ArticleFrontiers in genetics2021
SVInterpreter: A Comprehensive Topologically Associated Domain-Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural Variants.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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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.
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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.
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Who cites it
7 citing papers in PubMed.
- Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation.Global medical genetics · 2026Article
- Genomic structural variations contribute to inform prognosis in patients with cytogenetically normal acute myeloid leukemia.Blood cancer journal · 2026Article
- Structural variants in the 3D genome as drivers of disease.Nature reviews. Genetics · 2025Review
- Systematic assessment of structural variant annotation tools for genomic interpretation.Life science alliance · 2025Article
- Chromosome conformation capture technologies as tools to detect structural variations and their repercussion in chromatin 3D configuration.Frontiers in cell and developmental biology · 2023Review
- A Novel FrameshiftBiomedicines · 2022Article
- Detection of Structural Variants by NGS: Revealing Missing Alleles in Lysosomal Storage Diseases.Biomedicines · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of genomic sequencing, a number of balanced and unbalanced structural variants (SVs) can be detected per individual. Mainly due to incompleteness and the scattered nature of the available annotation data of the human genome, manual interpretation of the SV's clinical significance is laborious and cumbersome. Since bioinformatic tools developed for this task are limited, a comprehensive tool to assist clinical outcome prediction of SVs is warranted. Herein, we present
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