Reference standards for next-generation sequencing

Diagnostic yield of exome and genome sequencing after non-diagnostic multi-gene panels in patients with single-system diseases

BACKGROUND

Though next-generation sequencing (NGS) tests like exome sequencing (ES), genome sequencing (GS), and panels derived from exome and genome data (EGBP) are effective for rare diseases, the ideal diagnostic approach is debated. Limited research has explored reanalyzing raw ES and GS data post-negative EGBP results for diagnostics.

RESULTS

We analyzed complete ES/GS raw sequencing data from Mayo Clinic's Program for Rare and Undiagnosed Diseases (PRaUD) patients to assess whether supplementary findings could augment diagnostic yield. ES data from 80 patients (59 adults) and GS data from 20 patients (10 adults), averaging 43 years in age, were analyzed. Most patients had renal (n=44) and auto-inflammatory (n=29) phenotypes. Ninety-six cases had negative findings and in four cases additional genetic variants were found, including a variant related to a recently described disease (RRAGD-related hypomagnesemia), a variant missed due to discordant inheritance pattern (COL4A3), a variant with high allelic frequency (NPHS2) in the general population, and a variant associated with an initially untargeted phenotype (HNF1A).

CONCLUSION

ES and GS show diagnostic yields comparable to EGBP for single-system diseases. However, EGBP's limitations in detecting new disease-associated genes underscore the necessity for periodic updates.

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution

A main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.

A universal molecular control for DNA, mRNA and protein expression

The expression of genes encompasses their transcription into mRNA followed by translation into protein. In recent years, next-generation sequencing and mass spectrometry methods have profiled DNA, RNA and protein abundance in cells. However, there are currently no reference standards that are compatible across these genomic, transcriptomic and proteomic methods, and provide an integrated measure of gene expression. Here, we use synthetic biology principles to engineer a multi-omics control, termed pREF, that can act as a universal molecular standard for next-generation sequencing and mass spectrometry methods. The pREF sequence encodes 21 synthetic genes that can be in vitro transcribed into spike-in mRNA controls, and in vitro translated to generate matched protein controls. The synthetic genes provide qualitative controls that can measure sensitivity and quantitative accuracy of DNA, RNA and peptide detection. We demonstrate the use of pREF in metagenome DNA sequencing and RNA sequencing experiments and evaluate the quantification of proteins using mass spectrometry. Unlike previous spike-in controls, pREF can be independently propagated and the synthetic mRNA and protein controls can be sustainably prepared by recipient laboratories using common molecular biology techniques. Together, this provides a universal synthetic standard able to integrate genomic, transcriptomic and proteomic methods.

A novel synthetic nucleic acid mixture for quantification of microbes by mNGS

Metagenomic next-generation sequencing (mNGS) provides considerable advantages in identifying emerging and re-emerging, difficult-to-detect and co-infected pathogens; however, the clinical application of mNGS remains limited primarily due to the lack of quantitative capabilities. This study introduces a novel approach, KingCreate-Quantification (KCQ) system, for quantitative analysis of microbes in clinical specimens by mNGS, which co-sequence the target DNA extracted from the specimens along with a set of synthetic dsDNA molecules used as Internal-Standard (IS). The assay facilitates the conversion of microbial reads into their copy numbers based on IS reads utilizing a mathematical model proposed in this study. The performance of KCQ was systemically evaluated using commercial mock microbes with varying IS input amounts, different proportions of human genomic DNA, and at varying amounts of sequence analysis data. Subsequently, KCQ was applied in microbial quantitation in 36 clinical specimens including blood, bronchoalveolar lavage fluid, cerebrospinal fluid and oropharyngeal swabs. A total of 477 microbe genetic fragments were screened using the bioinformatic system. Of these 83 fragments were quantitatively compared with digital droplet PCR (ddPCR), revealing a correlation coefficient of 0.97 between the quantitative results of KCQ and ddPCR. Our study demonstrated that KCQ presents a practical approach for the quantitative analysis of microbes by mNGS in clinical samples.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Experience of copy number variation sequencing applied in spontaneous abortion

PURPOSE

We evaluated the value of copy number variation sequencing (CNV-seq) and quantitative fluorescence (QF)-PCR for analyzing chromosomal abnormalities (CA) in spontaneous abortion specimens.

METHODS

A total of 650 products of conception (POCs) were collected from spontaneous abortion between April 2018 and May 2020. CNV-seq and QF-PCR were performed to determine the characteristics and frequencies of copy number variants (CNVs) with clinical significance. The clinical features of the patients were recorded.

RESULTS

Clinically significant chromosomal abnormalities were identified in 355 (54.6%) POCs, of which 217 (33.4%) were autosomal trisomies, 42(6.5%) were chromosomal monosomies and 40 (6.2%) were pathogenic CNVs (pCNVs). Chromosomal trisomy occurs mainly on chromosomes 15, 16, 18, 21and 22. Monosomy X was not associated with the maternal or gestational age. The frequency of chromosomal abnormalities in miscarriages from women with a normal live birth history was 55.3%; it was 54.4% from women without a normal live birth history (P > 0.05). There were no significant differences among women without, with 1, and with ≥ 2 previous miscarriages regarding the rate of chromosomal abnormalities (P > 0.05); CNVs were less frequently detected in women with advanced maternal age than in women aged ≤ 29 and 30-34 years (P < 0.05).

CONCLUSION

Chromosomal abnormalities are the most common cause of pregnancy loss, and maternal and gestational ages are strongly associated with fetal autosomal trisomy aberrations. Embryo chromosomal examination is recommended regardless of the gestational age, modes of conception or previous abortion status.

