WisecondorX: improved copy number detection for routine shallow whole-genome sequencing

Area of Science:

• Genomics
• Bioinformatics
• Clinical Diagnostics

Background:

• Shallow whole-genome sequencing (sWGS) is increasingly used for copy number alteration (CNA) detection in clinical diagnostics.
• Existing CNA detection tools are often optimized for research, not clinical settings, and struggle with low-coverage or single-end sequencing data.
• Key requirements for clinical CNA tools include cost-effectiveness, speed, versatility, and robust performance.

Purpose of the Study:

• To compare the normalization methods of six prominent CNA detection tools.
• To evaluate tool performance based on variance, distributional assumptions, and accuracy in detecting true genomic variations.
• To introduce WisecondorX, an enhanced version of WISECONDOR, for broader clinical applicability.

Main Methods:

• Comparative analysis of six CNA detection tools focusing on their normalization techniques.
• Evaluation of tools using metrics such as variance, distributional assumptions, and detection accuracy.
• Development and validation of WisecondorX, an extension of the WISECONDOR algorithm.

Main Results:

• WISECONDOR demonstrated superior performance in variance control, distributional assumptions, and detection of true variations among the tested tools.
• The within-sample normalization technique employed by WISECONDOR proved highly effective.
• WISECONDOR's primary limitation was its specific optimization for non-invasive prenatal testing.

Conclusions:

• WISECONDOR is a highly effective tool for CNA detection, particularly regarding its normalization strategy.
• WisecondorX addresses the limitations of WISECONDOR, expanding its utility to diverse clinical applications beyond prenatal testing.
• WisecondorX offers a promising, freely available solution for routine diagnostic use of sWGS for CNA analysis.