Compute-Cluster for Deep-Learning Models for Mass Spectrometry based Proteomics data

基于质谱的蛋白质组数据深度学习模型的计算集群

• 批准号: 10389445
• 负责人: Fahad Saeed
• 金额: $ 10万
• 依托单位: FLORIDA INTERNATIONAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10389445
• 关键词: Administrative Supplement; Affect; Algorithms; Architecture; Big Data; Characteristics; Classification; Communities; Computational Technique; Custom; Data; Data Analyses; Data Set; Deductibles; Disease; Ensure; Goals; Grant; Health; High Performance Computing; Human; Investigation; Machine Learning; Mass Spectrum Analysis; Memory; Methods; Mission; Modeling; Organism; Outcome; Peptides; Performance; Post-Translational Protein Processing; Process; Proteins; Proteomics; Public Health; Reporting; Reproducibility; Research; Resolution; S-nitro-N-acetylpenicillamine; Sextuplet; Structure; System; Systems Biology; Techniques; Testing; Time; Training; United States National Institutes of Health; Work; algorithmic methodologies; base; cluster computing; cyber infrastructure; deep learning; deep learning algorithm; design; exhaustion; experimental study; high dimensionality; image processing; improved; indexing; innovation; insight; instrumentation; learning network; learning strategy; loss of function; mass spectrometer; microbiome; multidimensional data; multimodality; novel; open source; parent grant; protein aminoacid sequence; protein biomarkers; searchable database; simulation; success; tool; vector; virtual

Project Abstract/Summary Mass spectrometry (MS) data is high-dimensional data that is used for large-scale system biology proteomics. The current state of the art mass spectrometers can generate thousands of spectra from a single organism and experiment. This high-dimensional data is processed using database searches and denovo algorithms with varying degrees of success. The overarching objective of this study is to develop, test, integrate and evaluate novel image-processing and deep-learning algorithms that will allow us to deduce and identify reliable peptide sequences in a definitive and quantitative fashion. Our long-term goal is to improve on identification of MS based proteomics data using novel and scalable algorithms. The objective of this proposal is to investigate, design and implement machine-learning deep-learning algorithms for identification of peptides from MS data. Since deep-learning is very good at discovering intricate structures in high-dimensional data it will be ideal solution for discovering dark proteomics data and more accurate deduction of peptides. We predict that the integration of these methods, along with traditional numerical algorithms, will lead to a multimodal fusion-based approach for an optimized and accurate peptide deduction system for large-scale MS data. Further, we will design and implement data augmentation, memory-efficient indexing, and high-performance computing (HPC) to achieve these outcomes more efficiently with a shorter computational time. Therefore, this new line of investigation is significant since it has the potential to improve on long-stalled effort to increase accuracy, reliability and reproducibility of MS data analysis and search tools. The proximate expected outcome of this work is a novel set of deep-learning and image-processing tools which will allow much better insight in MS based proteomics data. The results will have an important positive impact immediately because these proposed research tasks will lay the groundwork to develop a new class of algorithms and will provide rapid, high-throughput, sensitive, and reproducible and reliable tools for MS based proteomics.

项目摘要/总结