An Open-Source Algorithm Isolating Overlapping Signatures in Microarray Data

描述（由申请人提供）： 重要的资源已经开发，包括大量的微阵列数据，代表对模式生物和人类的研究。许多结合微阵列方法的早期研究一直专注于鉴定在两种条件下以不同水平表达的基因，忽略了来自多重调控的潜在混淆转录。如果分析的目标是鉴定生物标志物，这是一个合乎逻辑的重点。然而，为了检测生物活性，有必要获得与过程而不是条件相关的转录特征。由于大多数基因的多重调控和有限的信息，这种多重调控，转录共调控的识别不能完成没有显着的数学建模。这里概述的工作将导致一个开源的，统计上强大的，灵活的算法，利用现有的生物学知识，通过途径数据库，基因本体和基因调控数据库的转录签名的识别。 该提案包括两个具体目标。首先，我们将创建一个新的马尔可夫链蒙特卡罗算法，可以直接推断生物过程的活动，通过使用富集分析。该算法将包括可交换的误差模型，其参数在采样期间估计。据我们所知，我们是第一个在允许多重调节的数学框架内提出对生物过程进行直接推理的小组。其次，我们将在一个用户友好的开源工具和R语言中编码算法，并将其作为GenePattern模块。 这项工作将提供一种算法，专门设计用于识别转录签名和变化的生物过程中的噪声数据使用先前的生物学知识。虽然这些数据现在在微阵列研究中很常见，但它很快也会出现在基因分型和蛋白质组学研究中。我们包括一个灵活的，参数化的误差模型将使该算法在这些新兴领域也很有用。在未来，我们打算把我们的工作集中在哺乳动物系统中的信号网络模型，依靠这项工作的结果，提供转录签名，以指导这些网络上的推理。 这项工作对于开发能够利用不断增长的功能基因组学数据来推断特定生物过程（如信号网络和代谢途径）的活性的系统具有重要意义。这些信息对于理解人类疾病和对治疗的反应至关重要，特别是对于新的分子靶向治疗。

DESCRIPTION (provided by applicant): Significant resources have been developed that include large amounts of microarray data, representing studies on both model organisms and humans. Many early studies incorporating microarray methods have been focused on identification of genes that are expressed at different levels in two conditions, ignoring potential confounding transcription from multiple regulation. This is a logical focus if the goal of the analysis is identification of biomarkers. However, in order to detect biological activity, it is necessary to obtain transcriptional signatures linked to processes rather than to conditions. Due to multiple regulation of the majority of genes and limited information concerning such multiple regulation, identification of transcriptional coregulation cannot be accomplished without significant mathematical modeling. The work outlined here will lead to an open-source, statistically powerful, and flexible algorithm for identification of transcriptional signatures that leverages existing biological knowledge available through pathway databases, gene ontology, and databases of gene regulation. The proposal consists of two specific aims. First, we will create a novel Markov chain Monte Carlo algorithm that can directly infer the activity of biological processes through the use of enrichment analysis. The algorithm will include swappable error models whose parameters are estimated during sampling. To the best of our knowledge, we are the first group to propose direct inference on biological processes within a mathematical framework allowing for multiple regulation. Second, we will encode the algorithm in a user friendly open-source tool and within the R language and as a GenePattern module. This work will provide an algorithm specifically designed to identify transcriptional signatures and changes in biological processes from noisy data using prior biological knowledge. While such data is now typical in microarray studies, it will soon exist in genotyping and proteomic studies as well. Our inclusion of a flexible, parameterized error model will make this algorithm useful in these emerging fields as well. In the future, we intend to focus our work on models of signaling networks in mammalian systems, relying on the results of this work to provide transcriptional signatures to guide inference on the these networks. This work has significant implications for the development of systems capable of utilizing the growing functional genomics data to infer the activity of specific biological processes, such as signaling networks and metabolic pathways. Such information is vital to understanding human disease and the response to therapy, especially with new molecularly targeted therapeutics.