Understanding genetic variability: exploring large-scale copy number variants through non-invasive prenatal testing in European populations.

Large-scale copy number variants (CNVs) are structural alterations in the genome that involve the duplication or deletion of DNA segments, contributing to genetic diversity and playing a crucial role in the evolution and development of various diseases and disorders, as they can lead to the dosage imbalance of one or more genes. Massively parallel sequencing (MPS) has revolutionized the field of genetic analysis and contributed significantly to routine clinical diagnosis and screening. It offers a precise method for detecting CNVs with exceptional accuracy. In this context, a non-invasive prenatal test (NIPT) based on the sequencing of cell-free DNA (cfDNA) from pregnant women's plasma using a low-coverage whole genome MPS (WGS) approach represents a valuable source for population studies. Here, we analyzed genomic data of 12,732 pregnant women from the Slovak (9,230), Czech (1,583), and Hungarian (1,919) populations. We identified 5,062 CNVs ranging from 200 kbp and described their basic characteristics and differences between the subject populations. Our results suggest that re-analysis of sequencing data from routine WGS assays has the potential to obtain large-scale CNV population frequencies, which are not well known and may provide valuable information to support the classification and interpretation of this type of genetic variation. Furthermore, this could contribute to expanding knowledge about the central European genome without investing in additional laboratory work, as NIPTs are a relatively widely used screening method.

A Practical Guide to Whole Genome Sequencing in the NICU.

The neonatal period is a peak time for the presentation of genetic disorders that can be diagnosed using whole genome sequencing (WGS). While any one genetic disorder is individually rare, they collectively contribute to significant morbidity, mortality, and health-care costs. As the cost of WGS continues to decline and becomes increasingly available, the ordering of rapid WGS for NICU patients with signs or symptoms of an underlying genetic condition is now feasible. However, many neonatal clinicians are not comfortable with the testing, and unfortunately, there is a dearth of geneticists to facilitate testing for every patient that needs it. Here, we will review the science behind WGS, diagnostic capabilities, limitations of testing, time to consider testing, test initiation, interpretation of results, developing a plan of care that incorporates genomic information, and returning WGS results to families.

Population Structure and Selection Signal Analysis of Nanyang Cattle Based on Whole-Genome Sequencing Data.

With a rich breeding history, Nanyang cattle (NY cattle) have undergone extensive natural and artificial selection, resulting in distinctive traits such as high fertility, excellent meat quality, and disease resistance. This makes them an ideal model for studying the mechanisms of environmental adaptability. To assess the population structure and genetic diversity of NY cattle, we performed whole-genome resequencing on 30 individuals. These data were then compared with published whole-genome resequencing data from 432 cattle globally. The results indicate that the genetic structure of NY cattle is significantly different from European commercial breeds and is more similar to North-Central Chinese breeds. Furthermore, among all breeds, NY cattle exhibit the highest genetic diversity and the lowest population inbreeding levels. A genome-wide selection signal analysis of NY cattle and European commercial breeds using Fst, θπ-ratio, and θπ methods revealed significant selection signals in genes associated with reproductive performance and immunity. Our functional annotation analysis suggests that these genes may be responsible for reproduction (MAP2K2, PGR, and GSE1), immune response (NCOA2, HSF1, and PAX5), and olfaction (TAS1R3). We provide a comprehensive overview of sequence variations in the NY cattle genome, revealing insights into the population structure and genetic diversity of NY cattle. Additionally, we identify candidate genes associated with important economic traits, offering valuable references for future conservation and breeding efforts of NY cattle.

Structural rearrangements as a recurrent pathogenic mechanism for SETBP1 haploinsufficiency.

Chromosomal structural rearrangements consist of anomalies in genomic architecture that may or may not be associated with genetic material gain and loss. Evaluating the precise breakpoint is crucial from a diagnostic point of view, highlighting possible gene disruption and addressing to appropriate genotype-phenotype association. Structural rearrangements can either occur randomly within the genome or present with a recurrence, mainly due to peculiar genomic features of the surrounding regions. We report about three non-related individuals, harboring chromosomal structural rearrangements interrupting SETBP1, leading to gene haploinsufficiency. Two out of them resulted negative to Chromosomal Microarray Analysis (CMA), being the rearrangement balanced at a microarray resolution. The third one, presenting with a complex three-chromosome rearrangement, had been previously diagnosed with SETBP1 haploinsufficiency due to a partial gene deletion at one of the chromosomal breakpoints. We thoroughly characterized the rearrangements by means of Optical Genome Mapping (OGM) and Whole Genome Sequencing (WGS), providing details about the involved sequences and the underlying mechanisms. We propose structural variants as a recurrent event in SETBP1 haploinsufficiency, which may be overlooked by laboratory routine genomic analyses (CMA and Whole Exome Sequencing) or only partially determined when associated with genomic losses at breakpoints. We finally introduce a possible role of SETBP1 in a Noonan-like phenotype.

