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HaplotagLR: An efficient and configurable utility for haplotagging long reads
Monica J Holmes1, Babak Mahjour2, Christopher P Castro1
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
Plos One
|March 13, 2024
Summary
HaplotagLR is a new tool that improves haplotagging for genomic sequence analysis. It accurately assigns sequencing reads to parental haplotypes, enhancing understanding of genetic variation and disease risk.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Understanding genetic variant functional effects is key in genomics.
- Millions of variants per genome influence traits and disease risk.
- Neighboring variant interactions are vital for accurate functional effect prediction.
Purpose of the Study:
- To introduce HaplotagLR, a user-friendly haplotagging tool for long sequencing reads.
- To address limitations of existing haplotagging methods, such as error rate control.
Main Methods:
- HaplotagLR utilizes a multinomial model and phased variant lists.
- It is user-configurable and incorporates an error model for False Discovery Rate (FDR) control.
Main Results:
- HaplotagLR outperforms leading haplotagging methods in simulated data, especially for specificity.
- It demonstrates 7% greater sensitivity on real sequencing data.
Conclusions:
- HaplotagLR enhances the utility of haplotagging for analyzing genetic variation.
- The tool provides a foundation for future advancements in haplotagging methodologies.
