Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads

Area of Science:

• Genomics
• Bioinformatics

Background:

• Identifying structural variants (SVs) is crucial for understanding health and disease.
• Linked-read sequencing technologies like stLFR offer cost-effective haplotype reconstruction but require specialized algorithms.
• Existing computational tools are limited in their ability to leverage these advanced sequencing platforms for SV detection.

Purpose of the Study:

• To develop a novel computational approach, Aquila_stLFR, for accurate SV detection using stLFR linked-reads.
• To enable haplotype-based assembly for improved SV characterization from single-sample sequencing data.

Main Methods:

• Aquila_stLFR partitions long fragment reads into haplotype-specific blocks using a reference genome.
• Independent assembly of each haplotype enables a complete diploid genome reconstruction.
• The algorithm leverages the inherent phasing capability of stLFR data.

Main Results:

• Aquila_stLFR demonstrates high sensitivity in detecting medium to large deletions (50 bp–10 kb).
• The method shows high specificity for medium-sized insertions (50 bp–1 kb).
• Performance was benchmarked on the NA24385 sample, validating its effectiveness.

Conclusions:

• Aquila_stLFR provides an effective computational solution for SV detection utilizing stLFR technology.
• The approach facilitates more comprehensive genomic analysis by resolving SVs through diploid assembly.
• This advancement aids in the characterization of genetic variations critical for health and disease research.