Poly(ADP-ribosyl)ation regulates insulator function and intrachromosomal interactions in Drosophila.

Insulators mediate inter- and intrachromosomal contacts to regulate enhancer-promoter interactions and establish chromosome domains. The mechanisms by which insulator activity can be regulated to orchestrate changes in the function and three-dimensional arrangement of the genome remain elusive. Here, we demonstrate that Drosophila insulator proteins are poly(ADP-ribosyl)ated and that mutation of the poly(ADP-ribose) polymerase (Parp) gene impairs their function. This modification is not essential for DNA occupancy of insulator DNA-binding proteins dCTCF and Su(Hw). However, poly(ADP-ribosyl)ation of K566 in CP190 promotes protein-protein interactions with other insulator proteins, association with the nuclear lamina, and insulator activity in vivo. Consistent with these findings, the nuclear clustering of CP190 complexes is disrupted in Parp mutant cells. Importantly, poly(ADP-ribosyl)ation facilitates intrachromosomal interactions between insulator sites measured by 4C. These data suggest that the role of insulators in organizing the three-dimensional architecture of the genome may be modulated by poly(ADP-ribosyl)ation.

The effect of variant interference on de novo assembly for viral deep sequencing.

BACKGROUND: Viruses have high mutation rates and generally exist as a mixture of variants in biological samples. Next-generation sequencing (NGS) approaches have surpassed Sanger for generating long viral sequences, yet how variants affect NGS de novo assembly remains largely unexplored. RESULTS: Our results from > 15,000 simulated experiments showed that presence of variants can turn an assembly of one genome into tens to thousands of contigs. This "variant interference" (VI) is highly consistent and reproducible by ten commonly-used de novo assemblers, and occurs over a range of genome length, read length, and GC content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by in silico simulations, where selective removal of minor variant reads from clinical datasets allow the "rescue" of full viral genomes from fragmented contigs. CONCLUSIONS: These results call for careful interpretation of contigs and contig numbers from de novo assembly in viral deep sequencing.

Genomic characterization of Haemophilus influenzae: a focus on the capsule locus.

BACKGROUND: Haemophilus influenzae (Hi) can cause invasive diseases such as meningitis, pneumonia, or sepsis. Typeable Hi includes six serotypes (a through f), each expressing a unique capsular polysaccharide. The capsule, encoded by the genes within the capsule locus, is a major virulence factor of typeable Hi. Non-typeable (NTHi) does not express capsule and is associated with invasive and non-invasive diseases. METHODS: A total of 395 typeable and 293 NTHi isolates were characterized by whole genome sequencing (WGS). Phylogenetic analysis and multilocus sequence typing were used to characterize the overall genetic diversity. Pair-wise comparisons were used to evaluate the capsule loci. A WGS serotyping method was developed to predict the Hi serotype. WGS serotyping results were compared to slide agglutination (SAST) or real-time PCR (rt-PCR) serotyping. RESULTS: Isolates of each Hi serotype clustered into one or two subclades, with each subclade being associated with a distinct sequence type (ST). NTHi isolates were genetically diverse, with seven subclades and 125 STs being detected. Regions I and III of the capsule locus were conserved among the six serotypes (≥82% nucleotide identity). In contrast, genes in Region II were less conserved, with only six gene pairs from all serotypes showing ≥56% nucleotide identity. The WGS serotyping method was 99.9% concordant with SAST and 100% concordant with rt-PCR in determining the Hi serotype. CONCLUSIONS: Genomic analysis revealed a higher degree of genetic diversity among NTHi compared to typeable Hi. The WGS serotyping method accurately predicted the Hi capsule type and can serve as an alternative method for Hi serotyping.

Whole-Genome Sequencing for Characterization of Capsule Locus and Prediction of Serogroup of Invasive Meningococcal Isolates.

