Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, 19716, USA.
Nat Commun. 2018 Oct 24;9(1):4425. doi: 10.1038/s41467-018-06798-7.
Gene synthesis enables creation and modification of genetic sequences at an unprecedented pace, offering enormous potential for new biological functionality but also increasing the need for biosurveillance. In this paper, we introduce a bioinformatics technique for determining whether a gene is natural or synthetic based solely on nucleotide sequence. This technique, grounded in codon theory and machine learning, can correctly classify genes with 97.7% accuracy on a novel data set. We then classify ∼19,000 unique genes from the Addgene non-profit plasmid repository to investigate whether natural and synthetic genes have differential use in heterologous expression. Phylogenetic analysis of distance between source and expression organisms reveals that researchers are using synthesis to source genes from more genetically-distant organisms, particularly for longer genes. We provide empirical evidence that gene synthesis is leading biologists to sample more broadly across the diversity of life, and we provide a foundational tool for the biosurveillance community.
基因合成以空前的速度实现了遗传序列的创建和修饰，为新的生物功能提供了巨大的潜力，但也增加了生物监测的需求。在本文中，我们介绍了一种基于核苷酸序列仅确定基因是天然的还是合成的生物信息学技术。该技术基于密码子理论和机器学习，在一个新的数据集上可以以 97.7%的准确率正确分类基因。然后，我们对来自 Addgene 非营利质粒库的约 19000 个独特基因进行分类，以研究天然和合成基因在异源表达中的差异使用情况。来源和表达生物体之间距离的系统发育分析表明，研究人员正在使用合成技术从遗传上更远的生物体中获取基因，特别是对于较长的基因。我们提供了经验证据表明，基因合成正在促使生物学家更广泛地从生命的多样性中采样，并且我们为生物监测社区提供了一个基础工具。
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