (2010).</li><li>Veiga, D. F. 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在这项研究中，我们评估了基于外显子和基于事件的方法，以检测批量RNA-Seq和3'末端测序数据中的AS和APA。基于外显子的AS方法既产生差异表达的外显子列表，又产生按总体基因水平差异剪接活性的统计显着性排序的基因水平排名（表1-2，4-5）。对于diffSplice包，差异用法是通过在外显子水平上拟合加权线性模型来确定的，以估计外显子的差异对数倍数变化与同一基因内其他外显子的平...

多年来，RNA-seq已被广泛用于估计差异基因表达和基因发现1。此外，它还可用于估计由于表达不同亚型的基因而导致的不同外显子水平使用情况，从而有助于更好地了解转录后水平的基因调控。大多数真核基因通过交替剪接（AS）产生不同的亚型，以增加mRNA表达的多样性。AS事件可分为不同的模式：跳过完全外显子（SE），其中（“盒式”）外显子与其侧翼内含子一起从转录本中完全去除;当外显子两端存在两个或多个剪接位点时，备选（供体）5'剪接位点选择（A5SS）和备选方案3'（受体）剪接位点选择（A3SS）;当内含子保留在成熟的mRNA转录本中时保留内含子（RI）和相互排除外显子使用（MXE），其中一次只能保留两个可用外显子中的一个2，3。替代聚腺苷酸化（APA）在使用替代聚（A）位点从单个转录本产生多种mRNA亚型4的基因表达中也起着重要作用。大多数聚腺苷酸化位点（pA）位于3'非翻译区域（3'UTR），产生具有不同3'UTR长度的mRNA亚型。由于 3' UTR 是识别调控元件的中心枢纽，因此不同的 3' UTR 长度会影响 mRNA 的定位、稳定性和翻译5。有一类 3' 末端测序测定经过优化，可检测 APA，这些APA在协议6的细节上有所不同。此处描述的管道是为 PolyA-seq 设计的，但可以适用于所述的其他协议。
在这项研究中，我们提出了一系列差异外显子分析方法7，8 （图1），可分为两大类：基于外显子（DEXSeq9，diffSplice10）和基于事件的（复制转录本剪接的多变量分析（rMATS）11）。基于外显子的方法将单个外显子条件下的倍数变化与整体基因折叠变化的度量进行比较，以调用差异表达的外显子使用情况，并由此计算AS活性的基因水平测量。基于事件的方法使用外显子-内含子跨越结读取来检测和分类特定的剪接事件，例如外显子跳跃或内含子保留，并在输出3中区分这些AS类型。因此，这些方法为AS12，13的完整分析提供了补充观点。我们选择了DEXSeq（基于DESeq214 DGE封装）和diffSplice（基于Limma10 DGE封装）进行研究，因为它们是差分拼接分析中使用最广泛的软件包之一。rMATS被选为基于事件的分析的常用方法。另一种流行的基于事件的方法是MISO（亚型混合物）1。对于APA，我们采用基于外显子的方法。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62636/62636fig01.jpg"> 图1.分析管道。 分析中使用的步骤的流程图。步骤包括：获取数据，执行质量检查和读取对齐，然后使用已知外显子，内含子和pA位点的注释对读取进行计数，过滤以去除低计数和标准化。使用diffSplice/DEXSeq方法分析PolyA-seq数据以寻找替代pA位点，使用diffSplice/DEXseq方法分析外显子水平的替代剪接的体RNA-Seq，并使用rMATS分析AS事件。 <a href="https://www.jove.com/files/ftp_upload/62636/62636fig01large.jpg" target="_blank">请点击此处查看此图的大图。</a>
本调查中使用的RNA-seq数据是从基因表达综合（GEO）（GSE138691）15获得的。我们使用本研究的小鼠RNA-seq数据，分为两个条件组：野生型（WT）和肌盲样1型敲除（Mbnl1 KO），每个重复三个。为了证明差异聚腺苷酸化位点的使用分析，我们获得了小鼠胚胎成纤维细胞（MEFs）PolyA-seq数据（GEO加入GSE60487）16。数据有四个条件组：野生型（WT），肌肉盲样1型/2型双敲除（Mbnl1 / 2 DKO），Mbnl 1 / 2 DKO与Mbnl3敲低（KD）和Mbnl1 / 2 DKO与Mbnl3对照（Ctrl）。每个条件组由两个仿行组成。
<table border="1" fo:font-size="5px" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td></td>
			<td>加入全球环境展望</td>
			<td>SRA 运行编号</td>
			<td>示例名称</td>
			<td>条件</td>
			<td>复制</td>
			<td>组织</td>
			<td>测 序</td>
			<td>读取长度</td>
		</tr><tr><td rowspan="6">核糖核酸序列</td>
			<td>GSM4116218</td>
			<td>SRR10261601</td>
			<td>Mbnl1KO_Thymus_1</td>
			<td>Mbnl1 淘汰赛</td>
			<td>代表 1</td>
			<td>胸腺</td>
			<td>配对端</td>
			<td>100 基点</td>
		</tr><tr><td>GSM4116219</td>
			<td>SRR10261602</td>
			<td>Mbnl1KO_Thymus_2</td>
			<td>Mbnl1 淘汰赛</td>
