作者要感谢卫生研究院（CIHR）和基因组阿尔伯塔，加拿大基因组的一个部门的加拿大学院，给予资金支持。

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作者什么都没有透露。

此处描述了用于创建简单的代谢途径（TCA循环）的协议可以适于创建各种为任何种类的机器可读的，生物复杂途径。此外，该协议还介绍了如何可以复制或传播其他用户创建的现有途径。构建使用这种工具的路径，需要反应，相互作用，运输过程和子通路，其每一个都通过重叠元件连接的重复步骤的分步添加。将所有这些一起允许一个创造丰富多彩，赏心悦目途径图表，提供可观的生物细节和有用的生物环境。在这个协议中所描述的步骤是相对简单的，它需要建立一个通路图中的时间取决于通路的大小和复杂性。只要稍加练习，大多数人可以呈现高品质的途径图包括约15-20反应或处理一个在约15分钟ð几个细胞成分。全新的用户最多可能需要30至40分钟可生成类似规模和复杂性的一个途径。以产生一个通路所需要的时间是与反应/需要被渲染过程的数目大致线性的。 创建通过这个基于Web的工具，一个高品质的途径取决于源材料的质量和细节（书籍，在线数据库，实验数据，手绘草图途径）和途径"艺术家"的挑剔。那些希望生成更高质量的途径图表应该特别注意部分1.11，1.15和协议的1.20，因为这些部分描述了反应元件（反应物/产品，酶，边缘，图像缩放框，标签的创建和编辑，和膜）。最好的途径图将智能从B中找到了途径尽可能包括那些作为许多现有交涉合并信息ooks，海报，文件和在线数据库。以产生高品质的途径的另一个关键是仔细检查反应的正确性产生一个反应（通过部分中的协议的1.11）之前。取所涉及的时间和精力来确保反应物，产物和酶（其中有许多在PathWhiz的大型数据库中已存在）都是正确的每个物种是非常重要的。同样重要的是要意识到反应的细胞定位的，并包括以提供正确的生物上下文关键细胞或亚细胞组分。这可以通过检查和确证通过在线数据库如的UniProt反应来完成。其手头所有需要的信息，用将要创建将大大减少错误的途径的粗糙，手绘草图和花在绘图或渲染的总时间一起。 正如预期的那样，更大，更复杂的途径将需要更长的时间来呈现，particularly如果期望的要素和过程中尚未PathWhiz的数据库。当与较大的通路的工作，它通常是明智的，从自动保存模式切换到手动节省模式中，为了防止操作之间的长延迟。当复制一个通路，的时间量，用户可能等待要生成的途径取决于该途径的元素数。大多数途径可在1-2分钟内复制。当传播途径，新传播途径的成功渲染取决于两个物种的相似程度，如PathWhiz使用BLAST 17序列进行搜索，以找到物种之间的同源酶。大通路会变慢传播，因为BLAST将需要在酶的数量更多运行。试图传播显著不同物种之间的通路（酵母和人类之间说吧）会导致途径正在与一些未知的蛋白质呈现。这些"遥远"Propagated途径通常需要额外的人工编辑。由于路径图和可带到一个途径细节的高度可视化的性质，它始终是一个好主意，在电脑上工作，有相当大的屏幕（&gt; 20英寸或"50厘米）和一个良好的互联网连接（&gt; 5 Mbps）的。 如果遇到与渲染或屏幕清爽问题，用户可能必须做的故障排除的少量。如果一个大型的，复杂的途径时间过长更新，用户可能需要刷新页面。如果像预期的那样途径不传播，用户可能必须做一些手工编辑，以确保所有元素都正确显示。另外，作为更具体的例子中，如果不显示反应的元素，用户可能必须确保所有元件或酶适当选择和边缘是不隐藏。如果遇到问题，在主标题中的"帮助"链接可能是有用的。的教程该"指南"选项卡，用户手册下可用可用"用户指南"选项卡下。这两种解释许多的详细工具的功能。指南可以被用来排除或者解释为一个特定特征潜在的局限性，当用户锁定一个通路，例如和后来的用户希望进行编辑。 通过该协议，并通过在附图中提供的实施例强调的，该工具提供了许多在任何（或大部分）其它途径绘图工具（ 见表1）未发现独特的功能。首先，它是完全基于网络的和完全独立于平台的。第二，它支持多色的，生物复合物，视觉上令人满意的，完全的超链接通路图表，也可以被转换为机器可读格式（BioPAX，SBGN-ML 18 SBML 19，PWML 14）的呈现和轻便的产生。第三，途径图属泰德此工具可以浏览，搜索，选择，并通过一个易于使用的在线数据库，查看界面轻松地探索。四，网络工具旨在支持社区的捐款途径，允许"通路人群采购"，鼓励新途径和新途径要素共享和生成。 通过这个基于web工具产生的途径，可用于各种应用。细节丰富，完全超链接的途径可以很容易地整合到蛋白质组学，代谢组学和系统生物学应用的特定生物的数据库。 Internet访问途径是教育和培训尤其有用，因为可以通过基于Web的图像细节往往比什么都通过静态图像或通过一个单一的课本或期刊页面显示大得多。这个基于Web的工具还支持途径表示了更适合于印刷和出版的产生。其结果是，通过该基于web工具产生许多图像中出现的文件，海报和幻灯演示。导出到通路基于文本的数据交换文件格式（如BioPAX和SBML）允许使用该Web服务器生成的途径可以直接在计算机分析系统生物学和代谢建模应用中使用。传播物种之间通路允许推断也可以在关于生物过程，尤其是已被最近测序的物种之一。虽然目前PathWhiz不存在现有的所有途径，它的公共途径数据库继续增长，导致新的，人群来源的途径收集的出现。这些藏品不仅很容易扩展到新的物种，他们将有希望导致其独特的生物学和生物化学更深入的了解。

