invMap: a sensitive mapping tool for long noisy reads with inversion structural variants

TL;DR

invMap is a new tool for accurately mapping long, noisy DNA sequences to detect inversion structural variants, which are hard to identify with existing methods.

Contribution

invMap introduces a novel mapping algorithm that improves inversion detection accuracy in long-read sequencing data.

Findings

invMap outperforms existing methods in locating aligned regions and calling inversion structural variants.

Testing on simulated and real human genome data shows invMap detects more inversion variants than competing tools.

The method is robust across different genomes and sequencing coverages.

Abstract

Longer reads produced by PacBio or Oxford Nanopore sequencers could more frequently
span the breakpoints of structural variations (SVs) than shorter reads. Therefore,
existing long-read mapping methods often generate wrong alignments and variant calls.
Compared to deletions and insertions, inversion events are more difficult to be detected
since the anchors in inversion regions are nonlinear to those in SV-free regions. To
address this issue, this study presents a novel long-read mapping algorithm (named as
invMap).

For each long noisy read, invMap first locates the aligned region with a specifically
designed scoring method for chaining, then checks the remaining anchors in the aligned
region to discover potential inversions. We benchmark invMap on simulated datasets
across different genomes and sequencing coverages, experimental results demonstrate that
invMap is more accurate to locate aligned regions and call SVs for inversions than the
competing methods. The real human genome sequencing dataset of NA12878 illustrates that
invMap can effectively find more candidate variant calls for inversions than the
competing methods.

The invMap software is available at https://github.com/zhang134/invMap.git.

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