Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations.

Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.

Genomic diagnostics in polycystic kidney disease: an assessment of real-world use of whole-genome sequencing.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is common, with a prevalence of 1/1000 and predominantly caused by disease-causing variants in PKD1 or PKD2. Clinical diagnosis is usually by age-dependent imaging criteria, which is challenging in patients with atypical clinical features, without family history, or younger age. However, there is increasing need for definitive diagnosis of ADPKD with new treatments available. Sequencing is complicated by six pseudogenes that share 97% homology to PKD1 and by recently identified phenocopy genes. Whole-genome sequencing can definitively diagnose ADPKD, but requires validation for clinical use. We initially performed a validation study, in which 42 ADPKD patients underwent sequencing of PKD1 and PKD2 by both whole-genome and Sanger sequencing, using a blinded, cross-over method. Whole-genome sequencing identified all PKD1 and PKD2 germline pathogenic variants in the validation study (sensitivity and specificity 100%). Two mosaic variants outside pipeline thresholds were not detected. We then examined the first 144 samples referred to a clinically-accredited diagnostic laboratory for clinical whole-genome sequencing, with targeted-analysis to a polycystic kidney disease gene-panel. In this unselected, diagnostic cohort (71 males :73 females), the diagnostic rate was 70%, including a diagnostic rate of 81% in patients with typical ADPKD (98% with PKD1/PKD2 variants) and 60% in those with atypical features (56% PKD1/PKD2; 44% PKHD1/HNF1B/GANAB/ DNAJB11/PRKCSH/TSC2). Most patients with atypical disease did not have clinical features that predicted likelihood of a genetic diagnosis. These results suggest clinicians should consider diagnostic genomics as part of their assessment in polycystic kidney disease, particularly in atypical disease.

Culture-free genome-wide locus sequence typing (GLST) provides new perspectives on Trypanosoma cruzi dispersal and infection complexity.

Analysis of genetic polymorphism is a powerful tool for epidemiological surveillance and research. Powerful inference from pathogen genetic variation, however, is often restrained by limited access to representative target DNA, especially in the study of obligate parasitic species for which ex vivo culture is resource-intensive or bias-prone. Modern sequence capture methods enable pathogen genetic variation to be analyzed directly from host/vector material but are often too complex and expensive for resource-poor settings where infectious diseases prevail. This study proposes a simple, cost-effective 'genome-wide locus sequence typing' (GLST) tool based on massive parallel amplification of information hotspots throughout the target pathogen genome. The multiplexed polymerase chain reaction amplifies hundreds of different, user-defined genetic targets in a single reaction tube, and subsequent agarose gel-based clean-up and barcoding completes library preparation at under 4 USD per sample. Our study generates a flexible GLST primer panel design workflow for Trypanosoma cruzi, the parasitic agent of Chagas disease. We successfully apply our 203-target GLST panel to direct, culture-free metagenomic extracts from triatomine vectors containing a minimum of 3.69 pg/µl T. cruzi DNA and further elaborate on method performance by sequencing GLST libraries from T. cruzi reference clones representing discrete typing units (DTUs) TcI, TcIII, TcIV, TcV and TcVI. The 780 SNP sites we identify in the sample set repeatably distinguish parasites infecting sympatric vectors and detect correlations between genetic and geographic distances at regional (< 150 km) as well as continental scales. The markers also clearly separate TcI, TcIII, TcIV and TcV + TcVI and appear to distinguish multiclonal infections within TcI. We discuss the advantages, limitations and prospects of our method across a spectrum of epidemiological research.

Framework for quality assessment of whole genome cancer sequences.

Bringing together cancer genomes from different projects increases power and allows the investigation of pan-cancer, molecular mechanisms. However, working with whole genomes sequenced over several years in different sequencing centres requires a framework to compare the quality of these sequences. We used the Pan-Cancer Analysis of Whole Genomes cohort as a test case to construct such a framework. This cohort contains whole cancer genomes of 2832 donors from 18 sequencing centres. We developed a non-redundant set of five quality control (QC) measurements to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses and allow for exclusion of samples of poor quality. We have found that this is an effective framework of quality measures. The implementation of the framework is available at: https://dockstore.org/containers/quay.io/jwerner_dkfz/pancanqc:1.2.2 .

DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework.

Although sequencing a human genome has become affordable, identifying genetic variants from whole-genome sequence data is still a hurdle for researchers without adequate computing equipment or bioinformatics support. GATK is a gold standard method for the identification of genetic variants and has been widely used in genome projects and population genetic studies for many years. This was until the Google Brain team developed a new method, DeepVariant, which utilizes deep neural networks to construct an image classification model to identify genetic variants. However, the superior accuracy of DeepVariant comes at the cost of computational intensity, largely constraining its applications. Accordingly, we present DeepVariant-on-Spark to optimize resource allocation, enable multi-GPU support, and accelerate the processing of the DeepVariant pipeline. To make DeepVariant-on-Spark more accessible to everyone, we have deployed the DeepVariant-on-Spark to the Google Cloud Platform (GCP). Users can deploy DeepVariant-on-Spark on the GCP following our instruction within 20 minutes and start to analyze at least ten whole-genome sequencing datasets using free credits provided by the GCP. DeepVaraint-on-Spark is freely available for small-scale genome analysis using a cloud-based computing framework, which is suitable for pilot testing or preliminary study, while reserving the flexibility and scalability for large-scale sequencing projects.

The DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis.

It is becoming increasingly important to understand the mechanism of regulatory elements on target genes in long-range genomic distance. 3C (chromosome conformation capture) and its derived methods are now widely applied to investigate three-dimensional (3D) genome organizations and gene regulation. Digestion-ligation-only Hi-C (DLO Hi-C) is a new technology with high efficiency and cost-effectiveness for whole-genome chromosome conformation capture. Here, we introduce the DLO Hi-C tool, a flexible and versatile pipeline for processing DLO Hi-C data from raw sequencing reads to normalized contact maps and for providing quality controls for different steps. It includes more efficient iterative mapping and linker filtering. We applied the DLO Hi-C tool to different DLO Hi-C datasets and demonstrated its ability in processing large data with multithreading. The DLO Hi-C tool is suitable for processing DLO Hi-C and in situ DLO Hi-C datasets. It is convenient and efficient for DLO Hi-C data processing.

Operational models and criteria for incorporating microbial whole genome sequencing in hospital microbiology - A systematic literature review.

BACKGROUND: Microbial whole genome sequencing (WGS) has many advantages over standard microbiological methods. However, it is not yet widely implemented in routine hospital diagnostics due to notable challenges. OBJECTIVES: The aim was to extract managerial, financial and clinical criteria supporting the decision to implement WGS in routine diagnostic microbiology, across different operational models of implementation in the hospital setting. METHODS: This was a systematic review of literature identified through PubMed and Web of Science. English literature studies discussing the applications of microbial WGS without limitation on publication date were eligible. A narrative approach for categorization and synthesis of the sources identified was adopted. RESULTS: A total of 98 sources were included. Four main alternative operational models for incorporating WGS in clinical microbiology laboratories were identified: full in-house sequencing and analysis, full outsourcing of sequencing and analysis and two hybrid models combining in-house/outsourcing of the sequencing and analysis components. Six main criteria (and multiple related sub-criteria) for WGS implementation emerged from our review and included cost (e.g. the availability of resources for capital and operational investment); manpower (e.g. the ability to provide training programmes or recruit trained personnel), laboratory infrastructure (e.g. the availability of supplies and consumables or sequencing platforms), bioinformatics requirements (e.g. the availability of valid analysis tools); computational infrastructure (e.g. the availability of storage space or data safety arrangements); and quality control (e.g. the existence of standardized procedures). CONCLUSIONS: The decision to incorporate WGS in routine diagnostics involves multiple, sometimes competing, criteria and sub-criteria. Mapping these criteria systematically is an essential stage in developing policies for adoption of this technology, e.g. using a multicriteria decision tool. Future research that will prioritize criteria and sub-criteria that were identified in our review in the context of operational models will inform decision-making at clinical and managerial levels with respect to effective implementation of WGS for routine use. Beyond WGS, similar decision-making challenges are expected with respect to future integration of clinical metagenomics.

In-depth comparative analysis of Illumina® MiSeq run metrics: Development of a wet-lab quality assessment tool.

Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high-quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K-means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In-Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.