Invasive meningococcal disease is mainly caused by Neisseria meningitidis serogroups A, B, C, X, W, and Y. The serogroup is typically determined by slide agglutination serogrouping (SASG) and real-time PCR (RT-PCR). We describe a whole-genome sequencing (WGS)-based method to characterize the capsule polysaccharide synthesis (cps) locus, classify N. meningitidis serogroups, and identify mechanisms for nongroupability using 453 isolates from a global strain collection. We identified novel genomic organizations within functional cps loci, consisting of insertion sequence (IS) elements in unique positions that did not disrupt the coding sequence. Genetic mutations (partial gene deletion, missing genes, IS insertion, internal stop, and phase-variable off) that led to nongroupability were identified. The results of WGS and SASG were in 91% to 100% agreement for all serogroups, while the results of WGS and RT-PCR showed 99% to 100% agreement. Among isolates determined to be nongroupable by WGS (31 of 453), the results of all three methods agreed 100% for those without a capsule polymerase gene. However, 61% (WGS versus SASG) and 36% (WGS versus RT-PCR) agreements were observed for the isolates, particularly those with phase variations or internal stops in cps loci, which warrant further characterization by additional tests. Our WGS-based serogrouping method provides comprehensive characterization of the N. meningitidis capsule, which is critical for meningococcal surveillance and outbreak investigations.

Regulation of chromatin organization and inducible gene expression by a Drosophila insulator.

Insulators are multiprotein-DNA complexes thought to affect gene expression by mediating inter- and intrachromosomal interactions. Drosophila insulators contain specific DNA-binding proteins plus common components, such as CP190, that facilitate these interactions. Here, we examine changes in the distribution of Drosophila insulator proteins during the heat-shock and ecdysone responses. We find that CP190 recruitment to insulator sites is the main regulatable step in controlling insulator function during heat shock. In contrast, both CP190 and DNA-binding protein recruitment are regulated during the ecdysone response. CP190 is necessary to stabilize specific chromatin loops and for proper activation of transcription of genes regulated by this hormone. These findings suggest that cells may regulate recruitment of insulator proteins to DNA to activate insulator activity at specific sites and create distinct patterns of nuclear organization that are necessary to achieve proper gene expression in response to different stimuli.

High-Throughput Next-Generation Sequencing of Polioviruses.

The poliovirus (PV) is currently targeted for worldwide eradication and containment. Sanger-based sequencing of the viral protein 1 (VP1) capsid region is currently the standard method for PV surveillance. However, the whole-genome sequence is sometimes needed for higher resolution global surveillance. In this study, we optimized whole-genome sequencing protocols for poliovirus isolates and FTA cards using next-generation sequencing (NGS), aiming for high sequence coverage, efficiency, and throughput. We found that DNase treatment of poliovirus RNA followed by random reverse transcription (RT), amplification, and the use of the Nextera XT DNA library preparation kit produced significantly better results than other preparations. The average viral reads per total reads, a measurement of efficiency, was as high as 84.2% ± 15.6%. PV genomes covering >99 to 100% of the reference length were obtained and validated with Sanger sequencing. A total of 52 PV genomes were generated, multiplexing as many as 64 samples in a single Illumina MiSeq run. This high-throughput, sequence-independent NGS approach facilitated the detection of a diverse range of PVs, especially for those in vaccine-derived polioviruses (VDPV), circulating VDPV, or immunodeficiency-related VDPV. In contrast to results from previous studies on other viruses, our results showed that filtration and nuclease treatment did not discernibly increase the sequencing efficiency of PV isolates. However, DNase treatment after nucleic acid extraction to remove host DNA significantly improved the sequencing results. This NGS method has been successfully implemented to generate PV genomes for molecular epidemiology of the most recent PV isolates. Additionally, the ability to obtain full PV genomes from FTA cards will aid in facilitating global poliovirus surveillance.

Integrated tRNA, transcript, and protein profiles in response to steroid hormone signaling.

The accurate and efficient transfer of genetic information into amino acid sequences is carried out through codon-anticodon interactions between mRNA and tRNA, respectively. In this way, tRNAs function at the interface between gene expression and protein synthesis. Whether tRNA levels are dynamically regulated and to what degree tRNA abundance influences the cellular proteome remains largely unexplored. Here we profile tRNA, transcript and protein levels in Drosophila Kc167 cells, a plasmatocyte cell line that, upon treatment with 20-hydroxyecdysone, differentiates into macrophages. We find that high abundance tRNAs associate with codons that are overrepresented in the Kc167 cell proteome, whereas tRNAs that are in low supply associate with codons that are underrepresented. Ecdysone-induced differentiation of Kc167 cells leads to changes in mRNA codon usage in a manner consistent with the developmental progression of the cell. At both early and late time points, ecdysone treatment concomitantly increases the abundance of tRNAThr(CGU), which decodes a differentiation-associated codon that becomes enriched in the macrophage proteome. These results together suggest that tRNA levels may provide a meaningful regulatory mechanism for defining the cellular proteomic landscape.