生物途径图是像蓝图生命科学家。他们也许用于描述生物过程和基因，蛋白质和代谢物之间的上下文连接的最简洁和信息的路由。这是因为，图像被更有效地处理，并往往更容易被人类比文本1的理解。质量，细节和通路图的内容可以有很大的不同。这些差异通常依赖于途径的预期目的和通路艺术家的技能。如挂图或教科书教育目的创建的途径往往是由专业艺术家创作。其结果是，这些途径图表更为悦目和代谢物结构的完整描述，亚细胞成分，细胞结构，组织和器官提供相当多的生物细节。这些"教科书"表示通常包括详细的注释和评注。另外，专为互联网应用途径往往图表不得不牺牲艺术性和视觉的丰富性有利于简化机器可读的"连线"的图表。这些线框图更容易图像映射和超链接。简化路径图是依据这些流行的网络通路数据库作为KEGG 2，MetaCyc 3，4 Wikipathways和Reactome 5。计算机兼容途径数据库的出现也带动了电脑兼容的途径绘图工具的外观。换句话说，人们不必是一个专业的歌手或专业的程序员来产生可用路径图。例如，BioCyc的途径的工具6和Wikipathway的PathVisio软件7允许用户自由生成和BioPAX 8份额机器可读通路一次/或HTML格式。此外，还有一些其他的单机免费软件包的以及商业软件包支持各种机器可读的，线框途径，如Cytoscape的9，GenMAPP 10中PathCase 11的产生，和VisANT 12。 互联网通路图的简化主要是在许多Web浏览器和基于Web的工具，渲染中发现的历史局限性增长。然而，在网络技术显著进展已在过去的几年中进行。这表明它可能是可能产生的互动，互联网兼容的途径图表，也同样丰富多彩，就像美观和一样完整的生物与在教科书中。这项工作导致PathWhiz的发展。 PathWhiz是使用Ruby on Rails的（http://rubyonrails.org，4.2.0版）网络framewor实施ķ纳入一个MySQL关系型数据库（https://www.mysql.com，版本50年1月5日）来管理所有的数据通路，包括实体关系，外部引用，描述，可视化的规格和化学结构。前端Web客户端是由Ruby on Rails的Backbone.js的带（http://backbonejs.org，1.0.0版本）作为编辑器的前端Web框架相结合的控制。 最初为人类只小分子通路数据库（SMPDB）13的研制策，PathWhiz 14已经被扩展，以支持新一代的途径为许多其他生物，并作为一般通路的形象和知识数据库的功能。特别是，该网络工具允许创建全系列生化/生物途径包括代谢，蛋白质相互作用，分子信号，生理和药物/疾病途径的。这个途径的绘图工具与其它大多数有所不同途径产生工具在三个主要方面:1）它是一个Web服务器，而不是一个独立的，可安装的软件包; 2）它支持的轻便生成和化学化合物，蛋白质，核酸，细胞膜，亚细胞结构，组织和器官的交互式可视化; 3）它允许用户方便地借阅，建立或改善其他用户的工作，从而使"众包"的途径产生。作为Web服务器，它具有超过可下载，特定于平台的软件工具的几个优点。特别是，它与任何平台，操作系统和现代网络浏览器兼容。此外，它不要求用户为了开始创建一个途径（尽管用户可以自由地以跟踪和控制他们创建的通路的辅助创造"私人帐号"）进行登记。或许这个工具的最吸引人的特点是，可以很容易地加入到生物和生化细节的量通过预渲染图像的调色板和蛋白质生化数据的广泛数据库的任何途径。这允许"非艺术家"和"非程序员"轻松创造丰富多彩，美观和细节丰富的途径是网络的兼容和全机读。 PathWhiz和其它途径的绘图工具之间的更详细的比较在表1中提供。 一些热门的生命科学数据库已经使用这种途径绘图工具来创建数据库特有的，在线，互动的途径图。例如， 大肠埃希氏菌代谢组学数据库（ECMDB）15最近更新了其途径库通过基于Web的工具绘制1650余途径。在ECMDB每种途径现在显示为一个丰富的彩色，充分超链接的图像地图详细代谢物和蛋白质的结构描述，以及一个简化的黑白KEGG-LIKE线图。这种大规模的更新途径导致了以前没有被包括在其他大肠杆菌代谢数据库许多中间代谢产物的发现。其它数据库，如人类代谢组数据库（HMDB）15，不仅依靠PathWhiz途径描绘和描述的代谢和信号通路，也描绘参与的疾病，如癌症的代谢变化。 HMDB目前包括101代谢途径，376药物作用的途径，233疾病相关的通路和16个信号通路，均通过本网站工具生成的。 以下方案详细介绍PathWhiz如何可以用来轻松地创建，复制，传播和生化途径有多种用途和应用。

