Related Experiment Video
Updated: Dec 22, 2025
04:52
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
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intansv: an R package for integrative analysis of structural variations
Lihua Jia1,2, Na Liu1, Fangfang Huang1
1National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou, Henan, China.
Peerj
|May 8, 2020
Summary
Structural variations impact human traits and diseases. The intansv R package integrates multiple prediction tools and provides annotation and visualization for structural variations, improving reliability and function understanding.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Identifying structural variations (SVs) is crucial for understanding phenotype variations and diseases.
- Existing SV prediction tools lack a golden standard, necessitating integration for reliable results.
- Annotation and visualization of SVs are essential for functional interpretation but currently underserved.
Purpose of the Study:
- To introduce intansv, an R package designed to address the limitations in SV analysis.
- To enable the integration of predictions from multiple SV detection programs.
- To provide annotation and visualization capabilities for structural variations.
Main Methods:
- Development of the intansv R package.
- Integration of outputs from various structural variation prediction tools.
- Implementation of annotation and visualization functionalities for SVs.
Main Results:
- intansv successfully integrates predictions from multiple SV callers.
- The package provides comprehensive annotation and visualization of structural variations.
- It offers a unified platform for more reliable and interpretable SV analysis.
Conclusions:
- intansv enhances the reliability and interpretability of structural variation analysis.
- The package fills a critical gap in the bioinformatics tool landscape for SVs.
- It facilitates a deeper understanding of the functional impact of structural variations on phenotypes and diseases.
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