Development of Human Rhinovirus RNA Reference Material Using Digital PCR

The human rhinovirus (RV) is a positive-stranded RNA virus that causes respiratory tract diseases affecting both the upper and lower halves of the respiratory system. RV enhances its replication by concentrating RNA synthesis within a modified host membrane in an intracellular compartment. RV infections often occur alongside infections caused by other respiratory viruses, and the RV virus may remain asymptomatic for extended periods. Alongside qualitative detection, it is essential to accurately quantify RV RNA from clinical samples to explore the relationships between RV viral load, infections caused by the virus, and the resulting symptoms observed in patients. A reference material (RM) is required for quality evaluation, the performance evaluation of molecular diagnostic products, and evaluation of antiviral agents in the laboratory. The preparation process for the RM involves creating an RV RNA mixture by combining RV viral RNA with RNA storage solution and matrix. The resulting RV RNA mixture is scaled up to a volume of 25 mL, then dispensed at 100 µL per vial and stored at -80 °C. The process of measuring the stability and homogeneity of RV RMs was conducted by employing reverse transcription droplet digital polymerase chain reaction (RT-ddPCR). Digital PCR is useful for the analysis of standards and can help to improve measurement compatibility: it represents the equivalence of a series of outcomes for reference materials and samples being analyzed when a few measurement procedures are employed, enabling objective comparisons between quantitative findings obtained through various experiments. The number of copies value represents a measured result of approximately 1.6 × 105 copies/μL. The RM has about an 11% bottle-to-bottle homogeneity and shows stable results for 1 week at temperatures of 4 °C and -20 °C and for 12 months at a temperature of -80 °C. The developed RM can enhance the dependability of RV molecular tests by providing a precise reference value for the absolute copy number of a viral target gene. Additionally, it can serve as a reference for diverse studies.

Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance

BACKGROUND

Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome.

RESULTS

We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data.

CONCLUSIONS

The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Establishing reference material for the quest towards standardization in environmental microbial metagenomic studies

Breakthroughs in DNA-based technologies, especially in metagenomic sequencing, have drastically enhanced researchers' ability to explore environmental microbiome and the associated interplays within. However, as new methodologies are being actively developed for improvements in different aspects, metagenomic workflows become diversified and heterogeneous. Through a single-variable control approach, we quantified the microbial profiling variations arising from 6 common technical variables associated with metagenomic workflows for both simple and complex samples. The incurred variations were constantly the lowest in replicates of DNA isolation and DNA sequencing library construction. Different DNA extraction kits often caused the highest variation among all the tested variables. Additionally, sequencing run batch was an important source of variability for targeted platforms. As such, the development of an environmental reference material for complex environmental samples could be beneficial in benchmarking accrued non-biological variability within and between protocols and insuring reliable and reproducible sequencing outputs immediately upstream of bioinformatic analysis. To develop an environment reference material, sequencing of a well-homogenized environmental sample composed of activated sludge was performed using different pre-analytical assays in replications. In parallel, a certified mock community was processed and sequenced. Assays were ranked based on the reconstruction of the theoretical mock community profile. The reproducibility of the best-performing assay and the microbial profile of the reference material were further ascertained. We propose the adoption of our complex environmental reference material, which could reflect the degree of diversity in environmental microbiome studies, to facilitate accurate, reproducible, and comparable environmental metagenomics-based studies.

Climate change drives loss of bacterial gut mutualists at the expense of host survival in wild meerkats

Climate change and climate-driven increases in infectious disease threaten wildlife populations globally. Gut microbial responses are predicted to either buffer or exacerbate the negative impacts of these twin pressures on host populations. However, examples that document how gut microbial communities respond to long-term shifts in climate and associated disease risk, and the consequences for host survival, are rare. Over the past two decades, wild meerkats inhabiting the Kalahari have experienced rapidly rising temperatures, which is linked to the spread of tuberculosis (TB). We show that over the same period, the faecal microbiota of this population has become enriched in Bacteroidia and impoverished in lactic acid bacteria (LAB), a group of bacteria including Lactococcus and Lactobacillus that are considered gut mutualists. These shifts occurred within individuals yet were compounded over generations, and were better explained by mean maximum temperatures than mean rainfall over the previous year. Enriched Bacteroidia were additionally associated with TB exposure and disease, the dry season and poorer body condition, factors that were all directly linked to reduced future survival. Lastly, abundances of LAB taxa were independently and positively linked to future survival, while enriched taxa did not predict survival. Together, these results point towards extreme temperatures driving an expansion of a disease-associated pathobiome and loss of beneficial taxa. Our study provides the first evidence from a longitudinally sampled population that climate change is restructuring wildlife gut microbiota, and that these changes may amplify the negative impacts of climate change through the loss of gut mutualists. While the plastic response of host-associated microbiotas is key for host adaptation under normal environmental fluctuations, extreme temperature increases might lead to a breakdown of coevolved host-mutualist relationships.

Establishment of health related physical fitness evaluation system for school adolescents aged 12-16 in Pakistan: a cross-sectional study

Background

The decline in adolescent physical fitness is a significant global public health concern, and Pakistan is no exception. The country's absence of a health-related physical fitness (HRPF) evaluation system has compounded this issue. To bridge this gap, this study aims to develop a scientifically-based HRPF evaluation system for the adolescent population that meets international standards. The evaluation system identifies at-risk children and improves adolescent health outcomes, including obesity, cardiovascular and musculoskeletal disorders, chronic diseases, and psychological illnesses, through crucial physical fitness evaluation. This study specifically aims to establish an HRPF evaluation system for school adolescents aged 12-16 in Pakistan.

Methods

A cross-sectional study was conducted among 2,970 school adolescents aged 12-16 years in the South Punjab, Pakistan. The study used a stratified sampling technique to select participants. The HRPF evaluation system included four components: cardiorespiratory endurance, core muscular endurance, muscular strength, and body composition. Data were collected through standardized tests and anthropometric measurements.

Results

The study's results indicated that the HRPF evaluation scoring system was feasible and valid for evaluating the HRPF of school adolescents in the South Punjab region of Pakistan. The results of the evaluation system categorized participants into five groups based on their performance: excellent (6.2%), good (24.9%), medium (50.7%), poor (17%), and very poor (1.2%).

