scholarly journals Point-Counterpoint: Should We Be Performing Metagenomic Next-Generation Sequencing for Infectious Disease Diagnosis in the Clinical Laboratory?

2019 ◽  
Vol 58 (3) ◽  
Author(s):  
Steve Miller ◽  
Charles Chiu ◽  
Kyle G. Rodino ◽  
Melissa B. Miller
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Disease Diagnosis ◽  
Metagenomic Sequencing ◽  
Infectious Agents ◽  
Next Generation ◽  
Sequencing Data ◽  
Clinical Laboratories ◽  
Generation Sequencing ◽  
Selection Of

INTRODUCTION With established applications of next-generation sequencing in inherited diseases and oncology, clinical laboratories are evaluating the use of metagenomics for identification of infectious agents directly from patient samples, to aid in the diagnosis of infections. Metagenomic next-generation sequencing for infectious diseases promises an unbiased approach to detection of microbes that does not depend on growth in culture or the targeting of specific pathogens. However, the issues of contamination, interpretation of results, selection of databases used for analysis, and prediction of antimicrobial susceptibilities from sequencing data remain challenges. In this Point-Counterpoint, Steve Miller and Charles Chiu discuss the pros of using direct metagenomic sequencing, while Kyle Rodino and Melissa Miller argue for the use of caution.

scholarly journals Implementation of Cloud based Next Generation Sequencing data analysis in a clinical laboratory

2014 ◽  
Vol 7 (1) ◽  
pp. 314 ◽  
Author(s):  
Getiria Onsongo ◽  
Jesse Erdmann ◽  
Michael D Spears ◽  
John Chilton ◽  
Kenneth B Beckman ◽  
...  
Keyword(s):  
Data Analysis ◽  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Sequencing Data Analysis

scholarly journals Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing

2019 ◽  
Vol 66 (1) ◽  
pp. 239-246 ◽  
Author(s):  
Chao Wu ◽  
Xiaonan Zhao ◽  
Mark Welsh ◽  
Kellianne Costello ◽  
Kajia Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Next Generation ◽  
Single Nucleotide Variants ◽  
Test Set ◽  
Clinical Laboratories ◽  
Bona Fide ◽  
Validation Set ◽  
Generation Sequencing

Abstract BACKGROUND Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. We present a machine learning–based method to distinguish artifacts from bona fide single-nucleotide variants (SNVs) detected by next-generation sequencing from nonformalin-fixed paraffin-embedded tumor specimens. METHODS A cohort of 11278 SNVs identified through clinical sequencing of tumor specimens was collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A 3-class (real, artifact, and uncertain) model was developed on the training set, fine-tuned with the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label “uncertain” variants. RESULTS The optimized classifier demonstrated 100% specificity and 97% sensitivity over 5587 SNVs of the test set. Overall, 1252 of 1341 true-positive variants were identified as real, 4143 of 4246 false-positive calls were deemed artifacts, whereas only 192 (3.4%) SNVs were labeled as “uncertain,” with zero misclassification between the true positives and artifacts in the test set. CONCLUSIONS We presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received definitive labels and thus were exempt from manual review. This framework could improve quality and efficiency of the variant review process in clinical laboratories.

scholarly journals Bioinformatics for Clinical Next Generation Sequencing

2015 ◽  
Vol 61 (1) ◽  
pp. 124-135 ◽  
Author(s):  
Gavin R Oliver ◽  
Steven N Hart ◽  
Eric W Klee
Keyword(s):  
Next Generation Sequencing ◽  
Service Providers ◽  
Clinical Laboratory ◽  
Work Flow ◽  
Next Generation ◽  
Sequencing Data ◽  
Regulatory Requirements ◽  
Bioinformatics Analyses ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract BACKGROUND Next generation sequencing (NGS)-based assays continue to redefine the field of genetic testing. Owing to the complexity of the data, bioinformatics has become a necessary component in any laboratory implementing a clinical NGS test. CONTENT The computational components of an NGS-based work flow can be conceptualized as primary, secondary, and tertiary analytics. Each of these components addresses a necessary step in the transformation of raw data into clinically actionable knowledge. Understanding the basic concepts of these analysis steps is important in assessing and addressing the informatics needs of a molecular diagnostics laboratory. Equally critical is a familiarity with the regulatory requirements addressing the bioinformatics analyses. These and other topics are covered in this review article. SUMMARY Bioinformatics has become an important component in clinical laboratories generating, analyzing, maintaining, and interpreting data from molecular genetics testing. Given the rapid adoption of NGS-based clinical testing, service providers must develop informatics work flows that adhere to the rigor of clinical laboratory standards, yet are flexible to changes as the chemistry and software for analyzing sequencing data mature.

