scholarly journals SARS-CoV-2 Evolution among Oncological Population: In-Depth Virological Analysis of a Clinical Cohort

2021 ◽  
Vol 9 (10) ◽  
pp. 2145
Author(s):  
Florian Laubscher ◽  
Samuel Cordey ◽  
Alex Friedlaender ◽  
Cecilia Schweblin ◽  
Sarah Noetzlin ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Cohort Analysis ◽  
Rapid Evolution ◽  
Nasopharyngeal Swab ◽  
Bioinformatics Pipeline ◽  
Oncological Patient ◽  
Patient Cohort ◽  
Oncological Patients ◽  
Minority Variants

Background: Oncological patients have a higher risk of prolonged SARS-CoV-2 shedding, which, in turn, can lead to evolutionary mutations and emergence of novel viral variants. The aim of this study was to analyze biological samples of a cohort of oncological patients by deep sequencing to detect any significant viral mutations. Methods: High-throughput sequencing was performed on selected samples from a SARS-CoV-2-positive oncological patient cohort. Analysis of variants and minority variants was performed using a validated bioinformatics pipeline. Results: Among 54 oncological patients, we analyzed 12 samples of 6 patients, either serial nasopharyngeal swab samples or samples from the upper and lower respiratory tracts, by high-throughput sequencing. We identified amino acid changes D614G and P4715L as well as mutations at nucleotide positions 241 and 3037 in all samples. There were no other significant mutations, but we observed intra-host evolution in some minority variants, mainly in the ORF1ab gene. There was no significant mutation identified in the spike region and no minority variants common to several hosts. Conclusions: There was no major and rapid evolution of viral strains in this oncological patient cohort, but there was minority variant evolution, reflecting a dynamic pattern of quasi-species replication.

scholarly journals MiDRMpol: A High-Throughput Multiplexed Amplicon Sequencing Workflow to Quantify HIV-1 Drug Resistance Mutations against Protease, Reverse Transcriptase, and Integrase Inhibitors

Viruses ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 806
Author(s):  
Shambhu G. Aralaguppe ◽  
Anoop T. Ambikan ◽  
Manickam Ashokkumar ◽  
Milner M. Kumar ◽  
Luke Elizabeth Hanna ◽  
...  
Keyword(s):  
Drug Resistance ◽  
High Throughput ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Polymorphism Analysis ◽  
Resistance Mutations ◽  
Subtype C ◽  
Bioinformatics Pipeline ◽  
Drug Resistance Mutations ◽  
Hiv 1

The detection of drug resistance mutations (DRMs) in minor viral populations is of potential clinical importance. However, sophisticated computational infrastructure and competence for analysis of high-throughput sequencing (HTS) data lack at most diagnostic laboratories. Thus, we have proposed a new pipeline, MiDRMpol, to quantify DRM from the HIV-1 pol region. The gag-vpu region of 87 plasma samples from HIV-infected individuals from three cohorts was amplified and sequenced by Illumina HiSeq2500. The sequence reads were adapter-trimmed, followed by analysis using in-house scripts. Samples from Swedish and Ethiopian cohorts were also sequenced by Sanger sequencing. The pipeline was validated against the online tool PASeq (Polymorphism Analysis by Sequencing). Based on an error rate of <1%, a value of >1% was set as reliable to consider a minor variant. Both pipelines detected the mutations in the dominant viral populations, while discrepancies were observed in minor viral populations. In five HIV-1 subtype C samples, minor mutations were detected at the <5% level by MiDRMpol but not by PASeq. MiDRMpol is a computationally as well as labor efficient bioinformatics pipeline for the detection of DRM from HTS data. It identifies minor viral populations (<20%) of DRMs. Our method can be incorporated into large-scale surveillance of HIV-1 DRM.

scholarly journals High-throughput metabarcoding characterises fungal endophyte diversity in the phyllosphere of a barley crop