Characteristic SNPs defining the major multidrug-resistant Mycobacterium tuberculosis clusters identified by EuSeqMyTB to support routine surveillance, EU/EEA, 2017 to 2019.

BackgroundThe EUSeqMyTB project, conducted in 2020, used whole genome sequencing (WGS) for surveillance of drug-resistant Mycobacterium tuberculosis in the European Union/European Economic Area (EU/EEA) and identified 56 internationally clustered multidrug-resistant (MDR) tuberculosis (TB) clones.AimWe aimed to define and establish a rapid and computationally simple screening method to identify probable members of the main cross-border MDR-TB clusters in WGS data to facilitate their identification and track their future spread.MethodsWe screened 34 of the larger cross-border clusters identified in the EuSeqMyTB pilot study (2017-19) for characteristic single nucleotide polymorphism (SNP) signatures that could identify and define members of each cluster. We also linked this analysis with published clusters identified in previous studies and identified more distant genetic relationships between some of the current clusters.ResultsA panel of 30 characteristic SNPs is presented that can be used as an initial (routine) screen for members of each cluster. For four of the clusters, no unique defining SNP could be identified; three of these are closely related (within approximately 20 SNPs) to one or more other clusters and likely represent a single established MDR-TB clade composed of multiple recent subclusters derived from the previously described ECDC0002 cluster.ConclusionThe identified SNP signatures can be integrated into routine pipelines and contribute to the more effective monitoring, rapid and widespread screening for TB. This SNP panel will also support accurate communication between laboratories about previously identified internationally transmitted MDR-TB genotypes.

Multi-platform whole genome sequencing for tuberculosis clinical and surveillance applications.

Whole genome sequencing (WGS) of Mycobacterium tuberculosis offers valuable insights for tuberculosis (TB) control. High throughput platforms like Illumina and Oxford Nanopore Technology (ONT) are increasingly used globally, although ONT is known for higher error rates and is less established for genomic studies. Here we present a study comparing the sequencing outputs of both Illumina and ONT platforms, analysing DNA from 59 clinical isolates in highly endemic TB regions of Thailand. The resulting sequence data were used to profile the M. tuberculosis pairs for their lineage, drug resistance and presence in transmission chains, and were compared to publicly available WGS data from Thailand (n = 1456). Our results revealed isolates that are predominantly from lineages 1 and 2, with consistent drug resistance profiles, including six multidrug-resistant strains; however, analysis of ONT data showed longer phylogenetic branches, emphasising the technologies higher error rate. An analysis incorporating the larger dataset identified fifteen of our samples within six potential transmission clusters, including a significant clade of 41 multi-drug resistant isolates. ONT's extended sequences also revealed strain-specific structural variants in pe/ppe genes (e.g. ppe50), which are candidate loci for vaccine development. Despite some limitations, our results show that ONT sequencing is a promising approach for TB genomic research, supporting precision medicine and decision-making in areas with less developed infrastructure, which is crucial for tackling the disease's significant regional burden.

Implementation of a high-throughput whole genome sequencing approach with the goal of maximizing efficiency and cost effectiveness to improve public health.

This manuscript describes the development of a streamlined, cost-effective laboratory workflow to meet the demands of increased whole genome sequence (WGS) capacity while achieving mandated quality metrics. From 2020 to 2021, the Wadsworth Center Bacteriology Laboratory (WCBL) used a streamlined workflow to sequence 5,743 genomes that contributed sequence data to nine different projects. The combined use of the QIAcube HT, Illumina DNA Prep using quarter volume reactions, and the NextSeq allowed the WCBL to process all samples that required WGS while also achieving a median turn-around time of 7 days (range 4 to 10 days) and meeting minimum sequence quality requirements. Public Health Laboratories should consider implementing these methods to aid in meeting testing requirements within budgetary restrictions. IMPORTANCE: Public Health Laboratories that implement whole genome sequencing (WGS) technologies may struggle to find the balance between sample volume and cost effectiveness. We present a method that allows for sequencing of a variety of bacterial isolates in a cost-effective manner. This report provides specific strategies to implement high-volume WGS, including an innovative, low-cost solution utilizing a novel quarter volume sequencing library preparation. The methods described support the use of high-throughput DNA extraction and WGS within budgetary constraints, strengthening public health responses to outbreaks and disease surveillance.

