Interpretation Of Next-generation Sequencing Data Research Articles

What can pediatricians learn from adult inherited metabolic diseases?

The field of inherited metabolic diseases (IMD) has initially emerged and developed over decades in pediatric departments. Still, today, about 50% of patients with IMD are adults, and adult metabolic medicine (AMM) is getting more structured at national and international levels. There are several domains in which pediatricians can learn from AMM. First, long-term evolution of IMD patients, especially those treated since childhood, is critical to determine nutritional and neuropsychiatric outcomes in adults so that these outcomes can be better monitored, and patient care adjusted as much as possible from childhood. Conversely, the observation of attenuated phenotypes in adults of IMD known to present with severe phenotypes in children calls for caution in the development of newborn screening programs and, more largely, in the interpretation of next-generation sequencing data. Third, it is important for pediatricians to be familiar with adult-onset IMD as they expand our understanding of metabolism, including in children, such as oxysterols and glycogen metabolism. Last, the identification of common molecular and cellular mechanisms in neurodevelopment and neurodegeneration opens the way to synergistic therapeutic developments that will benefit both fields of pediatric and adult medicine. Overall, these observations underline the need of strong interdisciplinarity between pediatricians and adult specialists for the diagnosis and the treatment of IMD well beyond the issues of patient transition from pediatric to adult medicine.

The development of a next-generation sequencing panel targeting cannabinoid synthase genes to distinguish between marijuana and hemp.

Hemp and marijuana, both derived from Cannabis sativa L. (C. sativa), are subject to divergent legal regulations due to their different Δ9-tetrahydrocannabinol (Δ9-THC) contents. Cannabinoid synthase genes are considered the key enzymes that determine the chemical composition or chemotype of a particular cultivar. However, existing methods for crop type differentiation based on previous synthase gene theories have limitations in terms of precision and specificity, and a wider range of cannabis varieties must be considered when examining cannabis-based genetic markers. A custom next-generation sequencing (NGS) panel was developed targeting all synthase genes, including Δ9-THC acid synthase, cannabidiolic acid synthase, and cannabichromenic acid synthase, as well as the pseudogenes across diverse C. sativa samples, spanning reference hemp and marijuana, commercial hemp derivatives, and seized marijuana extracts. Interpretation of NGS data revealed a relationship between genotypes and underlying chemotypes, with the principal component analysis indicating a clear distinction between hemp and marijuana clusters. This differentiation was attributed to variations in both synthase genes and pseudogene variants. Finally, this study proposes a genetic cannabis classification method using a differentiation flow chart with novel synthase markers. The flow chart successfully differentiated hemp from marijuana with a 1.3% error rate (n=147).

Challenges in clinical interpretation of next-generation sequencing data: Advantages and Pitfalls

Next-generation sequencing (NGS) technologies have completely changed the field of genomics by making it possible to rapidly and inexpensively produce genome-scale sequence data with higher precision and resolution. In recent years, rapid technological advancements driven by companies and academic institutions have expanded the range of NGS applications, from research to the clinic. Latest scientific developments have highlighted the effects of NGS technology on complex and Mendelian diseases, particularly cancer. However, the rapid adoption of NGS poses significant challenges in data processing, storage, management, and interpretation, as well as sequencing quality control, making it challenging to integrate the sequence data into clinical practice. Here, we first outline the technical features and performance of the most recent NGS platforms. Furthermore, we summarize the applications of NGS technologies in clinical diagnostics, data interpretation, and valuable bioinformatic tools and databases for analysis. Common issues in NGS processes are also discussed.

Clinical pathway on pediatric cardiomyopathies: a genetic testing strategy proposed by the Italian Society of Pediatric Cardiology

Pediatric cardiomyopathies are rare diseases, heterogeneous in clinical presentation, etiology and prognosis. Etiological diagnosis, where genetic analysis plays a key role, is of fundamental importance for defining diagnostic and therapeutic pathways. Furthermore, the identification of the genetic substrate represents a prerequisite for cascade screening in the proband's family members and to allow conscious reproductive choices. To date, genetic testing is performed with the analysis of gene panels (targeted panels) or with the study of the entire exome (whole exome sequencing) using next generation sequencing (NGS) technology. The great genetic heterogeneity and the temporal variability of the clinical manifestations lead to unique problems for pediatric cardiomyopathies, distinct from those of the adult, such as the possible indications for access to the test, the type of test to be used (exome or panel of genes), the importance of analyzing parents, especially in cases with neonatal onset; moreover, the correct execution of bioinformatics analysis and the interpretation of NGS data play a crucial role in the impact of the results on clinical management.

