Bio-On-Magnetic-Beads (BOMB): Open platform for high-throughput nucleic acid extraction and manipulation

Oberacker, Phil; Stepper, Peter; Bond, Donna M.; Höhn, Sven; Focken, Jule; Meyer, Vivien; Schelle, Luca; Sugrue, Victoria J.; Jeunen, Gert‐Jan; Moser, Tim; Hore, Steven R.; Meyenn, Ferdinand von; Hipp, Katharina; Hore, Timothy A; Jurkowski, Tomasz P.

doi:10.1101/414516

Cited by 41 publications

(63 citation statements)

References 34 publications

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“…This backbone was the same ColE1 backbone as was used in Green et al 2 for transcribing trigger RNAs, but with a GFPmut3b-ASV gene inserted. All amplicons were cleaned from their reaction buffers by using carboxyl-coated magnetic beads 54 (protocol 4.3): 1× concentration of beads to clean the longer linear backbone product, and 2× bead concentration to clean the smaller insert products. Both inserts were ligated separately into the ColE1 backbone in front of the GFPmut3b-ASV gene using golden gate cloning, as follows.…”

Section: Methodsmentioning

confidence: 99%

A deep learning approach to programmable RNA switches

et al. 2020

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Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2 = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04–0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

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Section: Methodsmentioning

confidence: 99%

A deep learning approach to programmable RNA switches

et al. 2020

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“…The prospectively designed TEM-1 β -lactamase library was cloned using BbsI sites. Both insert and vector PCRs were bead purified using homemade SPRI beads 80 .…”

Section: Library Cloning and Transformationmentioning

confidence: 99%

Low-N protein engineering with data-efficient deep learning

Biswas

Khimulya

Alley

et al. 2020

Preprint

189

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Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high-throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two highly dissimilar proteins, avGFP and TEM-1 β-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous multi-year, high-throughput efforts. Because it distills information from both global and local sequence landscapes, our model approximates protein function even before receiving experimental data, and generalizes from only single mutations to propose high-functioning epistatically non-trivial designs. With reproducible >500% improvements in activity from a single assay in a 96-well plate, we demonstrate the strongest generalization observed in machine-learning guided protein design to date. Taken together, our approach enables efficient use of resource intensive high-fidelity assays without sacrificing throughput. By encouraging alignment with endpoint objectives, low-N design will accelerate engineered proteins into the fermenter, field, and clinic.

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“…3g). Furthermore, adding isopropanol to the LBS (for a final concentration of 50%) has been used to isolate cell-free DNA from sputum and urine (44) to promote DNA precipitation onto the MB (45). When using the Thermo Fisher chemistry, we found that isopropanol decreased recovery of mtDNA and nDNA significantly in plasma (Fig.…”

Section: Optimization Of Extraction Parametersmentioning

confidence: 90%

An automated, high throughput methodology optimized for quantitative cell-free mitochondrial and nuclear DNA isolation from plasma

Ware

Desai

Lopez

et al. 2020

Preprint

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Circulating, cell-free mitochondrial DNA (ccf-mtDNA) and nuclear DNA (ccf-nDNA) are under investigation as biomarkers for various diseases. Optimal ccf-mtDNA isolation parameters, like those outlined for ccf-nDNA, have not been established. Here, we optimized a protocol for both ccf-mtDNA and ccf-nDNA recovery using a magnetic bead-based isolation process on an automated 96-well platform. Using the optimized protocol, our data show 6-fold improved yields of ccf-mtDNA when compared to the starting protocol. Digestion conditions, liquid handling characteristics, and magnetic particle processor programming all contributed to increased recovery and improved reproducibility. To our knowledge, this is the first high-throughput approach optimized for mtDNA and nDNA recovery and serves as an important starting point for clinical studies.Graphical Abstract

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Bio-On-Magnetic-Beads (BOMB): Open platform for high-throughput nucleic acid extraction and manipulation

Cited by 41 publications

References 34 publications

A deep learning approach to programmable RNA switches

A deep learning approach to programmable RNA switches

Low-N protein engineering with data-efficient deep learning

An automated, high throughput methodology optimized for quantitative cell-free mitochondrial and nuclear DNA isolation from plasma

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