scholarly journals CCMG practice guideline: laboratory guidelines for next-generation sequencing

2019 ◽  
Vol 56 (12) ◽  
pp. 792-800 ◽  
Author(s):  
Stacey Hume ◽  
Tanya N Nelson ◽  
Marsha Speevak ◽  
Elizabeth McCready ◽  
Ron Agatep ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Massively Parallel Sequencing ◽  
Clinical Genetics ◽  
Next Generation ◽  
Clinical Genetic ◽  
Inherited Disorders ◽  
Laboratory Service ◽  
Clinical Laboratories ◽  
Generation Sequencing

PurposeThe purpose of this document is to provide guidance for the use of next-generation sequencing (NGS, also known as massively parallel sequencing or MPS) in Canadian clinical genetic laboratories for detection of genetic variants in genomic DNA and mitochondrial DNA for inherited disorders, as well as somatic variants in tumour DNA for acquired cancers. They are intended for Canadian clinical laboratories engaged in developing, validating and using NGS methods.Methods of statement developmentThe document was drafted by the Canadian College of Medical Geneticists (CCMG) Ad Hoc Working Group on NGS Guidelines to make recommendations relevant to NGS. The statement was circulated for comment to the CCMG Laboratory Practice and Clinical Practice committees, and to the CCMG membership. Following incorporation of feedback, the document was approved by the CCMG Board of Directors.DisclaimerThe CCMG is a Canadian organisation responsible for certifying medical geneticists and clinical laboratory geneticists, and for establishing professional and ethical standards for clinical genetics services in Canada. The current CCMG Practice Guidelines were developed as a resource for clinical laboratories in Canada and should not be considered to be inclusive of all information laboratories should consider in the validation and use of NGS for a clinical laboratory service.

scholarly journals Building the Framework for Standardized Clinical Laboratory Reporting of Next-generation Sequencing Data for Resistance-associated Mutations in Mycobacterium tuberculosis Complex

2019 ◽  
Vol 69 (9) ◽  
pp. 1631-1633 ◽  
Author(s):  
Jeffrey A Tornheim ◽  
Angela M Starks ◽  
Timothy C Rodwell ◽  
Jennifer L Gardy ◽  
Timothy M Walker ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Drug Susceptibility Testing ◽  
Next Generation Sequencing Data ◽  
Molecular Testing ◽  
Next Generation ◽  
Sequencing Data ◽  
Standardized Report ◽  
Generation Sequencing

Abstract Tuberculosis is the primary infectious disease killer worldwide, with a growing threat from multidrug-resistant cases. Unfortunately, classic growth-based phenotypic drug susceptibility testing (DST) remains difficult, costly, and time consuming, while current rapid molecular testing options are limited by the diversity of antimicrobial-resistant genotypes that can be detected at once. Next-generation sequencing (NGS) offers the opportunity for rapid, comprehensive DST without the time or cost burden of phenotypic tests and can provide useful information for global surveillance. As access to NGS expands, it will be important to ensure that results are communicated clearly, consistent, comparable between laboratories, and associated with clear guidance on clinical interpretation of results. In this viewpoint article, we summarize 2 expert workshops regarding a standardized report format, focusing on relevant variables, terminology, and required minimal elements for clinical and laboratory reports with a proposed standardized template for clinical reporting NGS results for Mycobacterium tuberculosis.

scholarly journals Targeted Next-Generation Sequencing for Human Leukocyte Antigen Typing in a Clinical Laboratory: Metrics of Relevance and Considerations for Its Successful Implementation

2017 ◽  
Vol 141 (6) ◽  
pp. 806-812 ◽  
Author(s):  
Manish J. Gandhi ◽  
Deborah Ferriola ◽  
Yanping Huang ◽  
Jamie L. Duke ◽  
Dimitri Monos
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Successful Implementation ◽  
Next Generation ◽  
Human Leukocyte Antigen Typing ◽  
Clinical Laboratories ◽  
Exon 1 ◽  
Generation Sequencing