			<td>代表 2</td>
			<td>胸腺</td>
			<td>配对端</td>
			<td>100 基点</td>
		</tr><tr><td>GSM4116220</td>
			<td>SRR10261603</td>
			<td>Mbnl1KO_Thymus_3</td>
			<td>Mbnl1 淘汰赛</td>
			<td>代表 3</td>
			<td>胸腺</td>
			<td>配对端</td>
			<td>100 基点</td>
		</tr><tr><td>GSM4116221</td>
			<td>SRR10261604</td>
			<td>WT_Thymus_1</td>
			<td>野生型</td>
			<td>代表 1</td>
			<td>胸腺</td>
			<td>配对端</td>
			<td>100 基点</td>
		</tr><tr><td>GSM4116222</td>
			<td>SRR10261605</td>
			<td>WT_Thymus_2</td>
			<td>野生型</td>
			<td>代表 2</td>
			<td>胸腺</td>
			<td>配对端</td>
			<td>100 基点</td>
		</tr><tr><td>GSM4116223</td>
			<td>SRR10261606</td>
			<td>WT_Thymus_3</td>
			<td>野生型</td>
			<td>代表 3</td>
			<td>胸腺</td>
			<td>配对端</td>
			<td>100 基点</td>
		</tr><tr><td rowspan="8">3P-序列</td>
			<td>GSM1480973</td>
			<td>SRR1553129</td>
			<td>WT_1</td>
			<td>野生型（WT）</td>
			<td>代表 1</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>40 基点</td>
		</tr><tr><td>GSM1480974</td>
			<td>SRR1553130</td>
			<td>WT_2</td>
			<td>野生型（WT）</td>
			<td>代表 2</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>40 基点</td>
		</tr><tr><td>GSM1480975</td>
			<td>SRR1553131</td>
			<td>DKO_1</td>
			<td>Mbnl 1/2 双淘汰赛 （DKO）</td>
			<td>代表 1</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>40 基点</td>
		</tr><tr><td>GSM1480976</td>
			<td>SRR1553132</td>
			<td>DKO_2</td>
			<td>Mbnl 1/2 双淘汰赛 （DKO）</td>
			<td>代表 2</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>40 基点</td>
		</tr><tr><td>GSM1480977</td>
			<td>SRR1553133</td>
			<td>DKOsiRNA_1</td>
			<td>Mbnl 1/2 双敲除与 Mbnl 3 siRNA （KD）</td>
			<td>代表 1</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>40 基点</td>
		</tr><tr><td>GSM1480978</td>
			<td>SRR1553134</td>
			<td>DKOsiRNA_2</td>
			<td>Mbnl 1/2 双敲除与 Mbnl 3 siRNA （KD）</td>
			<td>代表 2</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>36 基点</td>
		</tr><tr><td>GSM1480979</td>
			<td>SRR1553135</td>
			<td>DKONTsiRNA_1</td>
			<td>Mbnl 1/2 双敲除与非靶向 siRNA （Ctrl）</td>
			<td>代表 1</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>40 基点</td>
		</tr><tr><td>GSM1480980</td>
			<td>SRR1553136</td>
			<td>DKONTsiRNA_2</td>
			<td>Mbnl 1/2 双敲除与非靶向 siRNA （Ctrl）</td>
			<td>代表 2</td>
			<td>小鼠胚胎成纤维细胞 （MEF）</td>
			<td>单端</td>
			<td>40 基点</td>
		</tr></tbody></table>表 1.用于分析的RNA-Seq和PolyA-seq数据集摘要。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Not relevent for computational study</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

