Evidence map›Paper›PMID 40803872›Full record

ArticleGenome research2025

FocalSV enables target region-based structural variant assembly and refinement using single-molecule long-read sequencing data.

Can Luo, Zimeng Jamie Zhou, Yichen Henry Liu, Xin Maizie Zhou

Abstract read
In one paragraph

Article in Genome research, 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
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0citing papers in PubMed
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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

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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

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5 · Who and what money

Authors and funding

4 authors.

Can Luo *Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA.
Zimeng Jamie Zhou *Department of Computer Science, Vanderbilt University, Nashville, Tennessee 37235, USA.
Yichen Henry LiuDepartment of Computer Science, Vanderbilt University, Nashville, Tennessee 37235, USA.ORCID 0009-0009-7817-1081
Xin Maizie ZhouDepartment of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA; maizie.zhou@vanderbilt.edu.ORCID 0000-0003-4015-4787

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

Structural variants (SVs) play a critical role in shaping the diversity of the human genome, and their detection holds significant potential for advancing precision medicine. Despite notable progress in single-molecule long-read sequencing technologies, accurately identifying SV breakpoints and resolving their sequence remains a major challenge. Current alignment-based tools often struggle with precise breakpoint detection and sequence characterization, whereas whole-genome assembly-based methods are computationally demanding and less practical for targeted analyses. Neither approach is ideally suited for scenarios where regions of interest are predefined and require precise SV characterization. To address this gap, we introduce FocalSV, a targeted SV detection framework that integrates both assembly- and alignment-based signals. By combining the precision of local assemblies with the efficiency of region-specific analysis, FocalSV enables more accurate SV detection. FocalSV supports user-defined target regions and can automatically identify and expand regions with potential structural variants to enable more comprehensive detection. FocalSV is evaluated on 10 germline data sets and two paired normal-tumor cancer data sets, demonstrating superior performance in both precision and efficiency.

Indexed as

Genome, HumanGenomic Structural VariationSequence Analysis, DNASoftwareHigh-Throughput Nucleotide SequencingHumans

Identifiers

PMID40803872
PMCPMC12487817

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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.