2021 ◽  
Author(s):  
Carla Bridget Milazzo ◽  
Katherine Grace Zulak ◽  
Mariano Jordi Muria-Gonzalez ◽  
Darcy Jones ◽  
Matthew Power ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Large Subunit ◽  
Fungal Endophytes ◽  
Environmental Dna ◽  
Fungal Endophyte ◽  
Bioinformatics Pipeline ◽  
Pests And Diseases ◽  
Key Factor ◽  
Agroecosystem Management

Over the last decade, the microbiome has received increasing attention as a key factor in macroorganism fitness. Sustainable pest management requires an understanding of the complex microbial endophyte communities existing symbiotically within plants and the way synthetic pesticides interact with them. Fungal endophytes are known to benefit plant growth and fitness and may deter pests and diseases. Recent advances in high-throughput sequencing (HTS) have enabled integrative microbiome studies especially in agricultural contexts. Here we profile the fungal endophyte community in the phyllosphere of two barley (Hordeum vulgare) cultivars exposed to two systemic foliar fungicides using metabarcoding, a HTS tool that constructs community profiles from environmental DNA (eDNA). We studied the fungal nuclear ribosomal large subunit (LSU) D2 and ITS2 DNA markers through a bioinformatics pipeline introduced here. We found 88 and 128 unique amplicon sequence variants (ASVs) using the D2 and ITS2 metabarcoding assays, respectively. With principal coordinate analysis (PCoA) and PERMANOVA, ASV diversity did not change in response to barley cultivar or fungicide treatment, however the community structure of unsprayed plants did change between two collection times eight days apart. The workflow described here can be applied to other microbiome studies in agriculture and we hope it encourages further research into crop microbiomes to improve agroecosystem management.

scholarly journals Virus Detection by High-Throughput Sequencing of Small RNAs: Large-Scale Performance Testing of Sequence Analysis Strategies

2019 ◽  
Vol 109 (3) ◽  
pp. 488-497 ◽  
Author(s):  
Sebastien Massart ◽  
Michela Chiumenti ◽  
Kris De Jonghe ◽  
Rachel Glover ◽  
Annelies Haegeman ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Performance Test ◽  
Virus Detection ◽  
Performance Testing ◽  
Plant Viruses ◽  
Reference Sequence ◽  
Bioinformatics Pipeline ◽  
Double Blind

Recent developments in high-throughput sequencing (HTS), also called next-generation sequencing (NGS), technologies and bioinformatics have drastically changed research on viral pathogens and spurred growing interest in the field of virus diagnostics. However, the reliability of HTS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at detecting viruses in HTS data have been reported but little attention has been paid thus far to their sensitivity and reliability for diagnostic purposes. Therefore, we compared the ability of 21 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 12 plant viruses through a double-blind large-scale performance test using 10 datasets of 21- to 24-nucleotide small RNA (sRNA) sequences from three different infected plants. The sensitivity of virus detection ranged between 35 and 100% among participants, with a marked negative effect when sequence depth decreased. The false-positive detection rate was very low and mainly related to the identification of host genome-integrated viral sequences or misinterpretation of the results. Reproducibility was high (91.6%). This work revealed the key influence of bioinformatics strategies for the sensitive detection of viruses in HTS sRNA datasets and, more specifically (i) the difficulty in detecting viral agents when they are novel or their sRNA abundance is low, (ii) the influence of key parameters at both assembly and annotation steps, (iii) the importance of completeness of reference sequence databases, and (iv) the significant level of scientific expertise needed when interpreting pipeline results. Overall, this work underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.

scholarly journals Improved detection of artifactual viral minority variants in high-throughput sequencing data

2015 ◽  
Vol 5 ◽  
Author(s):  
Matthijs R. A. Welkers ◽  
Marcel Jonges ◽  
Rienk E. Jeeninga ◽  
Marion P. G. Koopmans ◽  
Menno D. de Jong
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Minority Variants

scholarly journals V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput sequencing data

2020 ◽  
Author(s):  
Susana Posada-Céspedes ◽  
David Seifert ◽  
Ivan Topolsky ◽  
Karin J. Metzner ◽  
Niko Beerenwinkel
Keyword(s):  
Genetic Diversity ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Viral Infections ◽  
Markov Models ◽  
Large Data ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Bioinformatics Pipeline ◽  
The Impact

