CYTO-SV-ML: A Machine Learning Tool for Cytogenetic Structural Variant Analysis in Somatic Cell Type Using Genome Sequences

CYTO-SV-ML: A Machine Learning Tool for Cytogenetic Structural Variant Analysis in Somatic Cell Type Using Genome Sequences

(1) Background: Although whole genome sequencing (WGS) has enabled the comprehensive analyses of structural variants (SVs), more accurate and efficient methods are needed to distinguish large somatic SVs (SV size ≥ 1 Mb) traditionally detected through cytogenetic testing from germline SVs. (2) Metho...

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Bibliographic Details
Main Authors: Tao Zhang, Paul Auer, Stephen R. Spellman, Jing Dong, Wael Saber, Yung-Tsi Bolon
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Life
Subjects:
structural variants
somatic cells
cytogenetic abnormality
transplant
whole genome sequencing
machine learning
Online Access:https://www.mdpi.com/2075-1729/15/6/929
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