Browsing by Author "Wommack, K. Eric"
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Item Coordination of SARS-CoV-2 wastewater and clinical testing of university students demonstrates the importance of sampling duration and collection time(Science of the Total Environment, 2022-03-29) Anderson-Coughlin, Brienna L.; Shearer, Adrienne E.H.; Omar, Alexis N.; Litt, Pushpinder K.; Bernberg, Erin; Murphy, Marcella; Anderson, Amy; Sauble, Lauren; Ames, Bri; Damminger, Oscar Jr; Ladman, Brian S.; Dowling, Timothy; Wommack, K. Eric; Kniel, Kalmia E.Wastewater surveillance has been a useful tool complementing clinical testing during the COVID-19 pandemic. However, transitioning surveillance approaches to small populations, such as dormitories and assisted living facilities poses challenges including difficulties with sample collection and processing. Recently, the need for reliable and timely data has coincided with the need for precise local forecasting of the trajectory of COVID-19. This study compared wastewater and clinical data from the University of Delaware (Fall 2020 and Spring 2021 semesters), and evaluated wastewater collection practices for enhanced virus detection sensitivity. Fecal shedding of SARS-CoV-2 is known to occur in infected individuals. However, shedding concentrations and duration has been shown to vary. Therefore, three shedding periods (14, 21, and 30 days) were presumed and included for analysis of wastewater data. SARS-CoV-2 levels detected in wastewater correlated with clinical virus detection when a positive clinical test result was preceded by fecal shedding of 21 days (p < 0.05) and 30 days (p < 0.05), but not with new cases (p = 0.09) or 14 days of shedding (p = 0.17). Discretely collected wastewater samples were compared with 24-hour composite samples collected at the same site. The discrete samples (n = 99) were composited examining the influence of sampling duration and time of day on SARS-CoV-2 detection. SARS-CoV-2 detection varied among dormitory complexes and sampling durations of 3-hour, 12-hour, and 24-hour (controls). Collection times frequently showing high detection values were between the hours of 03:00 to 05:00 and 23:00 to 08:00. In each of these times of day 33% of samples (3/9) were significantly higher (p < 0.05) than the control sample. The remainder (6/9) of the collection times (3-hour and 12-hour) were not different (p > 0.05) from the control. This study provides additional framework for continued methodology development for microbiological wastewater surveillance as the COVID-19 pandemic progresses and in preparation for future epidemiological efforts.Item Improvements in viral gene annotation using large language models and soft alignments(BMC Bioinformatics, 2024-04-25) Harrigan, William L.; Ferrell, Barbra D.; Wommack, K. Eric; Polson, Shawn W.; Schreiber, Zachary D.; Belcaid, MahdiBackground The annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings. Results Central to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect. Conclusion The embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.Item Single cell genomics indicates horizontal gene transfer and viral infections in a deep subsurface Firmicutes population(Frontiers Media S.A., 2015-04-22) Labonté, Jessica M.; Field, Erin K.; Lau, Maggie; Chivian, Dylan; Van Heerden, Esta; Wommack, K. Eric; Kieft, Thomas L.; Onstott, Tullis C.; Stepanauskas, Ramunas; Jessica M. Labonté, Erin K.Field, Maggie Lau, Dylan Chivian, Esta Van Heerden, K. Eric Wommack, Thomas L. Kieft, Tullis C.Onstott and Ramunas Stepanauskas; Wommack, K. EricA major fraction of Earth's prokaryotic biomass dwells in the deep subsurface, where cellular abundances per volume of sample are lower, metabolism is slower, and generation times are longer than those in surface terrestrial and marine environments. How these conditions impact biotic interactions and evolutionary processes is largely unknown. Here we employed single cell genomics to analyze cell-to-cell genome content variability and signatures of horizontal gene transfer (HGT) and viral infections in five cells of Candidatus Desulforudis audaxviator, which were collected from a 3 km-deep fracture water in the 2.9 Ga-old Witwatersrand Basin of South Africa. Between 0 and 32% of genes recovered from single cells were not present in the original, metagenomic assembly of Desulforudis, which was obtained from a neighboring subsurface fracture. We found a transposable prophage, a retron, multiple clustered regularly interspaced short palindromic repeats (CRISPRs) and restriction-modification systems, and an unusually high frequency of transposases in the analyzed single cell genomes. This indicates that recombination, HGT and viral infections are prevalent evolutionary events in the studied population of microorganisms inhabiting a highly stable deep subsurface environment.