Three subclasses of a Drosophila insulator show distinct and cell type-specific genomic distributions.

Insulators are protein-bound DNA elements that are thought to play a role in chromatin organization and the regulation of gene expression by mediating intra- and interchromosomal interactions. Suppressor of Hair-wing [Su(Hw)] and Drosophila CTCF (dCTCF) insulators are found at distinct loci throughout the Drosophila melanogaster genome and function by recruiting an additional protein, Centrosomal Protein 190 (CP190). We performed chromatin immunoprecipitation (ChIP) and microarray analysis (ChIP-chip) experiments with whole-genome tiling arrays to compare Su(Hw), dCTCF, boundary element-associated factor (BEAF), and CP190 localization on DNA in two different cell lines and found evidence that BEAF is a third subclass of CP190-containing insulators. The DNA-binding proteins Su(Hw), dCTCF, and BEAF show unique distribution patterns with respect to the location and expression level of genes, suggesting diverse roles for these three subclasses of insulators in genome organization. Notably, cell line-specific localization sites for all three DNA-binding proteins as well as CP190 indicate multiple levels at which insulators can be regulated to affect gene expression. These findings suggest a model in which insulator subclasses may have distinct functions that together organize the genome in a cell type-specific manner, resulting in differential regulation of gene expression.

The BEAF-32 insulator coordinates genome organization and function during the evolution of Drosophila species.

Understanding the relationship between genome organization and expression is central to understanding genome function. Closely apposed genes in a head-to-head orientation share the same upstream region and are likely to be coregulated. Here we identify the Drosophila BEAF-32 insulator as a cis regulatory element separating close head-to-head genes with different transcription regulation modes. We then compare the binding landscapes of the BEAF-32 insulator protein in four different Drosophila genomes and highlight the evolutionarily conserved presence of this protein between close adjacent genes. We find that changes in binding of BEAF-32 to sites in the genome of different Drosophila species correlate with alterations in genome organization caused by DNA rearrangements or genome size expansion. The cross-talk between BEAF-32 genomic distribution and genome organization contributes to new gene-expression profiles, which in turn translate into specific and distinct phenotypes. The results suggest a mechanism for the establishment of differences in transcription patterns during evolution.

Genome-wide phosphoacetylation of histone H3 at Drosophila enhancers and promoters.

Transcription regulation is mediated by enhancers that bind sequence-specific transcription factors, which in turn interact with the promoters of the genes they control. Here, we show that the JIL-1 kinase is present at both enhancers and promoters of ecdysone-induced Drosophila genes, where it phosphorylates the Ser10 and Ser28 residues of histone H3. JIL-1 is also required for CREB binding protein (CBP)-mediated acetylation of Lys27, a well-characterized mark of active enhancers. The presence of these proteins at enhancers and promoters of ecdysone-induced genes results in the establishment of the H3K9acS10ph and H3K27acS28ph marks at both regulatory sequences. These modifications are necessary for the recruitment of 14-3-3, a scaffolding protein capable of facilitating interactions between two simultaneously bound proteins. Chromatin conformation capture assays indicate that interaction between the enhancer and the promoter is dependent on the presence of JIL-1, 14-3-3, and CBP. Genome-wide analyses extend these conclusions to most Drosophila genes, showing that the presence of JIL-1, H3K9acS10ph, and H3K27acS28ph is a general feature of enhancers and promoters in this organism.

Drosophila CTCF tandemly aligns with other insulator proteins at the borders of H3K27me3 domains.