identification of alternative splicing and polyadenylation in rna-seq data

除了对RNA-Seq进行典型分析以测量实验/生物学条件下的差异基因表达（DGE）外，RNA-seq数据还可用于探索外显子水平的其他复杂调控机制。选择性剪接和聚腺苷酸化通过产生不同的亚型来调节转录后水平的基因表达，在基因的功能多样性中起着至关重要的作用，并且将分析限制在整个基因水平上可能会错过这一重要的调控层。在这里，我们演示了详细的分步分析，以使用Bioconductor和其他封装和功能（包括DEXSeq，Limma封装的diffSplice和rMATS）来识别和可视化不同条件下的差异外显子和聚腺苷酸化位点的使用。

运行上述分步工作流程后，AS和APA分析输出和代表性结果以表格和数据图的形式生成，生成如下。
如： AS分析的主要输出（差异拼接的补充表1;DEXSeq的表2）是显示不同条件差异用法的外显子列表，以及显示其一个或多个组成外显子的显着整体剪接活性的基因列表，按统计学显着性排名。补充表1，选项卡2显示了显着的外显?...

Watch this Scientific Journal Video about Identification of Alternative Splicing and Polyadenylation in RNA-seq Data at JoVE.com

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

选择性剪接（AS）和替代聚腺苷酸化（APA）扩大了转录本亚型及其产物的多样性。在这里，我们描述了生物信息学协议，以分析批量RNA-seq和3'末端测序测定，以检测和可视化不同实验条件下变化的AS和APA。

RNA-seq数据中替代剪接和聚腺苷酸化的鉴定

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also ...

