ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species.
ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species.
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SCERTF：糖疗法物种的基准位置重量矩阵的综合数据库。
DOI：
10.1093/nar/gkr1180
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发表时间：
2012-01
影响因子：
14.9
通讯作者：
Stormo GD
中科院分区：
文献类型：
--
作者：
Spivak AT;Stormo GD
酿酒酵母（Saccharomyces cerevisiae）是研究转录调控的主要模型，大多数酵母转录因子的特异性已通过多种方法确定。然而，目前还不清楚哪些位置权重矩阵（PWM）是最有用的;对于酵母中的大约200个TF，文献中有超过1200个PWM。为了解决这个问题，我们创建了ScerTF，这是一个包含来自11个不同来源的1226个基序的综合数据库。我们确定了一个单一的矩阵，每个TF，最好的预测在体内数据的基准矩阵对染色质免疫沉淀和TF删除实验。我们还使用体内数据来优化阈值，以确定每个矩阵的监管网站。为了校正不同方法的偏差，我们开发了一种联合收割机矩阵的策略。这些对齐的矩阵优于几个TF的最佳可用矩阵。我们使用矩阵来预测基因组中共同出现的调控元件，并确定了许多已知的TF组合。此外，我们预测新的组合，并提供证据的基因表达数据的组合调控。该数据库可通过网站http://ural.wustl.edu/ScerTF查阅。该网站允许用户使用监管网站或矩阵搜索数据库，以识别最有可能结合输入序列的TF。
Saccharomyces cerevisiae is a primary model for studies of transcriptional control, and the specificities of most yeast transcription factors (TFs) have been determined by multiple methods. However, it is unclear which position weight matrices (PWMs) are most useful; for the roughly 200 TFs in yeast, there are over 1200 PWMs in the literature. To address this issue, we created ScerTF, a comprehensive database of 1226 motifs from 11 different sources. We identified a single matrix for each TF that best predicts in vivo data by benchmarking matrices against chromatin immunoprecipitation and TF deletion experiments. We also used in vivo data to optimize thresholds for identifying regulatory sites with each matrix. To correct for biases from different methods, we developed a strategy to combine matrices. These aligned matrices outperform the best available matrix for several TFs. We used the matrices to predict co-occurring regulatory elements in the genome and identified many known TF combinations. In addition, we predict new combinations and provide evidence of combinatorial regulation from gene expression data. The database is available through a web interface at http://ural.wustl.edu/ScerTF. The site allows users to search the database with a regulatory site or matrix to identify the TFs most likely to bind the input sequence.
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影响因子：
16
作者：
Badis, Gwenael;Chan, Esther T.;van Bakel, Harm;Pena-Castillo, Lourdes;Tillo, Desiree;Tsui, Kyle;Carlson, Clayton D.;Gossett, Andrea J.;Hasinoff, Michael J.;Warren, Christopher L.;Gebbia, Marinella;Talukder, Shaheynoor;Yang, Ally;Mnaimneh, Sanie;Terterov, Dimitri;Coburn, David;Yeo, Ai Li;Yeo, Zhen Xuan;Clarke, Neil D.;Lieb, Jason D.;Ansari, Aseem Z.;Nislow, Corey;Hughes, Timothy R.
通讯作者：
Hughes, Timothy R.
影响因子：
14.8
作者：
Berger, Michael F.;Bulyk, Martha L.
通讯作者：
Bulyk, Martha L.
DOI：
10.1126/science.1162327
发表时间：
2009-06-26
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Badis G;Berger MF;Philippakis AA;Talukder S;Gehrke AR;Jaeger SA;Chan ET;Metzler G;Vedenko A;Chen X;Kuznetsov H;Wang CF;Coburn D;Newburger DE;Morris Q;Hughes TR;Bulyk ML
通讯作者：
Bulyk ML
DOI：
10.1073/pnas.0701356104
发表时间：
2007-04-24
影响因子：
11.1
作者：
Morozov, Alexandre V.;Siggia, Eric D.
通讯作者：
Siggia, Eric D.
影响因子：
7
作者：
Gordan, Raluca;Hartemink, Alexander J.;Bulyk, Martha L.
通讯作者：
Bulyk, Martha L.