<table><tbody><tr><td>Computer with colour screen</td><td>N/A</td><td>N/A</td><td>&amp;nbsp;&gt;20 inches or &gt;50 cm</td></tr><tr><td>Internet connection&amp;nbsp;</td><td>N/A</td><td>N/A</td><td>&gt;5 mbps</td></tr><tr><td>Modern web browser&amp;nbsp;</td><td>N/A</td><td>N/A</td><td>Google Chrome (v. 31 and above), Internet Explorer (v. 9 and above), Safari (v. 7 and above), Opera (v. 15 and above) and Firefox (v. 23 and above)</td></tr></tbody></table>

a web tool for generating high quality machine-readable biological pathways

PathWhiz是专为方便丰富多彩，互动性，视觉愉悦途径图表，含有丰富的生物信息创建一个Web服务器。通过这个在线应用程序生成的途径是机器可读的，并与基本上所有的网络浏览器和电脑操作系统完全兼容。它采用了专门开发的，支持web的通路绘制接口，其允许选择和预拉伸的生物或生化实体的不同组合的放置来描绘的反应，相互作用，运输过程和结合事件。实体这个调色板由化学化合物，蛋白质，核酸，细胞膜，亚细胞结构，组织和器官的。所有在它的视觉元素的可以交互调节和定制。此外，由于该工具是Web服务器，所有的通路和通路元素是公开访问。这种途径的"众包"是指PathWhiz已经包含了以前绘制的途径和路径元素的大量和迅速增长的集合。在这里，我们描述了快速和容易创造新的途径和现有途径的变更的协议。为了进一步方便途径编辑和创作工具中包含的复制和传播功能。复制功能允许现有的途径用作模板来创建或编辑新的途径。传播功能，允许人们利用现有的途径，并自动在不同物种传播它。用这个工具创建的途径可以是"再病急乱投医"为不同的格式（KEGG状或教科书等），具有不同背景，远销BioPAX，SBGN-ML，SBML或PWML数据交换格式有色和下载为PNG或SVG图像。途径可以容易地并入在线数据库，集成到演示，海报或出版物，或专供在线可视化和勘探中。该公关otocol已成功应用于产生超过2,000通路图，目前已在很多网上数据库，包括HMDB，DrugBank，SMPDB和ECMDB发现。