Several multiprotein DNA complexes capable of insulator activity have been identified in Drosophila melanogaster, yet only CTCF, a highly conserved zinc finger protein, and the transcription factor TFIIIC have been shown to function in mammals. CTCF is involved in diverse nuclear activities, and recent studies suggest that the proteins with which it associates and the DNA sequences that it targets may underlie these various roles. Here we show that the Drosophila homolog of CTCF (dCTCF) aligns in the genome with other Drosophila insulator proteins such as Suppressor of Hairy wing [SU(HW)] and Boundary Element Associated Factor of 32 kDa (BEAF-32) at the borders of H3K27me3 domains, which are also enriched for associated insulator proteins and additional cofactors. RNAi depletion of dCTCF and combinatorial knockdown of gene expression for other Drosophila insulator proteins leads to a reduction in H3K27me3 levels within repressed domains, suggesting that insulators are important for the maintenance of appropriate repressive chromatin structure in Polycomb (Pc) domains. These results shed new insights into the roles of insulators in chromatin domain organization and support recent models suggesting that insulators underlie interactions important for Pc-mediated repression. We reveal an important relationship between dCTCF and other Drosophila insulator proteins and speculate that vertebrate CTCF may also align with other nuclear proteins to accomplish similar functions.

Distinct isoforms of the Drosophila Brd4 homologue are present at enhancers, promoters and insulator sites.

Brd4 is a double bromodomain protein that has been shown to interact with acetylated histones to regulate transcription by recruiting Positive Transcription Elongation Factor b to the promoter region. Brd4 is also involved in gene bookmarking during mitosis and is a therapeutic target for the treatment of acute myeloid leukemia. The Drosophila melanogaster Brd4 homologue is called Fs(1)h and, like its vertebrate counterpart, encodes different isoforms. We have used ChIP-seq to examine the genome-wide distribution of Fs(1)h isoforms. We are able to distinguish the Fs(1)h-L and Fs(1)h-S binding profiles and discriminate between the genomic locations of the two isoforms. Fs(1)h-S is present at enhancers and promoters and its amount parallels transcription levels. Correlations between the distribution of Fs(1)h-S and various forms of acetylated histones H3 and H4 suggest a preference for binding to H3K9acS10ph. Surprisingly, Fs(1)h-L is located at sites in the genome where multiple insulator proteins are also present. The results suggest that Fs(1)h-S may be responsible for the classical role assigned to this protein, whereas Fs(1)h-L may have a new and unexpected role in chromatin architecture by working in conjunction with insulator proteins to mediate intra- or inter-chromosome interactions.

Genome-wide association study identifies novel loci association with fasting insulin and insulin resistance in African Americans.

Insulin resistance (IR) is a key determinant of type 2 diabetes (T2D) and other metabolic disorders. This genome-wide association study (GWAS) was designed to shed light on the genetic basis of fasting insulin (FI) and IR in 927 non-diabetic African Americans. 5 396 838 single-nucleotide polymorphisms (SNPs) were tested for associations with FI or IR with adjustments for age, sex, body mass index, hypertension status and first two principal components. Genotyped SNPs (n = 12) with P < 5 × 10(-6) in African Americans were carried forward for de novo genotyping in 570 non-diabetic West Africans. We replicated SNPs in or near SC4MOL and TCERG1L in West Africans. The meta-analysis of 1497 African Americans and West Africans yielded genome-wide significant associations for SNPs in the SC4MOL gene: rs17046216 (P = 1.7 × 10(-8) and 2.9 × 10(-8) for FI and IR, respectively); and near the TCERG1L gene with rs7077836 as the top scoring (P = 7.5 × 10(-9) and 4.9 × 10(-10) for FI and IR, respectively). In silico replication in the MAGIC study (n = 37 037) showed weak but significant association (adjusted P-value of 0.0097) for rs34602777 in the MYO5A gene. In addition, we replicated previous GWAS findings for IR and FI in Europeans for GCKR, and for variants in four T2D loci (FTO, IRS1, KLF14 and PPARG) which exert their action via IR. In summary, variants in/near SC4MOL, and TCERG1L were associated with FI and IR in this cohort of African Americans and were replicated in West Africans. SC4MOL is under-expressed in an animal model of T2D and plays a key role in lipid biosynthesis, with implications for the regulation of energy metabolism, obesity and dyslipidemia. TCERG1L is associated with plasma adiponectin, a key modulator of obesity, inflammation, IR and diabetes.

Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection.