Context.— Numerous feasibility studies to type human leukocyte antigens (HLAs) by next-generation sequencing (NGS) have led to the development of vendor-supported kits for HLA typing by NGS. Some clinical laboratories have introduced HLA-NGS, and many are investigating the introduction. Standards from accrediting agencies form the regulatory framework for introducing this test into clinical laboratories. Objectives.— To provide an assessment of metrics and considerations relevant to the successful implementation of clinical HLA-NGS typing, and to provide as a reference a validated HLA-NGS protocol used clinically since December 2013 at the Children's Hospital of Philadelphia (Philadelphia, Pennsylvania). Data Sources.— The HLA-NGS has been performed on 2532 samples. The initial 1046 and all homozygous samples were also typed by an alternate method. The HLA-NGS demonstrated 99.7% concordance with the alternate method. Ambiguous results were most common at the DPB1 locus because of a lack of phasing between exons 2 and 3 or the unsequenced exon 1 (533 of 2954 alleles; 18.04%) and the DRB1 locus because of not sequencing exon 1 (75 of 3972 alleles; 1.89%). No ambiguities were detected among the other loci. Except for 2 false homozygous samples, all homozygous samples (1891) demonstrated concordance with the alternate method. The article is organized to address the critical elements in the preanalytic, analytic, and postanalytic phases of introducing this assay into the clinical laboratory. Conclusions.— The results demonstrate that HLA typing by NGS is a highly accurate, reproducible, efficient method that provides more-complete sequencing information for the length of the HLA gene and can be the single methodology for HLA typing in clinical immunogenetics laboratories.

scholarly journals Next-Generation Sequencing Informatics: Challenges and Strategies for Implementation in a Clinical Environment

2016 ◽  
Vol 140 (9) ◽  
pp. 958-975 ◽  
Author(s):  
Somak Roy ◽  
William A. LaFramboise ◽  
Yuri E. Nikiforov ◽  
Marina N. Nikiforova ◽  
Mark J. Routbort ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Cost Effective ◽  
Small Degree ◽  
Clinical Informatics ◽  
Next Generation ◽  
Clinical Environment ◽  
Next Generation Sequencing Technology ◽  
Clinical Laboratories ◽  
Generation Sequencing

Context.—Next-generation sequencing (NGS) is revolutionizing the discipline of laboratory medicine, with a deep and direct impact on patient care. Although it empowers clinical laboratories with unprecedented genomic sequencing capability, NGS has brought along obvious and obtrusive informatics challenges. Bioinformatics and clinical informatics are separate disciplines with typically a small degree of overlap, but they have been brought together by the enthusiastic adoption of NGS in clinical laboratories. The result has been a collaborative environment for the development of novel informatics solutions. Sustaining NGS-based testing in a regulated clinical environment requires institutional support to build and maintain a practical, robust, scalable, secure, and cost-effective informatics infrastructure. Objective.—To discuss the novel NGS informatics challenges facing pathology laboratories today and offer solutions and future developments to address these obstacles. Data Sources.—The published literature pertaining to NGS informatics was reviewed. The coauthors, experts in the fields of molecular pathology, precision medicine, and pathology informatics, also contributed their experiences. Conclusions.—The boundary between bioinformatics and clinical informatics has significantly blurred with the introduction of NGS into clinical molecular laboratories. Next-generation sequencing technology and the data derived from these tests, if managed well in the clinical laboratory, will redefine the practice of medicine. In order to sustain this progress, adoption of smart computing technology will be essential. Computational pathologists will be expected to play a major role in rendering diagnostic and theranostic services by leveraging “Big Data” and modern computing tools.

scholarly journals 2563. Clinical Metagenomic Next-Generation Sequencing for Diagnosis of Meningitis and Encephalitis

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S72-S72
Author(s):  
Michael Wilson ◽  
Hannah Sample ◽  
Kelsey Zorn ◽  
Samia N Naccache ◽  
Steve Miller ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Tertiary Care ◽  
Significant Proportion ◽  
Disease Diagnosis ◽  
Clinical Impact ◽  
Clinical Microbiology Laboratory ◽  
Metagenomic Sequencing ◽  
Next Generation ◽  
Tertiary Care Hospitals ◽  
Generation Sequencing

