Rodeo: A Platform for Discovery and Analysis of Protein Network Motifs

哈佛大学获得了一笔拨款，用于建立一个新的公开可用的信息学工具，称为Rodeo，该工具将允许整合与破译蛋白质-蛋白质相互作用网络相关的数据，并将提供许多分析这些数据的方法，包括图论数学。Rodeo将允许整合和分析几个数据库，包括来自50多个基因组的一套全面的同源物，一套新的来自人类的蛋白质-蛋白质（p-p）相互作用数据，以及来自其他生物体的超过70,000个p-p相互作用数据，一套来自新实验和NCBI主持的实验的大量基因表达数据。以及一个包含近10万个来自一种生物体（橡子虫）的表达序列标签的数据库，这可能是理解现代脊椎动物进化的发育复杂性的关键。Rodeo将允许对数据进行查询，而不仅仅是来自一个生物体，而是来自多个生物体，提供一种搜索趋同进化模式的方法，这是理解过程的重要一步。Rodeo还将允许图案的可视化，从字面上“看到”网络复杂性的模式。Rodeo还将成为蛋白质相互作用基序的教育和研究的宝贵工具，以及将图算法应用于生物学中复杂问题的一般资源。Rodeo不仅对对相互作用组的网络结构感兴趣的高级科学家开放，而且对任何想要更多地了解细胞复杂性的生物学学生开放。具体而言，将在哈佛医学院制定一个为期两天的“motif发现”培训课程，该课程将免费向国际科学界开放。这将由PI和高级人员共同教授，包括关于生物学图形模型和算法的介绍性讲座，以及Rodeo的实践培训。其次，将通过Rodeo网站门户创建虚拟培训，其中将包含大量文档，以及我们的“主题发现”培训研讨会的流媒体视频。第三，牛仔竞技会将在两个关于生物计算的主要研讨会上举办教程，这些教程也将通过牛仔竞技会的门户网站提供。最后，Rodeo将被纳入医学院系统生物学系研究生的培训课程。

Harvard University is awarded a grant to build a new publicly available informatics tool, called Rodeo that will allow the integration of data relevant to deciphering protein-protein interaction networks and will provide a number of methods for analyzing such data, including graph-theoretic mathematics. Rodeo will allow the integration and analysis of several databases including a comprehensive set of orthologs from over 50 genomes, a new set of protein-protein (p-p) interaction data from humans together with over 70,000 p-p interactions from other organisms, a massive set of gene expression data from new experiments and experiments hosted by NCBI, and a database of almost 100,000 Expressed Sequence Tags from an organism (the acorn worm) that may hold the key for understanding the developmental complexity which evolved in modern day vertebrate animals. Rodeo will allow the interrogation of data not just from one organism, but across multiple organisms, providing a way to search for convergently evolved patterns a vital step to understanding process. Rodeo will also allow the visualization of motifs to literally 'see' patterns of network complexity. Rodeo will also be a valuable tool for education and research on protein-protein interaction motifs, and a general resource on the application of graph algorithms to complex questions in biology. Rodeo will be made accessible to not just advanced scientists interested in network structures of interactomes, but to any student of biology concerned with learning more about the complexity of the cell. Specifically a training program will be developed consisting of a 2-day training course on 'motif discovery' at Harvard Medical School that will be open to the international scientific community at no cost. This will be co-taught by the PI and senior personnel on the proposal, with introductory lectures on graph models and algorithms for biology, as well as hands-on training with Rodeo. Secondly, a virtual training via the Rodeo web portal will be created that will contain extensive documentation, as well as streaming video of our 'motif discovery' training seminars. Thirdly, Rodeo tutorials will be held at two of the major symposia on biocomputing, which will also be available through the Rodeo web portal. And last not least, Rodeo will be incorporated into the training curriculum for graduate students in the Department of Systems Biology at the Medical School.