Evidence map›Paper›PMID 35288552›Full record

ArticleNature communications2022

Rescuing low frequency variants within intra-host viral populations directly from Oxford Nanopore sequencing data.

Yunxi Liu, Joshua Kearney, Medhat Mahmoud, Bryce Kille, Fritz J Sedlazeck, Todd J Treangen

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Anti-CRISPR-mediated continuous directed evolution of CRISPR-Cas9 in human cells.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Nanopore sequencing in veterinary medicine: from concepts to clinical applications.Frontiers in cellular and infection microbiology · 2025
    Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yunxi Liu *Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Joshua Kearney *Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Medhat MahmoudHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA.ORCID 0000-0002-2553-4231
Bryce KilleDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Fritz J SedlazeckDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.ORCID 0000-0001-6040-2691
Todd J TreangenDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA. treangen@rice.edu.ORCID 0000-0002-3760-564X

Funding

Viral Diversity and Pathogenicity in Mucosal Respiratory and Gastrointestinal DiseaseU19AI144297 · NIAID · BAYLOR COLLEGE OF MEDICINE · PI MARESSO, ANTHONY W · 2019 to 2024
$30.1M
TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
Project 3: Functional Microbiome and Host Signatures in Transition from Commensal to pathogenP01AI152999 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ARIAS, CESAR AUGUSTO, SAVIDGE, TOR C. · 2020 to 2025
$12.0M
NIAID NIH HHS P01 AI152999NIAID NIH HHS U19 AI144297NLM NIH HHS T15 LM007093U.S. Department of Health & Human Services | Centers for Disease Control and Prevention (CDC) 75D30121C11180U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) 1P01AI152999-01U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) 1U19AI144297
6 · The paper itself

Abstract

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost. Tracking low frequency intra-host variants provides important insights with respect to elucidating within-host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable computational solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluate Variabel on both synthetic data (SARS-CoV-2) and patient derived datasets (Ebola virus, norovirus, SARS-CoV-2); our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel .

Indexed as

COVID-19NanoporesNanopore SequencingHigh-Throughput Nucleotide SequencingHumansSARS-CoV-2

Identifiers

PMID35288552
PMCPMC8921239

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.