The Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning
The Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning
复制标题
合成成功计算器：利用机器学习预测 DNA 片段的快速合成
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
H. Salis
中科院分区：
文献类型：
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作者：
Sean M. Halper;Ayaan Hossain;H. Salis
长DNA片段的合成和组装极大地加速了合成生物学和生物技术研究。然而，较长的周转时间或综合失败会在设计-构建-测试-学习周期中造成不可预测的瓶颈。我们开发了一个机器学习模型，称为合成成功计算器，以预测长DNA片段是否可以在短周转时间内容易地合成。该模型还确定了与合成结果相关的序列决定因素。我们使用生物物理特征和1076个DNA片段序列的编译数据集训练了一个随机森林分类器，以实现高预测性能（251个未知序列的F1得分为0.928）。特征重要性分析表明，重复的DNA序列是合成失败的最重要的贡献者。然后，我们将合成成功计算器应用于大型序列数据集，发现84.9%的大肠杆菌MG 1655基因组，但NCBI中只有34.4%的采样质粒可以容易地合成。总的来说，合成成功计算器可以单独应用，以防止合成失败或嵌入优化算法中，以设计可以快速合成和组装的大型遗传系统。
The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We developed a machine learning model, called the Synthesis Success Calculator, to predict whether a long DNA fragment can be readily synthesized with a short turnaround time. The model also identifies the sequence determinants associated with the synthesis outcome. We trained a random forest classifier using biophysical features and a compiled dataset of 1076 DNA fragment sequences to achieve high predictive performance (F1 score of 0.928 on 251 unseen sequences). Feature importance analysis revealed that repetitive DNA sequences were the most important contributor to synthesis failures. We then applied the Synthesis Success Calculator across large sequence datasets and found that 84.9% of the Escherichia coli MG1655 genome, but only 34.4% of sampled plasmids in NCBI, could be readily synthesized. Overall, the Synthesis Success Calculator can be applied on its own to prevent synthesis failures or embedded within optimization algorithms to design large genetic systems that can be rapidly synthesized and assembled.
影响因子：
7.7
作者：
Lutz, Stefan
通讯作者：
Lutz, Stefan
影响因子：
4.7
作者：
Jack BR;Leonard SP;Mishler DM;Renda BA;Leon D;Suárez GA;Barrick JE
通讯作者：
Barrick JE
影响因子：
15
作者：
Casini, Arturo;Chang, Fang-Yuan;Gordon, D. Benjamin
通讯作者：
Gordon, D. Benjamin