SCOPE: a web server for practical de novo motif discovery

SCOPE is a novel parameter-free method for the de novo identification of potential regulatory motifs in sets of coordinately regulated genes. The SCOPE algorithm combines the output of three component algorithms, each designed to identify a particular class of motifs. Using an ensemble learning approach, SCOPE identifies the best candidate motifs from its component algorithms. In tests on experimentally determined datasets, SCOPE identified motifs with a significantly higher level of accuracy than a number of other web-based motif finders run with their default parameters. Because SCOPE has no adjustable parameters, the web server has an intuitive interface, requiring only a set of gene names or FASTA sequences and a choice of species. The most significant motifs found by SCOPE are displayed graphically on the main results page with a table containing summary statistics for each motif. Detailed motif information, including the sequence logo, PWM, consensus sequence and specific matching sites can be viewed through a single click on a motif. SCOPE's efficient, parameter-free search strategy has enabled the development of a web server that is readily accessible to the practising biologist while providing results that compare favorably with those of other motif finders. The SCOPE web server is at <http://genie.dartmouth.edu/scope>

Bounded Search for de Novo Identification of Degenerate Cis-Regulatory Elements

The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach

A Novel Ensemble Learning Method for de Novo Computational Identification of DNA Binding Sites

Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. We found that SCOPE\u27s performance on 78 experimentally characterized regulons from four species was a substantial and statistically significant improvement over that of its component algorithms. SCOPE outperformed a broad range of existing motif discovery algorithms on the same dataset by a statistically significant margin

Session 11: \u3cem\u3eCan machine learning predict particle deposition at specific intranasal regions based on computational fluid dynamics inputs/outputs and nasal geometry measurements?\u3c/em\u3e

Along with machine learning modeling, numerical simulations of respiratory airflow and particle transport can be used to improve targeted deposition at the upper respiratory infection site of numerous airborne diseases. Given the need for more patient data from varied demographics, we propose a machine learning-enabled protocol for determining optimal formulation design parameters that may match nasal spray device settings for successful drug delivery. We measured 11 anatomical parameters (including nasopharyngeal volume, nostril heights, and mid-nasal cavity volume) for 10 CT-based nasal geometries representative of the population for this aim. We also ran 160 computational fluid dynamics simulations of drug delivery on the same geometries for various breathing situations, using varied pressure gradients to drive inhaled air transport to evaluate drug deposition at the various upper airway areas for nasal inhalers. Using this test data, we constructed 18 machine-learning models to estimate the targeted deposition at the different regions of the upper airway. This study contributes to developing a customized, efficient intranasal delivery system for prophylactics, treatments, and immunizations; the findings will apply to a broad spectrum of respiratory disorders

Computational projection of virion transmission rates to the lower airway from the initial sars-cov-2 infection at the nasopharynx

https://www.rhinologyonline.org/Rhinology_online_issues/manuscript_240.pdfAccepted manuscrip

Controlling long-term SARS-CoV-2 infections is important for slowing viral evolution

The rapid emergence and expansion of novel SARS-CoV-2 variants is an unpleasant surprise that threatens our ability to achieve herd immunity for COVID-19. These fitter SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more traits that provide an evolutionary advantage, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we explore the mechanism by which fitter variants of SARS-CoV-2 arise during long-term infections using a mathematical model of viral evolution and identify means by which this evolution can be slowed. While viral load and infection duration play a strong role in favoring the emergence of such variants, the overall probability of emergence and subsequent transmission from any given infection is low, suggesting that viral variant emergence and establishment is a product of random chance. To the extent that luck plays a role in favoring the emergence of novel viral variants with an evolutionary advantage, targeting these low-probability random events might allow us to tip the balance of fortune away from these advantageous variants and prevent them from being established in the population.https://www.nature.com/articles/s41598-021-02148-8First author draf

Controlling long-term SARS-CoV-2 infections can slow viral evolution and reduce the risk of treatment failure.

The rapid emergence and expansion of novel SARS-CoV-2 variants threatens our ability to achieve herd immunity for COVID-19. These novel SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more evolutionarily advantageous traits, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we used a stochastic evolutionary modeling framework to explore the emergence of fitter variants of SARS-CoV-2 during long-term infections. We found that increased viral load and infection duration favor emergence of such variants. While the overall probability of emergence and subsequent transmission from any given infection is low, on a population level these events occur fairly frequently. Targeting these low-probability stochastic events that lead to the establishment of novel advantageous viral variants might allow us to slow the rate at which they emerge in the patient population, and prevent them from spreading deterministically due to natural selection. Our work thus suggests practical ways to achieve control of long-term SARS-CoV-2 infections, which will be critical for slowing the rate of viral evolution.DGE-1762114 - National Science FoundationPublished versio

Individually Optimal Choices Can Be Collectively Disastrous in COVID-19 Disease Control

Background: The word \u27pandemic\u27 conjures dystopian images of bodies stacked in the streets and societies on the brink of collapse. Despite this frightening picture, denialism and noncompliance with public health measures are common in the historical record, for example during the 1918 Influenza pandemic or the 2015 Ebola epidemic. The unique characteristics of SARS-CoV-2-its high basic reproduction number (R0), time-limited natural immunity and considerable potential for asymptomatic spread-exacerbate the public health repercussions of noncompliance with interventions (such as vaccines and masks) to limit disease transmission. Our work explores the rationality and impact of noncompliance with measures aimed at limiting the spread of SARS-CoV-2. Methods: In this work, we used game theory to explore when noncompliance confers a perceived benefit to individuals. We then used epidemiological modeling to predict the impact of noncompliance on control of SARS-CoV-2, demonstrating that the presence of a noncompliant subpopulation prevents suppression of disease spread. Results: Our modeling demonstrates that noncompliance is a Nash equilibrium under a broad set of conditions and that the existence of a noncompliant population can result in extensive endemic disease in the long-term after a return to pre-pandemic social and economic activity. Endemic disease poses a threat for both compliant and noncompliant individuals; all community members are protected if complete suppression is achieved, which is only possible with a high degree of compliance. For interventions that are highly effective at preventing disease spread, however, the consequences of noncompliance are borne disproportionately by noncompliant individuals. Conclusions: In sum, our work demonstrates the limits of free-market approaches to compliance with disease control measures during a pandemic. The act of noncompliance with disease intervention measures creates a negative externality, rendering suppression of SARS-CoV-2 spread ineffective. Our work underscores the importance of developing effective strategies for prophylaxis through public health measures aimed at complete suppression and the need to focus on compliance at a population level

Rapid relaxation of pandemic restrictions after vaccine rollout favors growth of SARS-CoV-2 variants: a model-based analysis

The development and deployment of several SARS-CoV-2 vaccines in a little over a year is an unprecedented achievement of modern medicine. The high levels of efficacy against transmission for some of these vaccines makes it feasible to use them to suppress SARS-CoV-2 altogether in regions with high vaccine acceptance. However, viral variants with reduced susceptibility to vaccinal and natural immunity threaten the utility of vaccines, particularly in scenarios where a return to pre-pandemic conditions occurs before the suppression of SARS-CoV-2 transmission. In this work we model the situation in the United States in May-June 2021, to demonstrate how pre-existing variants of SARS-CoV-2 may cause a rebound wave of COVID-19 in a matter of months under a certain set of conditions. A high burden of morbidity (and likely mortality) remains possible, even if the vaccines are partially effective against new variants and widely accepted. Our modeling suggests that variants that are already present within the population may be capable of quickly defeating the vaccines as a public health intervention, a serious potential limitation for strategies that emphasize rapid reopening before achieving control of SARS-CoV-2.Published versio