Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Swarm: A federated cloud framework for large-scale variant analysis.

Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.

Hummingbird: efficient performance prediction for executing genomic applications in the cloud.

MOTIVATION: A major drawback of executing genomic applications on cloud computing facilities is the lack of tools to predict which instance type is the most appropriate, often resulting in an over- or under- matching of resources. Determining the right configuration before actually running the applications will save money and time. Here, we introduce Hummingbird, a tool for predicting performance of computing instances with varying memory and CPU on multiple cloud platforms. RESULTS: Our experiments on three major genomic data pipelines, including GATK HaplotypeCaller, GATK Mutect2 and ENCODE ATAC-seq, showed that Hummingbird was able to address applications in command line specified in JSON format or workflow description language (WDL) format, and accurately predicted the fastest, the cheapest and the most cost-efficient compute instances in an economic manner. AVAILABILITY AND IMPLEMENTATION: Hummingbird is available as an open source tool at: https://github.com/StanfordBioinformatics/Hummingbird. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays.

BACKGROUND: Benchmarking the performance of complex analytical pipelines is an essential part of developing Lab Developed Tests (LDT). Reference samples and benchmark calls published by Genome in a Bottle (GIAB) consortium have enabled the evaluation of analytical methods. The performance of such methods is not uniform across the different genomic regions of interest and variant types. Several benchmarking methods such as hap.py, vcfeval, and vcflib are available to assess the analytical performance characteristics of variant calling algorithms. However, assessing the performance characteristics of an overall LDT assay still requires stringing together several such methods and experienced bioinformaticians to interpret the results. In addition, these methods are dependent on the hardware, operating system and other software libraries, making it impossible to reliably repeat the analytical assessment, when any of the underlying dependencies change in the assay. Here we present a scalable and reproducible, cloud-based benchmarking workflow that is independent of the laboratory and the technician executing the workflow, or the underlying compute hardware used to rapidly and continually assess the performance of LDT assays, across their regions of interest and reportable range, using a broad set of benchmarking samples. RESULTS: The benchmarking workflow was used to evaluate the performance characteristics for secondary analysis pipelines commonly used by Clinical Genomics laboratories in their LDT assays such as the GATK HaplotypeCaller v3.7 and the SpeedSeq workflow based on FreeBayes v0.9.10. Five reference sample truth sets generated by Genome in a Bottle (GIAB) consortium, six samples from the Personal Genome Project (PGP) and several samples with validated clinically relevant variants from the Centers for Disease Control were used in this work. The performance characteristics were evaluated and compared for multiple reportable ranges, such as whole exome and the clinical exome. CONCLUSIONS: We have implemented a benchmarking workflow for clinical diagnostic laboratories that generates metrics such as specificity, precision and sensitivity for germline SNPs and InDels within a reportable range using whole exome or genome sequencing data. Combining these benchmarking results with validation using known variants of clinical significance in publicly available cell lines, we were able to establish the performance of variant calling pipelines in a clinical setting.

Introgression of a Novel Ug99-Effective Stem Rust Resistance Gene into Wheat and Development of Dasypyrum villosum Chromosome-Specific Markers via Genotyping-by-Sequencing (GBS).

Dasypyrum villosum is a wild relative of common wheat (Triticum aestivum L.) with resistance to Puccinia graminis f. tritici, the causal agent of stem rust, including the highly virulent race TTKSK (Ug99). In order to transfer resistance, T. durum-D. villosum amphiploids were initially developed and used as a bridge to create wheat-D. villosum introgression lines. Conserved ortholog set (COS) markers were used to identify D. villosum chromosome introgression lines, which were then subjected to seedling P. graminis f. tritici resistance screening with race TTKSK. A COS marker-verified line carrying chromosome 2V with TTKSK resistance was further characterized by combined genomic in situ and fluorescent in situ analyses to confirm a monosomic substitution line MS2V(2D) (20″ + 1' 2V[2D]). This is the first report of stem rust resistance on 2V, which was temporarily designated as SrTA10276-2V. To facilitate the use of this gene in wheat improvement, a complete set of previously developed wheat-D. villosum disomic addition lines was subjected to genotyping-by-sequencing analysis to develop D. villosum chromosome-specific markers. On average, 350 markers per chromosome were identified. These markers can be used to develop diagnostic markers for D. villosum-derived genes of interest in wheat improvement.

