Evidence map›Paper›PMID 37386186›Full record

ReviewNature methods2023

A survey of algorithms for the detection of genomic structural variants from long-read sequencing data.

Mian Umair Ahsan, Qian Liu, Jonathan Elliot Perdomo, Li Fang, Kai Wang

Abstract readReview
In one paragraph

Review in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing 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

66 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Somatic mutations and genome mosaicism in aging and disease.Experimental & molecular medicine · 2026
    Review
  7. Bony fish genomes: Status and gaps.Journal of fish biology · 2026
    Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

6 more citing papers are in PubMed but not listed here.

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

5 authors.

Mian Umair AhsanRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0003-4725-2451
Qian LiuRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Jonathan Elliot PerdomoRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Li FangRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0001-5456-0255
Kai WangRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA. wangk@chop.edu.ORCID http://orcid.org/0000-0002-5585-982X

Funding

Detection and annotation of structural variants from long-read sequencingR01GM132713 · NIGMS · CHILDREN'S HOSP OF PHILADELPHIA · PI WANG, KAI · 2019 to 2022
$1.9M
NIGMS NIH HHS GM132713NIGMS NIH HHS R01 GM132713
6 · The paper itself

Abstract

As long-read sequencing technologies are becoming increasingly popular, a number of methods have been developed for the discovery and analysis of structural variants (SVs) from long reads. Long reads enable detection of SVs that could not be previously detected from short-read sequencing, but computational methods must adapt to the unique challenges and opportunities presented by long-read sequencing. Here, we summarize over 50 long-read-based methods for SV detection, genotyping and visualization, and discuss how new telomere-to-telomere genome assemblies and pangenome efforts can improve the accuracy and drive the development of SV callers in the future.

Indexed as

AlgorithmsGenomeGenome, HumanGenomic Structural VariationHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNA

Identifiers

PMID37386186
PMCPMC11208083

What OpenQuestion holds

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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.