Shortening the development cycle time of Trans Proteomic Pipeline tools with high performance computing

通过高性能计算缩短跨蛋白质组管道工具的开发周期

• 批准号: 10389405
• 负责人: Eric Deutsch
• 金额: $ 10万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10389405
• 关键词: Address; Affect; Aging; Algorithms; Award; Biological; Communities; Computer software; Data; Data Set; Development; Disease; Drug Interactions; E-learning; Feedback; High Performance Computing; Maintenance; Mass Spectrum Analysis; Memory; Post-Translational Protein Processing; Process; Proteins; Proteomics; Research; Research Project Grants; Running; Sampling; Software Tools; Speed; Statistical Data Interpretation; Stress; System; Technology; Time; Training; Variant; Visualization; algorithm development; base; computational platform; computing resources; data analysis pipeline; experimental study; improved; ion mobility; large datasets; open source; programs; protein complex; public health relevance; software development; solid state; tool; usability

Project Summary Mass spectrometry (MS) based proteomics is currently the most widely used technology for the analysis of complex protein mixtures. It has the ability to detect and quantify the abundance of thousands of proteins and their variants, post-translational modifications, protein:protein and protein:drug interactions per experiment. The Trans-Proteomic Pipeline (TPP) is a free and open-source suite of software tools for the analysis of mass spectrometry (MS) proteomics data that has been widely used by the community for over 15 years. Our ongoing award for the maintenance and development of the TPP has four aims in which we 1) enhance the TPP to process datasets from emerging workflows, 2) add statistical analysis and visualization capabilities for large datasets, 3) respond to the needs of the community with functional and usability enhancements, and 4) expand our user base with improved training and on-line learning. Great progress has been made on these aims during the project period. However, current development for one of the main tools that we are currently focusing on has been severely hindered by aging computational servers purchased in 2012 and inadequate development of computing resources. We propose to address this problem via the purchase and deployment of a pair of high- memory, high-core count, fast-disk compute servers, substantially accelerating our development cycle so that the algorithm development can be completed more rapidly, and the completed algorithm can be further rapidly optimized to run with more modest hardware requirements routinely. Deployment of these machines will streamline our software development by providing rapid feedback on software changes and further developments and their impact on search speed and enable us to provide tools to our users more rapidly, accelerating the progress of many other research programs that use the TPP for data processing.

项目概要 基于质谱 (MS) 的蛋白质组学是目前应用最广泛的蛋白质组学分析技术 复杂的蛋白质混合物。它能够检测和量化数千种蛋白质的丰度， 每个实验的变体、翻译后修饰、蛋白质：蛋白质和蛋白质：药物相互作用。这 Trans-Proteomic Pipeline (TPP) 是一套免费的开源软件工具，用于分析蛋白质组 光谱测定 (MS) 蛋白质组学数据已被社区广泛使用超过 15 年。我们正在进行的 维护和发展 TPP 的奖项有四个目标，其中我们 1) 加强 TPP 处理来自新兴工作流程的数据集，2）为大型项目添加统计分析和可视化功能 数据集，3）通过功能和可用性增强来响应社区的需求，以及 4）扩展 我们的用户群经过改进的培训和在线学习。期间这些目标取得了巨大进展 项目期间。然而，我们目前关注的主要工具之一的当前开发已经 2012 年购买的计算服务器老化以及开发不足严重阻碍了 计算资源。我们建议通过购买和部署一对高 内存、高核心数、快速磁盘计算服务器，大大加快了我们的开发周期，以便 可以更快地完成算法开发，并且可以进一步快速地完成算法 优化为通常以更适度的硬件要求运行。这些机器的部署将 通过提供有关软件变更的快速反馈并进一步简化我们的软件开发 发展及其对搜索速度的影响，使我们能够更快地为用户提供工具， 加速许多其他使用 TPP 进行数据处理的研究项目的进展。

项目成果

Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames.

• DOI: 10.1016/j.molcel.2023.01.023
• 发表时间: 2023-03-16
• 期刊: MOLECULAR CELL
• 影响因子: 16
• 作者: Sandmann, Clara-L.;Schulz, Jana F.;Ruiz-Orera, Jorge;Kirchner, Marieluise;Ziehm, Matthias;Adami, Eleonora;Marczenke, Maike;Christ, Annabel;Liebe, Nina;Greiner, Johannes;Schoenenberger, Aaron;Muecke, Michael B.;Liang, Ning;Moritz, Robert L.;Sun, Zhi;Deutsch, Eric W.;Gotthardt, Michael;Mudge, Jonathan M.;Prensner, John R.;Willnow, Thomas E.;Mertins, Philipp;van Heesch, Sebastiaan;Hubner, Norbert
• 通讯作者: Hubner, Norbert

A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics.

• DOI: 10.1038/s41597-020-00744-3
• 发表时间: 2020-11-23
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Weerakoon H;Potriquet J;Shah AK;Reed S;Jayakody B;Kapil C;Midha MK;Moritz RL;Lepletier A;Mulvenna J;Miles JJ;Hill MM
• 通讯作者: Hill MM

A comprehensive Candida albicans PeptideAtlas build enables deep proteome coverage.

全面的白色念珠菌 PeptideAtlas 构建可实现深度蛋白质组覆盖。

• DOI: 10.1016/j.jprot.2015.10.019
• 发表时间: 2016
• 期刊: Journal of proteomics
• 影响因子: 3.3
• 作者: Vialas,Vital;Sun,Zhi;Reales-Calderón,JoseA;Hernáez,MaríaL;Casas,Vanessa;Carrascal,Montserrat;Abián,Joaquín;Monteoliva,Lucía;Deutsch,EricW;Moritz,RobertL;Gil,Concha
• 通讯作者: Gil,Concha

Flexible and Fast Mapping of Peptides to a Proteome with ProteoMapper.

使用 ProteoMapper 将肽灵活快速地映射到蛋白质组。

• DOI: 10.1021/acs.jproteome.8b00544
• 发表时间: 2018
• 期刊: Journal of proteome research
• 影响因子: 4.4
• 作者: Mendoza,Luis;Deutsch,EricW;Sun,Zhi;Campbell,DavidS;Shteynberg,DavidD;Moritz,RobertL
• 通讯作者: Moritz,RobertL

Platelet derived growth factor receptor β (PDGFRβ) is a host receptor for the human malaria parasite adhesin TRAP.

• DOI: 10.1038/s41598-021-90722-5
• 发表时间: 2021-05-31
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Steel RWJ;Vigdorovich V;Dambrauskas N;Wilder BK;Arredondo SA;Goswami D;Kumar S;Carbonetti S;Swearingen KE;Nguyen T;Betz W;Camargo N;Fisher BS;Soden J;Thomas H;Freeth J;Moritz RL;Noah Sather D;Kappe SHI
• 通讯作者: Kappe SHI