Automated Visualization of Genetic Designs Using DNAplotlib

Bartoli, Vittorio; Dixon, Daniel; Gorochowski, Thomas E.

doi:10.1007/978-1-4939-7795-6_22

Cited by 13 publications

(10 citation statements)

References 14 publications

(16 reference statements)

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“…All genetic diagrams are shown using Synthetic Biology Open Language Visual (SBOL Visual) notation 52 . SBOL Visual diagrams were generated using the DNAplotlib Python package version 1.0 53 which were then annotated and composed with OmniGraffle version 7.9.2.…”

Section: Methodsmentioning

confidence: 99%

Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing

Tarnowski

Gorochowski

2021

Preprint

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Transcriptional terminators signal where transcribing RNA polymerases (RNAPs) should halt and disassociate from DNA. However, because termination is stochastic, two different forms of transcript could be produced: one ending at the terminator and the other reading through. An ability to control the abundance of these transcript isoforms would offer bioengineers a mechanism to regulate multi-gene constructs at the level of transcription. Here, we explore this possibility by repurposing terminators as ‘transcriptional valves’ which can tune the proportion of RNAP read-through. Using one-pot combinatorial DNA assembly we construct 1183 transcriptional valves for T7 RNAP and show how nanopore-based direct RNA sequencing (dRNA-seq) can be used to simultaneously characterize the entire pool at a nucleotide resolution in vitro and unravel genetic design principles to tune and insulate their function using nearby sequence context. This work provides new avenues for controlling transcription and demonstrates the value of long-read sequencing for exploring complex sequence-function landscapes.

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Section: Methodsmentioning

confidence: 99%

Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing

Tarnowski

Gorochowski

2021

Preprint

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“…Computational analysis was performed by custom scripts run using Python 3.6.8 and the following packages: tensorflow 1.14.0, keras 2.2.4, scikit-learn 0.21.2, scikit-image 0.15.0, numpy 1.16.4 and matplotlib 3.1.1. Genetic designs are visualised using DNAplotlib 1.0 56,57 and Synthetic Biology Open Language Visual (SBOL Visual) symbols 58 . Figures were composed using Omnigraffle 7.16 and Affinity Designer 1.8.3.…”

Section: General Computational Analysis and Toolsmentioning

confidence: 99%

Cheetah: a computational toolkit for cybergenetic control

Pedone

Cesare

Zamora

et al. 2020

Preprint

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1Advances in microscopy, microfluidics and optogenetics enable single cell monitoring and 2 environmental regulation and offer the means to control cellular phenotypes. The development 3 of such systems is challenging and often results in bespoke setups that hinder reproducibility. To 4 address this, we introduce Cheetah -a flexible computational toolkit that simplifies the integration 5 of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an 6 image segmentation system based on the versatile U-Net convolutional neural network. This is 7 supplemented with functionality to robustly count, characterise and control cells over time. We 8 demonstrate Cheetah's core capabilities by analysing long-term bacterial and mammalian cell 9 growth and by dynamically controlling protein expression in mammalian cells. In all cases, 10 Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, 11 allowing for more accurate control signals to be generated. Availability of this easy-to-use 12 platform will make control engineering techniques more accessible and offer new ways to probe 13 and manipulate living cells. 14 Introduction 15Modern automated microscopy techniques enable researchers to collect vast amounts of single-16 cell imaging data at high temporal resolutions. This has resulted in time-lapse microscopy 17 becoming the go to method for studying cellular dynamics, enabling the quantification of 18 processes such as stochastic fluctuations during gene expression 1-3 , emerging oscillatory 19 patterns in protein concentrations 4 , lineage selection 5,6 , and many more 7 . 20To make sense of microscopy images, segmentation is performed whereby an image is 21 broken up into regions corresponding to specific features of interest (e.g. cells and the 22 background). Image segmentation allows for the accurate quantification of cellular phenotypes 23 encoded by visual cues (e.g. fluorescence) by ensuring only those pixels corresponding to a cell 24 are considered. A range of segmentation algorithms have been proposed to automatically 25 analyse images of various organisms and tissues 3,8-11 . The most common of these are 26 thresholding 12 and seeded watershed 13 methods, which are available in many scientific image 27 processing toolkits. Commercial software packages also implement this type of functionality, 28 enabling both automated image acquisition and analysis (e.g. NIS-Elements, Nikon). While these 29 proprietary systems are user-friendly requiring no programming skills to be used, they are often 30 difficult to tailor for specific needs and cannot be easily extended to new forms of analysis. 31More recently, deep learning-based approaches to image segmentation have emerged 32 7,14-17 . Compared to the more common thresholding-based approaches 12 , deep learning methods 33 tend to require more significant computational resources when running on traditional computer 34 architectures and often require the time-consuming manual step of generating...

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“…To support the creation of SBOLv-compliant diagrams, numerous computational tools have emerged. These include programming libraries like DNAplotlib ( 10 , 11 ) and VisBOL ( 12 , 13 ), as well as graphical end-user environments like SBOLDesigner ( 14 ) and SBOLCanvas ( 15 ). These tools all rely on a set of SBOL glyphs that evolve over time and so a key challenge is managing how glyphs are drawn and kept up to date.…”

Section: Introductionmentioning

confidence: 99%

paraSBOLv: a foundation for standard-compliant genetic design visualization tools

2021

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Diagrams constructed from standardized glyphs are central to communicating complex design information in many engineering fields. For example, circuit diagrams are commonplace in electronics and allow for a suitable abstraction of the physical system that helps support the design process. With the development of the Synthetic Biology Open Language Visual (SBOLv), bioengineers are now positioned to better describe and share their biological designs visually. However, development of computational tools to support the creation of these diagrams is currently hampered by an excessive burden in maintenance due to the large and expanding number of glyphs present in the standard. Here, we present a Python package called paraSBOLv that enables access to the full suite of SBOLv glyphs through use of machine-readable parametric glyph definitions. These greatly simplify the rendering process while allowing extensive customization of the resulting diagrams. We demonstrate how adoption of paraSBOLv can accelerate the development of highly specialized biodesign visualization tools or even form the basis for more complex software by removing the burden of maintaining glyph specific rendering code. Looking forward, we suggest that incorporation of machine-readable parametric glyph definitions into the SBOLv standard could further simplify the development of tools to produce standard-compliant diagrams and integration of visual standards across fields.

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Automated Visualization of Genetic Designs Using DNAplotlib

Cited by 13 publications

References 14 publications

Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing

Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing

Cheetah: a computational toolkit for cybergenetic control

paraSBOLv: a foundation for standard-compliant genetic design visualization tools

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