Evidence map›Paper›PMID 40060913›Full record

ArticleiScience2025

Systematic benchmarking of tools for structural variation detection using short- and long-read sequencing data in pigs.

Sang He, Bangmin Song, Yueting Tang, Xiaolu Qu, Xingzheng Li, Xintong Yang, Qi Bao, Lingzhao Fang, Jicai Jiang, Zhonglin Tang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. 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

11 authors.

Sang HeShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Bangmin SongShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Yueting TangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Xiaolu QuShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Xingzheng LiShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Xintong YangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Qi BaoShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Lingzhao FangCenter for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark.
Jicai JiangDepartment of Animal Science, North Carolina State University, Raleigh, NC 27695, USA.
Zhonglin TangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Guoqiang YiShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating diverse structural variation (SV) detection-relevant programs leveraging different algorithms has become a pressing need in humans and farm animals. We addressed this by sequencing five genetically diverse pig individuals (breeds) with short- and long-read DNA-sequencing platforms. We created the SV benchmark set for each breed and assessed the performance of 16 SV calling-relevant tools. Results showed that long-read platforms enabled detecting many SVs missed by short-read platforms with similar precision. Benchmark SVs, mainly 200-500 bp insertions/deletions, had high validation rates. The assembly-based SV calling program SVIM-asm showed superior detection performance and resource consumption. The SVs with more supporting reads, sizes under 1 kb, outside simple repeat area, in low GC content and runs of homozygosity regions, had higher detection accuracy. Alignment-based tools performed well even at 5

Indexed as

BioinformaticsOmicsStructural biology

Identifiers

PMID40060913
PMCPMC11889634

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.