OP019: A tertiary care clinical sequencing program for patients with suspected immune defects: Results from the first 1000 families

The use of genomics in clinical research has evolved substantially over the past decade. For patients with immune defects, like other rare diseases, a timely molecular diagnosis reduces unnecessary testing, guides medical management, and facilitates genetic counseling. Interpretation of next-generation sequencing data from panel, exome, genome, and structural variant assays remains a challenge. For patients with inborn errors of immunity (IEI), the role of multiple molecular diagnoses and integration of copy number variant (CNV) analysis remains largely unexplored.

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.

Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy. Method This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. Conclusion As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.

Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer.

Next generation sequencing (NGS) technology is an increasingly important clinical tool for therapeutic decision-making. However, interpretation of NGS data presents challenges at the point of care, due to limitations in understanding the clinical importance of gene variants and efficiently translating results into actionable information for the clinician. The present study compared two approaches for annotating and reporting actionable genes and gene mutations from tumor samples: The traditional approach of manual curation, annotation and reporting using an experienced molecular tumor bioinformationist; and a cloud-based cognitive technology, with the goal to detect gene mutations of potential significance in Chinese patients with lung cancer. Data from 285 gene-targeted exon sequencing previously conducted on 115 patient tissue samples between 2014 and 2016 and subsequently manually annotated and evaluated by the Guangdong Lung Cancer Institute (GLCI) research team were analyzed by the Watson for Genomics (WfG) cognitive genomics technology. A comparative analysis of the annotation results of the two methods was conducted to identify quantitative and qualitative differences in the mutations generated. The complete congruence rate of annotation results between WfG analysis and the GLCI bioinformatician was 43.48%. In 65 (56.52%) samples, WfG analysis identified and interpreted, on average, 1.54 more mutation sites in each sample than the manual GLCI review. These mutation sites were located on 27 genes, including EP300, ARID1A, STK11 and DNMT3A. Mutations in the EP300 gene were most prevalent, and present in 30.77% samples. The Tumor Mutation Burden (TMB) interpreted by WfG analysis (1.82) was significantly higher than the TMB (0.73) interpreted by GLCI review. Compared with manual curation by a bioinformatician, WfG analysis provided comprehensive insights and additional genetic alterations to inform clinical therapeutic strategies for patients with lung cancer. These findings suggest the valuable role of cognitive computing to increase efficiency in the comprehensive detection and interpretation of genetic alterations which may inform opportunities for targeted cancer therapies.

Next Generation Sequencing Based Forward Genetic Approaches for Identification and Mapping of Causal Mutations in Crop Plants: A Comprehensive Review.

The recent advancements in forward genetics have expanded the applications of mutation techniques in advanced genetics and genomics, ahead of direct use in breeding programs. The advent of next-generation sequencing (NGS) has enabled easy identification and mapping of causal mutations within a short period and at relatively low cost. Identifying the genetic mutations and genes that underlie phenotypic changes is essential for understanding a wide variety of biological functions. To accelerate the mutation mapping for crop improvement, several high-throughput and novel NGS based forward genetic approaches have been developed and applied in various crops. These techniques are highly efficient in crop plants, as it is relatively easy to grow and screen thousands of individuals. These approaches have improved the resolution in quantitative trait loci (QTL) position/point mutations and assisted in determining the functional causative variations in genes. To be successful in the interpretation of NGS data, bioinformatics computational methods are critical elements in delivering accurate assembly, alignment, and variant detection. Numerous bioinformatics tools/pipelines have been developed for such analysis. This article intends to review the recent advances in NGS based forward genetic approaches to identify and map the causal mutations in the crop genomes. The article also highlights the available bioinformatics tools/pipelines for reducing the complexity of NGS data and delivering the concluding outcomes.