AbstractHigh-throughput sequencing technologies are used increasingly, not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence, and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations. To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape. V-pipe is freely available at https://github.com/cbg-ethz/V-pipe.

scholarly journals A targeted subgenomic approach for phylogenomics based on microfluidic PCR and high throughput sequencing

2015 ◽  
Author(s):  
Simon Uribe-Convers ◽  
Matthew L Settles ◽  
David C Tank
Keyword(s):  
High Throughput ◽  
Evolutionary Biology ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Nuclear Genome ◽  
Sequence Information ◽  
Biological Sequence ◽  
Bioinformatics Pipeline ◽  
Genomic Libraries

Advances in high-throughput sequencing (HTS) have allowed researchers to obtain large amounts of biological sequence information at speeds and costs unimaginable only a decade ago. Phylogenetics, and the study of evolution in general, is quickly migrating towards using HTS to generate larger and more complex molecular datasets. In this paper, we present a method that utilizes microfluidic PCR and HTS to generate large amounts of sequence data suitable for phylogenetic analyses. The approach uses a Fluidigm microfluidic PCR array and two sets of PCR primers to simultaneously amplify 48 target regions across 48 samples, incorporating sample-specific barcodes and HTS adapters (2,304 unique amplicons per microfluidic array). The final product is a pooled set of amplicons ready to be sequenced, and thus, there is no need to construct separate, costly genomic libraries for each sample. Further, we present a bioinformatics pipeline to process the raw HTS reads to either generate consensus sequences (with or without ambiguities) for every locus in every sample or—more importantly—recover the separate alleles from heterozygous target regions in each sample. This is important because it adds allelic information that is well suited for coalescent-based phylogenetic analyses that are becoming very common in conservation and evolutionary biology. To test our subgenomic method and bioinformatics pipeline, we sequenced 576 samples across 96 target regions belonging to the South American clade of the genus Bartsia L. in the plant family Orobanchaceae. After sequencing cleanup and alignment, the experiment resulted in ~25,300bp across 486 samples for a set of 48 primer pairs targeting the plastome, and ~13,500bp for 363 samples for a set of primers targeting regions in the nuclear genome. Finally, we constructed a combined concatenated matrix from all 96 primer combinations, resulting in a combined aligned length of ~40,500bp for 349 samples.

scholarly journals Considerations for Optimization of High-Throughput Sequencing Bioinformatics Pipelines for Virus Detection

Viruses ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 528 ◽  
Author(s):  
Christophe Lambert ◽  
Cassandra Braxton ◽  
Robert Charlebois ◽  
Avisek Deyati ◽  
Paul Duncan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computational Cost ◽  
Virus Detection ◽  
Computation Time ◽  
Memory Storage ◽  
Risk Level ◽  
Bioinformatics Pipeline ◽  
Sequence Identification ◽  
Data Volume

High-throughput sequencing (HTS) has demonstrated capabilities for broad virus detection based upon discovery of known and novel viruses in a variety of samples, including clinical, environmental, and biological. An important goal for HTS applications in biologics is to establish parameter settings that can afford adequate sensitivity at an acceptable computational cost (computation time, computer memory, storage, expense or/and efficiency), at critical steps in the bioinformatics pipeline, including initial data quality assessment, trimming/cleaning, and assembly (to reduce data volume and increase likelihood of appropriate sequence identification). Additionally, the quality and reliability of the results depend on the availability of a complete and curated viral database for obtaining accurate results; selection of sequence alignment programs and their configuration, that retains specificity for broad virus detection with reduced false-positive signals; removal of host sequences without loss of endogenous viral sequences of interest; and use of a meaningful reporting format, which can retain critical information of the analysis for presentation of readily interpretable data and actionable results. Furthermore, after alignment, both automated and manual evaluation may be needed to verify the results and help assign a potential risk level to residual, unmapped reads. We hope that the collective considerations discussed in this paper aid toward optimization of data analysis pipelines for virus detection by HTS.

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