Conclusion

The study establishes an HRPF evaluation system for Pakistani school adolescents. This system lays the foundation for implementing effective strategies to improve their physical health. The findings offer valuable insights to policymakers, health professionals, and educators, enabling them to promote fitness and devise impactful interventions for enhancing HRPF in this population.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

EMQN: Recommendations for genetic testing in inherited cardiomyopathies and arrhythmias

Inherited cardiomyopathies and arrhythmias (ICAs) are a prevalent and clinically heterogeneous group of genetic disorders that are associated with increased risk of sudden cardiac death and heart failure. Making a genetic diagnosis can inform the management of patients and their at-risk relatives and, as such, molecular genetic testing is now considered an integral component of the clinical care pathway. However, ICAs are characterised by high genetic and allelic heterogeneity, incomplete / age-related penetrance, and variable expressivity. Therefore, despite our improved understanding of the genetic basis of these conditions, and significant technological advances over the past two decades, identifying and recognising the causative genotype remains challenging. As clinical genetic testing for ICAs becomes more widely available, it is increasingly important for clinical laboratories to consolidate existing knowledge and experience to inform and improve future practice. These recommendations have been compiled to help clinical laboratories navigate the challenges of ICAs and thereby facilitate best practice and consistency in genetic test provision for this group of disorders. General recommendations on internal and external quality control, referral, analysis, result interpretation, and reporting are described. Also included are appendices that provide specific information pertinent to genetic testing for hypertrophic, dilated, and arrhythmogenic right ventricular cardiomyopathies, long QT syndrome, Brugada syndrome, and catecholaminergic polymorphic ventricular tachycardia.

Vibrio-Sequins - dPCR-traceable DNA standards for quantitative genomics of Vibrio spp

BACKGROUND

Vibrio spp. are a diverse group of ecologically important marine bacteria responsible for several foodborne outbreaks of gastroenteritis around the world. Their detection and characterization are moving away from conventional culture-based methods towards next generation sequencing (NGS)-based approaches. However, genomic methods are relative in nature and suffer from technical biases arising from library preparation and sequencing. Here, we introduce a quantitative NGS-based method that enables the quantitation of Vibrio spp. at the limit of quantification (LOQ) through artificial DNA standards and their absolute quantification via digital PCR (dPCR).

RESULTS

We developed six DNA standards, called Vibrio-Sequins, together with optimized TaqMan assays for their quantification in individually sequenced DNA libraries via dPCR. To enable Vibrio-Sequin quantification, we validated three duplex dPCR methods to quantify the six targets. LOQs were ranging from 20 to 120 cp/µl for the six standards, whereas the limit of detection (LOD) was ~ 10 cp/µl for all six assays. Subsequently, a quantitative genomics approach was applied to quantify Vibrio-DNA in a pooled DNA mixture derived from several Vibrio species in a proof-of-concept study, demonstrating the increased power of our quantitative genomic pipeline through the coupling of NGS and dPCR.

CONCLUSIONS

We significantly advance existing quantitative (meta)genomic methods by ensuring metrological traceability of NGS-based DNA quantification. Our method represents a useful tool for future metagenomic studies aiming at quantifying microbial DNA in an absolute manner. The inclusion of dPCR into sequencing-based methods supports the development of statistical approaches for the estimation of measurement uncertainties (MU) for NGS, which is still in its infancy.

Design, Fabrication, and Applications of SERS Substrates for Food Safety Detection: Review

Sustainable and safe food is an important issue worldwide, and it depends on cost-effective analysis tools with good sensitivity and reality. However, traditional standard chemical methods of food safety detection, such as high-performance liquid chromatography (HPLC), gas chromatography (GC), and tandem mass spectrometry (MS), have the disadvantages of high cost and long testing time. Those disadvantages have prevented people from obtaining sufficient risk information to confirm the safety of their products. In addition, food safety testing, such as the bioassay method, often results in false positives or false negatives due to little rigor preprocessing of samples. So far, food safety analysis currently relies on the enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), HPLC, GC, UV-visible spectrophotometry, and MS, all of which require significant time to train qualified food safety testing laboratory operators. These factors have hindered the development of rapid food safety monitoring systems, especially in remote areas or areas with a relative lack of testing resources. Surface-enhanced Raman spectroscopy (SERS) has emerged as one of the tools of choice for food safety testing that can overcome these dilemmas over the past decades. SERS offers advantages over chromatographic mass spectrometry analysis due to its portability, non-destructive nature, and lower cost implications. However, as it currently stands, Raman spectroscopy is a supplemental tool in chemical analysis, reinforcing and enhancing the completeness and coverage of the food safety analysis system. SERS combines portability with non-destructive and cheaper detection costs to gain an advantage over chromatographic mass spectrometry analysis. SERS has encountered many challenges in moving toward regulatory applications in food safety, such as quantitative accuracy, poor reproducibility, and instability of large molecule detection. As a result, the reality of SERS, as a screening tool for regulatory announcements worldwide, is still uncommon. In this review article, we have compiled the current designs and fabrications of SERS substrates for food safety detection to unify all the requirements and the opportunities to overcome these challenges. This review is expected to improve the interest in the sensing field of SERS and facilitate the SERS applications in food safety detection in the future.

Haack T, Eggermann T. Quality assurance within the context of genome diagnostics (a german perspective)

The rapid and dynamic implementation of Next-Generation Sequencing (NGS)-based assays has revolutionized genetic testing, and in the near future, nearly all molecular alterations of the human genome will be diagnosable via massive parallel sequencing. While this progress will further corroborate the central role of human genetics in the multidisciplinary management of patients with genetic disorders, it must be accompanied by quality assurance measures in order to allow the safe and optimal use of knowledge ascertained from genome diagnostics. To achieve this, several valuable tools and guidelines have been developed to support the quality of genome diagnostics. In this paper, authors with experience in diverse aspects of genomic analysis summarize the current status of quality assurance in genome diagnostics, with the aim of facilitating further standardization and quality improvement in one of the core competencies of the field.

Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests

High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.

