Population-based structural variation discovery with Hydra-Multi.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Multisample analysis enhances SNP and INDEL discovery sensitivity and specificity.
• Structural variant (SV) detection is improved by multisample approaches, but faces challenges like library insert size variability and complex genomic rearrangements.
• Existing SV calling tools lack scalability, hindering analysis of large genomic datasets.

Purpose of the Study:

• To develop a scalable approach for multisample structural variant (SV) analysis.
• To enable SV detection in hundreds to thousands of human genomes using commodity hardware.
• To evaluate the accuracy, speed, and scalability of the developed method, Hydra-Multi.

Main Methods:

• Developed Hydra-Multi, a C++ based tool for multisample SV analysis.
• Utilized publicly available datasets from The 1000 Genomes Project and The Cancer Genome Atlas (TCGA).
• Assessed performance metrics including accuracy, speed, and scalability on large datasets.

Main Results:

• Hydra-Multi demonstrates scalability for analyzing hundreds to thousands of human genomes.
• The tool effectively addresses challenges in multisample SV calling, including library insert size variability and complex rearrangements.
• Performance evaluation using 1000 Genomes and TCGA data confirms the method's accuracy and speed.

Conclusions:

• Hydra-Multi provides a scalable solution for multisample SV analysis, overcoming limitations of existing tools.
• The approach facilitates large-scale genomic studies by enabling efficient analysis of numerous genomes.
• Hydra-Multi represents a significant advancement in structural variant detection for large cohorts.