Exodus: Sequencing-based pipeline for quantification of pooled variants

Ilya Vainberg-Slutskin, Noga Kowalsman, Yael Silberberg, Tal Cohen, Jenia Gold, Edith Kario, Iddo Weiner*, Inbar Gahali-Sass, Sharon Kredo-Russo, Naomi B. Zak, Merav Bassan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Summary: Next-Generation Sequencing is widely used as a tool for identifying and quantifying microorganisms pooled together in either natural or designed samples. However, a prominent obstacle is achieving correct quantification when the pooled microbes are genetically related. In such cases, the outcome mostly depends on the method used for assigning reads to the individual targets. To address this challenge, we have developed Exodus - a reference-based Python algorithm for quantification of genomes, including those that are highly similar, when they are sequenced together in a single mix. To test Exodus' performance, we generated both empirical and in silico next-generation sequencing data of mixed genomes. When applying Exodus to these data, we observed median error rates varying between 0% and 0.21% as a function of the complexity of the mix. Importantly, no false negatives were recorded, demonstrating that Exodus' likelihood of missing an existing genome is very low, even if the genome's relative abundance is low and similar genomes are present in the same mix. Taken together, these data position Exodus as a reliable tool for identifying and quantifying genomes in mixed samples. Exodus is open source and free to use at: https://github.com/ilyavs/exodus.

Original languageEnglish
Pages (from-to)3288-3290
Number of pages3
Issue number12
StatePublished - 15 Jun 2022
Externally publishedYes


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