Latent Dirichlet Allocation for Protein Inference in Quantitative Proteomics

定量蛋白质组学中蛋白质推断的潜在狄利克雷分配

• 批准号: 8771434
• 负责人: AARON M. COHEN
• 金额: $ 16.75万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8771434
• 关键词: Accounting; Area; Bayesian Modeling; Bioinformatics; Biological; Biological Markers; Biological Process; Cancer Center; Characteristics; Chronic Disease; Clinical; Complex; Computing Methodologies; Data; Development; Diabetes Mellitus; Disease; Enzymes; Equipment; Evaluation; Goals; Head and Neck Squamous Cell Carcinoma; Health; Individual; Knowledge; Laboratories; Lead; Liquid Chromatography; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measurement; Medical; Methods; Modeling; Molecular; Noise; Pathogenesis; Patients; Peptide Fragments; Peptides; Population Heterogeneity; Process; Protein Fragment; Proteins; Proteomics; Reading; Research Personnel; Research Project Grants; Sampling; Scientist; Sensitivity and Specificity; Shotguns; Solutions; Specimen; Techniques; Technology; Trypsin; Validation; anticancer research; base; improved; interest; mass spectrometer; novel; personalized medicine; protein aminoacid sequence; protein profiling; research study; text searching; tumor

DESCRIPTION (provided by applicant): One way to accelerate the understanding of the molecular basis of cancer is through the application of robust, quantitative, proteomic technologies and corresponding computational methodologies. Mass spectroscopy measurement technology for peptides (LC-MS/MS) is rapidly advancing, and there is a great need for more development of the corresponding bioinformatics analysis techniques to infer proteins from the peptide spectra. The Latent Dirichlet Allocation (LDA) for Protein Inference in Quantitative Proteomics research project will adapt LDA, an established method of topic modeling from text mining, to the problem of protein inference. Advances in protein inference will be of great utility and interest in cancer clinical proteomics studies. Successfully deploying these methods will directly lead to an increase in the ability of proteomics to augment cancer research in many important areas such as biomarker discovery, pathogenesis, and patient-specific tumor therapies. Two specific aims in support of these goals will be undertaken during the proposed project: * Aim 1. Investigate how to best apply latent Dirichlet allocation modeling techniques previously used in text mining to the problem of protein inference. Areas to explore include the application of biological and domain knowledge constraints to the model as well as parameter optimization techniques. Tune and evaluate the approach in terms of accuracy, sensitivity, and specificity on a set of simulated protein-peptide fragment data with various amounts of noise and errors in the peptide reading process. Further evaluation and validation will be performed using LC-MS/MS data produced from proteomic laboratory standards that provide a known solution to complex real-world data samples. * Aim 2. Demonstrate the utility of the latent Dirichlet allocation-based protein inference techniques by application to experimental cancer data. A head and neck squamous cell carcinoma (SCC) study from the Vanderbilt-Ingram Cancer Center providing public data will be utilized allowing the comparison of results using LDA with those obtained by current standard techniques in terms of prediction overlap, differences, and confidence levels.

描述（由申请人提供）：加速理解癌症分子基础的一种方法是通过应用稳健的定量蛋白质组学技术和相应的计算方法。多肽的质谱测量技术（LC-MS/MS）发展迅速，需要进一步发展相应的生物信息学分析技术，从多肽谱图中推断蛋白质。定量蛋白质组学研究项目中蛋白质推理的潜在狄利克雷分配（LDA）将LDA（一种从文本挖掘中建立的主题建模方法）应用于蛋白质推理问题。蛋白质推断的进展将在癌症临床蛋白质组学研究中具有巨大的实用性和兴趣。成功部署 这些方法将直接提高蛋白质组学在许多重要领域（例如生物标志物发现、发病机制和患者特异性肿瘤治疗）增强癌症研究的能力。在拟议的项目期间，将实现两个具体目标，以支持这些目标：* 目标1。研究如何最好地将文本挖掘中使用的潜在Dirichlet分配建模技术应用于蛋白质推理问题。探索的领域包括生物和领域知识的约束模型的应用，以及参数优化技术。在肽阅读过程中具有不同数量的噪声和误差的一组模拟蛋白质-肽片段数据上，根据准确度、灵敏度和特异性调整和评估该方法。将使用蛋白质组学实验室标准品产生的LC-MS/MS数据进行进一步评价和验证，这些标准品为复杂的真实世界数据样本提供了已知的解决方案。* 目标二。通过应用于实验性癌症数据，展示基于潜在狄利克雷分配的蛋白质推断技术的实用性。将利用范德比尔特-英格拉姆癌症中心提供的公开数据进行头颈部鳞状细胞癌（SCC）研究，以便在预测重叠、差异和置信水平方面将使用LDA的结果与通过当前标准技术获得的结果进行比较。