A new method for mutation inducing in rice by using DC electrophoresis bath and its mutagenic effects

Mutation breeding is a significant means of increasing breeding efficiency and accelerating breeding process. In present study, we explored a new method for mutations inducing in rice (Oryza sativa L.) by using direct current electrophoresis bath (DCEB). The results showed that 20 mM NaCl solution is the optimal buffer, and the mortality of rice seeds followed an upward trend with increasing voltage and processing time of DCEB. By exploring the mutagenic effects of γ-irradiation and DCEB on seed vigor and physiological damages, we found that the physiological damages induced by DCEB on seed vigor were significant compared with that by γ-irradiation. We screened two mutants with low filled grain percentage and one mutant with abnormal hull from the M₂ generations. These three mutants were confirmed to be authentic mutants based on 48 SSR markers followed by the protocol NY/T 1433-2014. Whole-genome resequencing detected a total of 503 and 537 polymorphisms in the two mutants, respectively, and the DCEB mutagenesis induced mainly InDel variants, while the exon region of mutant genes occupied a large proportion, especially the SNP variants, which occupied about 20% of the mutation sites in the exon region.

Variant calling and benchmarking in an era of complete human genome sequences

Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.

Clinical implications of ctDNA for EGFR-TKIs as first-line treatment in NSCLC

PURPOSE

This study aimed to explore the clinical implications of ctDNA for epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) as the first-line treatment in EGFR-mutated (EGFRm) non-small cell lung cancer (NSCLC) in real-world settings.

METHODS

A total of 122 patients with NSCLC who underwent tissue and liquid next generation sequencing (NGS) tests were included. 66 patients with detected EGFR mutation in both tumor-tissue and plasma were included into the EGFR^{t+, p+} group, and 56 patients with EGFR mutation detected only in tumor-tissue were included into the EGFR^{t+, p-} group. The differences in clinical characteristics, concomitant mutations and prognosis between the two groups were compared.

RESULTS

The detection rate of the EGFR^{t+, p+} group was 54.1% (66/122). EGFR^{t+, p+} in the NGS test was particularly relevant to the size of tumors, liver metastasis, bone metastasis and TP53 mutation. In patients with TP53 mutation in ctDNA, the detection rate of EGFR mutation in ctDNA was up to 91.3%. EGFR^{t+, p+} could be an independent prognostic factor for first-line EGFR-TKIs treatment. Combination therapy seems to be a promising approach to improve the outcome for EGFR^{t+, p+} (P = 0.017, HR 0.509 [95% CI 0.288-0.897]). Moreover, the combination of TP53 mutated status and EGFRm status in plasma showed a better completion of risk stratification for PFS (Log-rank P < 0.001).

CONCLUSIONS

Co-detection of EGFR mutation in tumor tissue and plasma is an independent prognostic factor for first-line EGFR-TKIs treatment. Moreover, combination therapy could be a promising approach to improve the outcome for these patients.

Reproductive genetics laboratory may impact euploid blastocyst and live birth rates: a comparison of 4 national laboratories' PGT-A results from vitrified donor oocytes

OBJECTIVE

To evaluate potential variation in the euploid blastocyst rate and live birth rate (LBR) per single frozen euploid blastocyst transfer, among 4 unique United States reproductive genetics laboratories. Analyses were limited to blastocysts derived from vitrified donor oocytes, to minimize variation in oocyte quality.

DESIGN

Retrospective cohort study from 2016 to 2020.

SETTING

Donor Egg Bank Database.

PATIENT(S)

Patients undergoing in vitro fertilization with donor oocytes. We excluded patients with uterine factor, male factor, or surgically extracted sperm. Only healthy women <34 years old were accepted as oocyte donors.

INTERVENTION(S)

Next generation sequencing platforms for chromosomal analysis MAIN OUTCOME MEASURE(S): Euploid blastocyst rate and LBR per euploid transfer. Secondary outcomes included the rate of aneuploidy, mosaic calls, biochemical pregnancy loss, and miscarriage rate.

RESULT(S)

A total of 2,633 embryos were included. Four laboratories had >200 embryos tested. Euploid blastocyst rate was significantly higher in laboratory A (73.6%) vs. B (63.3%), C (60.9%), and D (52.3%). Mosaic rate was significantly lower for Laboratories B (2.8%) and C (5.5%) vs. Laboratories A (9.9%) and D (11.5). The LBR was significantly higher in laboratory A (58.73%) vs. laboratory D (47.3%). There were no significant differences in the implantation or miscarriage rate among laboratories.

CONCLUSION(S)

In this large study, controlling for oocyte quality, some preimplantation genetic testing for aneuploidy (PGT-A) laboratories report a significantly higher euploid blastocyst rate with concurrent higher LBR. This type of comparison is important as it provides insight into the role of the PGT-A process in outcomes. Further research is needed to evaluate the differences in laboratory techniques and bioinformatic algorithms accounting for variable outcomes across PGT-A laboratories.

Whole-Genome Sequencing of Six Neglected Arboviruses Circulating in Africa Using Sequence-Independent Single Primer Amplification (SISPA) and MinION Nanopore Technologies

On the African continent, a large number of arthropod-borne viruses (arboviruses) with zoonotic potential have been described, and yet little is known of most of these pathogens, including their actual distribution or genetic diversity. In this study, we evaluated as a proof-of-concept the effectiveness of the nonspecific sequencing technique sequence-independent single primer amplification (SISPA) on third-generation sequencing techniques (MinION sequencing, Oxford Nanopore Technologies, Oxford, UK) by comparing the sequencing results from six different samples of arboviruses known to be circulating in Africa (Crimean-Congo hemorrhagic fever virus (CCHFV), Rift Valley fever virus (RVFV), Dugbe virus (DUGV), Nairobi sheep disease virus (NSDV), Middleburg virus (MIDV) and Wesselsbron virus (WSLV)). All sequenced samples were derived either from previous field studies or animal infection trials. Using this approach, we were able to generate complete genomes for all six viruses without the need for virus-specific whole-genome PCRs. Higher Cq values in diagnostic RT-qPCRs and the origin of the samples (from cell culture or animal origin) along with their quality were found to be factors affecting the success of the sequencing run. The results of this study may stimulate the use of metagenomic sequencing approaches, contributing to a better understanding of the genetic diversity of neglected arboviruses.

