Evidence map›Paper›PMID 38519720›Full record

ArticleNature biotechnology2025

De novo and somatic structural variant discovery with SVision-pro.

Songbo Wang, Jiadong Lin, Peng Jia, Tun Xu, Xiujuan Li, Yuezhuangnan Liu, Dan Xu, Stephen J Bush, Deyu Meng, Kai Ye

Abstract read
In one paragraph

Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. cuteHap: Haplotype-Aware Structural Variant Detection in Phased Long-Read Sequencing Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Benchmark for simple and complex genome inversions.bioRxiv : the preprint server for biology · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Long and Accurate: How HiFi Sequencing is Transforming Genomics.Genomics, proteomics & bioinformatics · 2025
    Review
  18. Article
  19. Review
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

10 authors.

Songbo WangDepartment of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Jiadong LinSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
Peng JiaDepartment of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.ORCID http://orcid.org/0000-0002-3429-919X
Tun XuSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.ORCID http://orcid.org/0000-0003-3194-1834
Xiujuan LiSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
Yuezhuangnan LiuSchool of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Dan XuSchool of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Stephen J BushSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
Deyu MengMOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
Kai YeDepartment of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn.ORCID http://orcid.org/0000-0002-2851-6741

Funding

National Natural Science Foundation of China (National Science Foundation of China) 12226004National Natural Science Foundation of China (National Science Foundation of China) 32070134National Natural Science Foundation of China (National Science Foundation of China) 32070663National Natural Science Foundation of China (National Science Foundation of China) 32270188National Natural Science Foundation of China (National Science Foundation of China) 62272375National Natural Science Foundation of China (National Science Foundation of China) 62302386
6 · The paper itself

Abstract

Long-read-based de novo and somatic structural variant (SV) discovery remains challenging, necessitating genomic comparison between samples. We developed SVision-pro, a neural-network-based instance segmentation framework that represents genome-to-genome-level sequencing differences visually and discovers SV comparatively between genomes without any prerequisite for inference models. SVision-pro outperforms state-of-the-art approaches, in particular, the resolving of complex SVs is improved, with low Mendelian error rates, high sensitivity of low-frequency SVs and reduced false-positive rates compared with SV merging approaches.

Indexed as

GenomicsGenomic Structural VariationSoftwareAlgorithmsGenome, HumanHigh-Throughput Nucleotide SequencingHumansNeural Networks, ComputerSequence Analysis, DNA

Identifiers

PMID38519720
PMCPMC11825360

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Registered trials

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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.