An Open-Source Algorithm Isolating Overlapping Signatures in Microarray Data

描述(由申请人提供)： 已经开发了大量的资源，其中包括大量的微阵列数据，代表了对模式生物和人类的研究。许多早期的结合微阵列方法的研究都集中在鉴定在两种条件下不同水平表达的基因，而忽略了来自多重调控的潜在的混杂转录。如果分析的目标是识别生物标记物，这是一个合乎逻辑的焦点。然而，为了检测生物活性，有必要获得与过程有关的转录特征，而不是与条件有关的转录特征。由于大多数基因的多重调控，以及关于这种多重调控的有限信息，如果没有重要的数学模型，则无法完成转录协同调控的识别。这里概述的工作将导致一个开源的、统计功能强大的、灵活的转录签名识别算法，该算法利用通过途径数据库、基因本体论和基因调控数据库获得的现有生物学知识。 该提案包括两个具体目标。首先，我们将创建一种新的马尔可夫链蒙特卡罗算法，该算法可以通过使用浓缩分析来直接推断生物过程的活动。该算法将包括可交换的误差模型，其参数在采样期间被估计。据我们所知，我们是第一个在允许多重监管的数学框架内对生物过程提出直接推断的小组。其次，我们将在一个用户友好的开源工具中对算法进行编码，并在R语言中将其编码为GenePattern模块。 这项工作将提供一种专门设计的算法，利用先前的生物学知识从噪声数据中识别转录签名和生物过程中的变化。虽然这样的数据现在在微阵列研究中是典型的，但它很快也将存在于基因分型和蛋白质组学研究中。我们加入了一个灵活的、参数化的误差模型，这将使该算法在这些新兴领域也很有用。在未来，我们打算将我们的工作重点放在哺乳动物系统中的信号网络模型上，依靠这项工作的结果来提供转录签名来指导对这些网络的推理。 这项工作对于开发能够利用不断增长的功能基因组数据来推断特定生物过程的活动的系统具有重要意义，例如信号网络和代谢途径。这些信息对于了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.