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各个方面提供分析、计算和技术服务 以及索尔克癌症中心成员进行的生物信息学研究。在生物学大数据时代， 生物信息学分析对于从大量和有噪声的高通量数据中获得生物学见解是至关重要的。的 核心的目标是让癌症中心成员获得最先进的生物信息学专业知识 和支持核心工作人员协助癌症中心成员对测序数据集进行综合分析， 提供癌症中心成员可以用来执行自己的分析的软件和工具， 软件使用培训，并为尖端分析开发定制工具和管道。的 Bioinformatics Core为常见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识别，基因组变体的注释， 来自ChIP-Seq、Clip-Seq、DamID或4C实验的峰的鉴定和注释， 这些峰，从RNA-Seq和小RNA-Seq数据集鉴定差异表达的基因， 从HiC数据集鉴定和注释相互作用，从RNA中鉴定新的剪接异构体， Seq数据集，重新分析已发表或生成的微阵列数据集，整合不同的基因组学 数据集（例如，鉴别差异化ChIP-Seq和差异化RNA-Seq数据集之间的相关性）和 分析的基因组数据集的途径/过度表达分析。根据需要，他们还开发新的工具 癌症中心成员的项目推动了研究的界限， 分析的出版质量可视化，并帮助准备科学手稿和赠款， 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.