Evidence map›Paper›PMID 41029866›Full record

ArticleGenome biology2025

SDFA: a standardized decomposition format and toolkit for efficient analysis of structural variants in large-scale population genomic studies.

Wenjie Peng, Liubin Zhang, Bin Tang, Junhao Liang, Zhi Liu, Lihang Ye, Yangyang Yuan, Yifei Wang, Ruijie Tan, Nan Lin and 4 more

Abstract read
In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Wenjie PengZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Liubin ZhangZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Bin TangZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Junhao LiangZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Zhi LiuZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Lihang YeZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Yangyang YuanSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Yifei WangZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Ruijie TanZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Nan LinZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Chao XueZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Hui JiangZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Li FangZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Miaoxin LiZhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China. limiaoxin@mail.sysu.edu.cn.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province No. 2022A1515110913National Natural Science Foundation of China 32300500National Natural Science Foundation of China 32470683
6 · The paper itself

Abstract

Structural variants (SVs) contribute significantly to genetic diversity yet present computational challenges during analysis. We introduce SDFA, a standardized decomposition format and toolkit for efficient analysis of SVs in large-scale population genomics. SDFA efficiently stores and retrieves all SV types while providing algorithms for consistent SV merging, memory-efficient annotation, and precise gene feature annotation across large cohorts. SDFA outperforms existing tools, achieving at least 17.64 times faster merging than four tools and 120.93 times faster annotation than three tools, and uniquely handles complex SVs. We validate SDFA on 895,054 SVs from 150,119 individuals in the UK Biobank dataset.

Indexed as

Genetics, PopulationGenomicsGenomic Structural VariationSoftwareAlgorithmsHumansCohort-wide mergeColumnar-block formatIndexed sliding-window annotationLarge-scale SV analysesNumeric annotation of gene featureStandardized decomposition

Identifiers

PMID41029866
PMCPMC12482569

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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.