D-Tailor: automated analysis and design of DNA sequences

Guimaraes, Joao C.; Rocha, Miguel; Arkin, Adam P.; Cambray, Guillaume

doi:10.1093/bioinformatics/btt742

Title: D-Tailor: automated analysis and design of DNA sequences

Journal Article · Mon Jan 06 00:00:00 EST 2014 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btt742· OSTI ID:1625285

Guimaraes, Joao C. ^[1]; Rocha, Miguel ^[2]; Arkin, Adam P. ^[3]; Cambray, Guillaume ^[4]

Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering; Univ. of California, Berkeley, CA (United States). California Inst. for Quantitative Biosciences; Univ. of Minho, Braga (Portugal). School of Engineering. Computer and Science and Technology Center
Univ. of Minho, Braga (Portugal). School of Engineering. Computer and Science and Technology Center
Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering; Univ. of California, Berkeley, CA (United States). California Inst. for Quantitative Biosciences; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physical Biosciences Division
Univ. of California, Berkeley, CA (United States). California Inst. for Quantitative Biosciences

Motivation: Current advances in DNA synthesis, cloning and sequencing technologies afford high-throughput implementation of artificial sequences into living cells. However, flexible computational tools for multi-objective sequence design are lacking, limiting the potential of these technologies. Results: We developed DNA-Tailor (D-Tailor), a fully extendable software framework, for property-based design of synthetic DNA sequences. D-Tailor permits the seamless integration of multiple sequence analysis tools into a generic Monte Carlo simulation that evolves sequences toward any combination of rationally defined properties. As proof of principle, we show that D-Tailor is capable of designing sequence libraries comprising all possible combinations among three different sequence properties influencing translation efficiency in Escherichia coli. The capacity to design artificial sequences that systematically sample any given parameter space should support the implementation of more rigorous experimental designs.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; National Science Foundation (NSF); Fundacao para a Ciencia e a Tecnologia (FCT); European Regional Development Fund (ERDF)

Grant/Contract Number:: AC02-05CH11231; 04-570/0540879; SFRH/BD/47819/2008; FCOMP-01-0124-FEDER-015079; PEst-OE/EEI/ UI0752/2011

OSTI ID:: 1625285

Journal Information:: Bioinformatics, Vol. 30, Issue 8; ISSN 1367-4803

Publisher:: International Society for Computational Biology - Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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