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Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data.

Davide Bolognini¹, Alberto Magi²

¹Unit of Medical Genetics, Meyer Children's Hospital, Florence, Italy.

Frontiers in Genetics

|December 6, 2021

Summary

Long-read sequencing with Oxford Nanopore Technologies aids structural variant (SV) discovery. This study evaluates SV callers and aligners for nanopore data, optimizing SV detection and genotyping for improved human health insights.

Keywords:

bioinformatics genomics long reads nanopore sequencing structural variation

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Area of Science:

Genomics
Bioinformatics
Human Genetics

Background:

Structural variants (SVs) are genomic rearrangements with significant health implications.
Short-read sequencing has limitations in comprehensively assessing SVs.
Long-read sequencing, particularly Oxford Nanopore Technologies, shows promise for SV detection.

Purpose of the Study:

To evaluate and compare the performance of long-read structural variant callers with various aligners for nanopore sequencing data.
To investigate the impact of read alignment, sequencing coverage, and variant allele depth on SV detection and genotyping.
To provide insights into the precision and recall of SV callsets generated from nanopore data.

Main Methods:

Comparative analysis of five long-read SV callers across four long-read aligners.
Utilized both real and synthetic nanopore sequencing datasets.
Focused on assessing SV detection and genotyping across different SV types and size ranges.

Main Results:

Performance varied significantly based on the combination of aligner and SV caller.
Read alignment, sequencing coverage, and variant allele depth influenced SV detection accuracy.
Identified optimal pipelines for specific SV types and sizes, improving precision and recall.

Conclusions:

The choice of aligner and SV caller is critical for accurate structural variant detection in nanopore sequencing data.
Understanding the interplay of sequencing parameters enhances the reliability of SV callsets.
The proposed computational pipeline (EViNCe) aids in evaluating and optimizing nanopore-based SV analysis for genomic research.