Preparing For the Next Pandemic: Learning Wild Mutational Patterns At Scale For Analyzing Sequence Divergence In Novel Pathogens
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Abstract
As we begin to recover from the COVID-19 pandemic, a key question is if we can avert such disasters in future. Current surveillance protocols generally focus on qualitative impact assessments of viral diversity 1 . These efforts are primarliy aimed at ecosystem and human impact monitoring, and do not help to precisely quantify emergence. Currently, the similarity of biological strains is measured by the edit distance or the number of mutations that separate their genomic sequences 2–6 , e.g. the number of mutations that make an avian flu strain human-adapted. However, ignoring the odds of those mutations in the wild keeps us blind to the true jump risk, and gives us little indication of which strains are more risky. In this study, we develop a more meaningful metric for comparison of genomic sequences. Our metric, the q-distance, precisely quantifies the probability of spontaneous jump by random chance. Learning from patterns of mutations from large sequence databases, the q-distance adapts to the specific organism, the background population, and realistic selection pressures; demonstrably improving inference of ancestral relationships and future trajectories. As important application, we show that the q-distance predicts future strains for seasonal Influenza, outperforming World Health Organization (WHO) recommended flu-shot composition almost consistently over two decades. Such performance is demonstrated separately for Northern and Southern hemisphere for different subtypes, and key capsidic proteins. Additionally, we investigate the SARS-CoV-2 origin problem, and precisely quantify the likelihood of different animal species that hosted an immediate progenitor, producing a list of related species of bats that have a quantifiably high likelihood of being the source. Additionally, we identify specific rodents with a credible likelihood of hosting a SARS-CoV-2 ancestor. Combining machine learning and large deviation theory, the analysis reported here may open the door to actionable predictions of future pandemics.
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SciScore for 10.1101/2020.07.17.20156364: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations & Conclusion: Calculation of q-distance is currently limited to similar and aligned sequences, e.g. coronoviruses across different hosts, or time frames, or Influenza strains from different subtypes, hosts or seasons. Furthermore, we need a sufficient diversity of observed strains before we can successfully …
SciScore for 10.1101/2020.07.17.20156364: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations & Conclusion: Calculation of q-distance is currently limited to similar and aligned sequences, e.g. coronoviruses across different hosts, or time frames, or Influenza strains from different subtypes, hosts or seasons. Furthermore, we need a sufficient diversity of observed strains before we can successfully construct the Qnet model; simply having a large number of sequences is not enough, those observations must have sufficient diversity so that the underlying constraints are actually revealed. A multi-variate regression analysis (See SI Methods) indicates that the most important factor for our approach to succeed is indeed the diversity of the sequence dataset, i.e., how many sufficiently distinct sequences have we collected (See Tab. IV). Finally, in the context of strain forecasting, we note that simply reducing the edit distance from the dominant strain is not guaranteed to translate to a better immunological protection. Nevertheless consistent improvement in this metric achieved purely via computational means suggests the possibility of improvement over current practice. In conclusion, we introduce a data-driven distance metric to track subtle deviations in sequences, and quantify jump risk of risky pathogens. Demonstrated ability of perdicting future flu strains via subtle variations in a limited set of immunologically important residues suggest that the tools developed here could be essential in preempting and actionably mitigating the next pandemic.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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