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Scalable single-cell metagenomic analysis with Bascet and Zorn

Gourle, H.; Yakovenko, I.; Verma, J.; Dicken, J.; Albrecht, F.; Mazutis, L.; Normark, J.; Sheng, N.; Strömberg, N.; Löfstedt, T.; Carroll, L. M.; Henriksson, J. M.

2025-06-21 microbiology
10.1101/2025.06.20.660799 bioRxiv
Show abstract

Single-cell metagenomic sequencing (scMetaG) can provide maximum-resolution insights into complex microbial communities. However, existing bioinformatic tools are not equipped to handle the massive amounts of data generated by novel high-throughput scMetaG methods. Here, we present a bioinformatic toolkit for complete, end-to-end scMetaG analysis: (i) Bascet, a command-line suite designed to scale to massive scMetaG datasets ([≥]1 million cells); (ii) Zorn, an R package/workflow manager that enables reproducible scMetaG data analysis, exploration, and visualization (http://zorn.henlab.org/). Enabled by recent advances in droplet microfluidics, we use Bascet and Zorn to develop and optimize a high-throughput scMetaG method on a ten-species mock community. To showcase their utility on a real-world sample, we use Bascet and Zorn to characterize a human saliva sample, generating single-amplified genomes (SAGs) from >10k prokaryotic cells. Overall, Bascet and Zorn enable reproducible scMetaG analysis, allowing users to query microbiomes at unprecedented resolution and scale.

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