在这个手稿中描述的Web服务器的主要途径产生工具示于图1和图2。每个选项卡提供的菜单选项也显示。 图3和图 4提供了一组通路创建过程的截屏。 图5提供了一组反应创建过程的截图。 图6示出在线通路观察者及其菜单。 PathWhiz可用于产生具有各种内容类型和样式的途径。这些包括"传统"的代谢途径（ 图7），疾病和药物的途径示出副作用（ 图8）和药物反应（ 图9），以及蛋白信号传导途径（ 图10）。通路可能具有相当biolo被色彩丰富gical细节或它们可被转化为简单的黑白表示（ 图11）。一旦完成，这些途径可以在交互通路查看器（ 图6）来查看，下载图像，或在几个不同的机器可读的数据交换格式导出用于进一步分析。注意，不同的数据交换格式的质量取决于在最初绘制途径输入的数据的质量。例如，增加更多的反应细节（ 即化学计量，生物学状态）会产生更多的全面BioPAX。另一方面，通路绘制重叠元素（视觉原因，例如表示结合元件或蛋白复合物）也可以产生在SBGN-ML重叠字形。 <img alt="图1" src="/files/ftp_upload/54869/54869fig1.jpg" /> 图1:通路编辑器界面。该编辑器界面是康波顶级主菜单栏，二级菜单和网格帆布:3个主要部分SED。上方主菜单栏（紫色）提供的链接来查看，编辑和创建途径元素。下部辅助菜单栏（灰色）提供链接来添加和编辑中的电流路径图视觉通路的元素，如反应，相互作用，运输过程中，子通路，化合物，蛋白质，核酸，以及膜，蜂窝/亚细胞图像，缩放框和标签。该菜单还包括两个选项卡允许所选元素的编辑或画布的编辑。该网格白画布下方菜单栏是其中反应通路和过程将增加。缩放框工程作为视觉提示来指示图像中的一个选定区域的放大率。它包括连接到一个重新相当大的四边形的小正方形的。小正方形放置在要被扩大或缩小的区域，而四边形的工作原理，其中一个可以添加第一个帆布在所选区域中发生（由较小的方形）在线反应。通过双击它来访问它的侧边栏编辑缩放框。编辑选项包括下拉列表模板，颜色和Z-指数。缩放框的渲染方向可以通过选择顶部被改变，右，在模板标签左侧或底部。当选择缩放框，黑色圆圈可以拖动来调整和重新格式化不同的缩放框组件。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图2" src="/files/ftp_upload/54869/54869fig2.jpg" /> 图2:通路编辑器菜单。编辑菜单提供的选项添加过程和要素，以及修改现有的元素和画布。 （ 一 ）"途径"链接提供了选择"编辑详细信息"和<html> "出口和视图"。 "编辑细节"选项允许该途径描述和参考的编辑而"出口和查看"选项允许的图像文件的生成或再生。 （ 二 ）"添加程序"链接提供了选项添加反应，相互作用，结合事件，传输事件，反应加上运输，或子途径到画布上。 （ 三 ）"添加空洞元素"链接提供选项用于添加的化合物，蛋白质，核酸，元素集合，或者一个边缘到画布上。这些元件将在画布的左上角显示。任意元素将出现在画布一起弹出边栏，其中用户可以搜索所需的元素或改变所述元件的细节。该元件应纳入该途径增加任何新空洞的元素，为了保持通路整洁之前。_upload / 54869 / 54869fig2large.jpg"目标="_空白"&gt;点击此处查看该图的放大版本。 <img alt="图3" src="/files/ftp_upload/54869/54869fig3.jpg" /> 图3:途径指数化。通路索引提供现有途径的集合和一个搜索框查询特定通路。途径可以通过名称，类型，种类和创建者使用的索引表顶部的过滤棒进行过滤。它们也可以通过名称使用在网页顶部的搜索栏搜索。对外开放的"高级搜索"可以让生物状态，类型，种类，化合物和蛋白质的组合更具体的搜索。高级搜索允许使用AND，OR，和NOT逻辑运算符来创建复杂的查询。每个通道包括5个按钮:"显示"，"编辑"，"抽奖"，"消灭"和"复制"。 "显示"按钮允许查看使用浏览器的途径。 "编辑"按钮允许途径元数据，包括名称，类型，种类，说明和参考的编辑。 "绘制"按钮允许包含通路画布的编辑。在"消灭"按钮可以清除从数据库中通路（如果用户有权限）。在"复制"按钮允许选择的途径复制。的"上一个"和"下一步"按钮允许用户通路页面之间导航。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig3large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图4" src="/files/ftp_upload/54869/54869fig4.jpg" /> 图4:创建新的路径表。 "新路径"按钮（ 见图3）导致这里显示的路径形式。此表单包含为途径的名称，类型，种类和描述字段。 "新路径"按钮，还允许从现有的途径开始，并添加引用。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig4large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图5" src="/files/ftp_upload/54869/54869fig5.jpg" /> 图5:创建新的反应形式。 "添加过程"链接允许用户添加新的进程，如反应或结合事件。添加一个进程创建了一个模型反应，从中可视化反应可以生成并添加到途径的图。反应模型和可视化是独立的实体。 （ 一 ）的反应场许可证搜索现有反应中，反应物，产物，或酶。生物领域国家允许搜索和选择现有biologicaL状态。一旦反应选择，相应的酶可以使用"添加酶"按钮，将弹出一个自动完成的酶框添加。渲染选项允许用户选择他们希望的反应呈现的方向。一个新的反应可以通过（b）的"新的反应"按钮，这导致在那里可以添加元素和酶一个新的反应形式来创建。一旦所有字段被填充，该反应可以通过"创建反应"按钮来创建。要编辑的基本反应模式应该退出途径插画，去反应指数，查找和编辑反应出现。为了防止数据的差异，并无意改变现有的途径，这是不可能改变的反应物/产品或从反应模型取出的酶，如果它已经有现有途径可视化。因此，在编辑模式反应不会自动更新现有的反应可视化效果。为了改变反应模式必须相应的可视化重新添加到该变化出现在路径图。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig5large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图6" src="/files/ftp_upload/54869/54869fig6.jpg" /> 图6:途径查看器。右上方的浏览器界面中的按钮提供基本的导航，缩放，以及屏幕切换操作。中央视口显示，可以通过单击并拖动，或者使用鼠标缩放进行导航的途径。显示的路径元素超链接到其他的途径和数据库（ 如 HMDB，DrugBank，的UniProt）。侧菜单栏显示途径与用户提供的参考描述。侧面菜单也显示标签"高亮"，"分析"，"做wnloads"和"设置"，"高亮"选项卡允许化合物和酶进行选择，并以红色突出显示该"分析"选项卡允许输入实验浓度数据，然后映射到使用颜色渐变的途径。 "下载"选项卡提供链接到相应的下载图像文件和数据交换文件。该PNG文件是一个较小的非矢量图像文件。SVG + BioPAX链接提供较大的矢量图像文件嵌入BioPAX，对于机器的可读性。在BioPAX，SBML，SBGN和PWML链接提供不同的机器可读的格式。"设置"选项卡允许显示的图像途径视觉定制。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig6large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图7" src="/files/ftp_upload/54869/54869fig7.jpg" /><br/&gt; 图7:代谢途径图像。这是一个描述特定化合物（D-丝氨酸）的生物合成和降解"传统"的代谢途径的一个例子。主要代谢产物被定位在画布和反应和输送箭头（边缘）显示了通路的流的中心。边缘和元素可以自动地"啪"起来， 即连接。元件捕捉点通过透明红色圆圈上的元件侧指示，并且边缘开始/结束点由上边缘端部透明的灰色圆圈表示。选中时捕捉点时翻上空盘旋，透明的绿和纯绿。为了捕捉一个边缘的元素，首先点击任一边缘开始/结束或元素捕捉点（它会变成绿色常亮）。然后点击边缘开始/结束或需要连接的元素捕捉点。