Advanced Proteome Informatics of Cancer

描述(由申请人提供)：国家癌症研究所在癌症蛋白质组学的新技术平台上进行了大量投资，特别是通过人类癌症的小鼠模型和癌症临床蛋白质组技术。蛋白质组对于了解癌症的功能基因组学和系统生物学，以及发现和验证治疗和预防的生物标记物候选和分子靶点至关重要。复杂的蛋白质组分析需要先进的信息学来处理样本的复杂性、蛋白质浓度的极端动态范围、翻译后修饰、可选的剪接异构体、对各种扰动的反应以及数据库中的差异。目前在生物信息学这一子学科中训练有素的科学家的匮乏是密歇根大学癌症高级蛋白质组信息学T32培训基金的重点。我们建立在大学范围内的计算医学和生物信息学中心(CCMB)的生物信息学研究生项目的成功经验的基础上。我们现在有31名博士和6名硕士研究生，另外还有16名博士和14名硕士毕业生。我们的教师和学生领导着人类蛋白质组组织(HuPO)的倡议，开发和全球部署部分分布式文件共享系统和蛋白质交换，以及创建蛋白质组信息学的新算法。我们有一个由癌症研究人员、生物信息学家、统计学家、化学家和软件工程师组成的强大社区，专注于蛋白质组数据分析中的主要挑战。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虚会接受癌症生物学、生物信息学和计算机科学方面的培训。我们已取得重大进展，与来自弱势少数群体背景的申请者建立了牢固的关系。在密歇根大学建立癌症高级蛋白质组信息学的NCI培训计划，将为该领域的独立职业提供一批装备良好的新科学家，加强教员研究，并支持NCI的目标。 相关性：癌症研究人员正在使用新的蛋白质组学技术生成非常大的、复杂的数据集，这些技术使他们能够同时研究数千种蛋白质。这种数据的雪崩需要科学家在蛋白质组信息学的专业和多学科领域接受过良好的培训。在这一快速增长的领域中，没有足够数量的科学家接受培训，这一T32建议解决这个问题。

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.