UCLA Pediatric Research Education Program in Bioinformatics, Computational Biology, and Omics

加州大学洛杉矶分校生物信息学、计算生物学和组学儿科研究教育项目

• 批准号: 10629061
• 负责人: Sherin U Devaskar
• 金额: $ 16.14万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629061
• 关键词: Address; Area; Basic Science; Big Data; Bioinformatics; Biological Sciences; Biometry; Caring; Child health care; Childhood; Clinical; Clinical Medicine; Clinical Research; Collaborations; Complex; Computational Biology; Computational Science; Congenital Abnormality; DNA Methylation; Data Analyses; Data Set; Development; Diagnosis; Discipline; Disease; Doctor of Philosophy; E-learning; Education; Education Gap; Educational Status; Educational process of instructing; Educational workshop; Endocrine System Diseases; Etiology; Faculty; Fostering; Foundational Skills; Functional disorder; Funding; Future; Genetic Diseases; Health; High-Throughput Nucleotide Sequencing; Hybrids; Immune System Diseases; Individual; Institution; Instruction; Intervention; Journals; Knowledge; Life; Life Cycle Stages; Link; Malignant Childhood Neoplasm; Malignant Neoplasms; Medical; Mentored Clinical Scientist Development Program; Mentors; Metabolic Diseases; Molecular; Organ; Pathway Analysis; Pathway interactions; Patient Care; Patients; Pediatric Research; Pediatrics; Physicians; Postdoctoral Fellow; Prevention strategy; Proteome; Research; Research Personnel; Research Support; Scholarship; Science; Scientist; Systemic disease; Talents; Techniques; Technology; Training; Training and Education; Translational Research; big biomedical data; big-data science; career; collaboratory; disorder risk; education research; educational atmosphere; epigenome; experience; flipped classroom; forging; gene network; genome sequencing; graduate medical education; interest; lectures; medical schools; member; metabolome; metagenome; microbiome analysis; multidisciplinary; new therapeutic target; next generation; pediatric department; pediatric patients; precision medicine; programs; recruit; single-cell RNA sequencing; skills; targeted treatment; tool; transcriptome; trend; undergraduate student; whole genome

PROJECT SUMMARY/ABSTRACT Bioinformatics, computational biology, and omics are important areas that have the potential to uncover new discoveries in the molecular/cellular mechanisms and gene networks that lead to congenital birth defects, metabolic and genetic disorders, organ dysfunction, disorders of the immune system, and cancer in Pediatric patients. The Department of Pediatrics, David Geffen School of Medicine at UCLA proposes to fill the gap for much needed specialized education and in-depth training of pediatric graduate medical education (GME) clinical trainees in biomedical big data analysis, promote the establishment of multidisciplinary networks and opportunities for collaboration in these specialized areas, and provide a bridge for trainees towards successful participation as clinicians or clinician-scientists in multi-disciplinary team science towards obtaining future funding for the discovery and amelioration of childhood disorders (i.e. T32, K23, K12, K08) and academic careers in diverse research pathways. This program will focus on recruiting pediatric GME trainees from diverse backgrounds, with an interest in clinician researcher or physician-scientist careers, and who have a desire to build foundational skills in big data analysis. Specifically, the program will target Pediatric residents in their 2nd or 3rd year of training and Pediatric subspecialty fellows at the start of their 2nd year of training. The 12-week program, set to occur at the start of the academic year, will include weekly didactic lectures, taught by UCLA faculty with expertise in bioinformatics, biostatistics, computational biology, and omics, covering key areas of big data science and omics, accompanied by workshops linked to the specific topic presented in the didactic lectures, and interactive journal club sessions. The interactive workshops, conducted by faculty and UCLA Quantitative and Computational Biosciences Collaboratory postdoctoral PhD fellows, will reinforce concepts from the didactic lectures through hands-on application of the knowledge via analysis of actual experimental datasets and use of statistical techniques. The journal club sessions will be in a flipped classroom format and will reinforce concepts covered in the didactic lectures and workshops to demonstrate the application of bioinformatics and computational biology technology that lead to new discovery and advancement of Pediatric patient care. The program will allow for remote access by pediatric trainees beyond the institution through virtual learning (hybrid format) and availability of educational content online. Key areas of skills building will include next generation whole genome sequencing, transcriptome, proteome, and metabolome analysis, single cell RNA sequencing, DNA methylation analysis, microbiome analysis, and gene network analysis. This program will provide a unique opportunity for pediatric GME trainees to receive more formal instruction and training in the very important areas of big data analysis and directly interact with the faculty on campus, beyond the Department of Pediatrics, with expertise in these areas who could potentially serve as research and career mentors, thus facilitating their successful academic journey.

项目摘要/摘要 生物信息学，计算生物学和OMIC是有可能发现新的重要领域 在分子/细胞机制和基因网络中的发现，导致先天性先天缺陷， 代谢和遗传疾病，器官功能障碍，免疫系统的疾病以及小儿癌症 患者。加州大学洛杉矶分校的David Geffen医学院儿科系提议填补空白 急需的专业教育和小儿研究生教育（GME）临床的深入培训 生物医学大数据分析的学员，促进建立多学科网络和 在这些专业领域的合作机会，并为受训者提供了桥梁 作为临床医生或临床医生科学家参与多学科团队科学，以获得未来的资金 为了发现和改善儿童疾病（即T32，K23，K12，K08）和学术职业 多样化的研究途径。该计划将着重于招募各种各样的儿科GME学员 背景，对临床医生研究人员或医师科学家职业感兴趣，并且有愿望 在大数据分析中建立基本技能。具体而言，该计划将针对儿科居民的第二名 或在培训第二年开始的第三年的培训和小儿专业研究员。 12周 计划将在学年开始时进行，包括UCLA教授的每周教学演讲 具有生物信息学，生物统计学，计算生物学和OMIC的专业知识的教师，涵盖了大的关键领域 数据科学和奥理，并伴随着与教学讲座中提出的特定主题相关的研讨会， 和互动期刊俱乐部会议。由教师和UCLA定量进行的互动研讨会 和计算生物科学协作博士博士博士学位研究员，将增强教学的概念 通过分析实际实验数据集的知识实践应用和使用，并使用 统计技术。期刊俱乐部会议将以翻转的教室格式，并将加强概念 由教学讲座和讲习班涵盖，以证明生物信息学和 计算生物学技术，导致新发现和进步儿科患者护理。这 计划将允许通过虚拟学习远离机构以外的小儿访问（Hybrid） 格式）和在线教育内容的可用性。建立技能的关键领域将包括下一代 整个基因组测序，转录组，蛋白质组和代谢组分析，单细胞RNA测序， DNA甲基化分析，微生物组分析和基因网络分析。该程序将提供独特的 小儿GME学员有机会在非常重要的领域接受更多正式的指导和培训 大数据分析，并直接与校园的教职员工互动，除了儿科系以外 在这些领域的专业知识，这些领域有可能担任研究和职业导师，从而促进他们的 成功的学术旅程。