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA.

Analysis of DNA methylation in cell-free DNA reveals clinically relevant biomarkers but requires specialized protocols such as whole-genome bisulfite sequencing. Meanwhile, millions of cell-free DNA samples are being profiled by whole-genome sequencing. Here, we develop FinaleMe, a non-homogeneous Hidden Markov Model, to predict DNA methylation of cell-free DNA and, therefore, tissues-of-origin, directly from plasma whole-genome sequencing. We validate the performance with 80 pairs of deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing data.

Evaluating the efficacy of MEANGS for mitochondrial genome assembly of cartilaginous and ray-finned fish species.

The assembly of complete and circularized mitochondrial genomes (mitogenomes) is essential for population genetics, phylogenetics and evolution studies. Recently, Song et al. developed a seed-free tool called MEANGS for de novo mitochondrial assembly from whole genome sequencing (WGS) data in animals, achieving highly accurate and intact assemblies. However, the suitability of this tool for marine fish remains unexplored. Additionally, we have concerns regarding the overlap sequences in their original results, which may impact downstream analyses. In this Letter to the Editor, the effectiveness of MEANGS in assembling mitogenomes of cartilaginous and ray-finned fish species was assessed. Moreover, we also discussed the appropriate utilization of MEANGS in mitogenome assembly, including the implementation of the data-cut function and circular detection module. Our observations indicated that with the utilization of these modules, MEANGS efficiently assembled complete and circularized mitogenomes, even when handling large WGS datasets. Therefore, we strongly recommend users employ the data-cut function and circular detection module when using MEANGS, as the former significantly reduces runtime and the latter aids in the removal of overlapped sequences for improved circularization. Furthermore, our findings suggested that approximately 2× coverage of clean WGS data was sufficient for MEANGS to assemble mitogenomes in marine fish species. Moreover, due to its seed-free nature, MEANGS can be deemed one of the most efficient software tools for assembling mitogenomes from animal WGS data, particularly in studies with limited species or genetic background information.

A comparative study of structural variant calling in WGS from Alzheimer's disease families.

Detecting structural variants (SVs) in whole-genome sequencing poses significant challenges. We present a protocol for variant calling, merging, genotyping, sensitivity analysis, and laboratory validation for generating a high-quality SV call set in whole-genome sequencing from the Alzheimer's Disease Sequencing Project comprising 578 individuals from 111 families. Employing two complementary pipelines, Scalpel and Parliament, for SV/indel calling, we assessed sensitivity through sample replicates (N = 9) with in silico variant spike-ins. We developed a novel metric, D-score, to evaluate caller specificity for deletions. The accuracy of deletions was evaluated by Sanger sequencing. We generated a high-quality call set of 152,301 deletions of diverse sizes. Sanger sequencing validated 114 of 146 detected deletions (78.1%). Scalpel excelled in accuracy for deletions ≤100 bp, whereas Parliament was optimal for deletions >900 bp. Overall, 83.0% and 72.5% of calls by Scalpel and Parliament were validated, respectively, including all 11 deletions called by both Parliament and Scalpel between 101 and 900 bp. Our flexible protocol successfully generated a high-quality deletion call set and a truth set of Sanger sequencing-validated deletions with precise breakpoints spanning 1-17,000 bp.

Wales Infants' and childreN's Genome Service (WINGS): providing rapid genetic diagnoses for unwell children.

INTRODUCTION: This study reviews the first 3 years of delivery of the first National Health Service (NHS)-commissioned trio rapid whole genome sequencing (rWGS) service for acutely unwell infants and children in Wales. METHODS: Demographic and phenotypic data were prospectively collected as patients and their families were enrolled in the Wales Infants' and childreN's Genome Service (WINGS). These data were reviewed alongside trio rWGS results. RESULTS: From April 2020 to March 2023, 82 families underwent WINGS, with a diagnostic yield of 34.1%. The highest diagnostic yields were noted in skeletal dysplasias, neurological or metabolic phenotypes. Mean time to reporting was 9 days. CONCLUSION: This study demonstrates that trio rWGS is having a positive impact on the care of acutely unwell infants and children in an NHS setting. In particular, the study shows that rWGS can be applied in an NHS setting, achieving a diagnostic yield comparable with the previously published diagnostic yields achieved in research settings, while also helping to improve patient care and management.

