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Detecting and phasing minor single-nucleotide variants from long-read sequencing data
Zhixing Feng1,2, Jose C Clemente3,4, Brandon Wong5
1Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. zhixing.feng@mssm.edu.
Nature Communications
|May 25, 2021
Summary
This study introduces iGDA, an open-source tool for accurately detecting and phasing minor single-nucleotide variants (SNVs) from long-read sequencing data. iGDA can identify variants at frequencies as low as 0.2% and reconstruct haplotypes in closely related microbial strains.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Cellular genetic heterogeneity is prevalent in conditions like cancer and microbial communities.
- Detecting minor genetic variants is crucial but challenging due to technological limitations.
- Long-read sequencing offers potential but suffers from high error rates.
Purpose of the Study:
- To develop an accurate tool for detecting and phasing minor single-nucleotide variants (SNVs) from long-read sequencing data.
- To address the challenge of high error rates in long-read sequencing for variant analysis.
- To enable the study of cellular genetic heterogeneity and microbial strain diversity.
Main Methods:
- Development of iGDA, an open-source bioinformatics tool.
- Utilizing raw long-read sequencing data (e.g., PacBio, Oxford Nanopore).
- Application to detect low-frequency SNVs and reconstruct haplotypes.
Main Results:
- iGDA accurately detects and phases minor SNVs with frequencies as low as 0.2%.
- The tool demonstrates high accuracy in reconstructing haplotypes for closely related microbial strains (divergence ≥0.011%).
- Successful application on long-read metagenomic data.
Conclusions:
- iGDA effectively overcomes limitations of long-read sequencing for variant detection and phasing.
- The tool facilitates deeper insights into cellular genetic heterogeneity and microbial community composition.
- iGDA provides a valuable resource for genomic and metagenomic research.