Library adaptors with integrated reference controls improve the accuracy and reliability of nanopore sequencing

Library adaptors are short oligonucleotides that are attached to RNA and DNA samples in preparation for next-generation sequencing (NGS). Adaptors can also include additional functional elements, such as sample indexes and unique molecular identifiers, to improve library analysis. Here, we describe Control Library Adaptors, termed CAPTORs, that measure the accuracy and reliability of NGS. CAPTORs can be integrated within the library preparation of RNA and DNA samples, and their encoded information is retrieved during sequencing. We show how CAPTORs can measure the accuracy of nanopore sequencing, evaluate the quantitative performance of metagenomic and RNA sequencing, and improve normalisation between samples. CAPTORs can also be customised for clinical diagnoses, correcting systematic sequencing errors and improving the diagnosis of pathogenic BRCA1/2 variants in breast cancer. CAPTORs are a simple and effective method to increase the accuracy and reliability of NGS, enabling comparisons between samples, reagents and laboratories, and supporting the use of nanopore sequencing for clinical diagnosis.

Nucleotide-based genetic networks: Methods and applications

Genomic variations have been acclaimed as among the key players in understanding the biological mechanisms behind migration, evolution, and adaptation to extreme conditions. Due to stochastic evolutionary forces, the frequency of polymorphisms is affected by changes in the frequency of nearby polymorphisms in the same DNA sample, making them connected in terms of evolution. This article presents all the ingredients to understand the cumulative effects and complex behaviors of genetic variations in the human mitochondrial genome by analyzing co-occurrence networks of nucleotides, and shows key results obtained from such analyses. The article emphasizes recent investigations of these co-occurrence networks, describing the role of interactions between nucleotides in fundamental processes of human migration and viral evolution. The corresponding co-mutation-based genetic networks revealed genetic signatures of human adaptation in extreme environments. This article provides the methods of constructing such networks in detail, along with their graph-theoretical properties, and applications of the genomic networks in understanding the role of nucleotide co-evolution in evolution of the whole genome.

Gut microbiota individuality is contingent on temporal scale and age in wild meerkats

Inter-individual differences in gut microbiota composition are hypothesized to generate variation in host fitness-a premise for the evolution of host-gut microbe symbioses. However, recent evidence suggests that gut microbial communities are highly dynamic, challenging the notion that individuals harbour unique gut microbial phenotypes. Leveraging a long-term dataset of wild meerkats, we reconcile these concepts by demonstrating that the relative importance of identity for shaping gut microbiota phenotypes depends on the temporal scale. Across meerkat lifespan, year-to-year variation overshadowed the effects of identity and social group in predicting gut microbiota composition, with identity explaining on average less than 2% of variation. However, identity was the strongest predictor of microbial phenotypes over short sampling intervals (less than two months), predicting on average 20% of variation. The effect of identity was also dependent on meerkat age, with the gut microbiota becoming more individualized and stable as meerkats aged. Nevertheless, while the predictive power of identity was negligible after two months, gut microbiota composition remained weakly individualized compared to that of other meerkats for up to 1 year. These findings illuminate the degree to which individualized gut microbial signatures can be expected, with important implications for the time frames over which gut microbial phenotypes may mediate host physiology, behaviour and fitness in natural populations.

A unique Levey-Jennings control chart used for internal quality control in human papillomavirus detection

OBJECTIVE

The purpose of this study was to provide an updated estimate of the prevalences of different types of human papillomavirus (HPV) in females in Chaoshan District and to establish an internal quality control (IQC) method for excluding false-positive results in HPV detection by using the Levey-Jennings control chart.

METHOD

HPV types were detected in 23,762 cervical samples by using PCR membrane hybridization. The means and standard deviations (SDs) of the positive rates were calculated, the Levey-Jennings chart was plotted, and the rules for "out of control" and "warning" were established. A set of standardized IQC for HPV DNA tests was developed based on the values and Levey-Jennings charts.

RESULT

In 466 batches, the positive rate exceeded the 1 + 2SD rule 24 times, but there was no consecutive exceedance, which was considered "in control". When the positive rate exceeded the 1 + 3SD rule 8 times with consecutive exceedance, it was considered "out of control". Further examination revealed that detections showing "out of control" had an undesirable random error, indicating that contamination may occur due to improper operation.

CONCLUSION

This unique Levey-Jennings control chart is a practical method for eliminating false-positive results in HPV DNA detection and should be widely applicable in molecular diagnostic laboratories.

Selective ablation of 3' RNA ends and processive RTs facilitate direct cDNA sequencing of full-length host cell and viral transcripts

Alternative splicing (AS) is necessary for viral proliferation in host cells and a critical regulatory component of viral gene expression. Conventional RNA-seq approaches provide incomplete coverage of AS due to their short read lengths and are susceptible to biases and artifacts introduced in prevailing library preparation methodologies. Moreover, viral splicing studies are often conducted separately from host cell transcriptome analysis, precluding an assessment of the viral manipulation of host splicing machinery. To address current limitations, we developed a quantitative full-length direct cDNA sequencing strategy to simultaneously profile viral and host cell transcripts. This nanopore-based approach couples processive reverse transcriptases with a novel one-step chemical ablation of 3' RNA ends (termed CASPR), which decreases ribosomal RNA reads and enriches polyadenylated coding sequences. We extensively validate our approach using synthetic reference transcripts and show that CASPR doubles the breadth of coverage per transcript and increases detection of long transcripts (>4 kb), while being functionally equivalent to PolyA+ selection for transcript quantification. We used our approach to interrogate host cell and HIV-1 transcript dynamics during viral reactivation and identified novel putative HIV-1 host factors containing exon skipping or novel intron retentions and delineated the HIV-1 transcriptional state associated with these differentially regulated host factors.

