Computational tools and algorithms for designing customized synthetic genes.
Computational tools and algorithms for designing customized synthetic genes.
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DOI：
10.3389/fbioe.2014.00041
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发表时间：
2014
影响因子：
5.7
通讯作者：
Papamichail D
中科院分区：
文献类型：
--
作者：
Gould N;Hendy O;Papamichail D
DNA合成技术的进步使人工基因、基因回路和细菌基因组的构建成为可能。合成结构从头设计的自由为研究序列特征突变的影响以及验证核酸和氨基酸编码的功能信息的假设提供了重要的力量。为了实现这一目标，已经实现了大量不同复杂程度的软件工具，使合成基因的序列优化设计基于合理定义的属性。第一代工具主要处理单一目标，如密码子使用优化和唯一限制位点合并。近年来出现了序列设计工具，旨在将序列进化为目标组合。设计符合多个目标的最佳蛋白质编码序列在计算上是困难的，并且大多数工具依赖于启发式对巨大的序列设计空间进行采样。在这篇综述中，我们研究了基因优化背后的一些算法问题，以及不同工具采用的重新设计基因和优化所需编码特征的方法。我们利用测试用例来证明每种方法的效率，以及确定它们的优点和局限性。
Advances in DNA synthesis have enabled the construction of artificial genes, gene circuits, and genomes of bacterial scale. Freedom in de novo design of synthetic constructs provides significant power in studying the impact of mutations in sequence features, and verifying hypotheses on the functional information that is encoded in nucleic and amino acids. To aid this goal, a large number of software tools of variable sophistication have been implemented, enabling the design of synthetic genes for sequence optimization based on rationally defined properties. The first generation of tools dealt predominantly with singular objectives such as codon usage optimization and unique restriction site incorporation. Recent years have seen the emergence of sequence design tools that aim to evolve sequences toward combinations of objectives. The design of optimal protein-coding sequences adhering to multiple objectives is computationally hard, and most tools rely on heuristics to sample the vast sequence design space. In this review, we study some of the algorithmic issues behind gene optimization and the approaches that different tools have adopted to redesign genes and optimize desired coding features. We utilize test cases to demonstrate the efficiency of each approach, as well as identify their strengths and limitations.