Advancing leukemia diagnostics: Role of Next Generation Sequencing (NGS) in acute myeloid leukemia.

AML diagnostics, initially based solely on morphological evaluation, now relies on multiple disciplines to reach its full potential. Only by integrating the results of cytomorphology, cytochemistry, immunophenotyping, cytogenetics and molecular genetics it is possible to fulfil WHO classification and ELN prognostication systems. Especially molecular genetics has gained a lot of interest over the last decade, mainly through the introduction of next generation sequencing (NGS). NGS application ranges from the investigation of single genes and panels to even whole exomes, transcriptomes and genomes. In routine AML diagnostics panels are the preferred NGS methodology. Here, we will review the power and limitations of NGS in the context of diagnosis, prognosis and precision medicine. Due to high dimensionality, NGS data interpretation is challenging but it also offers a unique investigatory chance and the opportunity to apply data mining techniques such as artificial intelligence. We will also reflect on how the incorporation of the improved knowledge base into routine diagnostics can pave the way for better treatment and more cure in AML.

Development and validation of Houston Methodist Variant Viewer version 3: updates to our application for interpretation of next-generation sequencing data.

ObjectivesInformatics tools that support next-generation sequencing workflows are essential to deliver timely interpretation of somatic variants in cancer. Here, we describe significant updates to our laboratory developed bioinformatics pipelines and data management application termed Houston Methodist Variant Viewer (HMVV).Materials and MethodsWe collected feature requests and workflow improvement suggestions from the end-users of HMVV version 1. Over 1.5 years, we iteratively implemented these features in five sequential updates to HMVV version 3.ResultsWe improved the performance and data throughput of the application while reducing the opportunity for manual data entry errors. We enabled end-user workflows for pipeline monitoring, variant interpretation and annotation, and integration with our laboratory information system. System maintenance was improved through enhanced defect reporting, heightened data security, and improved modularity in the code and system environments.Discussion and ConclusionValidation of each HMVV update was performed according to expert guidelines. We enabled an 8× reduction in the bioinformatics pipeline computation time for our longest running assay. Our molecular pathologists can interpret the assay results at least 2 days sooner than was previously possible. The application and pipeline code are publicly available at https://github.com/hmvv.

The relevance of tumor mutation profiling in interpretation of NGS data from cell-free DNA in non-small cell lung cancer patients

Studies have indicated that detection of circulating tumor DNA (ctDNA) prior to treatment is a negative prognostic marker in non-small cell lung cancer (NSCLC). ctDNA is currently identified by detection of tumor mutations. Commercial next-generation sequencing (NGS) assays for mutation analysis of ctDNA for routine practice usually include small gene panels and are not suitable for general mutation analysis. In this study, we investigated whether mutation analysis of cfDNA could be performed using a commercially available comprehensive NGS gene panel and bioinformatics workflow. Tumor DNA, plasma DNA and peripheral blood leukocyte DNA from 30 NSCLC patients were sequenced. In two patients (7%), tumor mutations in cfDNA were immediately called by the bioinformatic workflow. In 13 patients (43%), tumor mutations were not called, but were present in ctDNA and were identified based on the known tumor mutation profile. In the remaining 15 patients (50%), no concordant mutations were detected. In conclusion, we were able to identify tumor mutations in ctDNA from 57% of NSCLC patients using a comprehensive gene panel. We demonstrated that sequencing paired tumor DNA was helpful to interpret data and confirm ctDNA, and thus increased the ratio of patients with detectable ctDNA. This approach might be feasible for mutation analysis of ctDNA in routine diagnostic practice, especially in case of suboptimal plasma quality and quantity.

Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based quantification

Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.

Genetic Variants Contributing to Early Recurrent Pregnancy Loss Etiology Identified by Sequencing Approaches.

