Transcription factor binding profiles reveal cyclic expression of human protein-coding genes and non-coding RNAs.
Transcription factor binding profiles reveal cyclic expression of human protein-coding genes and non-coding RNAs.
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DOI：
10.1371/journal.pcbi.1003132
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发表时间：
2013
影响因子：
4.3
通讯作者：
Whitfield ML
中科院分区：
文献类型：
--
作者：
Cheng C;Ung M;Grant GD;Whitfield ML
细胞周期是一个复杂的和高度监督的过程，必须进行精确的监管，以实现成功的细胞分裂。尽管应用广泛，微阵列时程实验在识别细胞周期基因方面存在一些局限性。因此，我们提出了一个计算模型来预测人类细胞周期基因的基础上转录因子（TF）的结合和调控基序信息在其启动子。我们利用ENCODE ChIP-seq数据和基序信息作为预测因子来区分细胞周期基因和非细胞周期基因。我们的研究结果表明，反式TF特征和顺式基序特征都可以预测细胞周期基因，两种类型的特征的组合可以进一步提高预测精度。我们将我们的模型应用于GENCODE启动子的完整列表，以预测蛋白质编码基因和非编码RNA（如lincRNA）的新型细胞周期驱动启动子。我们发现相似百分比的lincRNA作为蛋白质编码基因受到细胞周期调控，这表明非编码RNA在细胞周期分裂中的重要性。我们在这里提出的模型不仅提供了一个实用的工具，识别新的细胞周期基因的高精度，但也有新的见解细胞周期调控的转录因子和顺式调控元件。细胞周期是一个复杂的和高度监督的过程，必须进行精确的监管，以实现成功的细胞分裂。微阵列时程实验已成功地用于鉴定细胞周期调控基因，但存在一些局限性，例如在鉴定低表达基因方面效果较差。我们提出了一种计算方法来预测细胞周期基因的基础上TF结合数据和基序信息在其启动子。具体而言，我们利用ENCODE项目生成的ChIP-seq TF结合数据和公共数据库中的TF结合基序信息。这些数据被处理并用作使用随机森林方法预测细胞周期基因的预测因子。我们的研究结果表明，反式TF特征和顺式基序特征对细胞周期基因都有预测作用，两种特征的结合可以进一步提高预测精度。我们将我们的模型应用于GENCODE启动子的完整列表，以预测蛋白质编码基因和非编码RNA（如lincRNA）的新型细胞周期驱动启动子。我们发现相似百分比的lincRNA作为蛋白质编码基因受到细胞周期调控，这表明非编码RNA在细胞周期分裂中的重要性。
Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements. Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Microarray time course experiments have been successfully used to identify cell cycle regulated genes but with several limitations, e.g. less effective in identifying genes with low expression. We propose a computational approach to predict cell cycle genes based on TF binding data and motif information in their promoters. Specifically, we take advantage of ChIP-seq TF binding data generated by the ENCODE project and the TF binding motif information available from public databases. These data were processed and utilized as predictor for predicting cell cycle genes using the Random Forest method. Our results show that both the trans- TF features and the cis- motif features are predictive to cell cycle genes, and a combination of the two types features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division.
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影响因子：
7
作者：
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者：
Hubbard TJ
影响因子：
14.9
作者：
Gauthier NP;Jensen LJ;Wernersson R;Brunak S;Jensen TS
通讯作者：
Jensen TS
影响因子：
21.3
作者：
Kittler, Ralf;Pelletier, Laurence;Buchholz, Frank
通讯作者：
Buchholz, Frank
影响因子：
14.9
作者：
Dreszer TR;Karolchik D;Zweig AS;Hinrichs AS;Raney BJ;Kuhn RM;Meyer LR;Wong M;Sloan CA;Rosenbloom KR;Roe G;Rhead B;Pohl A;Malladi VS;Li CH;Learned K;Kirkup V;Hsu F;Harte RA;Guruvadoo L;Goldman M;Giardine BM;Fujita PA;Diekhans M;Cline MS;Clawson H;Barber GP;Haussler D;James Kent W
通讯作者：
James Kent W
影响因子：
5.3
作者：
Ishida, S;Huang, E;Nevins, JR
通讯作者：
Nevins, JR