Data Analysis

这个项目的首要目标是测试这一假设，即磷酸化介导的 响应于扰动的信号事件可以通过改变它们的表观遗传来建立新的细胞状态。 的风景.为了实现这一目标，我们建议进行基于质谱（MS）的蛋白质组学分析 其特异性靶向细胞中磷酸化信号传导和染色质修饰的定量读数， 15，000个扰动条件。这些扰动将集中在信号级联的调节上， 小分子和基因失活的表观遗传标记。我们将研究几种不同的细胞模型 系统，包括从人胚胎开始的神经元谱系分化的全面研究 干细胞我们建议建立一个center.in，以开发必要的基础设施，管道，数据 管理和分析需要执行什么将是最大的一组相关实验， 迄今为止的MS蛋白质组学读数。我们还将探索下一代MS采集技术， 建立一个永久可开采的MS数据资源，供公众访问。贡献力量 将产生的数据和工具提供给基于集成网络的蜂窝签名库（LINCS）计划 为了通过磷酸化蛋白质组学在不同扰动之间建立联系， 染色质修饰签名与其他数据类型一起由其他人贡献给LINCS 中心.由此产生的分析将有助于确定新的治疗机会和协同作用， 磷酸信号和表观遗传系统的失调是两种最常见的分子病因 在越来越多的遗传、发育和环境疾病中发现。 在该项目的这一部分中，我们描述了数据分析管道、高级统计和 生物信息学技术，以及我们将用于执行该项目的数据存储策略。我们也 讨论我们中心以外的最终用户将如何访问、分析、可视化数据并与之交互， 我们通过我们将开发的基于网络的工具产生。

The overarching goal of this project is to test the hypothesis that modulation of phosphorylation-mediated signaling events in response to perturbations can establish new cellular states by altering their epigenetic landscapes. To achieve this goal, we propose performing mass spectrometry (MS)-based proteomic assays that specifically target quantitative readouts of phosphosignaling and chromatin modifications in cells on > 15,000 perturbational conditions. These perturbations will focus on modulation of signaling cascades and epigenetic marks by small molecules and gene inactivations. We will study several different cellular model systems, including comprehensive studies neuronal lineage differentiation starting from human embryonic stem cells. We propose to establish a center.in order to develop the necessary infrastructure, pipelines, data management, and analytics required to perform what would be the largest set of related experiments with MS proteomic read outs to date. We will also explore next-generation MS acquisition technologies to establish a permanently minable MS data resource that will be accessible to the public. We will contribute the resulting data and tools to the Library of Integrated Network-based Cellular Signatures (LINCS) program for the purpose of making connections among disparate perturbations through phosphoproteomic and chromatin modification signatures in concert with other data types to be contributed to LINCS by other centers. The resulting analyses will help identify novel therapeutic opportunities and synergies, as dysregulation of phosphosignaling and epigenetic systems are two of the most common molecular etiologies identified in a growing number of genetic, developmental, and environmental diseases. In this component of the project we describe the data analysis pipelines, advanced statistical and bioinformatic techniques, and data repository strategies that we will use to prosecute the project. We also discuss how end-users outside of our center will access, analyze, visualize, and interact with the data that we generate through web-based tools that we will develop.