Bioinformatics Core

这个核心的目的是提供转录和DNA-蛋白质相互作用的生物信息分析。 下一代测序仪已经改变了基因组研究，但对这些数据的分析仍然是 波特菲内克。我们的核心将提供必要的数据分析服务，以使这些数据可以访问和 可由本CORT的成员解释。本核心的私人助理和员工在以下领域拥有丰富的经验 对基因组数据的分析，以及对相关项目的基础生物学的熟悉。 尽管加州大学洛杉矶分校存在微阵列和测序核心，但这些都不能提供数据分析 一项服务。因此，没有内部计算专业知识的典型生物团队通常是 留下了数据，没有能力解释它。我们在这里提议的核心将消除这一障碍，因此 此计划项目中的所有小组将不仅能够从他们的 样本，还可以获得研究人员可以直接解释的经过处理和分析的数据 如果没有计算方面的专业知识。这一功能应该使基因组学研究更容易被 本计划项目的所有成员。我们还将与更有计算经验的人合作 本计划每个实验室的研究人员改进分析工具。这一核心的目标是： 1.RNA-SEQ数据分析：我们将对RNA-SEQ数据进行定量和变异检测分析。 2.ChlP-Seq数据的分析：我们将提供峰的位置、平均峰分布和模体。 3.基因组浏览器上的数据显示：在所有情况下，该核心都会将全基因组数据加载到我们的 安装UCSC基因组浏览器，使用户可以看到数据的单碱基分辨率 从每个样本中生成。我们还将把数据和分析工具上传到维基网站进行交流。 4.数据质量指标：我们将为序列数据生成质量指标，以提供对 样品的质量。

The purpose of this Core is to provide bioinformafic analysis of transcriptomic, and DNA-protein interacfions. Next-generation sequencers have transformed genomic research, yet the analysis of this data remains a botfieneck. Our Core will provide the essenfial data analysis service to render this data accessible and interpretable to the members of this CORT. The PI and staff of this Core have extensive experience in the analysis of genomic data, as well as familiarity with the underiying biology of the associated projects. Despite the fact that microarray and sequencing cores exist at UCLA, none of these provide data analysis as a service. Therefore the typical biology group that does not have internal computational expertise is often left with data and no ability to interpret it. The Core we are proposing here will remove this impediment so that all the groups within this Program Project will be able to not only collect sequencing data from their samples, but also obtain processed and analyzed data that can be directly interpreted by researchers without computational expertise. This functionality should render genomics research far more accessible to all members of this Program Project. We will also work with more computafionally more experienced researchers in each lab of this Program to refine analysis tools. The Aims of this Core are: 1. Analysis of RNA-seq data: we will provide quantificafion and variant detection analyses of RNA-seq data. 2. Analysis of ChlP-seq data: we will provide the locafion of peaks, average peak distribufions and motifs. 3. Data display on genome browser: in all cases this core will also load genome-wide data onto our installation of the UCSC genome browser so that users can see at single base resolufion the data generated from each sample. We will also upload data and analysis tools to the Wiki site for exchange. 4. Data Quality metrics: we will generate quality metrics for sequence data to provide an estimate of the quality of the sample.