An Efficient Lightweight Environment for Biomedical Computation

生物医学计算的高效轻量级环境

• 批准号: 7566686
• 负责人: ANTON NEKRUTENKO
• 金额: $ 47.93万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7566686
• 关键词: Address; Biological; Biomedical Computing; Biomedical Research; Communities; Complex; Computer Analysis; Computer software; Data; Data Quality; Data Set; Data Sources; Dependency; Engineering; Environment; Face; Galaxy; Generations; Genetic; Genetic Polymorphism; Genome; Genomics; Genotype; Goals; Growth; Human; Human Genome; Imagery; Individual; Internet; Investments; Java; Language; Letters; Medical Research; Metagenomics; Molecular Profiling; One-Step dentin bonding system; Output; Paper; Parents; Phylogenetic Analysis; Polymorphism Analysis; Problem Solving; Protocols documentation; Provider; Publishing; Reading; Research; Research Infrastructure; Research Personnel; Resources; Retrieval; Risk; Scientist; Secure; Services; Source; System; Systems Analysis; Time; Training; Translational Research; Translations; Trees; United States National Institutes of Health; Variant; Work; Writing; bench to bedside; case control; clinically relevant; comparative; computerized tools; data modeling; database of Genotypes and Phenotypes; design; empowered; feeding; flexibility; genome-wide; human subject; improved; insight; interoperability; meetings; next generation; novel; open source; programs; public health relevance; skills; success; tool

DESCRIPTION (provided by applicant): The translation from large volumes of experimental data to clinically relevant insights relies on sophisticated computational analysis tools that can handle the enormous high-throughput sequence, polymorphism, and functional datasets. Developing appropriate tools is necessary but not sufficient, because the independent analysis tools in themselves do not solve an increasingly problematic barrier blocking the bench-to-bedside path outlined in the NIH Roadmap for medical research: making powerful new computational tools readily accessible and useful for experimental biologists. Developing usable and consistent user interfaces requires significant effort, and few tool developers can afford to devote time and resources to this goal. Currently many powerful, independent analysis tools exist, but lack integrated, easy-to-use interfaces that would allow experimental biologists to take advantage of them. Thus, developing tools to analyze overwhelming amounts of data is no longer the main challenge in biomedical research. Instead the problem lies in making existing tools usable for bench biologists so that they can take full advantage of existing data. We have developed a system - GALAXY - that makes substantial progress toward solving this problem. For experimental biologists, it provides an intuitive and consistent interface for performing sophisticated analyses with minimal effort, regardless of the scale of data involved. For computational tool developers, it makes it easy to integrate existing tools with a modern user interface by writing a simple, concise interface description. For data providers, it features a simple, elegant data access protocol. Thus, GALAXY bridges a critically important gap between data resources, computational tools and users, by making it easy to modernize the interfaces of any existing tool, freeing developers of new tools from the need to develop interfaces from scratch, and facilitating tool interoperability and complex analyses by seamlessly integrating analysis outputs, applications and external data. Here we propose to develop novel features specifically designed for translational research. First, we will engineer a tool integration framework streamlining delivery of analysis software to experimentalists. Second, we will develop a statistical genetics toolkit allowing clinicians to manipulate and interpret human variation data on any scale. Third, we will implement the first integrated system for analysis of short-read sequencing data. Fourth, we will design utilities for manipulation of the most valuable comparative genomics resource - multi- genome alignments. Finally, we will build a workflow system to enable reproducible and collaborative analysis of genomic data. PUBLIC HEALTH RELEVANCE: Genomic data discovery is no longer a limiting factor for much of the medical research. The NIH Roadmap recognizes that many challenges in biomedical research will only be overcome through appropriate investment to improve integrative access to existing data and tools, so researchers can more effectively and rapidly trans- late their findings into practice. The proposed project addresses this challenge by allowing biomedical re- searchers to take advantage of the enormous sequence, polymorphism, and functional datasets easily and effectively.

描述（由申请人提供）：从大量实验数据到临床相关见解的转化依赖于复杂的计算分析工具，这些工具可以处理巨大的高通量序列、多态性和功能数据集。开发适当的工具是必要的，但还不够，因为独立分析工具本身并不能解决美国国立卫生研究院医学研究路线图中概述的实验室到临床路径中日益成问题的障碍：使强大的新计算工具易于为实验生物学家提供和使用。开发可用且一致的用户界面需要付出巨大的努力，并且很少有工具开发人员能够投入时间和资源来实现这一目标。目前存在许多强大的、独立的分析工具，但缺乏集成的、易于使用的界面，使实验生物学家能够利用它们。因此，开发分析大量数据的工具不再是生物医学研究的主要挑战。相反，问题在于如何使现有工具可供实验室生物学家使用，以便他们能够充分利用现有数据。我们开发了一个系统 - GALAXY - 在解决这个问题方面取得了实质性进展。对于实验生物学家来说，它提供了一个直观且一致的界面，可以轻松执行复杂的分析，无论涉及的数据规模如何。对于计算工具开发人员来说，通过编写简单、简洁的界面描述，可以轻松地将现有工具与现代用户界面集成。对于数据提供者来说，它具有简单、优雅的数据访问协议。因此，GALAXY 可以轻松地对任何现有工具的界面进行现代化改造，使新工具的开发人员无需从头开始开发界面，并通过无缝集成分析输出、应用程序和外部数据来促进工具的互操作性和复杂的分析，从而弥合了数据资源、计算工具和用户之间至关重要的差距。在这里，我们建议开发专门为转化研究设计的新颖功能。首先，我们将设计一个工具集成框架，简化向实验人员提供分析软件的过程。其次，我们将开发一个统计遗传学工具包，使临床医生能够操纵和解释任何规模的人类变异数据。第三，我们将实施第一个短读长测序数据分析集成系统。第四，我们将设计实用程序来操作最有价值的比较基因组学资源——多基因组比对。最后，我们将构建一个工作流程系统，以实现基因组数据的可重复和协作分析。公共卫生相关性：基因组数据发现不再是许多医学研究的限制因素。 NIH 路线图认识到，生物医学研究中的许多挑战只能通过适当的投资来克服，以改善对现有数据和工具的综合访问，以便研究人员能够更有效、更快速地将他们的发现转化为实践。拟议的项目通过允许生物医学研究人员轻松有效地利用庞大的序列、多态性和功能数据集来解决这一挑战。