Evidence map›Paper›PMID 31127310›Full record

ArticleNucleic acids research2019

High efficiency error suppression for accurate detection of low-frequency variants.

Ting Ting Wang, Sagi Abelson, Jinfeng Zou, Tiantian Li, Zhen Zhao, John E Dick, Liran I Shlush, Trevor J Pugh, Scott V Bratman

Abstract read
In one paragraph

Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
–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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. A Review of Circulating Tumor DNA (ctDNA) and the Liquid Biopsy in Cancer Diagnosis, Screening, and Monitoring Treatment Response.Medical science monitor : international medical journal of experimental and clinical research · 2025
    Review
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  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

9 authors.

Ting Ting WangDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Sagi AbelsonPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Jinfeng ZouPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Tiantian LiPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Zhen ZhaoPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
John E DickPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Liran I ShlushPrincess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Trevor J PughDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Scott V BratmanDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of cancer-associated somatic mutations has broad applications for oncology and precision medicine. However, this becomes challenging when cancer-derived DNA is in low abundance, such as in impure tissue specimens or in circulating cell-free DNA. Next-generation sequencing (NGS) is particularly prone to technical artefacts that can limit the accuracy for calling low-allele-frequency mutations. State-of-the-art methods to improve detection of low-frequency mutations often employ unique molecular identifiers (UMIs) for error suppression; however, these methods are highly inefficient as they depend on redundant sequencing to assemble consensus sequences. Here, we present a novel strategy to enhance the efficiency of UMI-based error suppression by retaining single reads (singletons) that can participate in consensus assembly. This 'Singleton Correction' methodology outperformed other UMI-based strategies in efficiency, leading to greater sensitivity with high specificity in a cell line dilution series. Significant benefits were seen with Singleton Correction at sequencing depths ≤16 000×. We validated the utility and generalizability of this approach in a cohort of >300 individuals whose peripheral blood DNA was subjected to hybrid capture sequencing at ∼5000× depth. Singleton Correction can be incorporated into existing UMI-based error suppression workflows to boost mutation detection accuracy, thus improving the cost-effectiveness and clinical impact of NGS.

Indexed as

MutationAllelesCell Line, TumorDNA Barcoding, TaxonomicFetal BloodGene FrequencyHCT116 CellsHigh-Throughput Nucleotide SequencingHumansLeukemia, Myeloid, AcuteLeukocytes, MononuclearNeoplasm ProteinsPrecision MedicineScientific Experimental ErrorSequence Analysis, DNANeoplasm Proteins

Identifiers

PMID31127310
PMCPMC6735726

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.