Improving the quality and workflow of bacterial genome sequencing and analysis: paving the way for a Switzerland-wide molecular epidemiological surveillance platform.

Facing multidrug resistant (MDR) bacterial pathogens is one of the most important challenges for our society. The spread of highly virulent and resistant pathogens can be described using molecular typing technologies; in particular, whole genome sequencing (WGS) data can be used for molecular typing purposes with high resolution. WGS data analysis can explain the spatiotemporal patterns of pathogen transmission. However, the transmission between compartments (human, animal, food, environment) is very complex. Interoperable and curated metadata are a key requirement for fully understanding this complexity. In addition, high quality sequence data are a key element between centres using WGS data for diagnostic and epidemiological applications. We aim to describe steps to improve WGS data analysis and to implement a molecular surveillance platform allowing integration of high resolution WGS typing data and epidemiological data.

Cost Analyses of Genomic Sequencing: Lessons Learned from the MedSeq Project.

OBJECTIVE: To summarize lessons learned while analyzing the costs of integrating whole genome sequencing into the care of cardiology and primary care patients in the MedSeq Project by conducting the first randomized controlled trial of whole genome sequencing in general and specialty medicine. METHODS: Case study that describes key methodological and data challenges that were encountered or are likely to emerge in future work, describes the pros and cons of approaches considered by the study team, and summarizes the solutions that were implemented. RESULTS: Major methodological challenges included defining whole genome sequencing, structuring an appropriate comparator, measuring downstream costs, and examining clinical outcomes. Discussions about solutions addressed conceptual and practical issues that arose because of definitions and analyses around the cost of genomic sequencing in trial-based studies. CONCLUSIONS: The MedSeq Project provides an instructive example of how to conduct a cost analysis of whole genome sequencing that feasibly incorporates best practices while being sensitive to the varied applications and diversity of results it may produce. Findings provide guidance for researchers to consider when conducting or analyzing economic analyses of whole genome sequencing and other next-generation sequencing tests, particularly regarding costs.

Whole-genome bisulfite sequencing with improved accuracy and cost.

DNA methylation patterns in the genome both reflect and help to mediate transcriptional regulatory processes. The digital nature of DNA methylation, present or absent on each allele, makes this assay capable of quantifying events in subpopulations of cells, whereas genome-wide chromatin studies lack the same quantitative capacity. Testing DNA methylation throughout the genome is possible using whole-genome bisulfite sequencing (WGBS), but the high costs associated with the assay have made it impractical for studies involving more than limited numbers of samples. We have optimized a new transposase-based library preparation assay for the Illumina HiSeq X platform suitable for limited amounts of DNA and providing a major cost reduction for WGBS. By incorporating methylated cytosines during fragment end repair, we reveal an end-repair artifact affecting 1%-2% of reads that we can remove analytically. We show that the use of a high (G + C) content spike-in performs better than PhiX in terms of bisulfite sequencing quality. As expected, the loci with transposase-accessible chromatin are DNA hypomethylated and enriched in flanking regions by post-translational modifications of histones usually associated with positive effects on gene expression. Using these transposase-accessible loci to represent the cis-regulatory loci in the genome, we compared the representation of these loci between WGBS and other genome-wide DNA methylation assays, showing WGBS to outperform substantially all of the alternatives. We conclude that it is now technologically and financially feasible to perform WGBS in larger numbers of samples with greater accuracy than previously possible.

Developing a framework to assess the costeffectiveness of COMPARE - a global platform for the exchange of sequence-based pathogen data.

Analysing the genomic data of pathogens with the help of next-generation sequencing (NGS) is an increasingly important part of disease outbreak investigations and helps guide responses. While this technology has already been successfully employed to elucidate and control disease outbreaks, wider implementation of NGS also depends on its cost-effectiveness. COMPARE - short for 'Collaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks' - is a major project, funded by the European Union, to develop a global platform for sharing and analysing NGS data and thereby improve the rapid identification, containment and mitigation of emerging infectious diseases and foodborne outbreaks. This article introduces the project and presents the results of a review of the literature, composed of previous relevant cost-benefit and cost-effectiveness analyses. The authors also outline the implications for a methodological framework to assess the costeffectiveness of COMPARE and similar systems.

