Predicting gene expression from sequence: a reexamination.
Predicting gene expression from sequence: a reexamination.
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DOI：
10.1371/journal.pcbi.0030243
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发表时间：
2007-11
影响因子：
4.3
通讯作者：
Liu JS
中科院分区：
文献类型：
--
作者：
Yuan Y;Guo L;Shen L;Liu JS
尽管基因表达的大部分信息都被编码在基因组中，但破译这些信息一直是非常具有挑战性的。我们重新检查了Beer和Tavazoie（BT）的方法，根据各自启动子序列中的信息预测酿酒酵母中2，587个基因的mRNA表达模式。我们没有拟合复杂的贝叶斯网络模型，而是只使用BT提供的序列-基序匹配分数来训练朴素贝叶斯分类器。基于与BT相同的标准和相同的交叉验证（CV）程序，我们的简单模型正确预测了79%基因的表达模式，与BT的73%准确率相比，这是有利的。事实上，我们的方法没有使用预测的结合位点的位置和方向信息，但实现了更高的预测精度，促使我们调查了一些生物预测的BT。我们发现，他们的一些预测，特别是那些有关的主题方向和位置，充其量是间接的。例如，BT为PAC和RRPE基序提出的组合规则并不是预测模型所推断的基因簇所独有的，并且有比BT的规则更简单的规则在统计学上更显着。我们还表明，BT使用的CV程序来估计他们的方法的预测精度是不适当的，可能高估了约10%的预测精度。通过与靶基因上游的某些序列特异性位点结合，一类称为转录因子（TF）的特殊蛋白质控制转录活性，即，下游基因的表达量。由TF结合的DNA序列模式被称为基序。Beer和Tavazoie（BT）在2004年发表在Cell上的文章中已经表明，仅基于其上游序列信息，可以很好地预测基因的表达模式，所述上游序列信息的形式为一组序列基序的匹配分数和相应的预测结合位点的位置和方向。在这里，我们报告了一个新的朴素贝叶斯方法，这样的预测任务。与BT的工作相比，我们的模型更简单，更鲁棒，并且仅使用模体匹配得分就实现了更高的预测精度。在我们的方法中，位置和方向信息不会进一步帮助全局预测。我们的研究结果也对BT基于他们的模型产生的几个生物学假设提出了质疑。最后，我们表明，交叉验证过程中使用的BT估计他们的方法的预测精度是不适当的，可能高估了约10%的准确性。
Although much of the information regarding genes' expressions is encoded in the genome, deciphering such information has been very challenging. We reexamined Beer and Tavazoie's (BT) approach to predict mRNA expression patterns of 2,587 genes in Saccharomyces cerevisiae from the information in their respective promoter sequences. Instead of fitting complex Bayesian network models, we trained naïve Bayes classifiers using only the sequence-motif matching scores provided by BT. Our simple models correctly predict expression patterns for 79% of the genes, based on the same criterion and the same cross-validation (CV) procedure as BT, which compares favorably to the 73% accuracy of BT. The fact that our approach did not use position and orientation information of the predicted binding sites but achieved a higher prediction accuracy, motivated us to investigate a few biological predictions made by BT. We found that some of their predictions, especially those related to motif orientations and positions, are at best circumstantial. For example, the combinatorial rules suggested by BT for the PAC and RRPE motifs are not unique to the cluster of genes from which the predictive model was inferred, and there are simpler rules that are statistically more significant than BT's ones. We also show that CV procedure used by BT to estimate their method's prediction accuracy is inappropriate and may have overestimated the prediction accuracy by about 10%. Through binding to certain sequence-specific sites upstream of the target genes, a special class of proteins called transcription factors (TFs) control transcription activities, i.e., expression amounts, of the downstream genes. The DNA sequence patterns bound by TFs are called motifs. It has been shown in an article by Beer and Tavazoie (BT) published in Cell in 2004 that a gene's expression pattern can be well-predicted based only on its upstream sequence information in the form of matching scores of a set of sequence motifs and the location and orientation of corresponding predicted binding sites. Here we report a new naïve Bayes method for such a prediction task. Compared to BT's work, our model is simpler, more robust, and achieves a higher prediction accuracy using only the motif matching score. In our method, the location and orientation information do not further help the prediction in a global way. Our result also casts doubt on several biological hypotheses generated by BT based on their model. Finally, we show that the cross-validation procedure used by BT to estimate their method's prediction accuracy is inappropriate and may have overestimated the accuracy by about 10%.
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DOI：
10.1073/pnas.0407365101
发表时间：
2004-11-16
影响因子：
11.1
作者：
Das, D;Banerjee, N;Zhang, MQ
通讯作者：
Zhang, MQ
影响因子：
16
作者：
Cho, RJ;Campbell, MJ;Davis, RW
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Davis, RW
影响因子：
46.9
作者：
Tompa, M;Li, N;Zhu, Z
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Zhu, Z
影响因子：
3.3
作者：
Spellman, PT;Sherlock, G;Futcher, B
通讯作者：
Futcher, B
影响因子：
30.8
作者：
Bussemaker, HJ;Li, H;Siggia, ED
通讯作者：
Siggia, ED