Fundamentals of Bioinformatics sandbox module

项目摘要 我们建议构建一个模块，重点是培训实验科学家与 使用各种基因组学平台生成的高维生物医学数据。在我们 结合培训科学家分析我们拥有的高维生物医学数据的经验， 确定了实验生物学家访问、接口和分析的三个主要障碍 生物医学数据。首先也是最重要的，是理解什么是“云”，以及如何 访问各种云计算系统。其次，如何定制云环境， 工具适合于感兴趣的分析，最后在使用新软件时减少错误 访问、操作和分析数字化生物医学数据。许多免费的资源 介绍云环境的技术并不是针对实验生物学家的， 缺乏一些学生掌握基本概念所需的实践空间和时间 在进入下一个话题之前。 我们提出的模块使用生物医学数据作为整个模块的训练集， 重点是克服学习生物信息学的三个常见障碍。每一课将 在上一课的基础上，全面了解云如何 环境可以被利用来与数字化生物医学数据交互。每节课将开始 有一个视频和示例代码，然后将在虚拟“沙箱”中实践 环境，然后是自我测试活动，使学生能够确定他们是否准备好了 下一课的材料。这种形式使基础主题的自主学习成为可能 专门解决数字化生物医学数据的规范障碍。

Project Summary We propose constructing a module focused on training experimental scientists to interface with high-dimensional biomedical data generated using a variety of genomics platforms. In our combined experience training scientists to analyze high-dimensional biomedical data we have identified three major barriers to experimental biologists accessing, interfacing, and analyzing biomedical data. First and foremost, is an understanding of what the ‘cloud’ is and how to access various cloud computing systems. Secondly how to customize a cloud environment with tools appropriate for an analysis of interest, and finally error mitigation when using new software to access, manipulate, and analyze digitized biomedical data. Many freely available resources that introduce the cloud environment are not targeted for experimental biologists, and commonly lack the practice space and time necessary for some students to grasp foundational concepts before moving onto the next topic. Our proposed module uses biomedical data as a training set throughout the module and focuses on overcoming the three common barriers to learning bioinformatics. Each lesson will build on the previous lesson to provide a complex and well-rounded understanding of how cloud environments can be leveraged to interact with digitized biomedical data. Each lesson will begin with a video and example code, which will then be practiced in the virtual ‘sandbox’ environment, and followed by a self-test activity enabling students to determine if they are ready for the material in the next lesson. This format enables self-paced learning of foundational topics that specifically address canonical barriers to working with digitized biomedical data.