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Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues

Ghaddar, B.; Blaser, M. J.; De, S.

2022-06-30 genomics
10.1101/2022.06.29.498176 bioRxiv
Show abstract

We developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. In benchmark studies, SAHMI correctly identifies known microbial infections present in diverse tissues. The application of SAHMI to single-cell and spatial genomic data enables co-detection of somatic cells and microorganisms and joint analysis of host-microbiome ecosystems.

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