Genome-wide identification and analysis of the ALTERNATIVE OXIDASE gene family in diploid and hexaploid wheat.

A comprehensive understanding of wheat responses to environmental stress will contribute to the long-term goal of feeding the planet. ALERNATIVE OXIDASE (AOX) genes encode proteins involved in a bypass of the electron transport chain and are also known to be involved in stress tolerance in multiple species. Here, we report the identification and characterization of the AOX gene family in diploid and hexaploid wheat. Four genes each were found in the diploid ancestors Triticum urartu, and Aegilops tauschii, and three in Aegilops speltoides. In hexaploid wheat (Triticum aestivum), 20 genes were identified, some with multiple splice variants, corresponding to a total of 24 proteins for those with observed transcription and translation. These proteins were classified as AOX1a, AOX1c, AOX1e or AOX1d via phylogenetic analysis. Proteins lacking most or all signature AOX motifs were assigned to putative regulatory roles. Analysis of protein-targeting sequences suggests mixed localization to the mitochondria and other organelles. In comparison to the most studied AOX from Trypanosoma brucei, there were amino acid substitutions at critical functional domains indicating possible role divergence in wheat or grasses in general. In hexaploid wheat, AOX genes were expressed at specific developmental stages as well as in response to both biotic and abiotic stresses such as fungal pathogens, heat and drought. These AOX expression patterns suggest a highly regulated and diverse transcription and expression system. The insights gained provide a framework for the continued and expanded study of AOX genes in wheat for stress tolerance through breeding new varieties, as well as resistance to AOX-targeted herbicides, all of which can ultimately be used synergistically to improve crop yield.

Mapping genes for resistance to stripe rust in spring wheat landrace PI 480035.

Stripe rust caused by Puccinia striiformis Westend. f. sp. tritici Erikks. is an economically important disease of wheat (Triticum aestivum L.). Hexaploid spring wheat landrace PI 480035 was highly resistant to stripe rust in the field in Washington during 2011 and 2012. The objective of this research was to identify quantitative trait loci (QTL) for stripe rust resistance in PI 480035. A spring wheat, "Avocet Susceptible" (AvS), was crossed with PI 480035 to develop a biparental population of 110 recombinant inbred lines (RIL). The population was evaluated in the field in 2013 and 2014 and seedling reactions were examined against three races (PSTv-14, PSTv-37, and PSTv-40) of the pathogen under controlled conditions. The population was genotyped with genotyping-by-sequencing and microsatellite markers across the whole wheat genome. A major QTL, QYr.wrsggl1-1BS was identified on chromosome 1B. The closest flanking markers were Xgwm273, Xgwm11, and Xbarc187 1.01 cM distal to QYr.wrsggl1-1BS, Xcfd59 0.59 cM proximal and XA365 3.19 cM proximal to QYr.wrsggl1-1BS. Another QTL, QYr.wrsggl1-3B, was identified on 3B, which was significant only for PSTv-40 and was not significant in the field, indicating it confers a race-specific resistance. Comparison with markers associated with previously reported Yr genes on 1B (Yr64, Yr65, and YrH52) indicated that QYr.wrsggl1-1BS is potentially a novel stripe rust resistance gene that can be incorporated into modern breeding materials, along with other all-stage and adult-plant resistance genes to develop cultivars that can provide durable resistance.

Fine-scale analysis of 16S rRNA sequences reveals a high level of taxonomic diversity among vaginal Atopobium spp.

Although vaginal microbial communities of some healthy women have high proportions of Atopobium vaginae, the genus Atopobium is more commonly associated with bacterial vaginosis, a syndrome associated with an increased risk of adverse pregnancy outcomes and the transmission of sexually transmitted diseases. Genetic differences within Atopobium species may explain why single species can be associated with both health and disease. We used 16S rRNA gene sequences from previously published studies to explore the taxonomic diversity of the genus Atopobium in vaginal microbial communities of healthy women. Although A. vaginae was the species most commonly found, we also observed three other Atopobium species in the vaginal microbiota, one of which, A. parvulum, was not previously known to reside in the human vagina. Furthermore, we found several potential novel species of the genus Atopobium and multiple phylogenetic clades of A. vaginae. The diversity of Atopobium found in our study, which focused only on samples from healthy women, is greater than previously recognized, suggesting that analysis of samples from women with BV would yield even more diversity. Classification of microbes only to the genus level may thus obfuscate differences that might be important to better understand health or disease.

mcaGUI: microbial community analysis R-Graphical User Interface (GUI).

