Evidence map›Paper›PMID 41891250›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2026

Bayesian Integrative Detection of Structural Variations With False Discovery Rate Control.

Sheng Lian, Jiandong Shi, Jingyu Hao, Zhen Zhang, Yongyi Luo, Taobo Hu, Depeng Wang, Xiaodan Fan, Shu Wang, Weichuan Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Sheng LianDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Jiandong ShiDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.ORCID 0000-0002-5178-8470
Jingyu HaoDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Zhen ZhangDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Yongyi LuoDepartment of Statistics and Data Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
Taobo HuDepartment of Breast Surgery, Peking University People's Hospital, Beijing, China.
Depeng WangGrandOmics Inc., Beijing, China.
Xiaodan FanDepartment of Statistics and Data Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.ORCID 0000-0002-2744-9030
Shu WangDepartment of Breast Surgery, Peking University People's Hospital, Beijing, China.
Weichuan YuDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.

Funding

HKSAR goverment 7015-23GHKSAR goverment MHP/033/20HKSAR goverment R4012-18HKSAR goverment T12-101/23-NNational Key Research and Development Program of China 2021YFE0203200
6 · The paper itself

Abstract

Recent advances in long-read sequencing technologies have empowered the detection of structural variations (SVs) associated with genetic diseases. Despite the availability of numerous SV callers and efforts to merge SVs from multiple tools, there remains limited research on quantifying the confidence levels for the reported results. In this work, we propose a Bayesian integration model that combines SV calls from different tools. Notably, we introduce an approach for false discovery rate (FDR) control and provide a quantitative measure for the merged SVs. Our model can handle cases where certain tools lack quality scores, showcasing its flexibility in incorporating additional tools. Through extensive simulation studies, we evaluate the performance of our method under various conditions, demonstrating the FDR estimation accuracy and improved F1 score. Furthermore, we validate our model using simulated human genome sequencing data and the HG002 dataset.

Indexed as

BiometryGenomic Structural VariationBayes TheoremFalse Positive ReactionsGenome, HumanHumansBayesian analysisfalse discovery rateintegrationstructural variation

Identifiers

PMID41891250
PMCPMC13022811

What OpenQuestion holds

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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.