Vulture: Cloud-enabled scalable mining of microbial reads in public scRNA-seq data

Chen, J.; Yin, D.; Wong, H. Y. H.; Duan, X.; Yu, K. H. O.; Ho, J. W. K.

doi:10.1101/2023.02.13.528411

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bioRxiv · 10.1101/2023.02.13.528411

Vulture: Cloud-enabled scalable mining of microbial reads in public scRNA-seq data

Chen, J.·Yin, D.·Wong, H. Y. H.·Duan, X.·Yu, K. H. O.·Ho, J. W. K.

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Abstract

The rapidly growing collection of public single-cell sequencing data have become a valuable resource for molecular, cellular and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our scalability benchmarking experiments, Vulture can outperform the state-of-the-art cloud-based pipeline Cumulus with a 40% and 80% reduction of runtime and cost, respectively. Furthermore, Vulture is 2-10 times faster than PathogenTrack and Venus, while generating comparable results. We applied Vulture to two COVID-19, three hepatocellular carcinoma (HCC), and two gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell-type specific enrichment of SARS-CoV2, hepatitis B virus (HBV), and H. pylori positive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license at https://github.com/holab-hku/Vulture.

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Chen, J., Yin, D., Wong, H. Y. H., Duan, X., Yu, K. H. O., Ho, J. W. K.. 2023-02-15. Vulture: Cloud-enabled scalable mining of microbial reads in public scRNA-seq data. https://doi.org/10.1101/2023.02.13.528411

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