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Machine learning and optimization methods have not been utilized to enrich viral diagnostics and surveillance
False
The approach described in the text combines a deep learning model with combinatorial optimization
True
The most advanced methods for predicting diagnostic activity usually make binary predictions based on thermodynamic criteria and heuristics
True
The text suggests that computational design has made limited progress in enriching diagnostics and surveillance despite the explosion of viral genomic data
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CRISPR-Cas13a can only be used for detecting viral nucleic acids.
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The decision threshold for the classifier was set to yield a precision of 0.975.
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The dataset and model focus exclusively on CRISPR-Cas13a.
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The model provides quantitative predictions that can be used within an optimization framework.
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CRISPR-based diagnostics use probe sequences known as guide RNAs
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The objective is to find the set P of probes that maximizes the expected activity when P detects S
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The number of probes is limited to avoid interference and kinetic impact
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A canonical greedy algorithm for submodular maximization offers provable guarantees in this case
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CRISPR-based systems use quantitative predictions of enzyme activity during target detection to enhance sensitivity.
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The largest dataset on diagnostic performance for CRISPR-based diagnostics has been focused on CRISPR-Cas9 using handcrafted features.
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Approaches to designing sensitive viral diagnostics have followed only one paradigm, which is identifying conserved genomic regions and designing assays targeting them.
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ADAPT is a manual system for designing diagnostics for viral species.
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The deep convolutional neural network (CNN) classifier outperformed other models only in the classification task, not in the regression task.
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The researchers measured the fluorescence growth rate, which is proportional to the enzymatic efficiency and concentration of a guide–target–Cas13a complex, to evaluate the efficiency by changing the complex concentration.
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The model search preferred to incorporate both convolutional and locally connected filters of the same width, which may help the model uncover fixed spatial dependencies, such as mismatch-sensitive regions.
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The accuracy of the model was not validated through comparisons with independent datasets, resulting in Spearman’s ρ values of 0.816 and 0.816.
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Study Notes
- Researchers trained a machine learning model to predict enzymatic activity of CRISPR-Cas13a1 using a dataset of 19,209 unique LwaCas13a guide–target pairs.
- Cas13a enzymes use guide RNAs to locate a target and subsequently exhibit collateral activity, leading to a diagnostic readout through the cleavage of fluorescent reporters.
- The library of guide–target pairs was designed to have a sequence composition representative of viral diversity, an average of 2.9 mismatches between each guide and target, and a variety of protospacer flanking site (PFS) alleles.
- The researchers measured the fluorescence growth rate, which is proportional to the enzymatic efficiency and concentration of a guide–target–Cas13a complex, to evaluate the efficiency by holding the complex concentration constant.
- They defined activity as the logarithm of the fluorescence growth rate and measured the fluorescence arising from the library’s guide–target pairs every ~20min to calculate each pair’s activity.
- The researchers used a two-step hurdle model for classification and regression, classifying a pair as inactive or active, and then regressing the activity for active pairs.
- The deep convolutional neural network (CNN) classifier, using nucleotide sequences alone, outperformed other models in both classification and regression tasks.
- The models accounted for measurement error and the data was divided in a way that ensured validation folds contained sets of cognate guide–target pairs, unrelated to the data in the training folds.
- The model search preferred to incorporate both convolutional and locally connected filters of different widths, which may help the model uncover fixed spatial dependencies, such as mismatch-sensitive regions.
- The model performed well on a hold-out test set and against a standard Cas13a design heuristic, yielding a lower false-positive rate and higher precision than the heuristic when the guide and target are not identical.
- The models retained accuracy when evaluated on individual PFS alleles and mismatch counts, but additional data similar to the current dataset would not be expected to significantly improve performance.
- The accuracy of the model was validated through comparisons with two independent datasets, resulting in Spearman’s ρ values of 0.816 and 0.816.
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Description
Test your knowledge on the machine learning model trained to predict enzymatic activity in CRISPR-Cas13a system. This quiz focuses on guide design principles, reporter sequence requirements, and the collateral activity of Cas13a enzymes.