Evaluation of a custom designed hybridisation assay for whole genome sequencing of human adenoviruses direct from clinical samples.

BACKGROUND: Human Adenoviruses are a common cause of disease and can cause significant morbidity and mortality in immunocompromised patients. Nosocomial transmission events can occur with whole genome sequencing playing a crucial role. This study evaluates the performance of a custom designed SureSelectXT target enrichment assay based on 14 adenovirus genomes for sequencing direct from clinical samples. METHODS: Modifications were made to the SureSelectXT low input protocol to enhance performance for viral targets. Consensus sequences were generated using an in-house designed three stage bioinformatics pipeline. We assessed, percentage of on target reads, average depth of coverage and percentage genome coverage to determine assay performance across a range of sample matrices. RESULTS: Whole genome sequences were successfully generated for 91.6 % of samples assessed. Adenovirus DNA concentration was a good indicator of enrichment success. Highly specific enrichment was observed with only 6 % of samples showing < 50 % on target reads. Respiratory and faecal samples performed well where bloods showed higher levels of non-specific enrichment likely confounded by low adenovirus DNA concentrations. Protocol performance did not appear impacted by Adenovirus type or species. CONCLUSION: Overall performance of this modified SureSelectXT protocol appears in line with previously published works although there are some confounding factors requiring further investigation. The use of a small RNA bait set has the potential to reduce associated costs which can be prohibitive.

Lessons Learned from Translating Genome Sequencing to Clinical Routine: Understanding the Accuracy of a Diagnostic Pipeline.

The potential of genome sequencing (GS), which allows detection of almost all types of genetic variation across nearly the entire genome of an individual, greatly expands the possibility for diagnosing genetic disorders. The opportunities provided with this single test are enticing to researchers and clinicians worldwide for human genetic research as well as clinical application. Multiple studies have highlighted the advantages of GS for genetic variant discovery, emphasizing its added value for routine clinical use. We have implemented GS as first-line genetic testing for patients with rare diseases. Here, we report on our experiences in establishing GS as a reliable diagnostic method for almost all types of genetic disorders, from validating diagnostic accuracy of sequencing pipelines to clinical implementation in routine practice.

Whole genome sequencing in clinical practice.

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.

A prospective bacterial whole-genome-sequencing-based surveillance programme for comprehensive early detection of healthcare-associated infection transmission in paediatric oncology patients.

BACKGROUND: Bacterial whole-genome sequencing (WGS) and determination of genetic relatedness is an important tool for investigation of epidemiologically suspected outbreaks. AIM: This prospective cohort study evaluated a comprehensive, prospective bacterial WGS-based surveillance programme for early detection of transmission of most bacterial pathogens among patients at a paediatric oncology hospital. METHODS: Cultured bacterial isolates from clinical diagnostic specimens collected prospectively from both inpatient and outpatient encounters between January 2019 and December 2021 underwent routine WGS and core genome multi-locus sequence typing to determine isolates' relatedness. Previously collected isolates from January to December 2018 were retrospectively analysed for identification of prior or ongoing transmission. Multi-patient clusters were investigated to identify potential transmission events based on temporal and spatial epidemiological links and interventions were introduced. FINDINGS: A total of 1497 bacterial isolates from 1025 patients underwent WGS. A total of 259 genetically related clusters were detected, of which 18 (6.9%) multi-patient clusters involving 38 (3.7%) patients were identified. Sixteen clusters involved two patients each, and two clusters involved three patients. Following investigation, epidemiologically plausible transmission links were identified in five (27.8%) multi-patient clusters. None of the multi-patient clusters were suspected by conventional epidemiological surveillance. CONCLUSION: Bacterial WGS-based surveillance for early detection of hospital transmission detected several limited multi-patient clusters that were unrecognized by conventional epidemiological methods. Genomic surveillance helped efficiently focus interventions while reducing unnecessary investigations.

Selective whole-genome sequencing of Plasmodium parasites directly from blood samples by nanopore adaptive sampling.