CleanSeq: A Pipeline for Contamination Detection, Cleanup, and Mutation Verifications from Microbial Genome Sequencing Data

Contaminations frequently occur in bacterial cultures, which significantly affect the reproducibility and reliability of the results from whole-genome sequencing (WGS). Decontaminated WGS data with clean reads is the only desirable source for detecting possible variants correctly. Improvements in bioinformatics are essential to analyze the contaminated WGS dataset. Existing pipelines usually contain contamination detection, decontamination, and variant calling separately. The efficiency and results from existing pipelines fluctuate since distinctive computational models and parameters are applied. It is then promising to develop a bioinformatical tool containing functions to discriminate and remove contaminated reads and improve variant calling from clean reads. In this study, we established a Python-based pipeline named CleanSeq for automatic detection and removal of contaminating reads, analyzing possible genome variants with proper verifications via local re-alignments. The application and reproducibility are proven in either simulated, publicly available datasets or actual genome sequencing reads from our experimental evolution study in Escherichia coli. We successfully obtained decontaminated reads, called out all seven consistent mutations from the contaminated bacterial sample, and derived five colonies. Collectively, the results demonstrated that CleanSeq could effectively process the contaminated samples to achieve decontaminated reads, based on which reliable results (i.e., variant calling) could be obtained.

Metagenomics-enabled microbial surveillance

Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.

Reference Samples to Compare Next-Generation Sequencing Test Performance for Oncology Therapeutics and Diagnostics

OBJECTIVES

Diversity of laboratory-developed tests (LDTs) using next-generation sequencing (NGS) raises concerns about their accuracy for selection of targeted therapies. A working group developed a pilot study of traceable reference samples to measure NGS LDT performance among a cohort of clinical laboratories.

METHODS

Human cell lines were engineered via CRISPR/Cas9 and prepared as formalin-fixed, paraffin-embedded cell pellets ("wet" samples) to assess the entire NGS test cycle. In silico mutagenized NGS sequence files ("dry" samples) were used to assess the bioinformatics component of the NGS test cycle. Single and multinucleotide variants (n = 36) of KRAS and NRAS were tested at 5% or 15% variant allele fraction to determine eligibility for therapy with the EGFR inhibitor panitumumab in the setting of metastatic colorectal cancer.

RESULTS

Twenty-one (21/21) laboratories tested wet samples; 19 of 21 analyzed dry samples. Of the laboratories that tested both the wet and dry samples, 7 (37%) of 19 laboratories correctly reported all variants, 3 (16%) of 19 had fewer than five errors, and 9 (47%) of 19 had five or more errors. Most errors were false negatives.

CONCLUSIONS

Genetically engineered cell lines and mutagenized sequence files are complementary reference samples for evaluating NGS test performance among clinical laboratories using LDTs. Variable accuracy in detection of genetic variants among some LDTs may identify different patient populations for targeted therapy.

Consistency and reproducibility of large panel next-generation sequencing: Multi-laboratory assessment of somatic mutation detection on reference materials with mismatch repair and proofreading deficiency

INTRODUCTION

Clinical precision oncology increasingly relies on accurate genome-wide profiling using large panel next generation sequencing; however, difficulties in accurate and consistent detection of somatic mutation from individual platforms and pipelines remain an open question.

OBJECTIVES

To obtain paired tumor-normal reference materials that can be effectively constructed and interchangeable with clinical samples, and evaluate the performance of 56 panels under routine testing conditions based on the reference samples.

METHODS

Genes involved in mismatch repair and DNA proofreading were knocked down using the CRISPR-Cas9 technology to accumulate somatic mutations in a defined GM12878 cell line. They were used as reference materials to comprehensively evaluate the reproducibility and accuracy of detection results of oncopanels and explore the potential influencing factors.

RESULTS

In total, 14 paired tumor-normal reference DNA samples from engineered cell lines were prepared, and a reference dataset comprising 168 somatic mutations in a high-confidence region of 1.8 Mb were generated. For mutations with an allele frequency (AF) of more than 5% in reference samples, 56 panels collectively reported 1306 errors, including 729 false negatives (FNs), 179 false positives (FPs) and 398 reproducibility errors. The performance metric varied among panels with precision and recall ranging from 0.773 to 1 and 0.683 to 1, respectively. Incorrect and inadequate filtering accounted for a large proportion of false discovery (including FNs and FPs), while low-quality detection, cross-contamination and other sequencing errors during the wet bench process were other sources of FNs and FPs. In addition, low AF (<5%) considerably influenced the reproducibility and comparability among panels.

CONCLUSIONS

This study provided an integrated practice for developing reference standard to assess oncopanels in detecting somatic mutations and quantitatively revealed the source of detection errors. It will promote optimization, validation, and quality control among laboratories with potential applicability in clinical use.

A Custom DNA-Based NGS Panel for the Molecular Characterization of Patients With Diffuse Gliomas: Diagnostic and Therapeutic Applications

