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invMap: a sensitive mapping tool for long noisy reads with inversion structural variants
Ze-Gang Wei1,2, Peng-Yu Bu1, Xiao-Dan Zhang1
1School of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji 721016, China.
Bioinformatics (Oxford, England)
|December 7, 2023
Summary
This study introduces invMap, a new algorithm for long-read sequencing data. InvMap accurately detects structural variations, specifically inversions, improving variant calling in genomics.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Longer sequencing reads from PacBio and Oxford Nanopore can span structural variation (SV) breakpoints.
- Existing mapping algorithms struggle with accurate alignment and variant calling for SVs, especially inversions, due to nonlinear anchor regions.
Purpose of the Study:
- To develop a novel long-read mapping algorithm, invMap, to improve the detection and calling of structural variations, particularly inversions.
- To address the limitations of current methods in handling nonlinear anchor regions characteristic of inversions.
Main Methods:
- invMap employs a scoring method for chaining to locate aligned regions within long, noisy reads.
- The algorithm then identifies potential inversions by examining remaining anchors within these aligned regions.
Main Results:
- Benchmarking on simulated datasets shows invMap achieves higher accuracy in locating aligned regions and calling inversions compared to existing methods.
- Analysis of the NA12878 human genome dataset demonstrates invMap's effectiveness in identifying more candidate inversion variant calls.
Conclusions:
- invMap offers improved accuracy and sensitivity for detecting inversions from long-read sequencing data.
- The developed algorithm enhances structural variation analysis in genomics.
