Biology subjects

Yakovenko, I.

Publications and source records attributed to Yakovenko, I..

2 recordsLinked to original sources

bioRxiv2025

Scalable single-cell metagenomic analysis with Bascet and Zorn

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.

microbiology↗

bioRxiv2024

CRISPR-MIP replaces PCR and reveals GC and oversampling bias in pooled CRISPR screens

Pooled CRISPR screening is a powerful tool for finding the most important genes related to a biological process of interest. The quality of the generated gene list is however influenced by a range of technical parameters, such as CRISPR (single guide) sgRNA target efficiency, and further innovations are still called for. One open problem is the precise estimation of sgRNA abundances, as required for the statistical analysis. We do so using molecular inversion probes (MIPs) combined with the use of unique molecular identifiers (UMIs), thus enabling deduplication and absolute counting of cells. We show that this is a viable approach that eliminates sequencing depth bias. Furthermore, we find that GC% bias affects PCR, calling for a reanalysis of published CRISPR screen data and sgRNA efficiency estimates. We propose our method as a new gold standard for sgRNA quantification, especially for genes that are not top ranked but still of broad interest.

molecular biology↗