An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis sigma66 promoters.
An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis sigma66 promoters.
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DOI：
10.1186/1471-2105-10-271
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发表时间：
2009-08-28
影响因子：
3
通讯作者：
Ardell DH
中科院分区：
文献类型：
--
作者：
Mallios RR;Ojcius DM;Ardell DH
启动子鉴定是探索细菌基因调控的第一步。已经证明细菌转录的起始依赖于启动子区域中DNA的稳定性和拓扑结构以及RNA聚合酶σ-因子与启动子之间的结合亲和力。然而，迄今为止，启动子预测算法尚未明确使用这些因素的集合作为预测因子。此外，大多数启动子模型都是在大肠杆菌的数据上训练的。虽然已经表明，不同细菌的转录机制是相似的，但大肠杆菌和沙眼衣原体之间的差异很可能大到足以推荐生物体特异性建模工作。在这里，我们提出了一个迭代的随机模型构建过程，结合了DNA稳定性，曲率，扭曲和应力诱导的DNA双链体不稳定沿着与持续时间隐马尔可夫模型参数的生物物理指标，从29个实验验证的序列模型沙眼衣原体σ66启动子。最初，训练集序列的迭代持续时间隐马尔可夫建模提供了沙眼衣原体RNA聚合酶σ66/DNA结合的评分算法。随后，逐步二元逻辑回归的迭代应用选择多个启动子预测因子并删除/替换训练集序列以确定最佳训练集。由此产生的模型以高度的准确度预测最终的训练集，并提供对启动子区域结构的见解。提供基于模型的全基因组预测，以便可以通过实验评估最佳启动子候选物，并开发精细模型。还提供了与其他三种算法的联合预测，以提高可靠性。该策略和所得模型支持DNA生物物理性质与RNA聚合酶σ-因子/DNA协同结合一起沿着有助于序列促进转录的能力的推测。这项工作提供了一个基线模型，可以演变为新的沙眼衣原体σ66启动子的援助，从提供的全基因组预测确定。所提出的方法是理想的有机体与几个确定的启动子和相对较小的基因组。
Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort. Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model Chlamydia trachomatis σ66 promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for Chlamydia trachomatis RNA polymerase σ66/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability. This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new Chlamydia trachomatis σ66 promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.
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影响因子：
3.5
作者：
Iyer, LM;Koonin, EV;Aravind, L
通讯作者：
Aravind, L
影响因子：
2.6
作者：
Grech, Brian;Maetschke, Stefan;Timms, Peter
通讯作者：
Timms, Peter
影响因子：
14.9
作者：
HARLEY, CB;REYNOLDS, RP
通讯作者：
REYNOLDS, RP
影响因子：
3.2
作者：
Hefty, P. Scott;Stephens, Richard S.
通讯作者：
Stephens, Richard S.
DOI：
10.1073/pnas.1331135100
发表时间：
2003-07-08
影响因子：
11.1
作者：
Belland, RJ;Zhong, GM;Caldwell, HD
通讯作者：
Caldwell, HD