A Novel Method for Signaling Pathway Analysis

信号通路分析的新方法

基本信息

批准号：
7612527
负责人：
SORIN DRAGHICI
金额：
$ 14.63万
依托单位：
ADVAITA CORPORATION
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-03-01 至 2010-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7612527
关键词：
Academia Adoption Aging Area Arts Bioinformatics Biological Biological Factors Biological Phenomena Biological Sciences Complex Computer Simulation Computer software Custom Data Data Analyses Data Collection Development Gene Expression Gene Expression Regulation Gene Order Genes Genetic Genomics Goals Industry Letters Malignant Neoplasms Manuals Methods Modeling Molecular Biology Monitor Obesity Pathway Analysis Pathway interactions Pharmaceutical Preparations Phase Play Positioning Attribute Publications Regulator Genes Regulatory Pathway Research Research Personnel Role Scientist Shotgun Sequencing Signal Pathway Signal Transduction Signaling Pathway Gene Small Business Technology Transfer Research Software Tools System Systems Biology Techniques Testing Therapeutic Intervention Translating Whole Organism Work base drug candidate drug development experience high throughput technology innovation neglect novel novel strategies platform-independent portability prototype public health relevance research study simulation software development statistics tool

项目摘要

DESCRIPTION (provided by applicant): A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions on various regulatory pathways. Currently, a statistical approach is universally used to identify the most relevant pathways in a given experiment. This approach only considers the set of genes present on each pathway and completely ignores other important biological factors. Here we show that in spite of its general adoption, and independently of the particular model used, this statistical analysis is unsatisfactory, and can often provide incorrect results. Using a systems biology approach, we developed an impact analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. Our preliminary work shows that the classical analysis produces both false positives and false negatives while the impact analysis provides biologically meaningful results. In this Phase I application, we are proposing to develop a prototype that would demonstrate the feasibility of a commercial software analysis package based on this novel approach. Our team has a very strong track record as demonstrated by: a large number of citations to our previous publications, a large user-base for our previously developed software (over 5,000 scientists from all 5 continents), and very strong letters of support. 1 PUBLIC HEALTH RELEVANCE: The classical statistical approaches, which are universally used to identify the most relevant biological pathways in a given experiment, only consider the number of di(R)erentially expressed genes on each pathway and completely ignores other important biological factors. However, in spite of its general adoption, these statistical approaches are unsatisfactory, and can often provide incorrect results. We propose a novel signaling pathway analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc.

描述（由申请人提供）：基因组学数据分析中的一个共同挑战是试图在各种监管途径上所有复杂的相互作用的背景下理解基本现象。当前，普遍使用统计方法来识别给定实验中最相关的途径。这种方法仅考虑每个途径上存在的基因集，而完全忽略了其他重要的生物学因素。在这里，我们表明，尽管它一般采用，并且独立于所使用的特定模型，但这种统计分析是不令人满意的，并且通常会提供不正确的结果。使用系统生物学方法，我们开发了一种影响分析，其中包括经典统计数据，但也考虑了其他关键因素，例如每个基因的表达变化的大小，它们在给定途径中的类型和位置，它们的相互作用等。我们的初步工作表明，经典分析同时产生虚假的负面分析，同时产生影响分析，而影响分析则具有生物学意义的结果。在此I阶段应用程序中，我们建议开发一种原型，该原型将基于这种新颖的方法证明商业软件分析软件包的可行性。我们的团队具有非常强大的记录，如以下内容：我们以前的出版物的大量引用，这是我们先前开发的软件（来自所有5大洲的5,000多个科学家）的大型用户库以及非常强大的支持信。 1公共卫生相关性：经典的统计方法，这些方法被普遍用于识别给定实验中最相关的生物学途径，仅考虑在每个途径上呈di（r）的DI（r）数量，而完全忽略了其他重要的生物学因素。但是，尽管一般采用了这些统计方法，但这些统计方法并不令人满意，并且通常会提供不正确的结果。我们提出了一个新的信号通路分析，其中包括经典统计数据，但也考虑了其他关键因素，例如每个基因表达的变化的大小，它们在给定途径，相互作用等中的类型和位置等。