Computational tools for top down mass spectrometry based proteoform identification and proteogenomics

基于自上而下质谱法的蛋白质型鉴定和蛋白质基因组学的计算工具

• 批准号: 9484290
• 负责人: Xiaowen Liu
• 金额: $ 29.19万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9484290
• 关键词: Address; Algorithmic Software; Algorithms; Alternative Splicing; Biological Markers; Breast Epithelial Cells; Cell Line; Cells; Communities; Complex; Computer software; Custom; Data; Data Analyses; Databases; Detection; Disease; Drug Targeting; Effectiveness; Evaluation; Event; Explosion; Feedback; Genes; Genetic Transcription; Graph; Histones; Human; Mass Spectrum Analysis; Modification; Molecular; Mutation; Pattern; Post-Translational Protein Processing; Problem Solving; Process; Protein Analysis; Proteins; Proteome; Proteomics; Research; Research Personnel; Sampling; Single Nucleotide Polymorphism; Site; Software Tools; Speed; System; Technology; Testing; Transcription Alteration; Variant; Yeasts; anticancer research; base; combinatorial; computerized tools; data modeling; design; experimental study; improved; insight; malignant breast neoplasm; molecular sequence database; novel; open source; protein degradation; proteogenomics; public health relevance; signature molecule; software development; tandem mass spectrometry; tool; transcriptome sequencing; tumor; user-friendly

 DESCRIPTION (provided by applicant): Mass spectrometry-based top-down proteomics has emerged as one of the most informative approaches in protein analysis because it provides the "bird-eye" view of all intact proteoforms generated from post-translational modifications and sequence variations. A major challenge in proteoform identification by database search is the combinatorial explosion of possible proteoforms resulting from combinations of sequence variations, post-translational modifications, and other molecular events, such as protein degradation. Here, we propose to a novel data model, called the mass graph, to efficiently represent a huge number of potential proteoforms, and design new mass graph-based alignment and filtering algorithms that precisely identify complex proteoforms at the proteome level. We will also develop a software pipeline that combines top-down mass spectrometry and RNA-Seq data to identify sample-specific proteoforms. The proposed research will be conducted by a group of researchers who have complementary expertise. All the proposed algorithms will be implemented as user-friendly open source software tools.

 描述(申请人提供)：基于质谱学的自上而下的蛋白质组学已经成为蛋白质分析中信息量最大的方法之一，因为它提供了对翻译后修饰和序列变异产生的所有完整蛋白质形式的“鸟瞰”视图。通过数据库搜索鉴定蛋白质形式的一个主要挑战是序列变异、翻译后修饰和其他分子事件(如蛋白质降解)的组合导致可能的蛋白质形式的组合爆炸。在这里，我们提出了一种新的数据模型，称为质量图，以有效地表示大量潜在的蛋白质形式，并设计新的基于质量图的比对和过滤算法，在蛋白质组水平上精确识别复杂的蛋白质形式。我们还将开发一种软件管道，将自上而下的质谱学和RNA-Seq数据结合起来，以识别特定样本的蛋白质形式。拟议的研究将由一组拥有互补专业知识的研究人员进行。所有提出的算法都将作为用户友好的开源软件工具实现。