Wham: Identifying Structural Variants of Biological Consequence

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Accurate identification of structural variants (SVs) from short read sequencing data is crucial for understanding genetic variation and its role in disease.
• Current methods for SV detection often lack the required accuracy, hindering disease-gene association studies.
• Existing approaches face challenges in integrating SV identification with association testing.

Purpose of the Study:

• To introduce Wham (Whole-genome Alignment Metrics), an integrated framework for robust structural variant calling and association testing.
• To provide a solution that bypasses the difficulties associated with using SVs in association studies.
• To benchmark Wham against established SV identification tools.

Main Methods:

• Development of the Wham (Whole-genome Alignment Metrics) software framework.
• Benchmarking Wham against Lumpy, Delly, and SoftSearch for SV identification.
• Application of Wham for identifying and associating SVs with phenotypes in diverse species.

Main Results:

• Wham demonstrates improved accuracy in structural variant calling compared to existing tools.
• The framework successfully identifies and associates SVs with phenotypes across human, pigeon, and vaccinia virus datasets.
• Wham provides a unified approach, simplifying the process of SV association testing.

Conclusions:

• Wham offers a significant advancement in structural variant analysis, enhancing the accuracy of both detection and association.
• The integrated framework facilitates more reliable disease-gene identification and the study of genetic variation consequences.
• Wham is freely available, promoting wider adoption and community support for structural variant research.