Bioinformatics Core

该核心的目的是帮助CEG研究人员将基因组和蛋白质数据转换为 通过使用适当的统计方法和计算工具获得有意义的信息（知识）。 生物信息学F&S核心有三个具体目标。 [1]第一个目的是帮助CEG研究人员设计和分析基因表达 实验这将包括咨询研究人员对不同类型的 实验重复，选择适当的实验设计，管理和评估的质量 获得的数据，使用适当的统计分析鉴定差异表达的基因，鉴定 通过表达数据的聚类和差异相关性， 表达或共表达的基因组，具有有关受影响途径、基因组位置和 相关基因的结构和其他生物学相关信息。除了使用目前最好的 核心成员将开发新的方法来鉴定差异表达的基因 使用经验贝叶斯框架和明确的模型， 测量和基因表达水平。 [2]第二个目的是帮助CEG研究人员在上下文中评估他们的实验数据 其他相关表达实验和生物学相关数据的类型。在这 生物信息学F&S核心将维护所有基因表达实验的数据库 并开发一个网络服务器，以方便访问有关内部和外部 生成基因表达数据。使用内部开发的统计模型，核心成员将开发 用于整合分析不同微阵列数据集的协议，并使用它们来分析累积的CEG 微阵列数据。这种分析的结果将包含隐含的基于结构的功能注释， 将通过适当的基于网络的 应用. [3]第三个目标是协助CEG调查人员管理和分析所产生的数据 通过其他基因组和蛋白质组学技术。核心成员将协助非表达微阵列 技术，例如基于基因芯片的SNP基因分型，评估转录因子结合位点（“ChIP-on- Chip”）、MicroRNA（miRNA）谱和CpG岛谱，这些谱将在公开发行中引入 基因组学核心核心成员还将协助优化使用不同的工具， 指纹图谱、质谱蛋白质分析和任何其他可能可用的蛋白质组学分析 在下一个融资周期。核心将积累来自CEG研究人员的肽质谱， 开发新的机器学习算法用于大规模肽指纹分析。 核心包括数据管理，统计分析，机器学习，微阵列数据 分析、蛋白质组学、序列分析和蛋白质结构建模。为了实现具体目标，核心将 利用最先进的数据管理解决方案，促进对所有相关方面的跟踪， 实验数据以及不同实验、实验平台和模型之间的数据集成 有机体核心成员将使用最新的分析方法，并开发新的方法， 识别单个细胞内的不同生物实体的行为的可再现的差异和相似性。 实验或跨多个数据库、平台、模型生物以及最终不同的数据类型。 最后，核心成员将使用适当的技术来促进基于网络的访问、分析和挖掘 实验数据和分析结果。

The objective of this Core is to assist CEG investigators in converting the genomic and protein data into meaningful information (knowledge) through the use of appropriate statistical methods and computational tools. The Bioinformatics F&S Core has three specific aims. [1] The first aim is to assist CEG researchers in designing and analyzing gene-expression experiments. This will encompass consulting researchers on the relative importance of different types of experimental replicates, choosing the appropriate experimental design, managing and assessing the quality of the obtained data, identifying differentially expressed genes using appropriate statistical analysis, identifying significant patterns of expression through the clustering of expression data, and correlating differentially expressed or co-expressed groups of genes with information about affected pathways, genomic location, and structure of involved genes and other biologically relevant information. In addition to using the best of currently available methods, Core members will develop new methods for identifying differentially expressed genes using the empirical Bayesian framework end explicit models relating variability of gene-expression measurements and the level of gene expression. [2] The second aim is to assist CEG investigators in evaluating their experimental data in the context of other relevant expression experiments and types of biologically relevant data available. In this respect, the Bioinformatics F&S Core will maintain the database of all gene-expression experiments performed by CEG members and develop a web server to facilitate access to relevant internally and externally generated gene-expression data. Using in-house developed statistical models, Core members will develop protocols for integrative analysis of diverse microarray datasets and use them to analyze accumulated CEG microarray data. Results of such analyses will incorporate structure-based functional annotations of implied interactions, and access to the results of the analysis will be facilitated through appropriate web-based applications. [3] The third aim is to assist CEG investigators with the management and analysis of data generated by other genomic and proteomic technologies. Core members will assist with non-expression microarray technologies such as GeneChip-based SNP-genotyping, assessing transcription factor binding sites ("ChlP-on- Chip"), MicroRNA (miRNA) profiles, and CpG Island profiles, which are being introduced in the general offering of the Genomics Core. Core members will also assist with optimal use of different tools for mass peptide fingerprinting, mass spectrometric protein profiling, and any other proteomic assay that might become available over the next funding period. The Core will accumulate peptide mass spectra from CEG researchers and develop novel machine learning algorithms for mass peptide fingerprinting. The Core includes experts in data management, statistical analysis, machine learning, microarray data analysis, proteomics, sequence analysis, and protein structure modeling. To fulfill specific aims, the Core will utilize state-of-the-art data-management solutions that will facilitate the tracking of all relevant aspects of experimental data and integration of data across different experiments, experimental platforms, and model organisms. Core members will use the most up-to-date analytical approaches and develop new methods for identifying reproducible differences and similarities in behavior of different biological entities within a single experiment or across multiple databases, platforms, model organisms, and ultimately different data types. Finally, Core members will use appropriate technologies for facilitating web-based access, analysis and mining of experimental data, and analytical results.