Evidence map›Paper›PMID 34031367›Full record

ArticleNature communications2021

Detecting and phasing minor single-nucleotide variants from long-read sequencing data.

Zhixing Feng, Jose C Clemente, Brandon Wong, Eric E Schadt

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Genetic analysis using long-read sequencing to overcome the difficulties inResearch and practice in thrombosis and haemostasis · 2025
    Article
  9. Review
  10. Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Homozygous, Intragenic Tandem Duplication ofAmerican journal of respiratory cell and molecular biology · 2024
    Article
  18. Review
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zhixing FengIcahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. zhixing.feng@mssm.edu.ORCID http://orcid.org/0000-0003-0308-8549
Jose C ClementeIcahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0002-3970-9856
Brandon WongDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-7644-1916
Eric E SchadtIcahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0002-7892-8808

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, and co-infection of multiple pathogens. Detecting and phasing minor variants play an instrumental role in deciphering cellular genetic heterogeneity, but they are still difficult tasks because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, provide an opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrate that iGDA can accurately reconstruct haplotypes in closely related strains of the same species (divergence ≥0.011%) from long-read metagenomic data.

Indexed as

NucleotidesAlgorithmsBacteriaBorreliaBorrelia burgdorferiCoinfectionComputational BiologyGenome, HumanHaplotypesHigh-Throughput Nucleotide SequencingHumansMetagenomeMethylationModels, StatisticalNanoporesNucleotides

Identifiers

PMID34031367
PMCPMC8144375

What OpenQuestion holds

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LicenceCC BY
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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.