Investigating the predictability of essential genes across distantly related organisms using an integrative approach.
Investigating the predictability of essential genes across distantly related organisms using an integrative approach.
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DOI：
10.1093/nar/gkq784
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发表时间：
2011-02
影响因子：
14.9
通讯作者：
Lu LJ
中科院分区：
文献类型：
--
作者：
Deng J;Deng L;Su S;Zhang M;Lin X;Wei L;Minai AA;Hassett DJ;Lu LJ
在研究不足的微生物中快速准确地识别新的必需基因将显着提高我们对细胞如何工作的理解和重新设计微生物的能力。然而，预测远亲生物的必需基因仍然是一个挑战。在这里，我们提出了一种基于机器学习的综合方法，可以在远亲细菌之间可靠地转移必需基因注释。我们专注于四种具有良好特征的必需基因的细菌物种，并测试了其中三对之间的可转移性。对于每一对，我们训练我们的分类器来学习与一个生物体中的必需基因相关的特征，并将其应用于另一个生物体中的预测。然后通过检查与目标生物体中已知必需基因的一致性来评估预测。在相同微生物中进行10倍交叉验证，AUC评分在0.86和0.93之间。跨生物体预测的AUC评分在0.69和0.89之间。可移植性可能受到生长条件、训练数据集的质量和进化距离的影响。因此，我们是第一个报告基因的重要性可以可靠地预测使用功能训练和测试在一个遥远的相关生物体。我们的方法比现有的方法更强大和便携，显着扩展了我们预测直系同源物以外的必需基因的能力。
Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.
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影响因子：
9.9
作者：
de Berardinis, Veronique;Vallenet, David;Castelli, Vanina;Besnard, Marielle;Pinet, Agnes;Cruaud, Corinne;Samair, Sumitta;Lechaplais, Christophe;Gyapay, Gabor;Richez, Celine;Durot, Maxime;Kreimeyer, Annett;Le Fevre, Francois;Schaechter, Vincent;Pezo, Valerie;Doering, Volker;Scarpelli, Claude;Medigue, Claudine;Cohen, Georges N.;Marliere, Philippe;Salanoubat, Marcel;Weissenbach, Jean
通讯作者：
Weissenbach, Jean
影响因子：
64.8
作者：
Jeong, H;Mason, SP;Oltvai, ZN
通讯作者：
Oltvai, ZN
DOI：
10.1016/j.bbrc.2004.03.138
发表时间：
2004-05-07
影响因子：
3.1
作者：
Fuglsang, A
通讯作者：
Fuglsang, A
大肠杆菌K-12的构造框架，单基因敲除突变体：Keio Collection。
影响因子：
9.9
作者：
通讯作者：
--
影响因子：
4
作者：
Brown, JR;Volker, C
通讯作者：
Volker, C