Bioinformatics and High-Dimensional Data Analysis Shared Resource

生物信息学与高维数据分析共享资源 摘要 近年来，高维数据的复杂性和重要性有了巨大的增长， 成功分析和集成所需的专门方法和技能。为了解决这个新出现的问题 需要，并解决之前的批评，生物信息学和高维数据分析共享 资源(生物信息学)是通过整合以前的信息学组件开发的 分布在生物统计和其他三个共享资源中，形成了一个以数据分析为重点的资源 扩大了范围，增强了专门知识，扩大了能力。重组后的资源提供了 用于基因组学、化学信息学、图像分析等复杂领域的最先进的信息学和数据分析 有问题。教研室主任杰里米·爱德华兹博士(CGEG)是基因组学和生物信息学专家，也是 导演Tudor Oprea，MD，Phd(CT)和Keith Lidke，Phd(TCBS)领导化学信息学和图像 分别分析资源的组件。重组后的资源现在处于有利地位，可以促进 跨学科研究，并在癌症研究中开发创新和重要的方法。这个 资源与生成大型复杂数据集的其他共享资源密切合作，并与 生物统计学共享资源的教职员工提供互补的专业知识。资源总监 与用户合作设计实验，开发新的数据分析和解释方法，以 整合治疗知识，并协助撰写赠款和为出版物处理数据。这个 资源维护自己的专门计算资源、适当的数据库和软件，用于 实现其多样化的目标。它还与联合国排雷行动中心研究方案密切合作，制定和整合 将新方法纳入研究项目，并积极促进和传播有关新方法的信息 方法和数据分析技术，通过维护最新的网页、促进培训和 与高层面数据收集和分析有关的教育，并在联东特派团作专题介绍 会议和务虚会。UNM癌症中心研究管理局(CCRA)管理该资源，该资源 通过协作模式运作。在上一个5年项目期间，来自所有4个特派团的16名联东特派团成员 研究项目使用了生物信息学的组成部分，以前的生物统计学和生物信息学共享 资源，共出版了10份出版物(2份有未决的PMCID)。在2013年7月的报告年度-- 2014年6月，联东特派团16名成员负责100%的资源使用情况，并得到7名成员的支持 同行评审的赠款。然而，联东综合团另有18名成员，由另外14名赠款支助，并负责 用于另外18份经同行审查的出版物，由其他3个共享的生物信息学部门提供支助 资源，这些资源现已合并。因此，重组后的生物信息学和高等- 预计多维数据分析共享资源的使用率和重要性将大幅提高 由于其增强的功能，以及随着更多涉及大数据的项目的发展。

BIOINFORMATICS & HIGH-DIMENSIONAL DATA ANALYSIS SHARED RESOURCE ABSTRACT Recent years have seen enormous growth in the complexity and importance of high-dimensional data and the specialized methods and skills required for its successful analysis and integration. To address this emerging need, and to address previous critiques, the Bioinformatics and High-Dimensional Data Analysis Shared Resource (Bioinformatics) was developed by consolidating informatics components that were previously distributed in Biostatistics and three other shared resources, forming a data analysis-focused Resource with broadened scope, enhanced expertise and expanded capabilities. The reorganized Resource provides state-of- the-art informatics and data analysis for genomics, cheminformatics, image analysis and other complex problems. Faculty Director Jeremy Edwards, PhD (CGEG) is a genomics and bioinformatics expert and co- Directors Tudor Oprea, MD,PhD (CT) and Keith Lidke, PhD (TCBS) lead the cheminformatics and image analysis components of the Resource, respectively. The reorganized Resource is now well positioned to promote interdisciplinary research and to develop innovative and significant methodologies in cancer research. The Resource works closely with the other Shared Resources that generate large, complex data sets and with the faculty in the Biostatistics Shared Resource who provide complementary expertise. The Resource Directors collaborate with users to design experiments, develop new methods for data analysis and interpretation, to integrate therapeutic knowledge and to assist with grant writing and data processing for publications. The Resource maintains its own specialized computational resources, appropriate databases and software for accomplishing its diverse goals. It also works closely with UNMCC Research Programs to develop and integrate new methodologies into research projects, and actively facilitates and disseminates information about new approaches and data analysis techniques by maintaining an up-to-date web page, promoting training and education related to high-dimensional data collection and analysis and by giving presentations at UNMCC meetings and retreats. The UNM Cancer Center Research Administration (CCRA) manages the Resource, which operates through a collaborative model. During the previous 5-yr project period, 16 UNMCC members from all 4 Research Programs used the bioinformatics component of the former Biostatistics and Bioinformatics Shared Resource, resulting in a total of 10 publications (2 have pending PMCIDs). In the reporting year of July 2013 – June 2014, 16 UNMCC members were responsible for 100% of total Resource usage and were supported by 7 peer-reviewed grants. However, another 18 UNMCC members, supported by 14 more grants and responsible for 18 additional peer-reviewed publications, were supported by bioinformatics components in 3 other shared resources, which have now been consolidated. Consequently, the reorganized Bioinformatics and High- Dimensional Data Analysis Shared Resource is projected to have dramatically increased use and importance due to its enhanced capabilities and as more projects develop that involve Big Data.