Bioinformatics Core

该核心的目的是提供转录组学和DNA-蛋白质相互作用的生物信息学分析。 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一个瓶颈。我们的核心将提供必要的数据分析服务，使这些数据可访问和解释的CORT的成员。该核心的PI和工作人员在基因组数据分析方面拥有丰富的经验，并熟悉相关项目的基础生物学。 尽管加州大学洛杉矶分校有微阵列和测序核心，但这些核心都不提供数据分析服务。因此，没有内部计算专业知识的典型生物学小组通常只剩下数据，没有能力解释它。我们在这里提出的核心将消除这一障碍，以便本计划项目中的所有小组不仅能够从他们的样本中收集测序数据，而且还能够获得处理和分析的数据，这些数据可以由没有计算专业知识的研究人员直接解释。这一功能应使基因组学研究更容易获得本计划项目的所有成员。我们还将与该计划的每个实验室中更有经验的计算研究人员合作，以改进分析工具。该核心的目标是： 1. RNA-seq数据分析：我们将提供RNA-seq数据的定量和变异检测分析。 2. ChIP-seq数据的分析：我们将提供峰的位置、平均峰分布和基序。 3.在基因组浏览器上显示数据：在所有情况下，该核心还将基因组范围的数据加载到我们安装的UCSC基因组浏览器上，以便用户可以以单碱基分辨率查看每个样本生成的数据。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，以提供样本质量的估计。

The purpose of this Core is to provide bioinformatics analysis of transcriptomic, and DNA-protein interactions. Next-generation sequencers have transformed genomic research, yet the analysis of this data remains a bottleneck. Our Core will provide the essential 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 underlying 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 computationally 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 quantification and variant detection analyses of RNA-seq data. 2. Analysis of ChlP-seq data: we will provide the location of peaks, average peak distributions 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 resolution 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.