Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.
Bioinformatics. 2010 Apr 1;26(7):860-6. doi: 10.1093/bioinformatics/btq049. Epub 2010 Feb 10.
Transcription factors (TFs) are crucial during the lifetime of the cell. Their functional roles are defined by the genes they regulate. Uncovering these roles not only sheds light on the TF at hand but puts it into the context of the complete regulatory network.
Here, we present an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs. We incorporate our approach into the Gomo algorithm, a computational tool for detecting associations between a user-specified DNA regulatory motif [expressed as a position weight matrix (PWM)] and Gene Ontology (GO) terms. Incorporating multiple species into the analysis significantly improves Gomo's ability to identify GO terms associated with the regulatory targets of TFs. Including three comparative species in the process of predicting TF roles in Saccharomyces cerevisiae and Homo sapiens increases the number of significant predictions by 75 and 200%, respectively. The predicted GO terms are also more specific, yielding deeper biological insight into the role of the TF. Adjusting motif (binding) affinity scores for individual sequence composition proves to be essential for avoiding false positive associations. We describe a novel DNA sequence-scoring algorithm that compensates a thermodynamic measure of DNA-binding affinity for individual sequence base composition. GOMO's prediction accuracy proves to be relatively insensitive to how promoters are defined. Because GOMO uses a threshold-free form of gene set analysis, there are no free parameters to tune. Biologists can investigate the potential roles of DNA regulatory motifs of interest using GOMO via the web (http://meme.nbcr.net).
转录因子（TFs）在细胞的生命周期中至关重要。它们的功能作用由它们所调控的基因决定。揭示这些作用不仅阐明了手头的 TF，而且将其置于完整的调控网络背景下。
在这里，我们提出了一种无需对齐和阈值的比较基因组学方法，用于为 DNA 调控基序分配功能作用。我们将我们的方法纳入到 Gomo 算法中，该算法是一种用于检测用户指定的 DNA 调控基序（表示为位置权重矩阵（PWM））与基因本体论（GO）术语之间关联的计算工具。将多个物种纳入分析显著提高了 Gomo 识别与 TF 调控靶标相关的 GO 术语的能力。在预测酿酒酵母和人类 TF 角色的过程中，将三个比较物种纳入其中，分别将显著预测的数量增加了 75%和 200%。预测的 GO 术语也更加具体，为 TF 的作用提供了更深入的生物学见解。调整单个序列组成的基序（结合）亲和力评分对于避免假阳性关联至关重要。我们描述了一种新的 DNA 序列评分算法，该算法补偿了单个序列碱基组成的 DNA 结合亲和力的热力学测量。GOMO 的预测准确性对于启动子的定义方式相对不敏感。由于 Gomo 使用无阈值的基因集分析形式，因此没有可调的参数。生物学家可以通过网络（http://meme.nbcr.net）使用 Gomo 研究感兴趣的 DNA 调控基序的潜在作用。
