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Published May 2, 2015 | Published + Supplemental Material
Journal Article Open

Pydna: a simulation and documentation tool for DNA assembly strategies using python

Abstract

Background: Recent advances in synthetic biology have provided tools to efficiently construct complex DNA molecules which are an important part of many molecular biology and biotechnology projects. The planning of such constructs has traditionally been done manually using a DNA sequence editor which becomes error-prone as scale and complexity of the construction increase. A human-readable formal description of cloning and assembly strategies, which also allows for automatic computer simulation and verification, would therefore be a valuable tool. Results: We have developed pydna, an extensible, free and open source Python library for simulating basic molecular biology DNA unit operations such as restriction digestion, ligation, PCR, primer design, Gibson assembly and homologous recombination. A cloning strategy expressed as a pydna script provides a description that is complete, unambiguous and stable. Execution of the script automatically yields the sequence of the final molecule(s) and that of any intermediate constructs. Pydna has been designed to be understandable for biologists with limited programming skills by providing interfaces that are semantically similar to the description of molecular biology unit operations found in literature. Conclusions: Pydna simplifies both the planning and sharing of cloning strategies and is especially useful for complex or combinatorial DNA molecule construction. An important difference compared to existing tools with similar goals is the use of Python instead of a specifically constructed language, providing a simulation environment that is more flexible and extensible by the user.

Additional Information

© 2015 Pereira et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Received: 13 August 2014 Accepted: 20 March 2015. Published online: 02 May 2015. Thanks to Dr. Aric Hagberg Los Alamos National Laboratory, U.S.A and Sérgio Simões, Universidade de São Paulo, Brasil for help with NetworkX and graph theory in general. Thanks to Henrik Bengtsson, Dept of Epidemiology & Biostatistics, University of California San Francisco, U.S.A. for critical reading of the manuscript. Thanks to the 2013 Bioinformatics 6605 N4 students A. Coelho, A. Faria, A. Neves D. Yelshyna and E. Costa for testing. This work was supported by the Fundação para a Ciência e Tecnologia (FCT) [PTDC/AAC-AMB/120940/2010, EXPL/BBB-BIO/1772/2013]; and the FEDER POFC-COMPETE [PEst-C/BIA/UI4050/2011]. FA and GR were supported by FCT fellowships [SFRH/BD/80934/2011 and SFRH/BD/42565/2007, respectively]. Authors' contributions: BJ concept, initial design, programming, initial manuscript draft and testing. MB programming and testing. FP, GR and FA interface design and testing. ÅC synthesized the lactose pathway. All authors were involved in writing the manuscript. All authors read and approved the final manuscript.

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Supplemental Material - s12859-015-0544-x-s2.zip

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Created:
August 20, 2023
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October 23, 2023