Supporting and evolving Gene Set Enrichment Analysis and the Molecular Signatures Database for cancer research

支持和发展癌症研究的基因集富集分析和分子特征数据库

基本信息

批准号：
9921305
负责人：
JILL P. MESIROV
金额：
$ 67.99万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9921305
关键词：
Address Algorithmic Analysis Algorithms Benchmarking Biological Biological Process Biomedical Research Cancer Patient Cancer Research Project Clustered Regularly Interspaced Short Palindromic Repeats Code Collaborations Collection Communities Companions Computer software Computing Methodologies Data Data Analyses Data Set Databases Disease Documentation Education and Outreach Educational workshop Electronic Mail Ensure Extensible Markup Language Family Gene Combinations Gene Expression Profiling Genes Genetic Genomics Goals Investigation Knock-out Letters Libraries Literature Malignant Neoplasms Methodology Methods Modality Molecular Molecular Analysis Molecular Profiling Network-based Open Reading Frames Pathway interactions Persons Pharmaceutical Preparations Phenotype Process Proteomics Publications Regulation Repression Reproducibility Research Research Personnel Resources Sampling Source Source Code Specificity Technology Testing Time Training Validation Work Xenograft procedure anticancer research base chromosomal location data exchange differential expression experience experimental study file format flexibility genome-wide genome-wide analysis improved interest knock-down knowledge base light weight mouse model next generation novel strategies open source overexpression portability relational database repository response small hairpin RNA small molecule success tool transcriptome transcriptome sequencing user-friendly web site

项目摘要

Project Abstract Gene Set Enrichment Analysis (GSEA) introduced in 2003, is now standard practice for analyzing genome- wide expression data. GSEA derives its power from identifying the activation/repression of sets of genes that share common biological function, chromosomal location or regulation and differentiate biological phenotypes or cellular states. This knowledge-based approach is effective in elucidating underlying biological mechanisms and generating hypotheses for further study and experimental validation. Since 2005, we have developed, distributed and supported a freely available GSEA software application along with a database of annotated gene sets – the Molecular Signatures Database (MSigDB). This popular resource has more than 113,000 registered users and over 10,200 citations in the literature, and the MSigDB has almost 18,000 annotated sets. The goal of this proposal is to continue to evolve and add value to the GSEA/MSigDB resource to best address the needs of the cancer research community, while maintaining the high level of professional quality and strong support that investigators have come to expect. We plan to increase the power and sensitivity of the GSEA method and enrich the MSigDB to further accelerate the pace of genomic research. Our specific aims are: Aim 1: Develop and deploy the next generation of the GSEA method and software to keep pace with the needs of the cancer research community. The new core algorithm will be based on information- theoretic approaches, guided by a collection of 100 relevant benchmarks and informed by an Advisory Board of established cancer researchers. To facilitate the use of GSEA by researchers at all levels of computational sophistication, we will distribute the GSEA analysis tools as both an open source code library and a suite of user friendly, reproducible, interactive, electronic notebooks. Aim 2: Extend the scope and specificity of the MSigDB, and evolve the underlying technology. In collaboration with the community, we will add valuable new collections to MSigDB including signatures of drug responses and genetic perturbations, sets for use with mouse models of cancer and PDXs, sets from pathway and network databases, and sets for use with proteomic data analysis. The MSigDB will be redesigned from its current XML file format and deployed as a lightweight, portable relational database that can better support its growing size, online exploration tools, and use by investigators and other software. Aim 3: Provide training and outreach activities for the cancer research community, and maintain and support the GSEA software and MSigDB. The success and popularity of the GSEA/MSigDB resource over the past decade;; our extensive experience in developing computational methods for genomics research and delivering them as user-friendly, high quality software;; our significant user base and many citations;; our large repository of gene sets;; and our successful delivery of documentation and training for users make us well poised to carry out the aims of this proposal.

项目摘要基因集富集分析 (GSEA) 于 2003 年推出，现已成为分析基因组的标准做法 GSEA 的力量来自于识别一组基因的激活/抑制。共享共同的生物功能、染色体位置或调控并区分生物表型或细胞状态。这种基于知识的方法可以有效地阐明潜在的生物机制自 2005 年以来，我们已经开发出用于进一步研究和实验验证的假设。分发并支持免费提供的 GSEA 软件应用程序以及带注释的数据库基因集 – 分子特征数据库 (MSigDB) 这个受欢迎的资源拥有超过 113,000 个。注册用户和文献引用超过 10,200 次，MSigDB 拥有近 18,000 个注释集。该提案的目标是继续发展 GSEA/MSigDB 资源并为其增加价值，以最好地解决癌症研究界的需求，同时保持高水平的专业素质和强大的实力。我们计划提高 GSEA 的能力和灵敏度。方法并丰富 MSigDB，进一步加快基因组研究的步伐，我们的具体目标是：目标 1：开发和部署下一代 GSEA 方法和软件，以跟上癌症研究界的需求。新的核心算法将基于以下信息：理论方法，以 100 个相关基准为指导，并以咨询为依据已成立的癌症研究人员委员会，以促进各级研究人员使用 GSEA。为了提高计算的复杂性，我们将以开源代码的形式分发 GSEA 分析工具图书馆和一套用户友好的、可复制的、交互式的电子笔记本。目标 2：扩展 MSigDB 的范围和特殊性，并发展底层技术。与社区合作，我们将向 MSigDB 添加有价值的新集合，包括签名药物反应和遗传扰动，用于癌症和 PDX 小鼠模型的组，来自通路和网络数据库以及用于蛋白质组数据分析的集。从当前的 XML 文件格式重新设计并部署为轻量级、可移植的关系数据库可以更好地支持其不断增长的规模、在线探索工具以及调查人员和其他软件的使用。目标 3：为癌症研究界提供培训和外展活动，并维持并支持GSEA软件和MSigDB。过去十年 GSEA/MSigDB 资源的成功和受欢迎程度；；我们在这方面的丰富经验开发基因组学研究的计算方法，并以用户友好、高质量的方式提供它们软件；；我们重要的用户群和许多引用；；我们的大型基因组存储库；；为用户提供文档和培训使我们能够很好地实现该提案的目标。