ShuffleAnalyzer: A Comprehensive Tool to Visualize DNA Shuffling.

IF 3.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS ACS Synthetic Biology Pub Date : 2024-08-16 Epub Date: 2024-07-11 DOI:10.1021/acssynbio.4c00251

Franz Schweiggert, Gregor Habeck, Patrick Most, Martin Busch, Jörg Schweiggert

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Abstract

DNA shuffling is a powerful technique for generating synthetic DNA via recombination of homologous parental sequences. Resulting chimeras are often incorporated into complex libraries for functionality screenings that identify novel variants with improved characteristics. To survey shuffling efficiency, subsequences of chimeras can be computationally assigned to their corresponding parental counterpart, yielding insight into frequency of recombination events, diversity of shuffling libraries and actual composition of final variants. Whereas tools for parental assignment exist, they do not provide direct visualization of the results, making the analysis time-consuming and cumbersome. Here we present ShuffleAnalyzer, a comprehensive, user-friendly, Python-based analysis tool that directly generates graphical outputs of parental assignments and is freely available under a BSD-3 license (https://github.com/joerg-swg/ShuffleAnalyzer/releases). Besides DNA shuffling, peptide insertions can be simultaneously analyzed and visualized, which makes ShuffleAnalyzer a highly valuable tool for integrated approaches often used in synthetic biology, such as AAV capsid engineering in gene therapy applications.

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ShuffleAnalyzer：可视化 DNA 洗牌的综合工具

DNA 洗牌是一种通过同源亲本序列重组生成合成 DNA 的强大技术。由此产生的嵌合体通常被纳入复杂的文库中进行功能筛选，从而发现具有更好特性的新型变体。为了调查洗牌效率，可以通过计算将嵌合体的子序列分配给相应的亲本，从而了解重组事件的频率、洗牌文库的多样性以及最终变体的实际组成。虽然存在亲本分配工具，但这些工具不能提供结果的直接可视化，使得分析耗时且繁琐。在此，我们介绍基于 Python- 的 ShuffleAnalyzer，它是一种全面、用户友好的分析工具，可直接生成亲本分配的图形输出，并在 BSD-3 许可证下免费提供 (https://github.com/joerg-swg/ShuffleAnalyzer/releases)。除了 DNA 洗牌外，还可以同时分析和可视化肽插入，这使得 ShuffleAnalyzer 成为合成生物学中常用的集成方法的一个非常有价值的工具，例如基因治疗应用中的 AAV 胶囊工程。

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来源期刊

ACS Synthetic Biology 生物-

CiteScore

8.00

自引率

10.60%

发文量

380

审稿时长

6-12 weeks

期刊介绍： The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.