Prior studies have shown that sleep duration peri-vaccination influences an individual's antibody response. However, whether peri-vaccination sleep affects real-world vaccine effectiveness is unknown. Here, we tested whether objectively measured sleep around COVID-19 vaccination affected breakthrough infection rates. DETECT is a study of digitally recruited participants who report COVID-19-related information, including vaccination and illness data. Objective sleep data are also recorded through activity trackers. We compared the impact of sleep duration, sleep efficiency, and frequency of awakenings on reported breakthrough infection after the 2nd vaccination and 1st COVID-19 booster. Logistic regression models were created to examine if sleep metrics predicted COVID-19 breakthrough infection independent of age and gender. Self-reported breakthrough COVID-19 infection following 2nd COVID-19 vaccination and 1st booster. 256 out of 5265 individuals reported a breakthrough infection after the 2nd vaccine, and 581 out of 2583 individuals reported a breakthrough after the 1st booster. There was no difference in sleep duration between those with and without breakthrough infection. Increased awakening frequency was associated with breakthrough infection after the 1st booster with 3.01 ± 0.65 awakenings/hour in the breakthrough group compared to 2.82 ± 0.65 awakenings/hour in those without breakthrough (P < 0.001). Cox proportional hazards modeling showed that age < 60 years (hazard ratio 2.15, P < 0.001) and frequency of awakenings (hazard ratio 1.17, P = 0.019) were associated with breakthrough infection after the 1st booster. Sleep duration was not associated with breakthrough infection after COVID vaccination. While increased awakening frequency during sleep was associated with breakthrough infection beyond traditional risk factors, the clinical implications of this finding are unclear.

The BEAF insulator regulates genes involved in cell polarity and neoplastic growth.

Boundary Element Associated Factor-32 (BEAF-32) is an insulator protein predominantly found near gene promoters and thought to play a role in gene expression. We find that mutations in BEAF-32 are lethal, show loss of epithelial morphology in imaginal discs and cause neoplastic growth defects. To investigate the molecular mechanisms underlying this phenotype, we carried out a genome-wide analysis of BEAF-32 localization in wing imaginal disc cells. Mutation of BEAF-32 results in miss-regulation of 3850 genes by at least 1.5-fold, 794 of which are bound by this protein in wing imaginal cells. Up-regulated genes encode proteins involved in cell polarity, cell proliferation and cell differentiation. Among the down-regulated genes are those encoding components of the wingless pathway, which is required for cell differentiation. Miss-regulation of these genes explains the unregulated cell growth and neoplastic phenotypes observed in imaginal tissues of BEAF-32 mutants.

Improving participant representation in the era of digital clinical studies.

Traditional clinical research relies on conventional strategies to invite and enroll research participants. However, these strategies often fail to reach potential participants from marginalized communities or that reflect the diversity of the nation, such as race, ethnicity, or geography. As we discuss here, the digital clinical study model sets the stage for improved and equitable participation in biomedical research.

The Power of Patient Engagement With Electronic Health Records as Research Participants.

Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants.

Inter-individual variation in objective measure of reactogenicity following COVID-19 vaccination via smartwatches and fitness bands.

The ability to identify who does or does not experience the intended immune response following vaccination could be of great value in not only managing the global trajectory of COVID-19 but also helping guide future vaccine development. Vaccine reactogenicity can potentially lead to detectable physiologic changes, thus we postulated that we could detect an individual's initial physiologic response to a vaccine by tracking changes relative to their pre-vaccine baseline using consumer wearable devices. We explored this possibility using a smartphone app-based research platform that enabled volunteers (39,701 individuals) to share their smartwatch data, as well as self-report, when appropriate, any symptoms, COVID-19 test results, and vaccination information. Of 7728 individuals who reported at least one vaccination dose, 7298 received an mRNA vaccine, and 5674 provided adequate data from the peri-vaccine period for analysis. We found that in most individuals, resting heart rate (RHR) increased with respect to their individual baseline after vaccination, peaked on day 2, and returned to normal by day 6. This increase in RHR was greater than one standard deviation above individuals' normal daily pattern in 47% of participants after their second vaccine dose. Consistent with other reports of subjective reactogenicity following vaccination, we measured a significantly stronger effect after the second dose relative to the first, except those who previously tested positive to COVID-19, and a more pronounced increase for individuals who received the Moderna vaccine. Females, after the first dose only, and those aged <40 years, also experienced a greater objective response after adjusting for possible confounding factors. These early findings show that it is possible to detect subtle, but important changes from an individual's normal as objective evidence of reactogenicity, which, with further work, could prove useful as a surrogate for vaccine-induced immune response.

Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.

BACKGROUND: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. METHODS: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. FINDINGS: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. INTERPRETATION: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. FUNDING: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.

VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data.

Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.