Shared Resource-Bioinformatics Core

生物信息学核心共享资源--项目摘要/摘要 生物信息学核心为基因组学的所有方面提供分析、计算和技术服务 以及由索尔克癌症中心成员进行的生物信息学研究。在生物学的大数据时代， 生物信息学分析对于从大量和有噪音的高通量数据中获得生物洞察力至关重要。这个 核心的目标是让癌症中心的成员能够获得最先进的生物信息学专业知识 和支持。核心中心的工作人员协助癌症中心成员对测序数据集进行综合分析， 提供软件和工具，癌症中心成员可以使用它们进行自己的分析，提供 软件使用方面的培训，并开发用于尖端分析的定制工具和管道。这个 生物信息学核心为常见请求的分析提供标准的最佳实践数据分析，例如： 来自全外显子组和全基因组测序数据的变体调用，基因组变体的注释， 来自CHIP-SEQ、CLIP-SEQ、DAMID或4C实验的峰的识别和注释，基序分析来自 这些峰，鉴定来自RNA-Seq和小RNA-Seq数据集的差异表达基因， 识别和注释来自HIC数据集的相互作用，识别来自RNA的新剪接异构体- SEQ数据集、已发表或生成的微阵列数据集的重新分析、不同基因组学的整合 数据集(例如，识别差异ChIP-Seq和差异RNA-Seq数据集之间的相关性)以及 已分析基因组数据集的通径/过度表达分析。根据需要，他们还开发新的工具 为癌症中心成员提供的技术，他们的项目推动了研究的边界，产生了 分析的出版物质量可视化，并帮助准备科学手稿和赠款，其中包括 生物信息学或基因组学组件。

Bioinformatics Core Shared Resource - Project Summary/Abstract The Bioinformatics Core provides analytical, computational, and technical services for all aspects of genomics and bioinformatics research performed by Salk Cancer Center members. In the era of big data in biology, bioinformatics analysis is crucial for deriving biological insights from large and noisy high-throughput data. The goal of the Core has been to give Cancer Center members access to state-of-the-art bioinformatics expertise and support. Staff in the Core assist Cancer Center members with integrative analysis of sequencing datasets, provide software and tools that Cancer Center members can use to perform their own analyses, provide training in software use, and develop customized tools and pipelines for cutting-edge analyses. The Bioinformatics Core provides standard, best practice data analysis for commonly requested analyses such as: variant calling from whole exome and whole genome sequencing data, annotation of genomic variants, identification and annotation of peaks from ChIP-Seq, Clip-Seq, DamID, or 4C experiments, motif analysis from these peaks, identification of differentially expressed genes from RNA-Seq and small RNA-Seq datasets, identification and annotation of interactions from HiC datasets, identification of novel splice isoforms from RNA- Seq datasets, reanalysis of published or generated microarray datasets, integration of diverse genomics datasets (e.g., identifying correlations between differential ChIP-Seq and differential RNA-Seq datasets) and pathway/overrepresentation analysis of analyzed genomic datasets. As needed, they also develop new tools and techniques for Cancer Center members whose projects push the boundaries of research, generate publication quality visualizations of analyses, and help prepare scientific manuscripts and grants containing bioinformatics or genomics components.