Recurrent pregnancy loss (RPL) affects up to 5% of couples. It is believed that genetic factors contribute to the disease's etiology and pathophysiology. Hundreds of genes represent coherent RPL candidates due to mammalian implantation's inherent complexity. Sanger sequencing (direct sequencing) of candidate genes has identified potential RPL causative genes (and variants), including those regulating embryo implantation and pregnancy maintenance. Although this approach is a reliable technique, the simultaneous analysis of large genomic regions is challenging. Next-generation sequencing (NGS) technology has thus emerged as a useful alternative for determining genetic variants and transcriptomic disturbances contributing to monogenic and polygenic diseases pathogenesis. However, interpreting results remains challenging as NGS experiments provide an enormous amount of complex data. The molecular aspects of specific diseases must be fully understood for accurate interpretation of NGS data. This review was thus aimed at describing (for the first time) the most relevant studies involving Sanger and NGS sequencing, leading to the description of variants related to RPL pathogenesis. Successful RPL-related NGS initiatives (including RNAseq-based studies) and future challenges are discussed. We consider that the information given here should be useful for clinicians, scientists, and students to enable a better understanding of RPL etiology. It may also provide a basis for the development of diagnostic/prognostic approaches contributing toward translational medicine.

Application of genome analysis strategies in the clinical testing for pediatric diseases.

Next‐generation sequencing (NGS) is being used in clinical testing. Government authorities in both China and the United States are overseeing the clinical application of NGS instruments and reagents. In addition, the US Association for Molecular Pathology and the College of American Pathologists have jointly released a guidance to standardize the analysis and interpretation of NGS data involved in clinical testing. At present, the analysis strategies and pipelines for NGS data related to the clinical detection of pediatric disease are similar to those used for adult diseases. However, for rare pediatric diseases without linkage to known genetic variants, it is currently difficult to detect the relevant pathogenic genes using NGS technology. Additionally, it is challenging to identify novel pathogenic genes of familial pediatric tumors. Therefore, characterization of the pathogenic genes associated with above diseases is important for the diagnosis and treatment of rare diseases in children. This article introduces the general pipelines for NGS data analyses of diseases and elucidates data analysis strategies for the pathogenic genes of rare pediatric diseases and familial pediatric tumors.

Comparative analysis of target gene exon sequencing by cognitive technology using next generation sequencing platform in Chinese patients with lung cancer.

e24254Background: Next generation sequencing (NGS) technology is becoming increasingly important as a clinical application for therapeutic decision. However, interpretation of NGS data is a further...

A recessive ataxia diagnosis algorithm for the next generation sequencing era.

Differential diagnosis of autosomal recessive cerebellar ataxias can be challenging. A ranking algorithm named RADIAL that predicts the molecular diagnosis based on the clinical phenotype of a patient has been developed to guide genetic testing and to align genetic findings with the clinical context. An algorithm that follows clinical practice, including patient history, clinical, magnetic resonance imaging, electromyography, and biomarker features, was developed following a review of the literature on 67 autosomal recessive cerebellar ataxias and personal clinical experience. Frequency and specificity of each feature were defined for each autosomal recessive cerebellar ataxia, and corresponding prediction scores were assigned. Clinical and paraclinical features of patients are entered into the algorithm, and a patient's total score for each autosomal recessive cerebellar ataxia is calculated, producing a ranking of possible diagnoses. Sensitivity and specificity of the algorithm were assessed by blinded analysis of a multinational cohort of 834 patients with molecularly confirmed autosomal recessive cerebellar ataxia. The performance of the algorithm was assessed versus a blinded panel of autosomal recessive cerebellar ataxia experts. The correct diagnosis was ranked within the top 3 highest-scoring diagnoses at a sensitivity and specificity of >90% for 84% and 91% of the evaluated genes, respectively. Mean sensitivity and specificity of the top 3 highest-scoring diagnoses were 92% and 95%, respectively. The algorithm outperformed the panel of ataxia experts (p = 0.001). Our algorithm is highly sensitive and specific, accurately predicting the underlying molecular diagnoses of autosomal recessive cerebellar ataxias, thereby guiding targeted sequencing or facilitating interpretation of next-generation sequencing data. Ann Neurol 2017;82:892-899.

Reference standards for next-generation sequencing.

