MetaProteomics Pipeline (MPP): Integrating a stack of metaproteomics data analysis tools into a full-featured and sustainable bioinformatics workflow

元蛋白质组管道 (MPP)：将一系列元蛋白质组数据分析工具集成到功能齐全且可持续的生物信息学工作流程中

• 批准号: 391179955
• 负责人: Dr. Stephan Fuchs
• 金额: --
• 依托单位: Abteilung 1 - Infektionskrankheiten
• 依托单位国家: 德国
• 项目类别: Research data and software (Scientific Library Services and Information Systems)
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/391179955?language=en
• 关键词: MetaProteomics; Pipeline; MPP; Integrating; stack

The main objective of this project proposal is to establish the Metaproteomics Pipeline (MPP), a sustainable, full-featured, and user-friendly data analysis workflow for metaproteomics research which integrates three existing software prototypes.Metaproteomics is an evolving field of microbiology studying microbial communities directly from their habitat by applying mass spectrometry-based protein analytics. This research area strongly demands tailored software solutions that enable researchers from various backgrounds (microbiology, ecology, medical diagnostics etc.) to apply adequate and state-of-the-art bioinformatics strategies for analysing highly complex and heterogeneous biological samples. Since such samples contain proteins of hundreds of potentially unknown species, data analysis and interpretation belong to the most challenging tasks. This includes the reliable identification of acquired mass spectra, the efficient grouping of ambiguous (peptide-sharing) protein identifications, the accurate quantification of identified proteins, and the meaningful integration of taxonomic and functional meta-information. To support these tasks and to overcome existing limitations, we developed the open source software tools MetaProteomeAnalyzer (MPA), Pipasic, and Prophane. These prototypes provide effective features for the meaningful analysis and interpretation of metaproteomics data. The scope of this project is to combine the existing prototypes into a single, end-to-end solution providing high usability, reliability, interoperability, and discoverability to the metaproteomics research community. In six different work packages we will focus on (i.) standard workflow definition and implementation, (ii.) automated quality assurance using benchmarking data, (iii.) data format definition for interoperability with other tools, (iv.) infrastructure for accessibility, (v.) documentation, training, and improved discoverability, and (vi.) dissemination of results and impact measurement.The pipeline will facilitate the time- and cost-efficient analysis of metaproteomics data. Users lacking in-depth knowledge either in metaproteomics or bioinformatics will be able to conduct data analysis without experiencing compatibility issues. Domain experts will be involved as pilot users already at an early stage to evaluate and validate the pipeline with respect to its performance on metaproteomics data. Moreover, an automated benchmarking will be provided not only for evaluating our developed pipeline, but also for testing other software in the field. Methods of semantic software annotation, documentation, training and distribution on community platforms will make the workflow accessible to a wider research community. Usage statistics and surveys from pilot and end users will help us to set up a sustainable bioinformatics pipeline which considers best-practices in data analysis and workflow design.

该项目的主要目标是建立一个可持续的、功能齐全的、用户友好的元蛋白质组学研究数据分析工作流（MPP），该工作流整合了三个现有的软件原型。元蛋白质组学是一个不断发展的微生物学领域，通过应用基于质谱的蛋白质分析直接从微生物群落的栖息地研究微生物群落。这一研究领域强烈需要量身定制的软件解决方案，使来自不同背景的研究人员（微生物学，生态学，医学诊断等）。应用适当和最先进的生物信息学策略来分析高度复杂和异质的生物样品。由于这些样本含有数百种潜在未知物种的蛋白质，因此数据分析和解释属于最具挑战性的任务。这包括获得的质谱的可靠鉴定，模糊（肽共享）蛋白质鉴定的有效分组，鉴定的蛋白质的准确定量，以及分类和功能元信息的有意义的整合。为了支持这些任务并克服现有的限制，我们开发了开源软件工具MetaProteomeAnalyzer（MPA），Pipasic和Prophane。这些原型为元蛋白质组学数据的有意义的分析和解释提供了有效的功能。该项目的范围是将现有的原型联合收割机组合成一个单一的端到端解决方案，为元蛋白质组学研究社区提供高可用性，可靠性，互操作性和可扩展性。在六个不同的工作包中，我们将重点关注（一）标准工作流程定义和实现，（ii.）使用基准数据的自动化质量保证，（iii.）与其他工具互操作的数据格式定义，（iv.）无障碍基础设施，（五）文档、培训和改进的可扩展性，以及（vi.）该管道将促进元蛋白质组学数据的时间和成本效益分析。缺乏元蛋白质组学或生物信息学深入知识的用户将能够进行数据分析，而不会遇到兼容性问题。领域专家将在早期阶段作为试点用户参与评估和验证管道在元蛋白质组学数据上的性能。此外，将提供一个自动化的基准测试，不仅用于评估我们开发的管道，而且还用于测试该领域的其他软件。语义软件注释，文档，培训和社区平台上的分发方法将使更广泛的研究社区可以访问工作流程。来自试点和最终用户的使用统计数据和调查将帮助我们建立一个可持续的生物信息学管道，该管道考虑了数据分析和工作流程设计的最佳实践。