UNLABELLED: Microbial communities have an important role in natural ecosystems and have an impact on animal and human health. Intuitive graphic and analytical tools that can facilitate the study of these communities are in short supply. This article introduces Microbial Community Analysis GUI, a graphical user interface (GUI) for the R-programming language (R Development Core Team, 2010). With this application, researchers can input aligned and clustered sequence data to create custom abundance tables and perform analyses specific to their needs. This GUI provides a flexible modular platform, expandable to include other statistical tools for microbial community analysis in the future. AVAILABILITY: The mcaGUI package and source are freely available as part of Bionconductor at http://www.bioconductor.org/packages/release/bioc/html/mcaGUI.html

Rapid gene-based SNP and haplotype marker development in non-model eukaryotes using 3'UTR sequencing.

BACKGROUND: Sweet cherry (Prunus avium L.), a non-model crop with narrow genetic diversity, is an important member of sub-family Amygdoloideae within Rosaceae. Compared to other important members like peach and apple, sweet cherry lacks in genetic and genomic information, impeding understanding of important biological processes and development of efficient breeding approaches. Availability of single nucleotide polymorphism (SNP)-based molecular markers can greatly benefit breeding efforts in such non-model species. RNA-seq approaches employing second generation sequencing platforms offer a unique avenue to rapidly identify gene-based SNPs. Additionally, haplotype markers can be rapidly generated from transcript-based SNPs since they have been found to be extremely utile in identification of genetic variants related to health, disease and response to environment as highlighted by the human HapMap project. RESULTS: RNA-seq was performed on two sweet cherry cultivars, Bing and Rainier using a 3' untranslated region (UTR) sequencing method yielding 43,396 assembled contigs. In order to test our approach of rapid identification of SNPs without any reference genome information, over 25% (10,100) of the contigs were screened for the SNPs. A total of 207 contigs from this set were identified to contain high quality SNPs. A set of 223 primer pairs were designed to amplify SNP containing regions from these contigs and high resolution melting (HRM) analysis was performed with eight important parental sweet cherry cultivars. Six of the parent cultivars were distantly related to Bing and Rainier, the cultivars used for initial SNP discovery. Further, HRM analysis was also performed on 13 seedlings derived from a cross between two of the parents. Our analysis resulted in the identification of 84 (38.7%) primer sets that demonstrated variation among the tested germplasm. Reassembly of the raw 3'UTR sequences using upgraded transcriptome assembly software yielded 34,620 contigs containing 2243 putative SNPs in 887 contigs after stringent filtering. Contigs with multiple SNPs were visually parsed to identify 685 putative haplotypes at 335 loci in 301 contigs. CONCLUSIONS: This approach, which leverages the advantages of RNA-seq approaches, enabled rapid generation of gene-linked SNP and haplotype markers. The general approach presented in this study can be easily applied to other non-model eukaryotes irrespective of the ploidy level to identify gene-linked polymorphisms that are expected to facilitate efficient Gene Assisted Breeding (GAB), genotyping and population genetics studies. The identified SNP haplotypes reveal some of the allelic differences in the two sweet cherry cultivars analyzed. The identification of these SNP and haplotype markers is expected to significantly improve the genomic resources for sweet cherry and facilitate efficient GAB in this non-model crop.

The genome of the domesticated apple (Malus × domestica Borkh.).

We report a high-quality draft genome sequence of the domesticated apple (Malus × domestica). We show that a relatively recent (>50 million years ago) genome-wide duplication (GWD) has resulted in the transition from nine ancestral chromosomes to 17 chromosomes in the Pyreae. Traces of older GWDs partly support the monophyly of the ancestral paleohexaploidy of eudicots. Phylogenetic reconstruction of Pyreae and the genus Malus, relative to major Rosaceae taxa, identified the progenitor of the cultivated apple as M. sieversii. Expansion of gene families reported to be involved in fruit development may explain formation of the pome, a Pyreae-specific false fruit that develops by proliferation of the basal part of the sepals, the receptacle. In apple, a subclade of MADS-box genes, normally involved in flower and fruit development, is expanded to include 15 members, as are other gene families involved in Rosaceae-specific metabolism, such as transport and assimilation of sorbitol.