Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

Vries, Jutte J.C. de; Brown, Julianne R.; Fischer, Nicole; Sidorov, Igor A.; Morfopoulou, Sofia; Huang, Jia-Bin; Munnink, Bas B. Oude; Sayıner, Arzu; Bulgurcu, Alihan; Rodriguez, Christophe; Gricourt, Guillaume; Keyaerts, Els; Beller, Leen; Bachofen, Claudia; Kubacki, Jakub; Cordey, Samuel; Laubscher, Florian; Schmitz, Dennis; Beer, Martin; Hoeper, Dirk; Huber, Michael; Kufner, Verena; Zaheri, Maryam; Lebrand, Aitana; Papa, Anna; Boheemen, Sander van; Kroes, Aloys C.M.; Breuer, Judith; López‐Labrador, F. Xavier; Claas, Eric C. J.

doi:10.1016/j.jcv.2021.104908

Cited by 45 publications

(65 citation statements)

References 41 publications

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“…Here, we calculated a ratio between samples and controls in a similar manner as previously described 13 , which effectively removed the majority of background, leaving a manageable list of possible pathogens. Recently, guidelines from the European Society of Clinical Virology (ESCV) have been published with regards to both experimental procedures and bioinformatic pipelines, outlining the importance of careful consideration of each step in order to gain trustworthy results with minimal contamination 14 , 15 , 29 . We propose that information of the leukocyte content would also aid in the effort to determine whether a possible pathogen might be detectable.…”

Section: Discussionmentioning

confidence: 99%

Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics

Olausson

Brunet

Vracar

et al. 2022

Sci Rep

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Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare microbes and novel pathogens. However, several major hurdles remain, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. Twenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. The detection threshold for each sample was calculated by using the total leukocyte content in the sample and environmental contaminants found in the bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to the calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to the calculations. False positive pathogens of computational or environmental origin were readily identified, by using a commonly available cell control. For bacteria, additional filters including a comparison between classifiers removed the remaining false positives and alleviated pathogen identification. Here we show a generalizable method for identification of pathogen species using DNA metagenomic sequencing. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens requires multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads.

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Section: Discussionmentioning

confidence: 99%

Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics

Olausson

Brunet

Vracar

et al. 2022

Sci Rep

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“…For Kraken discrepant classification results were observed, likely due to differences in the databases used by the participants. A recent European benchmark of thirteen bioinformatic pipelines currently in use for metagenomic virus diagnostics used datasets from clinical samples [16] Analyses using Centrifuge, and Genome Detective software resulted in sensitivities of 93% and 87% respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Performance testing is typically part of the implementation procedure in diagnostic laboratories to ensure the quality of diagnostic test results. Accurate bioinformatic identification of viral pathogens depends on both the classification algorithm and the database [14][15] [16]. Metagenomic sequencing in the past has been mainly oriented at profiling of bacterial genomes in the context of microbiome comparisons in research settings, and most bioinformatic tools currently available have been designed for that specific purpose [17] [18].…”

Section: Introductionmentioning

confidence: 99%

Performance of five metagenomic classifiers for virus pathogen detection using respiratory samples from a clinical cohort

Carbo

Sidorov

Rijn-Klink

et al. 2022

Preprint

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Viral metagenomics is increasingly being applied in clinical diagnostic settings for detection of pathogenic viruses. While a number of benchmarking studies have been published on the use of metagenomic classifiers for abundance and diversity profiling of bacterial populations, studies on the comparative performance of the classifiers for virus pathogen detection are scarce. In this study, metagenomic data sets (N=88) from a clinical cohort of patients with respiratory complaints were used for comparison of the performance of five taxonomic classifiers: Centrifuge, Clark, Kaiju, Kraken2, and Genome Detective. A total of 1,144 positive and negative PCR results for a total of 13 respiratory viruses were used as gold standard. Sensitivity and specificity of these classifiers ranged from 83-100% and 90-99% respectively, and was dependent on the classification level and data pre-processing. Exclusion of human reads generally resulted in increased specificity. Normalization of read counts for genome length resulted in minor overall performance, however negatively affected the detection of targets with read counts around detection level. Correlation of sequence read counts with PCR Ct-values varied per classifier, data pre-processing (R2 range 15.1-63.4%), and per virus, with outliers up to 3 log10 reads magnitude beyond the predicted read count for viruses with high sequence diversity. In this benchmarking study, sensitivity and specificity were within the ranges of use for diagnostic practice when the cut-off for defining a positive result was considered per classifier.

