Bioinformatics and Pathways Core

生物信息学和通路核心

• 批准号: 10629617
• 负责人: Jonathan Daniel Wren
• 金额: $ 48.5万
• 依托单位: OKLAHOMA MEDICAL RESEARCH FOUNDATION
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629617
• 关键词: Acceleration; Applications Grants; Bioinformatics; Biological; Centers of Research Excellence; ChIP-seq; Chemicals; Computer software; Data; Data Analyses; Databases; Dedications; Development; Developmental Biology; Disease; Doctor of Philosophy; Ensure; FAIR principles; Fee-for-Service Plans; Feedback; Future; Gene Expression; Genes; Genomic Segment; Genomics; Human Resources; Image Analysis; Institution; Literature; Meta-Analysis; Methods; Modeling; Nature; Network-based; Oklahoma; Participant; Pathway interactions; Peer Review; Phase; Phenotype; Property; Publications; Publishing; Research; Research Design; Research Personnel; Resources; Sample Size; Scientist; Services; Technology; Troglodytinae; cohesion; cost effective; data integration; design; differential expression; experience; experimental analysis; experimental study; high throughput analysis; improved; interest; machine learning method; novel; novel strategies; power analysis; public database; skills; task analysis; tool; transcriptome sequencing

ABSTRACT BIOINFORMATICS & PATHWAYS CORE The Bioinformatics and Pathways Core (BPC) will support users with bioinformatics analysis of high- throughput experiments such as RNA-seq, ChIP-seq, and genomic analyses, and provide statistical support in both the design and interpretation of experiments. During Phases I and II of this COBRE, BPC support has helped substantially to increase the competitiveness of Project Lead grant applications, based upon reviewer feedback. The BPC is led by Dr. Jonathan Wren, a bioinformatician with 19 years of experience and includes a dedicated Ph.D.-level Statistician and a Bioinformatics Scientist. Our Specific Aims are: Aim 1: Create a cost-effective resource for high-quality, advanced data analysis services, employing both fee-for-service and collaborative models. BPC personnel have extensive experience using state-of- the-art bioinformatics methods, as well as a track record of developing novel analysis methods. We will provide support for pre-experimental study design, including power analyses and utilize novel bioinformatics and statistical tools to solve specific biological questions posed by COBRE participants. The Core Director has developed a number of novel tools, such as software to: 1) Automate the identification and analysis of related entities of interest (e.g., genes, diseases, phenotypes, etc.) within the published literature; 2) Identify features, properties and experiments that overlap with genomic regions of interest; 3) Identify and analyze experiments in public microarray databases like NCBI’s Gene Expression Omnibus (GEO) through meta-analysis and 4) Interpret differentially expressed genes in terms of the correlations between them within public databases. Aim 2: Provide a set of experienced analysts with a diversity of skills and expertise to accommodate a variety of future analysis projects. This COBRE has enabled the BPC to develop novel software and to hire personnel with a set of skills broadly useful to helping biomedical researchers. And, as evidenced by our collaborative publications during COBRE Phases I and II, we have been successful at using our capabilities to help others advance their research. Phase III will enable us to establish a self-sustaining institutional Core. Aim 3: Continue to develop, evaluate, and refine new methods of data analysis and data integration. The BPC will develop increasingly sophisticated and powerful technologies that evolve with the demands of scientific investigators. We will improve tracking of resource utilization, advance technologies that improve online interactivity between the data provided and our analysis of it, help investigators make their data compliant with FAIR principles, develop methods for evaluating network-based data cohesion, and advance machine-learning methods for common image analysis tasks.

摘要生物信息学和途径核心 生物信息学和途径核心（BPC）将通过高级生物信息学为用户提供支持 吞吐量实验，例如RNA-SEQ，CHIP-SEQ和基因组分析，并提供统计支持 经验的设计和解释。 基于审核者 反馈。 专门的博士学位统计学家和生物信息学科学家。 目标1：为高质量的高级数据分析服务创建具有成本效益的资源 供服务和协作模型。 ART生物信息学方法以及我们将提供新的分析方法的记录 支持实验前的研究设计，不可能分析能力分析并利用新颖的生物信息学和 解决核心参与者提出的特定生物学问题的统计工具。 开发了许多新颖的工具，例如：1）自动化相关的识别和分析 感兴趣的实体（例如，基因，疾病，表型等）在已发表的文献中； 与感兴趣的基因组区域重叠的特性和实验； 3）识别和分析实验 在公共微阵列数据库中，如NCBI的基因表达综合（GEO），通过荟萃分析和4） 根据公共数据库中它们之间的相关性来解释差异表达的基因。 目标2：提供一组具有多种技能和专业知识的经验丰富的分析师，以适应 多种将来的分析项目。 具有一组技能的人员，可以帮助生物医学研究人员。 Cobres I和II期间的合作出版物 帮助其他人提高他们的研究。 目标3：继续开发，评估和完善数据分析和数据集成的新方法。 BPC Willop越来越复杂且强大的技术，随着要求的需求而发展 科学研究人员。 所提供的数据与我们对其分析之间的在线互动，帮助调查人员制作数据 符合公平原则，开发用于评估基于网络的数据凝聚力的方法并提高 通用图像分析任务的机器学习方法。