Advanced Proteome Informatics of Cancer

描述（由申请人提供）：国家癌症研究所对癌症蛋白质组学的新技术平台进行了大量投资，特别是通过人类癌症小鼠模型和癌症临床蛋白质组学技术。蛋白质组学对于理解癌症的功能基因组学和系统生物学，以及发现和验证生物标志物候选物和治疗和预防的分子靶点至关重要。蛋白质组学的复杂分析需要先进的信息学来处理标本的复杂性、蛋白质浓度的极端动态范围、翻译后修饰、可选择的剪接异构体、对各种扰动的响应以及数据库的差异。目前生物信息学这一分支学科中训练有素的科学家的短缺是密歇根大学高级癌症蛋白质组信息学T32培训资助的重点。我们正在建立在我们的成功经验与生物信息学研究生课程，基于全校范围内的计算医学和生物信息学中心（CCMB）。现有博士31人，硕士6人，博士16人，硕士14人。我们的教师和学生是人类蛋白质组组织（HUPO）倡议的领导者，Tranche分布式文件共享系统和ProteomExchange的开发和全球部署，以及蛋白质组信息学新算法的创建。我们有一个强大的社区，由癌症研究人员、生物信息学家、统计学家、化学家和软件工程师组成，专注于蛋白质组数据分析的主要挑战。学员将来自不同的背景，并将通过课程、研讨会、期刊俱乐部和年度务静会接受癌症生物学、生物信息学和计算机科学方面的培训。我们在与弱势少数族裔申请者建立牢固关系方面取得了实质性进展。在密歇根大学建立一个NCI高级癌症蛋白质组信息学培训项目，将为该领域的独立职业提供一批新的科学家，加强教师研究，并支持NCI的目标。

DESCRIPTION (provided by applicant): The National Cancer Institute has made a substantial investment in new technology platforms for cancer proteomics, especially through the Mouse Models of Human Cancers and the Clinical Proteomic Technologies for Cancer. The proteome is critical to understanding functional genomics and systems biology of cancers and to discovery and validation of biomarker candidates and molecular targets for therapy and prevention. Sophisticated analysis of proteomes requires advanced informatics to deal with the complexity of specimens, the extreme dynamic range of protein concentrations, post-translational modifications, alternative splice isoforms, responses to all sorts of perturbations, and differences in databases. The current scarcity of trained scientists in this subdiscipline of bioinformatics is the focus of this proposed T32 training grant in Advanced Proteome Informatics of Cancer at the University of Michigan. We are building upon our successful experience with the Bioinformatics Graduate Program, based in the university-wide Center for Computational Medicine and Bioinformatics (CCMB). We now have 31 PhD and 6 M.S. students, plus 16 PhD and 14 M.S. graduates. Our faculty and students are in the leadership of Human Proteome Organization (HUPO) initiatives, development and global deployment of the Tranche distributed file-sharing system and the ProteomExchange, and creation of new algorithms for proteome informatics. We have a robust community of cancer researchers, bioinformaticians, statisticians, chemists, and software engineers focused on major challenges in proteome data analysis. Trainees will come from diverse backgrounds and will receive training in cancer biology, bioinformatics, and computer science through courses, seminars, journal club, and annual retreats. We have made substantial progress building strong relationships with sources of applicants from disadvantaged minority backgrounds. Establishment of an NCI training program in Advanced Proteome Informatics of Cancer at the U of M will provide a new pool of scientists well-equipped for independent careers in this field, enhance faculty research, and support NCI goals. RELEVANCE: Very large, complex datasets are being generated by cancer researchers using new proteomics technologies that allow them to study thousands of proteins simultaneously. This avalanche of data requires scientists well-trained in the specialized and multidisciplinary field of Proteome Informatics. Insufficient numbers of scientists are being trained in this rapidly growing field and this T32 proposes to address this issue.