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“…After demultiplexing of the sequence reads using bcl2fastq (version 2.2.0) (Illumina, San Diego, CA, USA), FASTQ files were uploaded to the Galileo Analytics web application [13,15] which automatically processes data for quality assessment and pathogen detection using a custom database of DNA viruses involved in transplant-associated infections: ADV, CMV, EBV, HHV-6A, HHV-6B, HSV-1, HSV-2, JCV, VZV, B19V, and TTV. Human reads were removed before uploading the fastq files to the web application after mapping them to the human reference genome GRCh38 with Bowtie2 version 2.3.4 [6]. The analytics web application aligns sequence reads to the genomes of the DNA viruses in their calibration kit, scores these read alignments based on complexity, uniqueness, and alignment scores, and reports this in a signal value.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

“…Metagenomic next-generation sequencing (mNGS) is increasingly being applied for the identification of pathogens in undiagnosed cases suspected of infection [2][3][4]. Quantification of viral loads utilising mNGS remains a challenge [5][6][7][8]. Complicating factors are the varying amount of background sequences from the host and from bacterial origin, technical bias affecting target sequence depth, unselective attribution of reads, and the number of calibration curves that are needed simultaneously when using untargeted sequencing for viral load calculations.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Monitoring of DNA Viral Loads in Transplant Patients Using Quantitative Metagenomic Next-Generation Sequencing

et al. 2022

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Introduction: Immunocompromised patients are prone to reactivations and (re-)infections of multiple DNA viruses. Viral load monitoring by single-target quantitative PCRs (qPCR) is the current cornerstone for virus quantification. In this study, a metagenomic next-generation sequencing (mNGS) approach was used for the identification and load monitoring of transplantation-related DNA viruses. Methods: Longitudinal plasma samples from six patients that were qPCR-positive for cytomegalovirus (CMV), Epstein-Barr virus (EBV), BK polyomavirus (BKV), adenovirus (ADV), parvovirus B19 (B19V), and torque teno-virus (TTV) were sequenced using the quantitative metagenomic Galileo Viral Panel Solution (Arc Bio, LLC, Cambridge, MA, USA) reagents and bioinformatics pipeline combination. Qualitative and quantitative performance was analysed with a focus on viral load ranges relevant for clinical decision making. Results: All pathogens identified by qPCR were also identified by mNGS. BKV, CMV, and HHV6B were additionally detected by mNGS, and could be confirmed by qPCR or auxiliary bioinformatic analysis. Viral loads determined by mNGS correlated with the qPCR results, with inter-method differences in viral load per virus ranging from 0.19 log10 IU/mL for EBV to 0.90 log10 copies/mL for ADV. TTV, analysed by mNGS in a semi-quantitative way, demonstrated a mean difference of 3.0 log10 copies/mL. Trends over time in viral load determined by mNGS and qPCR were comparable, and clinical thresholds for initiation of treatment were equally identified by mNGS. Conclusions: The Galileo Viral Panel for quantitative mNGS performed comparably to qPCR concerning detection and viral load determination, within clinically relevant ranges of patient management algorithms.

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Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

Cited by 45 publications

References 41 publications

Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics

Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics

Performance of five metagenomic classifiers for virus pathogen detection using respiratory samples from a clinical cohort

Longitudinal Monitoring of DNA Viral Loads in Transplant Patients Using Quantitative Metagenomic Next-Generation Sequencing

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