IMPORTANCE: Malaria is caused by parasites of the genus Plasmodium, and reached a global disease burden of 247 million cases in 2021. To study drug resistance mutations and parasite population dynamics, whole-genome sequencing of patient blood samples is commonly performed. However, the predominance of human DNA in these samples imposes the need for time-consuming laboratory procedures to enrich Plasmodium DNA. We used the Oxford Nanopore Technologies' adaptive sampling feature to circumvent this problem and enrich Plasmodium reads directly during the sequencing run. We demonstrate that adaptive nanopore sequencing efficiently enriches Plasmodium reads, which simplifies and shortens the timeline from blood collection to parasite sequencing. In addition, we show that the obtained data can be used for monitoring genetic markers, or to generate nearly complete genomes. Finally, owing to its inherent mobility, this technology can be easily applied on-site in endemic areas where patients would benefit the most from genomic surveillance.

Leveraging whole-genome sequencing to estimate telomere length in plants.

Changes in telomere length are increasingly used to indicate species' response to environmental stress across diverse taxa. Despite this broad use, few studies have explored telomere length in plants. Thus, evaluation of new approaches for measuring telomeres in plants is needed. Rapid advances in sequencing approaches and bioinformatic tools now allow estimation of telomere content from whole-genome sequencing (WGS) data, a proxy for telomere length. While telomere content has been quantified extensively using quantitative polymerase chain reaction (qPCR) and WGS in humans, no study to date has compared the effectiveness of WGS in estimating telomere length in plants relative to qPCR approaches. In this study, we use 100 Populus clones re-sequenced using short-read Illumina sequencing to quantify telomere length comparing three different bioinformatic approaches (Computel, K-seek and TRIP) in addition to qPCR. Overall, telomere length estimates varied across different bioinformatic approaches, but were highly correlated across methods for individual genotypes. A positive correlation was observed between WGS estimates and qPCR, however, Computel estimates exhibited the greatest correlation. Computel incorporates genome coverage into telomere length calculations, suggesting that genome coverage is likely important to telomere length quantification when using WGS data. Overall, telomere estimates from WGS provided greater precision and accuracy of telomere length estimates relative to qPCR. The findings suggest WGS is a promising approach for assessing telomere length and, as the field of telomere ecology evolves, may provide added value to assaying response to biotic and abiotic environments for plants needed to accelerate plant breeding and conservation management.

Los cambios en la longitud de los telómeros se utilizan cada vez más para indicar la respuesta de las especies al estrés ambiental en diversos taxones. A pesar de este amplio uso, pocos estudios han explorado la longitud de los telómeros en las plantas. Por lo tanto, es necesario evaluar nuevos enfoques para medir los telómeros en las plantas. Los rápidos avances en los enfoques de secuenciación y las herramientas bioinformáticas ahora permiten estimar el contenido de los telómeros a partir de datos de secuenciación del genoma completo (WGS), un indicador de la longitud de los telómeros. Si bien el contenido de los telómeros se ha cuantificado ampliamente mediante la reacción en cadena de la polimerasa cuantitativa (qPCR) y WGS en humanos, ningún estudio hasta la fecha ha comparado la efectividad de WGS para estimar la longitud de los telómeros en plantas en relación con los enfoques de qPCR. En este estudio, utilizamos cien clones de álamos (Populus) resecuenciados mediante secuenciación Illumina de lectura corta para cuantificar la longitud de los telómeros comparando tres diferentes enfoques bioinformáticos, Computel, K-seek y TRIP, además de qPCR. En general, las estimaciones de la longitud de los telómeros variaron según los diferentes enfoques bioinformáticos, pero la longitud de los telómeros estuvo altamente correlacionada entre los métodos para genotipos individuales. Se observó una correlación positiva entre las estimaciones de WGS y qPCR; sin embargo, las estimaciones de Computel mostraron la mayor correlación. Computel incorpora la cobertura del genoma en los cálculos de la longitud de los telómeros, lo que sugiere que la cobertura del genoma probablemente es importante para la cuantificación de la longitud de los telómeros cuando se utilizan datos de WGS. En general, las estimaciones de los telómeros de WGS proporcionaron mayor precisión y exactitud de las estimaciones de la longitud de los telómeros en relación con la qPCR. Los hallazgos sugieren que WGS es un enfoque prometedor para evaluar la longitud de los telómeros y, a medida que evoluciona el campo de la ecología de los telómeros, puede proporcionar un valor agregado para analizar la respuesta a ambientes bióticos y abióticos de las plantas necesarias para acelerar los programas de mejoramiento genético y conservación.

Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning.