该协议通过提供分步工作流程来鉴定差异剪接位点、差异表达外显子和聚（A）位点，从而全面了解由替代剪接和聚腺苷酸化产生的基因亚型。该协议的主要优点是它评估了基于外显子和基于事件的方法，用于研究替代剪接。它还应用基于外显子的方法研究替代聚腺苷酸化。提供了包含AS和AP分析的代码和注释的R Markdown文件。建议按照 R Markdown 文件中的步骤进行操作，并仔细访问每个步骤的注释。若要使用 limma 的 diffSplice 识别差分拼接，请按照 R 笔记本文件进行操作。按照文本手稿中的说明准备输入文件。确保按顺序遵循稿件中的步骤 1 到 3 以准备输入文件，然后再继续。首先加载必要的库。要执行非特异性过滤，请首先提取先前获得的读取计数矩阵，并使用 edgeR 包中的 DGEList 函数创建特征列表，其中行表示基因，列表示样本。然后，使用 edgeR 包中的 CPM 函数将数据从原始规模转换为每百万计数，并保持计数大于可设置阈值的外显子。此数据集包含六个样本。因此，CPM 设置为大于 1 个样本，六个样本中至少设置为 3 个样本。使用 edgeR 包中的 calcNormFactors 函数使用修剪后的 M 平均值值对样本计数进行归一化。此函数将计算比例因子以调整库大小。使用先前生成的示例表创建设计矩阵，以定义每个样品的实验条件。运行 limma 包的 voom 函数以处理 RNA 测序数据以估计方差。此函数将生成精度权重以校正泊松计数噪声，并将外显子水平计数转换为记录百万分之二计数或logCPM。运行 lmfit 函数，将线性模型拟合到每个外显子的表达式数据。然后运行 eBayes 函数来计算拟合模型的基于经验的统计量，以检测差异外显子表达。为感兴趣的实验比较定义对比矩阵。使用对比。拟合函数，用于获取每对比较的系数和标准误差。在拟合模型上运行diffSplice，以测试野生型和敲除之间基因外显子使用的差异。使用 topSplice 函数探索排名靠前的结果，其中等于 t 的测试给出 AS 外显子的排名，等于 simes 的测试给出基因的排名。运行绘图拼接函数以绘制结果。在将感兴趣的基因放在基因ID参数中时，红点显示了差异表达的外显子。使用增强火山生物导体包生成火山图，以展示差异表达的外显子。要使用 rMATS，请确保在工作目录中使用 conda 或 GitHub 安装最新版本的 rMATS 版本 4.1.1。转到包含映射后获取的 bam 文件的文件夹。根据 rMATS 的要求，针对复制 bam 文件名及其路径（用逗号分隔）的两个条件准备文本文件。运行 rmas。py 使用两个生成的输入文本文件描述 BAM 文件的路径和注释。之前获得的GTF文件。这将生成一个输出文件夹rmats_out其中包含描述统计信息的文本文件，包括每个拼接事件的 P 值和包含水平。使用生物导体封装激射器来探索rMATS结果。将交汇点和外显子计数文本文件加载到脉泽对象中，并包括每个拼接事件至少五个平均读取，以根据覆盖范围过滤结果。要可视化rMATS结果，请首先从maser包中运行topEvents函数，以10%的错误发现率和至少10%的拼接百分比或PSI变化选择重要的拼接事件。检查感兴趣的单个基因的基因事件，并绘制该基因的每个剪接事件的PSI值。通过指定事件类型生成火山图。使用 rmats2sashimiplot 包以文本文件的形式使用 rMATS 获得的拼接事件结果生成生鱼片图。生鱼片图显示了Wnk1基因中跳过的外显子事件。每行代表一个RNA-seq样品，三个野生型重复和Mbnl1敲除。高度以RPKM为单位显示读取覆盖率，连接弧表示跨外显子的结读数。底部显示了带注释的基因模型替代亚型。在使用diffSplice和DEXSeq获得的火山图左上象限或右象限的基因中，可以观察到实质性的倍数变化和真实差异的有力统计证据。发现盒式外显子在基因Wnk1的不同条件之间有所不同。差异外显子使用图显示了Wnk1.6.45附近五个外显子位点的差异剪接证据，与野生型相比，粉红色突出显示的外显子可能在Mbnl1敲除样本中被剪接掉。交替剪接的基因的火山图有助于区分排除在野生型中的基因和包含在野生型中的基因。剪接事件SE，A5SS，A3SS，MXE和RI的类型使用这些事件的顶级重要基因的生鱼片图可视化。使用火山图观察基因的三个主要非翻译区域中的差异APA活性。使用事件图可视化从不同管道获得的显著差异的PA站点使用结果。在双敲除中，PA位点的使用在双敲除中都可以观察到显着的远端到近端转移，可以在基因FOSL1和Papola中观察到。使用诊断图确定全基因组水平上锚定在已知PA切割位点的侧翼区域的平均覆盖率。确保在生成计数指标时正确使用跨特定信息和允许多重重叠等参数。线性模型拟合和生成对比对对于正确比较非常重要。对于 rMATS，请确保在运行命令之前根据您的数据正确设置所有参数。从差异剪接活性中获得的基因可用于进行基因集富集分析。另一个称为MISO的工具可用于进一步的基于事件的分析。

Research

JoVE Journal

Biology

RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells

RNA-seq的凝血酶处理和控制人肺微血管内皮细胞的转录组分析

The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the ...

科研

生物学

Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq

核糖体RNA的蚊子肠道宏基因组RNA-seq的枯竭

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity ...

Alternative Cultures for Human Pluripotent Stem Cell Production, Maintenance, and Genetic Analysis

另类文化的人多能干细胞的生产，维护和遗传分析

Human pluripotent stem cells (hPSCs) hold great promise for regenerative medicine and biopharmaceutical applications. Currently, optimal culture and ...

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

检测选择性剪接的过程中上皮 - 间质转化

Alternative splicing plays a critical role in the epithelial-mesenchymal transition (EMT), an essential cellular program that occurs in various ...

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

实验和生物信息学议定书光周期滞育的亚洲虎蚊的RNA序列分析，<em&gt;白纹伊蚊</em

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, ...

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

化学遗传相互作用的快速鉴定<em&gt;酿酒酵母</em

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. ...

Methods to Discover Alternative Promoter Usage and Transcriptional Regulation of Murine Bcrp1

方法发现小鼠BCRP1的另类用法子和转录调控

Gene expression in different tissues is often controlled by alternative promoters that result in the synthesis of mRNA with unique &#8212; usually ...

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

赫连图斯安努斯大型热成像筛选装置新稳压器的鉴定

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water ...

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

使用RNA-seq研究分子进化和基因表达的生物信息学管道

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...