边缘将自动连接到捕捉点，并保持连接until在删除内容（通过双击边缘并拖动终点而去，或将其连接到不同的捕捉点）。这是要留意选择意外捕捉点很重要，因为在尝试走动边时，这会产生意想不到的后果。选择捕捉点的固体绿色是为了提醒用户抢购，他们目前已经选择点。捕捉点可以通过点击第二次被取消。边缘也可以重新连接到它们原来的元素。当选择一个边缘，参观"编辑选择"菜单链接，然后在"编辑边"链接。这将弹出的选项自动连接边缘在不同的方向。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig7large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图8" src="/files/ftp_upload/54869/54869fig8.jpg" /> &lt;BR /&gt; 图8:疾病通路图像。这是一种疾病的途径，显示受该疾病影响（肌氨酸Oncometabolite途径）器官的一个例子。附加图像元素被用来描述在代谢物浓度的增加或减少和它们的累积或耗散。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig8large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图9" src="/files/ftp_upload/54869/54869fig9.jpg" /> 图9:药物途径图像。这是一种药物的途径，其中显示出药物被代谢器官（布洛芬通路）的一个例子。围绕药物代谢物的颜色通常被描绘为粉红色。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig9large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <p类="jove_content"FO: - together.within页保留="1"&gt; <img alt="图10" src="/files/ftp_upload/54869/54869fig10.jpg" /> 图10:蛋白信号通路的图像。这是一个信号通路表示信令不同蛋白（EGFR途径）之间的反应的集合的一个例子。蛋白可与多个颜色来描绘和它们可以由蛋白质名称或子单元名称来表示。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig10large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图11" src="/files/ftp_upload/54869/54869fig11.jpg" /> 图11:七彩虹VS简单的途径形象。七彩途径可以与无论是白色或蓝色的背景（ 一 ）丰富的生物背景下产生。叶酸代谢在这里描绘。操作简单，KEGG般的途径也可以为G用一个简单的黑与白表示（B）enerated。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig11large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图12" src="/files/ftp_upload/54869/54869fig12.jpg" /> 图12:次优路径图像。图像描绘什么是最理想的途径（TCA循环）的模样。重叠的元素和穿越的边缘使通路不知所云。如果反应元素是不小心还是正确操纵画布上会发生这种情况。操纵元素对化合物（大，中，小复方可视化或药物可视化，可视化的辅助因子，简单的底部，左或右顶部可视化）两种以上不同的模板类型导致多种图像不一致。的模板类型示于步骤1.15.2。没有连接日Ë边缘影响图像导致通路的差解释的流动。 <a href="http://ecsource.jove.com/files/ftp_upload/54869/54869fig12large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td></td><td> PathWhiz </td><td> VANTED </td><td> PathVisio </td><td>通路工具</td><td> VisANT </td></tr><tr><td>网络服务器</td><td>是</td><td>没有</td><td>没有</td><td>没有</td><td>没有</td></tr><tr><td>可安装程序</td><td>没有</td><td>是</td><td>是</td><td>是</td><td>是</td></tr><tr><td>蛋白质途径</td><td>是</td><td>是</td><td>是</td><td>没有</td><td>是</td></tr><tr><td>代谢途径</td><td>是</td><td>是</td><td>是</td><td>是</td><td>是</td></tr><tr><td>保存为PNG / JPG</td><td>是</td><td>是</td><td>是</td><td>没有</td><td>没有</td></tr><tr><td>另存为HTML </td><td>是</td><td>没有</td><td>没有</td><td>是</td><td>没有</td></tr><tr><td>另存为SVG </td><td>是</td><td>是</td><td>是</td><td>没有</td><td>是</td></tr><tr><td>另存为PDF </td><td>是</td><td>是</td><td>是</td><td>是</td><td>是</td></tr><tr><td>除BioPAX </td><td>是</td><td>是</td><td>是</td><td>是</td><td>是</td></tr><tr><td>除SBML </td><td>是</td><td>是</td><td>是</td><td>是</td><td>是</td></tr><tr><td>除SBGN-ML </td><td>是</td><td>是</td><td>是</td><td>没有</td><td>没有</td></tr><tr><td>标识符映射</td><td>是</td><td>是</td><td>是</td><td>是</td><td>是</td></tr><tr><td>膜渲染</td><td>是</td><td>没有</TD&gt; <td>没有</td><td>没有</td><td>没有</td></tr><tr><td>细胞器渲染</td><td>是</td><td>没有</td><td>没有</td><td>没有</td><td>没有</td></tr><tr><td>器官渲染</td><td>是</td><td>没有</td><td>没有</td><td>没有</td><td>没有</td></tr><tr><td>色彩丰富的图像</td><td>是</td><td>没有</td><td>没有</td><td>没有</td><td>没有</td></tr><tr><td>途径说明</td><td>是</td><td>没有</td><td>没有</td><td>是</td><td>没有</td></tr><tr><td>通路DB链接</td><td>是</td><td>没有</td><td>是</td><td>是</td><td>没有</td></tr><tr><td>通路推理</td><td>是</td><td>没有</td><td>没有</td><td>是</td><td>没有</td></tr><tr><td> EXPT。数据覆盖</td><td>没有</td><td>是</td><td>没有</td><td>是</td><td>是</td></tr><tr><td>路径分析</td><td>没有</td><td>是</td><td>是</td><td&gt;是</td><td>是</td></tr></tbody></table> 表1:功能比较。常见的几种途径编辑/渲染工具功能比较。 补充文件1:三羧酸循环说明了PathWhiz途径的例子 。 <a href="https://www.jove.com/files/ftp_upload/54869/Supplementary_material_-_The_citric_acid_cycle.doc">点击这里下载补充文件。</a>