The management of patients with Central Nervous System (CNS) malignancies relies on the appropriate classification of these tumors. Recently, the World Health Organization (WHO) has published new criteria underlining the importance of an accurate molecular characterization of CNS malignancies, in order to integrate the information generated by histology. Next generation sequencing (NGS) allows single step sequencing of multiple genes, generating a comprehensive and specific mutational profile of the tumor tissue. We developed a custom NGS-based multi-gene panel (Glio-DNA panel) for the identification of the correct glioma oncotype and the detection of its essential molecular aberrations. Specifically, the Glio-DNA panel targets specific genetic and chromosomal alterations involving ATRX chromatin remodeler (ATRX), cyclin dependent kinase inhibitor 2A (CDKN2A), isocitrate dehydrogenase (NADP+) 1 (IDH1) and the telomerase reverse transcriptase (TERT) promoter while also recognizing the co-deletion of 1p/19q, loss of chromosome 10 and gain of chromosome 7. Furthermore, the Glio-DNA panel also evaluates the methylation level of the O-6-methylguanine-DNA methyltransferase (MGMT) gene promoter that predicts temozolomide efficacy. As knowledge of the mutational landscape of each glioma is mandatory to define a personalized therapeutic strategy, the Glio-DNA panel also identifies alterations involving “druggable” or “actionable” genes. To test the specificity of our panel, we used two reference mutated DNAs verifying that NGS allele frequency measurement was highly accurate and sensitive. Subsequently, we performed a comparative analysis between conventional techniques - such as immunohistochemistry or fluorescence in situ hybridization - and NGS on 60 diffuse glioma samples that had been previously characterized. The comparison between conventional testing and NGS showed high concordance, suggesting that the Glio-DNA panel may replace multiple time-consuming tests. Finally, the identification of alterations involving different actionable genes matches glioma patients with potential targeted therapies available through clinical trials. In conclusion, our analysis demonstrates NGS efficacy in simultaneously detecting different genetic alterations useful for the diagnosis, prognosis and treatment of adult patients with diffuse glioma.

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Towards accurate and reliable resolution of structural variants for clinical diagnosis

Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Establishment of reference standards for multifaceted mosaic variant analysis

Detection of somatic mosaicism in non-proliferative cells is a new challenge in genome research, however, the accuracy of current detection strategies remains uncertain due to the lack of a ground truth. Herein, we sought to present a set of ultra-deep sequenced WES data based on reference standards generated by cell line mixtures, providing a total of 386,613 mosaic single-nucleotide variants (SNVs) and insertion-deletion mutations (INDELs) with variant allele frequencies (VAFs) ranging from 0.5% to 56%, as well as 35,113,417 non-variant and 19,936 germline variant sites as a negative control. The whole reference standard set mimics the cumulative aspect of mosaic variant acquisition such as in the early developmental stage owing to the progressive mixing of cell lines with established genotypes, ultimately unveiling 741 possible inter-sample relationships with respect to variant sharing and asymmetry in VAFs. We expect that our reference data will be essential for optimizing the current use of mosaic variant detection strategies and for developing algorithms to enable future improvements.

Measurement(s)	genotype
Technology Type(s)	DNA sequencing
Factor Type(s)	genotyping
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Environment	cell line

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16970041

Prenatal detection of chromosomal abnormalities and copy number variants in fetuses with congenital gastrointestinal obstruction

Background

Congenital gastrointestinal obstruction (CGIO) mainly refers to the stenosis or atresia of any part from the esophagus to the anus and is one of the most common surgical causes in the neonatal period. The concept of genetic factors as an etiology of CGIO has been accepted, but investigations about CGIO have mainly focused on aneuploidy, and the focus has been on duodenal obstruction. The objective of this study was to evaluate the risk of chromosome aberrations (including numeric and structural aberrations) in different types of CGIO. A second objective was to assess the risk of abnormal CNVs detected by copy number variation sequencing (CNV-seq) in fetuses with different types of CGIO.

Methods

Data from pregnancies referred for invasive testing and CNV-seq due to sonographic diagnosis of fetal CGIO from 2015 to 2020 were obtained retrospectively from the computerized database. The rates of chromosome aberrations and abnormal CNV-seq findings for isolated CGIOs and complicated CGIOs and different types of CGIOs were calculated.

Results

Of the 240 fetuses with CGIO that underwent karyotyping, the detection rate of karyotype abnormalities in complicated CGIO was significantly higher than that of the isolated group (33.8% vs. 10.8%, p < 0.01). Ninety-three cases with normal karyotypes further underwent CNV-seq, and CNV-seq revealed an incremental diagnostic value of 9.7% over conventional karyotyping. In addition, the incremental diagnostic yield of CNV-seq analysis in complicated CGIOs (20%) was higher than that in isolated CGIOs (4.8%), and the highest prevalence of pathogenic CNVs/likely pathogenic CNVs was found in the duodenal stenosis/atresia group (17.5%), followed by the anorectal malformation group (15.4%). The 13q deletion, 10q26 deletion, 4q24 deletion, and 2p24 might be additional genetic etiologies of duodenal stenosis/atresia.

Conclusions

The risk of pathogenic chromosomal abnormalities and CNVs increased in the complicated CGIO group compared to that in the isolated CGIO group, especially when fetuses presented duodenal obstruction (DO) and anorectal malformation. CNV-seq was recommended to detect submicroscopic chromosomal aberrations for DO and anorectal malformation when the karyotype was normal. The relationship between genotypes and phenotypes needs to be explored in the future to facilitate prenatal diagnosis of fetal CGIO and yield new clues into their etiologies.

Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome

BACKGROUND

Next-generation sequencing (NGS) can identify mutations in the human genome that cause disease and has been widely adopted in clinical diagnosis. However, the human genome contains many polymorphic, low-complexity, and repetitive regions that are difficult to sequence and analyze. Despite their difficulty, these regions include many clinically important sequences that can inform the treatment of human diseases and improve the diagnostic yield of NGS.

RESULTS

To evaluate the accuracy by which these difficult regions are analyzed with NGS, we built an in silico decoy chromosome, along with corresponding synthetic DNA reference controls, that encode difficult and clinically important human genome regions, including repeats, microsatellites, HLA genes, and immune receptors. These controls provide a known ground-truth reference against which to measure the performance of diverse sequencing technologies, reagents, and bioinformatic tools. Using this approach, we provide a comprehensive evaluation of short- and long-read sequencing instruments, library preparation methods, and software tools and identify the errors and systematic bias that confound our resolution of these remaining difficult regions.

CONCLUSIONS

This study provides an analytical validation of diagnosis using NGS in difficult regions of the human genome and highlights the challenges that remain to resolve these difficult regions.

W. Emerging technologies and their impact on regulatory science

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.

The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies

Introduction

Over the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial.

Methods

We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths.

Results and Discussion

Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.

