Detailing regulatory networks through large scale data integration
Detailing regulatory networks through large scale data integration
复制标题
DOI：
10.1093/bioinformatics/btp588
复制
发表时间：
2009-12-15
期刊：
影响因子：
5.8
通讯作者：
Coller, Hilary A.
中科院分区：
文献类型：
--
作者：
Huttenhower, Curtis;Mutungu, K. Tsheko;Coller, Hilary A.
动机：细胞对环境变化的大部分调节反应发生在转录水平。特别是在高等生物中，转录因子（TF）、microRNA和表观遗传修饰可以联合收割机形成复杂的调控网络。该系统的一部分可以建模为一个集合的监管模块：共调节基因，在何种条件下，他们是共同调节和序列水平的监管motifs.Results：我们提出的组合算法表达和基于序列的聚类提取（COALESCE）系统的监管模块预测。该算法是有效的，足以发现在后生动物基因组（>20 000个基因）和非常大的微阵列纲要（>10 000个条件）的表达双簇和推定的调控基序。使用贝叶斯数据集成，它还可以包括不同的支持数据类型，如进化保守或核小体放置。我们使用共簇基因的功能评价、已知的酵母和土栖大肠杆菌TF靶标、合成数据和各种后生动物数据纲要来验证其性能。在所有情况下，COALESCE的表现与当前的双聚类和基序预测工具一样好或更好，在功能和TF/靶标分配方面具有高准确性，并且在合成数据上具有零假阳性。COALESCE提供了一个有效和灵活的平台，在这个平台内，可以整合大量不同的数据收集来预测后生动物的调控网络。
Motivation: Much of a cell's regulatory response to changing environments occurs at the transcriptional level. Particularly in higher organisms, transcription factors (TFs), microRNAs and epigenetic modifications can combine to form a complex regulatory network. Part of this system can be modeled as a collection of regulatory modules: co-regulated genes, the conditions under which they are co-regulated and sequence-level regulatory motifs.Results: We present the Combinatorial Algorithm for Expression and Sequence-based Cluster Extraction (COALESCE) system for regulatory module prediction. The algorithm is efficient enough to discover expression biclusters and putative regulatory motifs in metazoan genomes (>20 000 genes) and very large microarray compendia (>10 000 conditions). Using Bayesian data integration, it can also include diverse supporting data types such as evolutionary conservation or nucleosome placement. We validate its performance using a functional evaluation of co-clustered genes, known yeast and Escherichea coli TF targets, synthetic data and various metazoan data compendia. In all cases, COALESCE performs as well or better than current biclustering and motif prediction tools, with high accuracy in functional and TF/target assignments and zero false positives on synthetic data. COALESCE provides an efficient and flexible platform within which large, diverse data collections can be integrated to predict metazoan regulatory networks.