L'analyse des données sur le génome des agents pathogènes grâce au séquençage de nouvelle génération (SNG) joue un rôle de plus en plus important dans les enquêtes sur les foyers de maladies et contribue à l'élaboration de stratégies de réponse. Si cette technologie a été utilisée avec succès pour élucider la cause des certains foyers et pour les contrôler, une application plus large du SNG dépend également de sa rentabilité. La plate-forme COMPARE (plate-forme de gestion collaborative pour la détection et l'analyse des foyers émergents et ré-émergents et des toxi-infections alimentaires) est un projet de grande envergure financé par l'Union européenne, visant à mettre en place une plate-forme mondiale d'échanges et d'analyse des données de séquençage de nouvelle génération et à améliorer ainsi l'identification précoce, le confinement et l'atténuation des maladies infectieuses émergentes et des foyers de toxiinfections alimentaires. Les auteurs présentent le projet ainsi que les résultats d'une étude bibliographique intégrant des analyses pertinentes coûts­avantages et coûts­efficacité réalisées dans le passé. Ils soulignent également les enseignements de ces analyses pour l'élaboration d'un cadre méthodologique d'évaluation de la relation coûts­efficacité applicable au système COMPARE et à d'autres systèmes similaires.

El análisis de datos genómicos de los patógenos con ayuda de técnicas de secuenciación de próxima generación es un componente cada vez más importante de la investigación de brotes infecciosos, que resulta de utilidad para guiar las medidas de respuesta. Aunque estas técnicas ya se han utilizado con éxito para elucidar y combatir brotes de enfermedad, su aplicación generalizada también dependerá de la relación costo-eficacia que ofrezcan. COMPARE (acrónimo inglés de «plataforma de gestión colectiva para la detección y análisis de brotes (re)emergentes y de transmisión alimentaria¼) es un vasto proyecto financiado por la Unión Europea que apunta a instituir un dispositivo mundial de intercambio y análisis de datos de secuenciación de próxima generación y lograr así más eficacia en la rápida identificación, contención y mitigación de brotes de transmisión alimentaria y de enfermedades infecciosas emergentes. Los autores exponen el proyecto y presentan los resultados de un repaso bibliográfico de anteriores análisis de las relaciones costo-beneficio y costo-eficacia de estas técnicas. Además, explican brevemente lo que puede aportar un marco metodológico para evaluar la relación costo-eficacia del sistema COMPARE y de otros sistemas similares.

Rapid and affordable genome-wide bisulfite DNA sequencing by XmaI-reduced representation bisulfite sequencing.

AIM: To develop a reduced representation bisulfite sequencing (RRBS) approach for rapid and affordable genome-wide DNA methylation analysis. METHODS: We have selected restriction endonuclease XmaI to produce RRBS library fragments. After digestion and partial fill-in DNA fragments were ligated to barcoded adapters, bisulfite converted, size-selected, and sequenced on the Ion Torrent Personal Genome Machine. XmaI-RRBS results were compared with the previously published RRBS data. RESULTS: We have developed an XmaI-RRBS method for rapid and affordable genome-wide DNA methylation analysis, with library preparation taking only 4 days and sequencing possible within 4 h. We have also addressed several challenges in order to further improve the RRBS technology. XmaI-RRBS may be performed on degraded DNA samples and is compatible with the bench-top next-generation sequencing machines.

Comparative assessment of DNA methylation patterns between reduced representation bisulfite sequencing and Sequenom EpiTyper methylation analysis.

AIM: Validation of sequencing-based DNA methylation data is an important step for meaningful translation of findings. However, there has been limited assessment of different platforms to validate methylation data from next generation sequencing. METHODS: We performed a comparative methylation analysis between the genome-wide platform of reduced representation bisulfite sequencing with a targeted, Sequenom EpiTyper platform (four genes were analyzed in 15 cell lines covering 52 CpG sites). RESULTS: We show that the accuracy of validation substantially improves if results from multiple and adjacent CpG sites are combined rather than at single CpG sites. We demonstrate increased read number improves accuracy of reduced representation bisulfite sequencing results. Further, by using series of replicates, we document variation in samples analyzed by Sequenom EpiTyper, indicating the importance of including replicates to increase precision. CONCLUSION: The results reveal potential sources of bias and provide a guideline for refining study design for DNA methylation analysis.