Dataset Information

Standardizing Automated DNA Assembly: Best Practices, Metrics, and Protocols Using Robots.

ABSTRACT: The advancement of synthetic biology requires the ability to create new DNA sequences to produce unique behaviors in biological systems. Automation is increasingly employed to carry out well-established assembly methods of DNA fragments in a multiplexed, high-throughput fashion, allowing many different configurations to be tested simultaneously. However, metrics are required to determine when automation is warranted based on factors such as assembly methodology, protocol details, and number of samples. The goal of our synthetic biology automation work is to develop and test protocols, hardware, and software to investigate and optimize DNA assembly through quantifiable metrics. We performed a parameter analysis of DNA assembly to develop a standardized, highly efficient, and reproducible MoClo protocol, suitable to be used both manually and with liquid-handling robots. We created a key DNA assembly metric (Q-metric) to characterize a given automation method's advantages over conventional manual manipulations with regard to researchers' highest-priority parameters: output, cost, and time. A software tool called Puppeteer was developed to formally capture these metrics, help define the assembly design, and provide human and robotic liquid-handling instructions. Altogether, we contribute to a growing foundation of standardizing practices, metrics, and protocols for automating DNA assembly.

SUBMITTER: Walsh DI

PROVIDER: S-EPMC6819997 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Standardizing Automated DNA Assembly: Best Practices, Metrics, and Protocols Using Robots.

Walsh David I DI Pavan Marilene M Ortiz Luis L Wick Scott S Bobrow Johanna J Guido Nicholas J NJ Leinicke Sarah S Fu Dany D Pandit Shreya S Qin Lucy L Carr Peter A PA Densmore Douglas D

SLAS technology 20190215 3

The advancement of synthetic biology requires the ability to create new DNA sequences to produce unique behaviors in biological systems. Automation is increasingly employed to carry out well-established assembly methods of DNA fragments in a multiplexed, high-throughput fashion, allowing many different configurations to be tested simultaneously. However, metrics are required to determine when automation is warranted based on factors such as assembly methodology, protocol details, and number of s ...[more]

PMID: 30768372

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Project description:BackgroundExtraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. However, primary source language describing treatment-related end points can vary greatly, resulting in large labor efforts to manually standardize extractions before data are fit for use.ObjectivesTo minimize these labor efforts, we applied an augmented intelligence approach and developed automated tools to support standardization of extracted information via application of preexisting controlled vocabularies.MethodsWe created and applied a harmonized controlled vocabulary crosswalk, consisting of Unified Medical Language System (UMLS) codes, German Federal Institute for Risk Assessment (BfR) DevTox harmonized terms, and The Organization for Economic Co-operation and Development (OECD) end point vocabularies, to roughly 34,000 extractions from prenatal developmental toxicology studies conducted by the National Toxicology Program (NTP) and 6,400 extractions from European Chemicals Agency (ECHA) prenatal developmental toxicology studies, all recorded based on the original study report language.ResultsWe automatically applied standardized controlled vocabulary terms to 75% of the NTP extracted end points and 57% of the ECHA extracted end points. Of all the standardized extracted end points, about half (51%) required manual review for potential extraneous matches or inaccuracies. Extracted end points that were not mapped to standardized terms tended to be too general or required human logic to find a good match. We estimate that this augmented intelligence approach saved >350 hours of manual effort and yielded valuable resources including a controlled vocabulary crosswalk, organized related terms lists, code for implementing an automated mapping workflow, and a computationally accessible dataset.DiscussionAugmenting manual efforts with automation tools increased the efficiency of producing a findable, accessible, interoperable, and reusable (FAIR) dataset of regulatory guideline studies. This open-source approach can be readily applied to other legacy developmental toxicology datasets, and the code design is customizable for other study types. https://doi.org/10.1289/EHP13215.

Dataset Information

Standardizing Automated DNA Assembly: Best Practices, Metrics, and Protocols Using Robots.

Publications

Standardizing Automated DNA Assembly: Best Practices, Metrics, and Protocols Using Robots.

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