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Large indel detection in region-based phased diploid assemblies from linked-reads
Can Luo1, Brock A Peters2, Xin Maizie Zhou3,4
1Department of Biomedical Engineering, Vanderbilt University, Nashville, 37235, TN, USA.
BMC Genomics
|March 19, 2025
Summary
RegionIndel is an efficient pipeline for detecting large indel structural variants (SVs) using linked-reads. It achieves high accuracy in diploid assembly and variant genotyping, outperforming existing methods for population studies.
Area of Science:
- Genomics
- Bioinformatics
- Population Genetics
Background:
- Linked-reads technology enables genome partitioning and barcoding for various applications.
- Whole genome assembly using linked-reads is computationally intensive and unsuitable for large populations.
- Characterizing large indel structural variants (SVs) remains a challenge.
Purpose of the Study:
- To develop an efficient, region-based diploid assembly approach for accurate indel SV detection.
- To reduce computational costs associated with linked-read based variant detection.
- To enable large-scale population studies for SV analysis.
Main Methods:
- RegionIndel pipeline focuses on targeted regions (50kb default) using barcoded reads.
- Integrates haplotyping algorithm and local assembly to generate phased diploid contigs.
- Detects variants via pairwise comparison of contigs to reference sequences.
Main Results:
- RegionIndel demonstrated superior performance compared to existing SV callers in benchmark experiments.
- Achieved F1 scores of 74.8% for deletions and 61.8% for insertions (10x linked-reads).
- Achieved F1 scores of 64.3% for deletions and 36.7% for insertions (stLFR linked-reads).
- Reported high genotyping accuracy: 83.6% (10x) and 80.8% (stLFR).
Conclusions:
- RegionIndel successfully achieves diploid assembly and indel SV detection in targeted regions.
- Phased diploid contigs facilitate investigation of indel SVs with linked SNPs and small indels.
- The method is suitable for efficient, large-scale population studies.