Watch this Scientific Journal Video about A Web Tool for Generating High Quality Machine-readable Biological Pathways at JoVE.com

A Web Tool for Generating High Quality Machine-readable Biological Pathways

PathWhiz是产生生化和生物学通路全面，网上通路绘图工具。它采用公开访问的数据库和易于扩展的调色板由预先绘制的路径组件。本协议描述了如何轻松构建新的途径，复制和编辑现有的途径，并传播以前绘制的路径不同的生物。

一个网络工具，用以生成高质量机器可读的生物学途径

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological ...

a-web-tool-for-generating-high-quality-machine-readable-biological

Research

JoVE Journal

Biology

17.4K 次观看.  University of Alberta.  PathWhiz是产生生化和生物学通路全面，网上通路绘图工具。它采用公开访问的数据库和易于扩展的调色板由预先绘制的路径组件。本协议描述了如何轻松构建新的途径，复制和编辑现有的途径，并传播以前绘制的路径不同的生物。 

视频: A Web Tool for Generating High Quality Machine-readable Biological Pathways

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

利用CyVerse资源<em&gt; De Novo</em&gt;不合格（非模型）生物的比较转录组学

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

科研

遗传学

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

斑马鱼 RNASeq-based 基因表达数据的样品制备与分析

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Existing algorithms generate one solution for a biomarker detection dataset. This protocol demonstrates the existence of multiple similarly effective solutions and presents a user-friendly software to help biomedical researchers investigate their datasets for the proposed challenge. Computer scientists may also provide this feature in their biomarker detection algorithms.

