Publications by authors named "John Nicol"

Article Synopsis
  • The prediction of RNA structure from its sequence is challenging due to a lack of experimental data, which has slowed advancement in the field.
  • Researchers have developed a dataset called Ribonanza, consisting of chemical mapping data from two million RNA sequences, collected through crowdsourcing platforms like Eterna.
  • Utilizing this dataset, they created a deep learning model named RibonanzaNet, which, when fine-tuned, demonstrates superior performance in predicting various RNA behaviors, potentially improving understanding of RNA structures.
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Designing single molecules that compute general functions of input molecular partners represents a major unsolved challenge in molecular design. Here, we demonstrate that high-throughput, iterative experimental testing of diverse RNA designs crowdsourced from Eterna yields sensors of increasingly complex functions of input oligonucleotide concentrations. After designing single-input RNA sensors with activation ratios beyond our detection limits, we created logic gates, including challenging XOR and XNOR gates, and sensors that respond to the ratio of two inputs.

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Article Synopsis
  • mRNA-based medicines, like COVID-19 vaccines, have great potential but face challenges with stability due to their susceptibility to chemical degradation.
  • A machine learning competition called 'Stanford OpenVaccine' on Kaggle involved analyzing over 6,000 RNA constructs to predict their degradation, resulting in a successful model that accurately forecasted RNA stability.
  • The integration of crowdsourcing for both data collection and model training could be a beneficial approach for quickly addressing other scientific challenges in the future.
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Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers.

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Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics.

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RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood.

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Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers.

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RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery, and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood.

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The recent rise to prominence of healthcare leadership worldwide has prompted those involved in medical education to consider how to facilitate learning to lead effectively. Research has focused on formal curriculum activities. Curricular theory suggests that trainee doctors may also learn through the informal curriculum but there is a lack of medical education literature on this.

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Background: Visualization software can expose previously undiscovered patterns in genomic data and advance biological science.

Results: The Genoviz Software Development Kit (SDK) is an open source, Java-based framework designed for rapid assembly of visualization software applications for genomics. The Genoviz SDK framework provides a mechanism for incorporating adaptive, dynamic zooming into applications, a desirable feature of genome viewers.

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Unlabelled: Experimental techniques that survey an entire genome demand flexible, highly interactive visualization tools that can display new data alongside foundation datasets, such as reference gene annotations. The Integrated Genome Browser (IGB) aims to meet this need. IGB is an open source, desktop graphical display tool implemented in Java that supports real-time zooming and panning through a genome; layout of genomic features and datasets in moveable, adjustable tiers; incremental or genome-scale data loading from remote web servers or local files; and dynamic manipulation of quantitative data via genome graphs.

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