Discovery of large genomic inversions using long range information

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Discovering large structural variations, particularly inversions, is challenging due to repetitive genomic regions limiting short-read mapping.
• Existing algorithms struggle with large inversions, especially those within segmental duplications or common repeats.
• Current methods, like those from the 1000 Genomes Project, are limited to detecting shorter inversions.

Purpose of the Study:

• To introduce VALOR, a novel algorithm for discovering large inversions.
• To leverage long-range sequencing technologies for improved structural variation detection.
• To address the limitations of current algorithms in identifying complex genomic rearrangements.

Main Methods:

• Development of the VALOR algorithm for large inversion discovery.
• Utilization of long-range sequencing data, including 10X Genomics linked-read and pooled clone sequencing.
• Comparative analysis of VALOR against state-of-the-art structural variation discovery algorithms using whole genome shotgun sequencing data.

Main Results:

• VALOR accurately identifies previously validated large inversions with a low false discovery rate.
• A novel inversion was predicted using VALOR and subsequently validated via fluorescent in situ hybridization.
• Demonstrated utility of VALOR on pooled clone sequencing and 10X Genomics linked-read data from NA12878.

Conclusions:

• VALOR effectively detects large inversions, overcoming limitations of existing methods.
• The algorithm shows high accuracy and a low false discovery rate in structural variation analysis.
• VALOR represents a significant advancement in identifying complex genomic rearrangements using long-range sequencing.