用相似有效的二元分类性能选择多个生物标记子集

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker ...

癌症研究

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is pathogenic, one must query many databases to determine whether the gene of interest is linked to a genetic disease, whether the specific variant has been reported before, and what functional data is available in model organism databases that may provide clues about the gene&#8217;s function in human. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) is a one-stop data collection tool for human genes and variants and their orthologous genes in seven model organisms including in mouse, rat, zebrafish, fruit fly, nematode worm, fission yeast, and budding yeast. In this Protocol, we provide an overview of what MARRVEL can be used for and discuss how different datasets can be used to assess whether a variant of unknown significance (VUS) in a known disease-causing gene or a variant in a gene of uncertain significance (GUS) may be pathogenic. This protocol will guide a user through searching multiple human databases simultaneously starting with a human gene with or without a variant of interest. We also discuss how to utilize data from OMIM, ExAC/gnomAD, ClinVar, Geno2MP, DGV and DECHIPHER. Moreover, we illustrate how to interpret a list of ortholog candidate genes, expression patterns, and GO terms in model organisms associated with each human gene. Furthermore, we discuss the value protein structural domain annotations provided and explain how to use the multiple species protein alignment feature to assess whether a variant of interest affects an evolutionarily conserved domain or amino acid. Finally, we will discuss three different use-cases of this website. MARRVEL is an easily accessible open access website designed for both clinical and basic researchers and serves as a starting point to design experiments for functional studies.

Here, we present a protocol to access and analyze many human and model organism databases efficiently. This protocol demonstrates the use of MARRVEL to analyze candidate disease-causing variants identified from next-generation sequencing efforts.

导航 MARRVEL,一种基于 Web 的工具,集成了人类基因组学和模型有机体遗传学信息

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is ...

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.

代谢通路信息 GWAS 分析的路径关联研究工具

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway ...

<meta charset="utf8"></head><body>用于 CUT&RUN 数据分析的初学者友好型单语言管道

Introductory Analysis and Validation of CUT&#38;RUN Sequencing Data

The CUT&amp;RUN technique facilitates detection of protein-DNA interactions across the genome. Typical applications of CUT&amp;RUN include profiling changes in histone tail modifications or mapping transcription factor chromatin occupancy. Widespread adoption of CUT&amp;RUN is driven, in part, by technical advantages over conventional ChIP-seq that include lower cell input requirements, lower sequencing depth requirements, and increased sensitivity with reduced background signal due to a lack of cross-linking agents that otherwise mask antibody epitopes. Widespread adoption of CUT&amp;RUN has also been achieved through the generous sharing of reagents by the Henikoff lab and the development of commercial kits to accelerate adoption for beginners. As technical adoption of CUT&amp;RUN increases, CUT&amp;RUN sequencing analysis and validation become critical bottlenecks that must be surmounted to enable complete adoption by predominantly wet lab teams. CUT&amp;RUN analysis typically begins with quality control checks on raw sequencing reads to assess sequencing depth, read quality, and potential biases. Reads are then aligned to a reference genome sequence assembly, and several bioinformatics tools are subsequently employed to annotate genomic regions of protein enrichment, confirm data interpretability, and draw biological conclusions. Although multiple in silico analysis pipelines have been developed to support CUT&amp;RUN data analysis, their complex multi-module structure and usage of multiple programming languages render the platforms difficult for bioinformatics beginners who may lack familiarity with multiple programming languages but wish to understand the CUT&amp;RUN analysis procedure and customize their analysis pipelines. Here, we provide a single-language step-by-step CUT&amp;RUN analysis pipeline protocol designed for users with any level of bioinformatics experience. This protocol includes completing critical quality checks to validate that the sequencing data is suitable for biological interpretation. We expect that following the introductory protocol provided in this article combined with downstream peak annotation will allow users to draw biological insights from their own CUT&amp;RUN datasets.

<meta charset="utf8"></head><body>作者聚焦：研究重复 DNA 失调在癌症发生和免疫治疗耐药中的作用

This protocol guides bioinformatics beginners through an introductory CUT&amp;RUN analysis pipeline that enables users to complete an initial analysis and validation of CUT&amp;RUN sequencing data. Completing the analysis steps described here, combined with downstream peak annotation, will allow users to draw mechanistic insights into chromatin regulation.

