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ViralMSA: massively scalable reference-guided multiple sequence alignment of viral genomes
1Department of Computer Science and Engineering, UC San Diego, La Jolla, CA 92093, USA.
Bioinformatics (Oxford, England)
|August 21, 2020
Summary
ViralMSA is a new tool for aligning viral genomes, improving speed for large datasets. This reference-guided multiple sequence alignment (MSA) method offers rapid analysis for molecular epidemiology.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Molecular epidemiology relies on viral genomic sequence alignment for transmission cluster identification.
- Current multiple sequence alignment (MSA) methods face scalability challenges with increasing numbers of sequences.
Purpose of the Study:
- To develop a scalable and efficient tool for multiple sequence alignment (MSA) of large viral genome datasets.
- To address the limitations of existing MSA methods in handling ultra-large viral sequence data.
Main Methods:
- ViralMSA employs reference-guided alignment strategies.
- It utilizes algorithmic techniques inspired by read mappers for efficient processing.
- The tool is designed for user-friendliness and speed.
Main Results:
- ViralMSA achieves linear scalability with the number of sequences.
- It can align tens of thousands of full viral genomes within seconds.
- Alignments generated by ViralMSA are reference-based and omit insertions relative to the reference.
Conclusions:
- ViralMSA provides a significant advancement in the speed and scalability of viral genome alignment.
- The tool is suitable for analyzing ultra-large datasets in molecular epidemiology.
- ViralMSA is available as open-source software, facilitating broader adoption.
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