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：
10153712
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/10153712
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 Gene set enrichment analysis Genes Genetic Genetic Transcription Genomics Goals Investigation Knock-out Letters Libraries Literature Malignant Neoplasms Methodology Methods Modality Molecular 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 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 patient derived xenograft model portability relational database repository response small hairpin RNA small molecule success tool 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 2003年引入的基因集富集分析（GSEA）现在是分析基因组的标准实践广泛的表达数据。 GSEA通过确定基因集的激活/抑制而获得其力量共享共同的生物学功能，染色体位置或调节，并区分生物学表型或细胞状态。这种基于知识的方法有效阐明潜在的生物学机制并产生用于进一步研究和实验验证的假设。自2005年以来，我们已经发展了分发并支持免费的可用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）。这个流行的资源超过113,000 注册用户和文献中的10,200多种引用，MSIGDB的注释近18,000套。该提案的目的是继续发展并为GSEA/MSIGDB资源增加价值为最佳地址癌症研究界的需求，同时保持高水平的专业质量和强大支持调查人员已经期待的支持。我们计划提高GSEA的功率和灵敏度方法并丰富了MSIGDB，以进一步加速基因组研究的空间。我们的具体目的是：目标1：开发和部署下一代GSEA方法和软件，以保持空间癌症研究界的需求。新的核心算法将基于信息 - 理论方法，以100个相关基准的集合为指导，并由咨询成熟的癌症研究人员委员会。促进研究人员在各个级别的GSEA使用计算社会化，我们将分发GSEA分析工具作为开源代码图书馆和一套用户友好，可重复的，互动的，电子笔记本。目标2：扩展MSIGDB的范围和特异性，并发展基础技术。在与社区的合作，我们将为MSIGDB增加价值的新收藏，包括药物反应和遗传扰动，用于与癌症和PDX的小鼠模型一起使用的集合途径和网络数据库，并设置用于蛋白质组学数据分析。 MSIGDB将从其当前的XML文件格式重新设计，并部署为轻巧的，可移植的关系数据库可以更好地支持其不断增长的规模，在线探索工具以及研究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，并维护并支持GSEA软件和MSIGDB。在过去的十年中，GSEA/MSIGDB资源的成功和普及；我们丰富的经验开发用于基因组学研究的计算方法，并将其作为用户友好，高质量传递软件;;我们重要的用户群和许多引用；我们的大型基因库；和我们的成功提供给用户的文档和培训使我们有充分的中毒来执行该建议的目标。