CUT&#38;RUN 测序数据的介绍性分析和验证

The CUT&amp;RUN technique facilitates detection of protein-DNA interactions across the genome. Typical applications of CUT&amp;RUN include profiling ...

Pipeline for Analyzing Microarray Data in Esophageal Cancer Research

Development of Compendium for Esophageal Squamous Cell Carcinoma

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early detection. EC manifests in two major histological forms - adenocarcinoma (EAD) and squamous cell carcinoma (ESCC) - both exhibiting variations in incidence across geographically distinct populations. High-throughput technologies are transforming the understanding of diseases, including cancer. A significant challenge for the scientific community is dealing with scattered data in the literature. To address this, a simple pipeline is proposed for the analysis of publicly available microarray datasets and the collection of differentially regulated molecules between cancer and normal conditions. The pipeline can serve as a standard approach for differential gene expression analysis, identifying genes differentially expressed between cancer and normal tissues or among different cancer subtypes. The pipeline involves several steps, including Data preprocessing (involving quality control and normalization of raw gene expression data to remove technical variations between samples), Differential expression analysis (identifying genes differentially expressed between two or more groups of samples using statistical tests such as t-tests, ANOVA, or linear models), Functional analysis (using bioinformatics tools to identify enriched biological pathways and functions in differentially expressed genes), and Validation (involving validation using independent datasets or experimental methods such as qPCR or immunohistochemistry). Using this pipeline, a collection of differentially expressed molecules (DEMs) can be generated for any type of cancer, including esophageal cancer. This compendium can be utilized to identify potential biomarkers and drug targets for cancer and enhance understanding of the molecular mechanisms underlying the disease. Additionally, population-specific screening of esophageal cancer using this pipeline will help identify specific drug targets for distinct populations, leading to personalized treatments for the disease.

This protocol describes a useful tool for identifying significant molecular changes in cancer and leads to the development of new diagnostic and therapeutic approaches for esophageal squamous cell carcinoma.

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early ...

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.g., biomedical literature). The software identifies sub-cellular proteins and their functional partners within disease-relevant documents. Additional disease-relevant documents are identified via the software's label imputation method. To contextualize the resulting protein-disease associations and to integrate information from multiple relevant biomedical resources, a knowledge graph is automatically constructed for further analyses. We present one use case with a corpus of ~34 million text documents downloaded online to provide an example of elucidating the role of mitochondrial proteins in distinct cardiovascular disease phenotypes using this method. Furthermore, a deep learning model was applied to the resulting knowledge graph to predict previously unreported relationships between proteins and disease, resulting in 1,583 associations with predicted probabilities &gt;0.90 and with an area under the receiver operating characteristic curve (AUROC) of 0.91 on the test set. This software features a highly customizable and automated workflow, with a broad scope of raw data available for analysis; therefore, using this method, protein-disease associations can be identified with enhanced reliability within a text corpus.

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and diseases.

通过生物医学报告 阐明 细胞器通路在疾病中的作用的知识图谱方法

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for ...

一个网络工具，用以生成高质量机器可读的生物学途径

摘要

探索更多视频

此视频中的章节

利用CyVerse资源

斑马鱼 RNASeq-based 基因表达数据的样品制备与分析

用相似有效的二元分类性能选择多个生物标记子集

导航 MARRVEL,一种基于 Web 的工具,集成了人类基因组学和模型有机体遗传学信息

代谢通路信息 GWAS 分析的路径关联研究工具

CUT&RUN 测序数据的介绍性分析和验证

CUT&RUN 测序数据的介绍性分析和验证

CUT&RUN 测序数据的介绍性分析和验证

Development of Compendium for Esophageal Squamous Cell Carcinoma

通过生物医学报告阐明细胞器通路在疾病中的作用的知识图谱方法

一个网络工具，用以生成高质量机器可读的生物学途径

摘要

探索更多视频

此视频中的章节

利用CyVerse资源

斑马鱼 RNASeq-based 基因表达数据的样品制备与分析

用相似有效的二元分类性能选择多个生物标记子集

导航 MARRVEL,一种基于 Web 的工具,集成了人类基因组学和模型有机体遗传学信息

代谢通路信息 GWAS 分析的路径关联研究工具

CUT&RUN 测序数据的介绍性分析和验证

CUT&RUN 测序数据的介绍性分析和验证

CUT&RUN 测序数据的介绍性分析和验证

Development of Compendium for Esophageal Squamous Cell Carcinoma

通过生物医学报告 阐明 细胞器通路在疾病中的作用的知识图谱方法

通过生物医学报告阐明细胞器通路在疾病中的作用的知识图谱方法