Contamination with food-borne pathogens, such as Listeria monocytogenes, remains a big concern for food safety. Hence, rigorous and continuous microbial surveillance is a standard procedure. At this point, however, the food industry and authorities only focus on detection of Listeria monocytogenes without characterization of individual strains into groups of more or less concern. As whole genome sequencing (WGS) gains increasing interest in the industry, this methodology presents an opportunity to obtain finer resolution of microbial traits such as virulence. Within this study, we therefore aimed to explore the use of WGS in combination with Machine Learning (ML) to predict L. monocytogenes virulence potential on a sub-species level. The WGS datasets used in this study for ML model training consisted of i) national surveillance isolates (n = 169, covering 38 MLST types) and ii) publicly available isolates acquired through the GenomeTrakr network (n = 2880, spanning 80 MLST types). We used the clinical frequency, i.e., ratio of the number of clinical isolates to total amount of isolates, as estimate for virulence potential. The predictive performance of input features from three different genomic levels (i.e., virulence genes, pan-genome genes, and single nucleotide polymorphisms (SNPs)) and six machine learning algorithms (i.e., Support Vector Machine with a linear kernel, Support Vector Machine with a radial kernel, Random Forrest, Neural Networks, LogitBoost, and Majority Voting) were compared. Our machine learning models predicted sub-species virulence potential with nested cross-validation F1-scores up to 0.88 for the majority voting classifier trained on national surveillance data and using pan-genome genes as input features. The validation of the pre-trained ML models based on 101 previously in vivo studied isolates resulted in F1-scores up to 0.76. Furthermore, we found that the more rapid and less computationally intensive raw read alignment yields comparably accurate models as de novo assembly. The results of our study suggest that a majority voting classifier trained on pan-genome genes is the best and most robust choice for the prediction of clinical frequency. Our study contributes to more rapid and precise characterization of L. monocytogenes virulence and its variation on a sub-species level. We further demonstrated a possible application of WGS data in the context of microbial hazard characterization for food safety. In the future, predictive models may assist case-specific microbial risk management in the food industry. The python code, pre-trained models, and prediction pipeline are deposited at (https://github.com/agmei/LmonoVirulenceML).

Low-pass whole genome sequencing is a reliable and cost-effective approach for copy number variant analysis in the clinical setting.

INTRODUCTION: Next generation sequencing technology has greatly reduced the cost and time required for sequencing a genome. An approach that is rapidly being adopted as an alternative method for CNV analysis is the low-pass whole genome sequencing (LP-WGS). Here, we evaluated the performance of LP-WGS to detect copy number variants (CNVs) in clinical cytogenetics. MATERIALS AND METHODS: DNA samples with known CNVs detected by chromosomal microarray analyses (CMA) were selected for comparison and used as positive controls; our panel included 44 DNA samples (12 prenatal and 32 postnatal), comprising a total of 55 chromosome imbalances. The selected cases were chosen to provide a wide range of clinically relevant CNVs, the vast majority being associated with intellectual disability or recognizable syndromes. The chromosome imbalances ranged in size from 75 kb to 90.3 Mb, including aneuploidies and two cases of mosaicism. RESULTS: All CNVs were successfully detected by LP-WGS, showing a high level of consistency and robust performance of the sequencing method. Notably, the size of chromosome imbalances detected by CMA and LP-WGS were compatible between the two different platforms, which indicates that the resolution and sensitivity of the LP-WGS approach are at least similar to those provided by CMA. DISCUSSION: Our data show the potential use of LP-WGS to detect CNVs in clinical diagnosis and confirm the method as an alternative for chromosome imbalances detection. The diagnostic effectiveness and feasibility of LP-WGS, in this technical validation study, were evidenced by a clinically representative dataset of CNVs that allowed a systematic assessment of the detection power and the accuracy of the sequencing approach. Further, since the software used in this study is commercially available, the method can easily be tested and implemented in a routine diagnostic setting.

From Whole Genome Shotgun Sequencing to Viral Community Profiling: The ViromeScan Tool.

ViromeScan is an innovative metagenomic analysis tool that allows characterizing the taxonomy of viral communities from raw data of metagenomics sequencing, efficiently denoising samples from reads of other microorganisms. This means that users can use the same shotgun metagenomic sequencing data to fully characterize complex microbial ecosystems, including bacteria and viruses. Here we describe the analysis procedure with some examples, illustrating the processes computed by ViromeScan from raw data to the final output.