ArticleViruses2020
A Systematic Evaluation of High-Throughput Sequencing Approaches to Identify Low-Frequency Single Nucleotide Variants in Viral Populations.
Article in Viruses, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Genetic and biological characterization of a reassortant H3N2 swine influenza virus isolated in China with internal genes from the 2009 pandemic H1N1.BMC microbiology · 2026Article
- Molecular identification, isolation and functional characterization of a glutathione S-transferase gene CsGST in saffron (Crocus sativus L.).Scientific reports · 2026Article
- An in vitro approach reveals molecular mechanisms underlying endocrine disruptor-induced epimutagenesis.eLife · 2024Article
- Identification and quantitation of multiple variants in RNA virus genomes.Biology methods & protocols · 2024Article
- Article
- Accelerating the alignment processing speed of the comprehensive end-to-end whole-genome bisulfite sequencing pipeline, wg-blimp.Biology methods & protocols · 2023Article
- A general approach to identify low-frequency variants within influenza samples collected during routine surveillance.Microbial genomics · 2022Article
- Establishing an In Vitro System to Assess How Specific Antibodies Drive the Evolution of Foot-and-Mouth Disease Virus.Viruses · 2022Article
- Computational methods for chromosome-scale haplotype reconstruction.Genome biology · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
High-throughput sequencing such as those provided by Illumina are an efficient way to understand sequence variation within viral populations. However, challenges exist in distinguishing process-introduced error from biological variance, which significantly impacts our ability to identify sub-consensus single-nucleotide variants (SNVs). Here we have taken a systematic approach to evaluate laboratory and bioinformatic pipelines to accurately identify low-frequency SNVs in viral populations. Artificial DNA and RNA "populations" were created by introducing known SNVs at predetermined frequencies into template nucleic acid before being sequenced on an Illumina MiSeq platform. These were used to assess the effects of abundance and starting input material type, technical replicates, read length and quality, short-read aligner, and percentage frequency thresholds on the ability to accurately call variants. Analyses revealed that the abundance and type of input nucleic acid had the greatest impact on the accuracy of SNV calling as measured by a micro-averaged Matthews correlation coefficient score, with DNA and high RNA inputs (10
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