Detection of viral pathogens with multiplex Nanopore MinION sequencing: be careful with cross-talk

Mapping Intimacies ◽

10.1101/308262 ◽

2018 ◽

Author(s):

Yifei Xu ◽

Kuiama Lewandowski ◽

Sheila Lumley ◽

Steven Pullan ◽

Richard Vipond ◽

...

Keyword(s):

Influenza A ◽

Point Of Care ◽

Clinical Settings ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Viral Pathogens ◽

Oxford Nanopore ◽

Downstream Analysis ◽

Multiplex Sequencing ◽

Single Flow

AbstractMetagenomic sequencing with the Oxford Nanopore MinION sequencer offers potential for point-of-care testing of infectious diseases in clinical settings. To improve cost-effectiveness, multiplexing of several, barcoded samples upon a single flow cell will be required during sequencing. We generated a unique sequencing dataset to assess the extent and source of cross barcode contamination caused by multiplex MinION sequencing. Sequencing libraries for three different viruses, including influenza A, dengue and chikungunya, were prepared separately and sequenced on individual flow cells. We also pooled the respective libraries and performed multiplex sequencing. We identified 0.056% of total reads in the multiplex sequencing data that were assigned to incorrect barcodes. Chimeric reads were the predominant source of this error. Our findings highlight the need for careful filtering of multiplex sequencing data before downstream analysis, and the trade-off between sensitivity and specificity that applies to the barcode demultiplexing methods.

Assessment of Viral Targeted Sequence Capture Using Nanopore Sequencing Directly from Clinical Samples

Viruses ◽

10.3390/v12121358 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1358

Author(s):

Leonard Schuele ◽

Hayley Cassidy ◽

Erley Lizarazo ◽

Katrin Strutzberg-Minder ◽

Sabine Schuetze ◽

...

Keyword(s):

Influenza A ◽

Simultaneous Detection ◽

Clinical Samples ◽

Metagenomic Sequencing ◽

Genome Coverage ◽

Sequence Capture ◽

Shotgun Metagenomic Sequencing ◽

Oxford Nanopore ◽

The One ◽

Targeted Sequence Capture

Shotgun metagenomic sequencing (SMg) enables the simultaneous detection and characterization of viruses in human, animal and environmental samples. However, lack of sensitivity still poses a challenge and may lead to poor detection and data acquisition for detailed analysis. To improve sensitivity, we assessed a broad scope targeted sequence capture (TSC) panel (ViroCap) in both human and animal samples. Moreover, we adjusted TSC for the Oxford Nanopore MinION and compared the performance to an SMg approach. TSC on the Illumina NextSeq served as the gold standard. Overall, TSC increased the viral read count significantly in challenging human samples, with the highest genome coverage achieved using the TSC on the MinION. TSC also improved the genome coverage and sequencing depth in clinically relevant viruses in the animal samples, such as influenza A virus. However, SMg was shown to be adequate for characterizing a highly diverse animal virome. TSC on the MinION was comparable to the NextSeq and can provide a valuable alternative, offering longer reads, portability and lower initial cost. Developing new viral enrichment approaches to detect and characterize significant human and animal viruses is essential for the One Health Initiative.

A Long-Read Sequencing Approach for Direct Haplotype Phasing in Clinical Settings

International Journal of Molecular Sciences ◽

10.3390/ijms21239177 ◽

2020 ◽

Vol 21 (23) ◽

pp. 9177

Author(s):

Simone Maestri ◽

Maria Giovanna Maturo ◽

Emanuela Cosentino ◽

Luca Marcolungo ◽

Barbara Iadarola ◽

...

Keyword(s):

Diagnostic Testing ◽

Variant Calling ◽

Clinical Settings ◽

Sequencing Data ◽

Sequencing Platform ◽

Variant Discovery ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

Second Generation Sequencing

The reconstruction of individual haplotypes can facilitate the interpretation of disease risks; however, high costs and technical challenges still hinder their assessment in clinical settings. Second-generation sequencing is the gold standard for variant discovery but, due to the production of short reads covering small genomic regions, allows only indirect haplotyping based on statistical methods. In contrast, third-generation methods such as the nanopore sequencing platform developed by Oxford Nanopore Technologies (ONT) generate long reads that can be used for direct haplotyping, with fewer drawbacks. However, robust standards for variant phasing in ONT-based target resequencing efforts are not yet available. In this study, we presented a streamlined proof-of-concept workflow for variant calling and phasing based on ONT data in a clinically relevant 12-kb region of the APOE locus, a hotspot for variants and haplotypes associated with aging-related diseases and longevity. Starting with sequencing data from simple amplicons of the target locus, we demonstrated that ONT data allow for reliable single-nucleotide variant (SNV) calling and phasing from as little as 60 reads, although the recognition of indels is less efficient. Even so, we identified the best combination of ONT read sets (600) and software (BWA/Minimap2 and HapCUT2) that enables full haplotype reconstruction when both SNVs and indels have been identified previously using a highly-accurate sequencing platform. In conclusion, we established a rapid and inexpensive workflow for variant phasing based on ONT long reads. This allowed for the analysis of multiple samples in parallel and can easily be implemented in routine clinical practice, including diagnostic testing.

