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Background Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies. Results We compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database ( https://nanome.jax.org ) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts. Conclusions Our study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing.

Understanding the pathophysiology of SARS-CoV-2 infection is critical for therapeutic and public health strategies. Viral–host interactions can guide discovery of disease regulators, and protein structure function analysis points to several immune pathways, including complement and coagulation, as targets of coronaviruses. To determine whether conditions associated with dysregulated complement or coagulation systems impact disease, we performed a retrospective observational study and found that history of macular degeneration (a proxy for complement-activation disorders) and history of coagulation disorders (thrombocytopenia, thrombosis and hemorrhage) are risk factors for SARS-CoV-2-associated morbidity and mortality—effects that are independent of age, sex or history of smoking. Transcriptional profiling of nasopharyngeal swabs demonstrated that in addition to type-I interferon and interleukin-6-dependent inflammatory responses, infection results in robust engagement of the complement and coagulation pathways. Finally, in a candidate-driven genetic association study of severe SARS-CoV-2 disease, we identified putative complement and coagulation-associated loci including missense, eQTL and sQTL variants of critical complement and coagulation regulators. In addition to providing evidence that complement function modulates SARS-CoV-2 infection outcome, the data point to putative transcriptional genetic markers of susceptibility. The results highlight the value of using a multimodal analytical approach to reveal determinants and predictors of immunity, susceptibility and clinical outcome associated with infection. A combination of clinical and molecular analyses supports an association between disorders of immune complement or coagulation with poor outcome in patients with SARS-CoV-2 infection.

Recent studies have provided insights into the pathology of and immune response to COVID-19 1 – 8 . However, a thorough investigation of the interplay between infected cells and the immune system at sites of infection has been lacking. Here we use high-parameter imaging mass cytometry 9 that targets the expression of 36 proteins to investigate the cellular composition and spatial architecture of acute lung injury in humans (including injuries derived from SARS-CoV-2 infection) at single-cell resolution. These spatially resolved single-cell data unravel the disordered structure of the infected and injured lung, alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyperinflammatory cell state that is associated with lung damage. We leverage the temporal range of fatal outcomes of COVID-19 in relation to the onset of symptoms, which reveals increased macrophage extravasation and increased numbers of mesenchymal cells and fibroblasts concomitant with increased proximity between these cell types as the disease progresses—possibly as a result of attempts to repair the damaged lung tissue. Our data enable us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. We use this landscape to characterize the pathophysiology of the human lung from its macroscopic presentation to the single-cell level, which provides an important basis for understanding COVID-19 and lung pathology in general. Imaging mass cytometry of the human lung reveals the cellular composition and spatial architecture during COVID-19 and other acute injuries, enabling the characterization of lung pathophysiology from structural, immunological and clinical perspectives.

Assessing the reproducibility, accuracy and utility of massively parallel DNA sequencing platforms remains an ongoing challenge. Here the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Study benchmarks the performance of a set of sequencing instruments (HiSeq/NovaSeq/paired-end 2 × 250-bp chemistry, Ion S5/Proton, PacBio circular consensus sequencing (CCS), Oxford Nanopore Technologies PromethION/MinION, BGISEQ-500/MGISEQ-2000 and GS111) on human and bacterial reference DNA samples. Among short-read instruments, HiSeq 4000 and X10 provided the most consistent, highest genome coverage, while BGI/MGISEQ provided the lowest sequencing error rates. The long-read instrument PacBio CCS had the highest reference-based mapping rate and lowest non-mapping rate. The two long-read platforms PacBio CCS and PromethION/MinION showed the best sequence mapping in repeat-rich areas and across homopolymers. NovaSeq 6000 using 2 × 250-bp read chemistry was the most robust instrument for capturing known insertion/deletion events. This study serves as a benchmark for current genomics technologies, as well as a resource to inform experimental design and next-generation sequencing variant calling. Next-generation sequencing platforms are benchmarked using human, bacterial and metagenomics reference materials.

Background One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. Results In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. Conclusions This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

The common bed bug ( Cimex lectularius ) has been a persistent pest of humans for thousands of years, yet the genetic basis of the bed bug’s basic biology and adaptation to dense human environments is largely unknown. Here we report the assembly, annotation and phylogenetic mapping of the 697.9-Mb Cimex lectularius genome, with an N50 of 971 kb, using both long and short read technologies. A RNA-seq time course across all five developmental stages and male and female adults generated 36,985 coding and noncoding gene models. The most pronounced change in gene expression during the life cycle occurs after feeding on human blood and included genes from the Wolbachia endosymbiont, which shows a simultaneous and coordinated host/commensal response to haematophagous activity. These data provide a rich genetic resource for mapping activity and density of C. lectularius across human hosts and cities, which can help track, manage and control bed bug infestations. The common bedbug is a pest for humans, yet its molecular biology is poorly understood. Here, the authors sequence the common bedbug genome and profile gene expression across all life stages to show major changes in gene expression after feeding on human blood.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies. Measurement(s) Somatic Mutation Analysis Technology Type(s) whole genome sequencing • Whole Exome Sequencing Factor Type(s) sequencing platform • sample prepration • library preparation • bioinformatics method Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16713655

Lake Hillier is a hypersaline lake known for its distinctive bright pink color. The cause of this phenomenon in other hypersaline sites has been attributed to halophiles, Dunaliella , and Salinibacter , however, a systematic analysis of the microbial communities, their functional features, and the prevalence of pigment-producing-metabolisms has not been previously studied. Through metagenomic sequencing and culture-based approaches, our results evidence that Lake Hillier is composed of a diverse set of microorganisms including archaea, bacteria, algae, and viruses. Our data indicate that the microbiome in Lake Hillier is composed of multiple pigment-producer microbes, including Dunaliella , Salinibacter , Halobacillus , Psychroflexus , Halorubrum , many of which are cataloged as polyextremophiles. Additionally, we estimated the diversity of metabolic pathways in the lake and determined that many of these are related to pigment production. We reconstructed complete or partial genomes for 21 discrete bacteria (N = 14) and archaea (N = 7), only 2 of which could be taxonomically annotated to previously observed species. Our findings provide the first metagenomic study to decipher the source of the pink color of Australia’s Lake Hillier. The study of this pink hypersaline environment is evidence of a microbial consortium of pigment producers, a repertoire of polyextremophiles, a core microbiome and potentially novel species.

Multiple strains of a novel yeast belonging to genus Naganishia were isolated from environmental surfaces aboard the International Space Station (ISS). These strains exhibited a phenotype similar to Titan cell (~10 µm diameter) morphology when grown under a combination of simulated microgravity and 5% CO2 conditions. Confocal, scanning, and transmission electron microscopy revealed distinct morphological differences between the microgravity-grown cells and the standard Earth gravity-grown cells, including larger cells and thicker cell walls, altered intracellular morphology, modifications to extracellular fimbriae, budding, and the shedding of bud scars. Phylogenetic analyses via multi-locus sequence typing indicated that these ISS strains represented a single species in the genus Naganishia and were clustered with Naganishia diffluens. The name Naganishia tulchinskyi is proposed to accommodate these strains, with IF6SW-B1T as the holotype. The gene ontologies were assigned to the cell morphogenesis, microtubule-based response, and response to UV light, suggesting a variety of phenotypes that are well suited to respond to microgravity and radiation. Genomic analyses also indicated that the extracellular region, outer membrane, and cell wall were among the highest cellular component results, thus implying a set of genes associated with Titan-like cell plasticity. Finally, the highest molecular function matches included cytoskeletal motor activity, microtubule motor activity, and nuclear export signal receptor activity.