The potential of using blood circular RNA as liquid biopsy biomarker for human diseases

Circular RNA (circRNA) is a novel class of single-stranded RNAs with a closed loop structure. The majority of circRNAs are formed by a back-splicing process in pre-mRNA splicing. Their expression is dynamically regulated and shows spatiotemporal patterns among cell types, tissues and developmental stages. CircRNAs have important biological functions in many physiological processes, and their aberrant expression is implicated in many human diseases. Due to their high stability, circRNAs are becoming promising biomarkers in many human diseases, such as cardiovascular diseases, autoimmune diseases and human cancers. In this review, we focus on the translational potential of using human blood circRNAs as liquid biopsy biomarkers for human diseases. We highlight their abundant expression, essential biological functions and significant correlations to human diseases in various components of peripheral blood, including whole blood, blood cells and extracellular vesicles. In addition, we summarize the current knowledge of blood circRNA biomarkers for disease diagnosis or prognosis.

The Value of Circulating Circular RNA in Cancer Diagnosis, Monitoring, Prognosis, and Guiding Treatment

Liquid biopsy includes non-invasive analysis of circulating tumor-derived substances. It is a novel, innovative cancer screening tool that overcomes the limitations of current invasive tissue examinations in precision oncology. Circular RNA (circRNA) is a recent, novel, and attractive liquid biomarker showing stability, abundance, and high specificity in various diseases, especially in human cancers. This review focused on the emerging potential of human circRNA in body fluids as the liquid biopsy biomarkers for cancers and the methods used to detect the circRNA expression and summarized the construction of circRNA biomarkers in body fluids for treating human cancers and their limitations before they become part of routine clinical medicine. Furthermore, the future opportunities and challenges of translating circRNAs in liquid biopsy into clinical practices were explored.

Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats

Circadian rhythms in gut microbiota composition are crucial for metabolic function, yet the extent to which they govern microbial dynamics compared to seasonal and lifetime processes remains unknown. Here, we investigate gut bacterial dynamics in wild meerkats (Suricata suricatta) over a 20-year period to compare diurnal, seasonal, and lifetime processes in concert, applying ratios of absolute abundance. We found that diurnal oscillations in bacterial load and composition eclipsed seasonal and lifetime dynamics. Diurnal oscillations were characterised by a peak in Clostridium abundance at dawn, were associated with temperature-constrained foraging schedules, and did not decay with age. Some genera exhibited seasonal fluctuations, whilst others developed with age, although we found little support for microbial senescence in very old meerkats. Strong microbial circadian rhythms in this species may reflect the extreme daily temperature fluctuations typical of arid-zone climates. Our findings demonstrate that accounting for circadian rhythms is essential for future gut microbiome research.

Multilaboratory assessment of metagenomic next-generation sequencing for unbiased microbe detection

•

We developed a set of well-defined reference materials, which is very beneficial to monitor problems in mNGS workflows and identify optimal protocols.

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The high interlaboratory variability in the identification and quantitation of microbes indicates that the current mNGS protocols are in urgent need of standardization and optimization.

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The detection rate of mNGS for low-concentration microbes (less than 10³ cell/ml) is significantly lower than that of microbes with a concentration of 10⁴ cell/ml and higher.

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Only 56.7% to 83.3% of the laboratories showed a sufficient ability to obtain clear etiological diagnoses for three simulated cases combined with patient information.

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Addressing laboratory contamination(false positive) is an urgent task.

Introduction

Metagenomic next-generation sequencing (mNGS) assay for detecting infectious agents is now in the stage of being translated into clinical practice. With no approved approaches or guidelines available, laboratories adopt customized mNGS assays to detect clinical samples. However, the accuracy, reliability, and problems of these routinely implemented assays are not clear.

Objectives

To evaluate the performance of 90 mNGS laboratories under routine testing conditions through analyzing identical samples.

Methods

Eleven microbial communities were generated using 15 quantitative microbial suspensions. They were used as reference materials to evaluate the false negatives and false positives of participating mNGS protocols, as well as the ability to distinguish genetically similar organisms and to identify true pathogens from other microbes based on fictitious case reports.

Results

High interlaboratory variability was found in the identification and the quantitative reads per million reads (RPM) values of each microbe in the samples, especially when testing microbes present at low concentrations (1 × 10³ cell/ml or less). 42.2% (38/90) of the laboratories reported unexpected microbes (i.e. false positive problem). Only 56.7% (51/90) to 83.3% (75/90) of the laboratories showed a sufficient ability to obtain clear etiological diagnoses for three simulated cases combined with patient information. The analysis of the performance of mNGS in distinguishing genetically similar organisms in three samples revealed that only 56.6% to 63.0% of the laboratories recovered RPM ratios (RPM_{S. aureus}/RPM_{S. epidermidis}) within the range of a 2-fold change of the initial input ratios (indicating a relatively low level of bias).

Conclusion

The high interlaboratory variability found in both identifying microbes and distinguishing true pathogens emphasizes the urgent need for improving the accuracy and comparability of the results generated across different mNGS laboratories, especially in the detection of low-microbial-biomass samples.

Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology.

Detection of COVID-19 from speech signal using bio-inspired based cepstral features

The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easily recorded. The Mel Frequency Cepstral Coefficient (MFCC) analysis of speech signal is one of the oldest but potential analysis tools. The performance of this analysis mainly depends on the use of conversion between normal frequency scale to perceptual frequency scale and the frequency range of the filters used. Traditionally, in speech recognition, these values are fixed. But the characteristics of speech signals vary from disease to disease. In the case of detection of COVID-19, mainly the coughing sounds are used whose bandwidth and properties are quite different from the complete speech signal. By exploiting these properties the efficiency of the COVID-19 detection can be improved. To achieve this objective the frequency range and the conversion scale of frequencies have been suitably optimized. Further to enhance the accuracy of detection performance, speech enhancement has been carried out before extraction of features. By implementing these two concepts a new feature called COVID-19 Coefficient (C-19CC) is developed in this paper. Finally, the performance of these features has been compared.