Current challenges and best-practice protocols for microbiome analysis

Briefings in Bioinformatics ◽

10.1093/bib/bbz155 ◽

2019 ◽

Cited By ~ 6

Author(s):

Richa Bharti ◽

Dominik G Grimm

Keyword(s):

16S Rrna ◽

Best Practice ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Experimental Conditions ◽

Sequencing Errors ◽

Sequencing Technologies ◽

Control Assembly ◽

Computationally Intensive ◽

Downstream Analysis

Abstract Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

Assembly methods for nanopore-based metagenomic sequencing: a comparative study

10.1101/722405 ◽

2019 ◽

Cited By ~ 1

Author(s):

Adriel Latorre-Pérez ◽

Pascual Villalba-Bermell ◽

Javier Pascual ◽

Manuel Porcar ◽

Cristina Vilanova

Keyword(s):

Bacterial Genome ◽

Gene Clusters ◽

Systematic Evaluation ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Short Reads ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Sequencing Platforms

ABSTRACTBackgroundMetagenomic sequencing has lead to the recovery of previously unexplored microbial genomes. In this sense, short-reads sequencing platforms often result in highly fragmented metagenomes, thus complicating downstream analyses. Third generation sequencing technologies, such as MinION, could lead to more contiguous assemblies due to their ability to generate long reads. Nevertheless, there is a lack of studies evaluating the suitability of the available assembly tools for this new type of data.FindingsWe benchmarked the ability of different short-reads and long-reads tools to assembly two different commercially available mock communities, and observed remarkable differences in the resulting assemblies depending on the software of choice. Short-reads metagenomic assemblers proved unsuitable for MinION data. Among the long-reads assemblers tested, Flye and Canu were the only ones performing well in all the datasets. These tools were able to retrieve complete individual genomes directly from the metagenome, and assembled a bacterial genome in only two contigs in the best scenario. Despite the intrinsic high error of long-reads technologies, Canu and Flye lead to high accurate assemblies (~99.4-99.8 % of accuracy). However, errors still had an impact on the prediction of biosynthetic gene clusters.ConclusionsMinION metagenomic sequencing data proved sufficient for assembling low-complex microbial communities, leading to the recovery of highly complete and contiguous individual genomes. This work is the first systematic evaluation of the performance of different assembly tools on MinION data, and may help other researchers willing to use this technology to choose the most appropriate software depending on their goals. Future work is still needed in order to assess the performance of Oxford Nanopore MinION data on more complex microbiomes.

Nanopore metagenomic sequencing of full length human metapneumovirus (HMPV) within a unique sub-lineage

10.1101/496687 ◽

2018 ◽

Cited By ~ 2

Author(s):

Yifei Xu ◽

Kuiama Lewandowski ◽

Sheila Lumley ◽

Nicholas D Sanderson ◽

Alison Vaughan ◽

...

Keyword(s):

Phylogenetic Analyses ◽

Human Metapneumovirus ◽

Full Length ◽

Clinical Settings ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Complete Sequences ◽

Genomic Characterization ◽

Tract Disease ◽

The Uk

Human metapneumovirus (HMPV) has been recognized as an important pathogen which can cause a spectrum of respiratory tract disease. Here, we report Nanopore metagenomic sequencing of the first full length HMPV genome directly from a throat swab from a UK patient with complex lung disease and immunocompromise. We found a predominance (26.4%) of HMPV reads in the metagenomic sequencing data and consequently assembled the full genome at a high depth of coverage (mean 4,786). Through phylogenetic analyses, we identified this HMPV strain to originate from a unique genetic group in A2b, showing the presence of this group in the UK. Our study demonstrated the effectiveness of Nanopore metagenomic sequencing for diagnosing infectious diseases and recovering complete sequences for genomic characterization, highlighting the applicability of Nanopore sequencing in clinical settings.

