A GPU-accelerated compute framework for pathogen genomic variant identification to aid genomic epidemiology of infectious disease: a malaria case study.

As recently demonstrated by the COVID-19 pandemic, large-scale pathogen genomic data are crucial to characterize transmission patterns of human infectious diseases. Yet, current methods to process raw sequence data into analysis-ready variants remain slow to scale, hampering rapid surveillance efforts and epidemiological investigations for disease control. Here, we introduce an accelerated, scalable, reproducible, and cost-effective framework for pathogen genomic variant identification and present an evaluation of its performance and accuracy across benchmark datasets of Plasmodium falciparum malaria genomes.

Genomic Surveillance of SARS-CoV-2 in a University Community: Insights Into Tracking Variants, Transmission, and Spread of Gamma (P.1) Variant.

Background: Using a combination of data from routine surveillance, genomic sequencing, and phylogeographic analysis, we tracked the spread and introduction events of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants focusing on a large university community.

Methods: Here, we sequenced and analyzed 677 high-quality SARS-CoV-2 genomes from positive RNA samples collected from Purdue University students, faculty, and staff who tested positive for the virus between January 2021 and May 2021, comprising an average of 32% of weekly cases across the time frame.

Results: Our analysis of circulating SARS-CoV-2 variants over time revealed periods when variants of concern (VOC) Alpha (B.

ST-analyzer: a web-based user interface for simulation trajectory analysis.

Molecular dynamics (MD) simulation has become one of the key tools to obtain deeper insights into biological systems using various levels of descriptions such as all-atom, united-atom, and coarse-grained models. Recent advances in computing resources and MD programs have significantly accelerated the simulation time and thus increased the amount of trajectory data. Although many laboratories routinely perform MD simulations, analyzing MD trajectories is still time consuming and often a difficult task.

Prediction of protein relative enthalpic stability from molecular dynamics simulations of the folded and unfolded states.

For proteins of known structure, the relative enthalpic stability with respect to wild-type, ΔΔH(U), can be estimated by direct computation of the folded and unfolded state energies. We propose a model by which the change in stability upon mutation can be predicted from all-atom molecular dynamics simulations for the folded state and a peptide-based model for the unfolded state. The unfolding enthalpies are expressed in terms of environmental and hydration-solvent reorganization contributions that readily allow a residue-specific analysis of ΔΔH(U).