Evidence map›Paper›PMID 41646351›Full record

ArticleResearch square2026

Enhancing variant detection in complex genomes: leveraging linked reads for robust SNP, Indel, and structural variant analysis.

Can Luo, Yichen Henry Liu, Han Liu, Zhenmiao Zhang, Lu Zhang, Brock A Peters, Xin Maizie Zhou

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In one paragraph

Article in Research square, 2026. 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
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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

7 authors.

Can LuoDepartment of Biomedical Engineering, Vanderbilt University, 37235 Nashville, USA.
Yichen Henry LiuDepartment of Computer Science, Vanderbilt University, 37235 Nashville, USA.
Han LiuDepartment of Computer Science, Vanderbilt University, 37235 Nashville, USA.
Zhenmiao ZhangDepartment of Computer Science and Engineering, University of California San Diego, 92037 San Diego, USA.
Lu ZhangDepartment of Computer Science, Hong Kong Baptist University, Hong Kong, China.
Brock A PetersAdvanced Genomics Technology Lab, Complete Genomics Inc, 2904 Orchard Parkway, San Jose, CA 95134, USA.
Xin Maizie ZhouDepartment of Biomedical Engineering, Vanderbilt University, 37235 Nashville, USA.

Funding

Detecting structural variants in a large population of samples through high-throughput sequencing dataR35GM146960 · NIGMS · VANDERBILT UNIVERSITY · PI Xin Maizie Zhou · 2022 to 2026
$2.1M
NIGMS NIH HHS R35 GM146960
6 · The paper itself

Abstract

Background: Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is critical for comprehensive genomic analysis. While traditional short-read sequencing performs well for SNP and INDEL detection, it struggles to resolve SVs, especially in complex genomic regions, due to inherent read length limitations. Linked-read sequencing technologies, such as single-tube Long Fragment Read sequencing (stLFR), overcome these challenges by employing molecular barcodes, providing crucial long-range information. Methods: This study investigates traditional pair-end linked-reads and a conceptual extension of linked-read technology: barcoded single-end reads of 500 bp (SE500_stLFR) and 1000 bp (SE1000_stLFR), generated using the single-tube Long Fragment Read (stLFR) platform. Unlike conventional paired-end (PE100_stLFR) linked reads, these longer single-end reads could offer improved resolution for variant detection by leveraging extended read lengths per barcode. To explore the potential of stLFR reads, we developed stLFR-sim, a Python-based simulator that reproduces the stLFR linked-read sequencing workflow to enable realistic simulation and benchmarking of linked-read sequencing data. Using stLFR-sim, we simulated a diverse set of datasets for the HG002 sample using T2T-based realistic genome simulation. Variant detection performance was then systematically assessed across three stLFR configurations: standard PE100_stLFR, SE500_stLFR, and SE1000_stLFR. Results: Benchmarking against the Genome in a Bottle (GIAB) gold standard reveals distinct strengths of each configuration. Extended single-end reads (SE500_stLFR and SE1000_stLFR) significantly enhance SV detection, with SE1000_stLFR providing the best balance between precision and recall. In contrast, the shorter PE100_stLFR reads exhibit higher precision for SNP and INDEL calling, particularly within high-confidence regions, though with reduced performance in low-mappability contexts. To explore optimization strategies, we constructed hybrid libraries combining paired-end and single-end barcoded reads. These hybrid approaches integrate the complementary advantages of different read types, consistently outperforming single libraries across small variant types and genomic contexts. Conclusion: Collectively, our findings offer a robust comparative framework for evaluating stLFR sequencing strategies, highlight the promise of barcoded single-end reads for improving SV detection, and provide practical guidance for tailoring sequencing designs to the complexities of the genome.

Indexed as

INDEL callLinked-read sequencingSequencing simulationSNP callstLFRStructural variants call

Identifiers

PMID41646351
PMCPMC12869651

What OpenQuestion holds

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LicenceCC BY
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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.