Enrichment of long DNA fragments from mixed samples for Nanopore sequencing

10.1101/048850 ◽

2016 ◽

Cited By ~ 1

Author(s):

SE Eckert ◽

JZ-M Chan ◽

Darren Houniet ◽

J Breuer ◽

G Speight ◽

...

Keyword(s):

Influenza A ◽

Illumina Miseq ◽

Clinical Samples ◽

Viral Pathogens ◽

High Background ◽

Oxford Nanopore ◽

Target Dna ◽

Speed Up ◽

One Step ◽

Personalised Treatment

AbstractWhole-genome sequencing of pathogenic organisms directly from clinical samples combines detection and genotyping in one step. This can speed up diagnosis, especially for slow-growing organisms like Mycobacterium tuberculosis (Mtb), which need considerable time to grow in subculture, and can provide vital information for effective personalised treatment. Within the PATHSEEK project, we have developed a bait-capture approach to selectively enrich DNA/RNA from specific bacterial and viral pathogens present in clinical samples. Here, we present a variation of the method that allows enrichment of large fragments of target DNA for sequencing on an Oxford Nanopore MinIONTM sequencer. We enriched and sequenced cDNA from Influenza A (FluA), genomic DNA (gDNA) from human cytomegalovirus (CMV) and from two strains of Mtb, and present an evaluation of the method together with analysis of the sequencing results from a MinIONTM and an Illumina MiSeq sequencer. While unenriched FluA and CMV samples had no reads matching the target organism due to the high background of DNA from host cell lines, enriched samples had 56.7% and 90.9% on-target reads respectively for the best quality Nanopore reads.

Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks

10.1101/366526 ◽

2018 ◽

Cited By ~ 6

Author(s):

Ryan R. Wick ◽

Louise M. Judd ◽

Kathryn E. Holt

Keyword(s):

Ground Truth ◽

Cost Effective ◽

Complex Problem ◽

Deep Convolutional Neural Networks ◽

Dna Base ◽

Oxford Nanopore ◽

Sequencing Cost ◽

Read Signal ◽

Multiplex Sequencing ◽

Single Flow

AbstractMultiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barcode, and current demultiplexing tools fail to classify many reads. Here we present Deepbinner, a tool for Oxford Nanopore demultiplexing that uses a deep neural network to classify reads based on the raw electrical read signal. This ‘signal-space’ approach allows for greater accuracy than existing ‘base-space’ tools (Albacore and Porechop) for which signals must first be converted to DNA base calls, itself a complex problem that can introduce noise into the barcode sequence. To assess Deepbinner and existing tools, we performed multiplex sequencing on 12 amplicons chosen for their distinguishability. This allowed us to establish a ground truth classification for each read based on internal sequence alone. Deepbinner had the lowest rate of unclassified reads (7.8%) and the highest demultiplexing precision (98.5% of classified reads were correctly assigned). It can be used alone (to maximise the number of classified reads) or in conjunction with other demultiplexers (to maximise precision and minimise false positive classifications). We also found cross-sample chimeric reads (0.3%) and evidence of barcode switching (0.3%) in our dataset, which likely arise during library preparation and may be detrimental for quantitative studies that use multiplexing. Deepbinner is open source (GPLv3) and available at https://github.com/rrwick/Deepbinner.

NanoSPC: a scalable, portable, cloud compatible viral nanopore metagenomic data processing pipeline

Nucleic Acids Research ◽

10.1093/nar/gkaa413 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W366-W371

Author(s):

Yifei Xu ◽

Fan Yang-Turner ◽

Denis Volk ◽

Derrick Crook

Keyword(s):

Point Of Care ◽

Treatment Strategies ◽

Metagenomic Data ◽

Infection Prevention And Control ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Point Of Care Test ◽

Elastic Computing ◽

Analyse Data

Abstract Metagenomic sequencing combined with Oxford Nanopore Technology has the potential to become a point-of-care test for infectious disease in public health and clinical settings, providing rapid diagnosis of infection, guiding individual patient management and treatment strategies, and informing infection prevention and control practices. However, publicly available, streamlined, and reproducible pipelines for analyzing Nanopore metagenomic sequencing data are still lacking. Here we introduce NanoSPC, a scalable, portable and cloud compatible pipeline for analyzing Nanopore sequencing data. NanoSPC can identify potentially pathogenic viruses and bacteria simultaneously to provide comprehensive characterization of individual samples. The pipeline can also detect single nucleotide variants and assemble high quality complete consensus genome sequences, permitting high-resolution inference of transmission. We implement NanoSPC using Nextflow manager within Docker images to allow reproducibility and portability of the analysis. Moreover, we deploy NanoSPC to our scalable pathogen pipeline platform, enabling elastic computing for high throughput Nanopore data on HPC cluster as well as multiple cloud platforms, such as Google Cloud, Amazon Elastic Computing Cloud, Microsoft Azure and OpenStack. Users could either access our web interface (https://nanospc.mmmoxford.uk) to run cloud-based analysis, monitor process, and visualize results, as well as download Docker images and run command line to analyse data locally.

