Evidence map›Paper›PMID 41169152›Full record

ArticleBioinformatics (Oxford, England)2025

OctopuSV and TentacleSV: a one-stop toolkit for multi-sample, cross-platform structural variant comparison and analysis.

Qingxiang Guo, Yangyang Li, Ting-You Wang, Abhirami Ramakrishnan, Rendong Yang

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Qingxiang GuoDepartment of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.ORCID 0000-0002-3694-3428
Yangyang LiDepartment of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.
Ting-You WangDepartment of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.ORCID 0000-0003-4354-3198
Abhirami RamakrishnanDepartment of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.
Rendong YangDepartment of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.ORCID 0000-0002-9512-2240

Funding

Computational approaches to delineate non-canonical splicing eventsR35GM142441 · NIGMS · UNIVERSITY OF MINNESOTA · PI YANG, RENDONG · 2021 to 2025
$2.0M
Genome-wide mapping and characterization of exitrons in human cancerR01CA259388 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Rendong Yang · 2022 to 2026
$1.8M
NCI NIH HHS R01 CA259388NIGMS NIH HHS R35 GM142441NIH HHS R01CA259388NIH HHS R35GM142441
6 · The paper itself

Abstract

motivationStructural variants (SVs) influence gene regulation, disease progression, and diagnostics, yet integrating SV calls across platforms remains difficult due to inconsistent annotations, limited merging flexibility, and fragmented workflows. Ambiguous breakend (BND) annotations, which comprise many variant calls, are often discarded or misclassified, hindering variant characterization. Existing tools lack advanced merging operations essential for precise identification of disease-specific or somatic variants across samples or patient groups. Additionally, current SV analysis pipelines require extensive manual intervention and complex parameter tuning, compromising reproducibility and scalability. Addressing these gaps is crucial for improving the accuracy, interpretability, and clinical utility of SV analyses.

resultsWe developed OctopuSV and TentacleSV to address these long-standing challenges in SV analysis. OctopuSV features a specialized BND correction module that converts ambiguous BND annotations into canonical SV types, recovering important variants that are often overlooked by existing tools. Additionally, it provides advanced set operations (difference, complement, custom-defined) that enable sophisticated variant filtering without programming expertise, critical for identifying tumor-specific SVs or variants unique to specific sample groups. TentacleSV completes our solution by automating the entire SV analysis process from raw sequencing data to high-confidence callsets, ensuring consistency and reproducibility across projects. Benchmarking across short-read and long-read platforms showed superior F1 score, complete SV type consistency compared to existing tools. Our framework enables experimental biologists and clinical researchers to perform sophisticated analyses ranging from cancer subtype-specific SV identification to multi-sample comparative studies without requiring specialized programming skills. AVAILABILITY AND IMPLEMENTATION: All codes are available at https://github.com/ylab-hi/OctopuSV; https://github.com/ylab-hi/TentacleSV.

Indexed as

Computational BiologyGenomicsGenomic Structural VariationSoftwareHigh-Throughput Nucleotide SequencingHumansReproducibility of Results

Identifiers

PMID41169152
PMCPMC12646641

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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.