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Background The frequency of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is unclear and may be influenced by how symptoms are evaluated. In this study, we sought to determine the frequency of asymptomatic SARS-CoV-2 infections in a prospective cohort of health care workers (HCWs). Methods A prospective cohort of HCWs, confirmed negative for SARS-CoV-2 exposure upon enrollment, were evaluated for SARS-CoV-2 infection by monthly analysis of SARS-CoV-2 antibodies as well as referral for polymerase chain reaction testing whenever they exhibited symptoms of coronavirus disease 2019 (COVID-19). Participants completed the standardized and validated FLU-PRO Plus symptom questionnaire scoring viral respiratory disease symptom intensity and frequency at least twice monthly during baseline periods of health and each day they had any symptoms that were different from their baseline. Results Two hundred sixty-three participants were enrolled between August 25 and December 31, 2020. Through February 28, 2021, 12 participants were diagnosed with SARS-CoV-2 infection. Symptom analysis demonstrated that all 12 had at least mild symptoms of COVID-19, compared with baseline health, near or at time of infection. Conclusions These results suggest that asymptomatic SARS-CoV-2 infection in unvaccinated, immunocompetent adults is less common than previously reported. While infectious inoculum doses and patient factors may have played a role in the clinical manifestations of SARS-CoV-2 infections in this cohort, we suspect that the high rate of symptomatic disease was due primarily to participant attentiveness to symptoms and collection of symptoms in a standardized, prospective fashion. These results have implications for studies that estimate SARS-CoV-2 infection prevalence and for public health measures to control the spread of this virus.

The virosphere is largely unexplored and the majority of viruses are yet to be represented in public sequence databases. Bats are rich reservoirs of viruses, including several zoonoses. In this study, high throughput sequencing (HTS) of viral RNA extracted from swabs of four body sites per bat per timepoint is used to characterize the virome through a longitudinal study of a captive colony of fruit nectar bats, species Eonycteris spelaea in Singapore. Through unbiased shotgun and target enrichment sequencing, we identify both known and previously unknown viruses of zoonotic relevance and define the population persistence and temporal patterns of viruses from families that have the capacity to jump the species barrier. To our knowledge, this is the first study that combines probe-based viral enrichment with HTS to create a viral profile from multiple swab sites on individual bats and their cohort. This work demonstrates temporal patterns of the lesser dawn bat virome, including several novel viruses. Given the known risk for bat–human zoonoses, a more complete understanding of the viral dynamics in South-eastern Asian bats has significant implications for disease prevention and control. The findings of this study will be of interest to U.S. Department of Defense personnel stationed in the Asia-Pacific region and regional public health laboratories engaged in emerging infectious disease surveillance efforts.

Although the capability of DNA to form a variety of non-canonical (non-B) structures has long been recognized, the overall significance of these alternate conformations in biology has only recently become accepted en masse. In order to provide access to genome-wide locations of these classes of predicted structures, we have developed non-B DB, a database integrating annotations and analysis of non-B DNA-forming sequence motifs. The database provides the most complete list of alternative DNA structure predictions available, including Z-DNA motifs, quadruplex-forming motifs, inverted repeats, mirror repeats and direct repeats and their associated subsets of cruciforms, triplex and slipped structures, respectively. The database also contains motifs predicted to form static DNA bends, short tandem repeats and homo(purine•pyrimidine) tracts that have been associated with disease. The database has been built using the latest releases of the human, chimp, dog, macaque and mouse genomes, so that the results can be compared directly with other data sources. In order to make the data interpretable in a genomic context, features such as genes, single-nucleotide polymorphisms and repetitive elements (SINE, LINE, etc.) have also been incorporated. The database is accessed through query pages that produce results with links to the UCSC browser and a GBrowse-based genomic viewer. It is freely accessible at http://nonb.abcc.ncifcrf.gov.

Background Understanding the biological roles of microRNAs (miRNAs) is a an active area of research that has produced a surge of publications in PubMed, particularly in cancer research. Along with this increasing interest, many open-source bioinformatics tools to identify existing and/or discover novel miRNAs in next-generation sequencing (NGS) reads become available. While miRNA identification and discovery tools are significantly improved, the development of miRNA differential expression analysis tools, especially in temporal studies, remains substantially challenging. Further, the installation of currently available software is non-trivial and steps of testing with example datasets, trying with one’s own dataset, and interpreting the results require notable expertise and time. Subsequently, there is a strong need for a tool that allows scientists to normalize raw data, perform statistical analyses, and provide intuitive results without having to invest significant efforts. Findings We have developed miRNA Temporal Analyzer (mirnaTA), a bioinformatics package to identify differentially expressed miRNAs in temporal studies. mirnaTA is written in Perl and R (Version 2.13.0 or later) and can be run across multiple platforms, such as Linux, Mac and Windows. In the current version, mirnaTA requires users to provide a simple, tab-delimited, matrix file containing miRNA name and count data from a minimum of two to a maximum of 20 time points and three replicates. To recalibrate data and remove technical variability, raw data is normalized using Normal Quantile Transformation (NQT), and linear regression model is used to locate any miRNAs which are differentially expressed in a linear pattern. Subsequently, remaining miRNAs which do not fit a linear model are further analyzed in two different non-linear methods 1) cumulative distribution function (CDF) or 2) analysis of variances (ANOVA). After both linear and non-linear analyses are completed, statistically significant miRNAs (P