Abstract Background Metagenomic next-generation sequencing (mNGS) of CSF can identify nearly all pathogens in a single test. We previously validated a CSF mNGS assay in a licensed clinical laboratory. To date, the utility of mNGS for infectious disease diagnosis has been described in case reports and small case series, but not in a large-scale clinical trial. Methods The PDAID (“Precision Diagnosis of Acute Infectious Diseases”) study was a 1-year nationwide prospective study across 8 tertiary care hospitals to evaluate the performance and utility of a clinical metagenomic sequencing assay for diagnosis of meningitis, encephalitis, or myelitis from cerebrospinal fluid (CSF) (ClinicalTrials.gov number NCT02910037). We recruited acutely ill hospitalized inpatients lacking a diagnosis at the time of enrollment. CSF samples were processed and analyzed by mNGS testing within 1 week of receipt in the clinical microbiology laboratory, with sequencing results reported in the patient medical record and used to make contemporaneous treatment decisions. Weekly clinical microbial sequencing boards were convened to discuss mNGS results with treating physicians, and clinical impact evaluated by surveys, chart review, and direct clinician feedback. Results A total of 204 patients were enrolled. Patients were severely ill (ICU 48%, average length of stay 26 days, overall 30-day mortality 7.4%). Fifty-nine neurologic infections were diagnosed in 57 patients (27.9%). mNGS identified 15 (25.4%) infections that were missed by all conventional microbiological tests, including emerging and/or uncommon pathogens such as St. Louis encephalitis virus, hepatitis E virus acquired by lung transplant, and Nocardia farcinica. Twelve of the 15 mNGS-only diagnoses (80%) had clinical impact, with 9 of 15 (60%) guiding appropriate treatment. For diagnosis of infections by direct detection CSF testing, mNGS had 79.1% sensitivity and 98.8% specificity, versus 65.1% sensitivity and 99.4% specificity by conventional testing. Conclusion A significant proportion of neurologic infections are missed despite extensive diagnostic testing performed in tertiary care hospitals. Clinical metagenomic CSF testing was found to be useful in increasing the number of diagnosed neurologic infections and providing actionable information for physicians. Disclosures All authors: No reported disclosures.

scholarly journals Quantifying Live Microbial Load in Human Saliva Samples over Time Reveals Stable Composition and Dynamic Load

mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Clarisse Marotz ◽  
James T. Morton ◽  
Perris Navarro ◽  
Joanna Coker ◽  
Pedro Belda-Ferre ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Salivary Flow ◽  
Oral Microbiome ◽  
Microbial Load ◽  
Metagenomic Sequencing ◽  
Next Generation ◽  
Sequencing Data ◽  
Order Of Magnitude ◽  
Generation Sequencing ◽  
Over Time

ABSTRACT Evaluating microbial community composition through next-generation sequencing has become increasingly accessible. However, metagenomic sequencing data sets provide researchers with only a snapshot of a dynamic ecosystem and do not provide information about the total microbial number, or load, of a sample. Additionally, DNA can be detected long after a microorganism is dead, making it unsafe to assume that all microbial sequences detected in a community came from living organisms. By combining relic DNA removal by propidium monoazide (PMA) with microbial quantification with flow cytometry, we present a novel workflow to quantify live microbial load in parallel with metagenomic sequencing. We applied this method to unstimulated saliva samples, which can easily be collected longitudinally and standardized by passive collection time. We found that the number of live microorganisms detected in saliva was inversely correlated with salivary flow rate and fluctuated by an order of magnitude throughout the day in healthy individuals. In an acute perturbation experiment, alcohol-free mouthwash resulted in a massive decrease in live bacteria, which would have been missed if we did not consider dead cell signal. While removing relic DNA from saliva samples did not greatly impact the microbial composition, it did increase our resolution among samples collected over time. These results provide novel insight into the dynamic nature of host-associated microbiomes and underline the importance of applying scale-invariant tools in the analysis of next-generation sequencing data sets. IMPORTANCE Human microbiomes are dynamic ecosystems often composed of hundreds of unique microbial taxa. To detect fluctuations over time in the human oral microbiome, we developed a novel workflow to quantify live microbial cells with flow cytometry in parallel with next-generation sequencing, and applied this method to over 150 unstimulated, timed saliva samples. Microbial load was inversely correlated with salivary flow rate and fluctuated by an order of magnitude within a single participant throughout the day. Removing relic DNA improved our ability to distinguish samples over time and revealed that the percentage of sequenced bacteria in a given saliva sample that are alive can range from nearly 0% up to 100% throughout a typical day. These findings highlight the dynamic ecosystem of the human oral microbiome and the benefit of removing relic DNA signals in longitudinal microbiome study designs.

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