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Exodus: sequencing-based pipeline for quantification of pooled variants
Ilya Vainberg-Slutskin1, Noga Kowalsman1, Yael Silberberg1
1BiomX Ltd., Ness Ziona 7414002, Israel.
Bioinformatics (Oxford, England)
|May 13, 2022
Summary
Exodus accurately quantifies mixed genomes using Next-Generation Sequencing, even for highly similar microbes. This Python algorithm minimizes errors and avoids false negatives, ensuring reliable identification and quantification in complex samples.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Next-Generation Sequencing (NGS) is crucial for microbial identification and quantification.
- Accurate quantification is challenging for genetically related microorganisms in pooled samples.
- Read assignment methods significantly impact quantification outcomes for similar genomes.
Purpose of the Study:
- To develop a robust algorithm for accurate genome quantification in mixed samples.
- To address the challenge of quantifying highly similar or related microbial genomes.
- To provide a reliable tool for analyzing complex microbial communities.
Main Methods:
- Developed Exodus, a reference-based Python algorithm for genome quantification.
- Utilized a Snakemake framework for implementation.
- Generated and analyzed both empirical and in silico NGS data of mixed genomes.
Main Results:
- Exodus achieved median error rates between 0% and 0.21% across varying mix complexities.
- Demonstrated very low likelihood of false negatives, even with low abundance and similar genomes.
- Validated performance on diverse empirical and in silico datasets.
Conclusions:
- Exodus is a reliable tool for identifying and quantifying genomes in mixed microbial samples.
- The algorithm effectively handles quantification of highly similar genomes.
- Exodus is open-source and freely available for research use.