Respiratory Viral Pathogens Among U.S. Military Personnel at a Medical Treatment Facility in Hawaii From 2014 to 2019

Military Medicine ◽

10.1093/milmed/usab191 ◽

2021 ◽

Author(s):

Agnes S Montgomery ◽

Michael B Lustik ◽

Susan A Reichert-Scrivner ◽

Ronald L Woodbury ◽

Milissa U Jones ◽

...

Keyword(s):

Military Personnel ◽

Influenza A ◽

Clinical Symptoms ◽

Medical Center ◽

Signs And Symptoms ◽

Military Operations ◽

Military Population ◽

Viral Pathogens ◽

Viral Pathogen ◽

Medical Treatment Facility

ABSTRACT Introduction Acute respiratory diseases account for a substantial number of outpatient visits and hospitalizations among U.S. military personnel, significantly affecting mission readiness and military operations. We conducted a retrospective analysis of respiratory viral pathogen (RVP) samples collected from U.S. military personnel stationed in Hawaii and tested at Tripler Army Medical Center from January 2014 to May 2019 in order to describe the etiology, distribution, and seasonality of RVP exposure in a military population. Materials and Methods Samples were analyzed by viral culture or multiplex PCR. Distribution of respiratory viruses over time was analyzed as well as subject demographic and encounter data. Presenting signs and symptoms were evaluated with each RVP. Results A total of 2,576 military personnel were tested, of which 726 (28.2%) were positive for one or more RVP. Among positive tests, the three most common viral pathogens detected were influenza A (43.0%), rhinovirus (24.5%), and parainfluenza (7.6%). Symptoms were generally mild and most frequently included cough, fever, and body aches. Conclusion Our study evaluated respiratory virus prevalence, seasonality, and association with clinical symptoms for military personnel in an urban tropical setting in Oahu, HI, over a 5-year period. We show that viral prevalence and seasonality in Hawaii are distinct from those of the CONUS. Results contribute to the broader understanding of seasonality, clinical manifestation, and demographics of RVP among active duty military personnel stationed in Hawaii.

Characterizing and Evaluating the Zoonotic Potential of Novel Viruses Discovered in Vampire Bats

Viruses ◽

10.3390/v13020252 ◽

2021 ◽

Vol 13 (2) ◽

pp. 252

Author(s):

Laura M. Bergner ◽

Nardus Mollentze ◽

Richard J. Orton ◽

Carlos Tello ◽

Alice Broos ◽

...

Keyword(s):

Machine Learning ◽

Phylogenetic Analyses ◽

Human Infection ◽

Machine Learning Algorithms ◽

Zoonotic Potential ◽

Metagenomic Sequencing ◽

Learning Models ◽

Sequencing Data ◽

Vampire Bats ◽

Machine Learning Models

The contemporary surge in metagenomic sequencing has transformed knowledge of viral diversity in wildlife. However, evaluating which newly discovered viruses pose sufficient risk of infecting humans to merit detailed laboratory characterization and surveillance remains largely speculative. Machine learning algorithms have been developed to address this imbalance by ranking the relative likelihood of human infection based on viral genome sequences, but are not yet routinely applied to viruses at the time of their discovery. Here, we characterized viral genomes detected through metagenomic sequencing of feces and saliva from common vampire bats (Desmodus rotundus) and used these data as a case study in evaluating zoonotic potential using molecular sequencing data. Of 58 detected viral families, including 17 which infect mammals, the only known zoonosis detected was rabies virus; however, additional genomes were detected from the families Hepeviridae, Coronaviridae, Reoviridae, Astroviridae and Picornaviridae, all of which contain human-infecting species. In phylogenetic analyses, novel vampire bat viruses most frequently grouped with other bat viruses that are not currently known to infect humans. In agreement, machine learning models built from only phylogenetic information ranked all novel viruses similarly, yielding little insight into zoonotic potential. In contrast, genome composition-based machine learning models estimated different levels of zoonotic potential, even for closely related viruses, categorizing one out of four detected hepeviruses and two out of three picornaviruses as having high priority for further research. We highlight the value of evaluating zoonotic potential beyond ad hoc consideration of phylogeny and provide surveillance recommendations for novel viruses in a wildlife host which has frequent contact with humans and domestic animals.