Next-generation sequencing (NGS) provides a broad investigation of the genome, and it is being readily applied for the diagnosis of disease-associated genetic features. However, the interpretation of NGS data remains challenging owing to the size and complexity of the genome and the technical errors that are introduced during sample preparation, sequencing and analysis. These errors can be understood and mitigated through the use of reference standards - well-characterized genetic materials or synthetic spike-in controls that help to calibrate NGS measurements and to evaluate diagnostic performance. The informed use of reference standards, and associated statistical principles, ensures rigorous analysis of NGS data and is essential for its future clinical use.

Next-generation sequencing–based clinical testing for lung cancer in Japan

The development of targeted therapies for lung cancer based on the presence of corresponding specific biomarkers has highlighted the need for molecular diagnostic tests capable of the analysis of multiple actionable genetic alterations in a single tumor sample. Amplicon-based next-generation sequencing (NGS)— as opposed to conventional Sanger-based sequencing—has been introduced to facilitate the performance of multiple genomic tests with small amounts of tissue. In Japan, several institutions including the National Cancer Center and university hospitals have initiated NGS-based clinical testing for cancer patients and are able to provide access to investigational drugs or approved targeted agents matched to detected molecular alterations. However, no NGS system has yet been approved for the detection of somatic mutations by the Pharmaceuticals and Medical Devices Agency (PMDA) of Japan. Further development of precision medicine in clinical practice in Japan will require changes to the medical curriculum to support the interpretation and annotation of NGS data. In this review, we introduce NGS-based clinical sequencing projects that are ongoing in Japan—in particular, those focusing on lung cancer—and we discuss issues relating to the integration of NGS into clinical practice.

Houston Methodist Variant Viewer: An Application to Support Clinical Laboratory Interpretation of Next-generation Sequencing Data for Cancer

Introduction: Next-generation-sequencing (NGS) is increasingly used in clinical and research protocols for patients with cancer. NGS assays are routinely used in clinical laboratories to detect mutations bearing on cancer diagnosis, prognosis and personalized therapy. A typical assay may interrogate 50 or more gene targets that encompass many thousands of possible gene variants. Analysis of NGS data in cancer is a labor-intensive process that can become overwhelming to the molecular pathologist or research scientist. Although commercial tools for NGS data analysis and interpretation are available, they are often costly, lack key functionality or cannot be customized by the end user. Methods: To facilitate NGS data analysis in our clinical molecular diagnostics laboratory, we created a custom bioinformatics tool termed Houston Methodist Variant Viewer (HMVV). HMVV is a Java-based solution that integrates sequencing instrument output, bioinformatics analysis, storage resources and end user interface. Results: Compared to the predicate method used in our clinical laboratory, HMVV markedly simplifies the bioinformatics workflow for the molecular technologist and facilitates the variant review by the molecular pathologist. Importantly, HMVV reduces time spent researching the biological significance of the variants detected, standardizes the online resources used to perform the variant investigation and assists generation of the annotated report for the electronic medical record. HMVV also maintains a searchable variant database, including the variant annotations generated by the pathologist, which is useful for downstream quality improvement and research projects. Conclusions: HMVV is a clinical grade, low-cost, feature-rich, highly customizable platform that we have made available for continued development by the pathology informatics community.

Next-Generation Sequencing and Result Interpretation in Clinical Oncology: Challenges of Personalized Cancer Therapy.

The tools of next-generation sequencing (NGS) technology, such as targeted sequencing of candidate cancer genes and whole-exome and -genome sequencing, coupled with encouraging clinical results based on the use of targeted therapeutics and biomarker-guided clinical trials, are fueling further technological advancements of NGS technology. However, NGS data interpretation is associated with challenges that must be overcome to promote the techniques' effective integration into clinical oncology practice. Specifically, sequencing of a patient's tumor often yields 30-65 somatic variants, but most of these variants are "passenger" mutations that are phenotypically neutral and thus not targetable. Therefore, NGS data must be interpreted by multidisciplinary decision-support teams to determine mutation actionability and identify potential "drivers," so that the treating physician can prioritize what clinical decisions can be pursued in order to provide cancer therapy that is personalized to the patient and his or her unique genome.