Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq

Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here researchers from the Broad Institute of Harvard and MIT developed Cumulus—a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. The researchers benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

Cumulus: a scalable, feature-rich, accessible cloud-based framework
for sc/snRNA-seq analysis

a, Cumulus data analysis workflow. Cumulus takes raw base call files as input and outputs diverse analysis results, with three key computational steps: mkfastq, count and analysis. b, sc/snRNA-seq analysis tasks in Pegasus. c, Cumulus enables flexible interactive data visualization and analysis. Users can instantly visualize Cumulus analysis results with Cirrocumulus, or publicly available visualization tools such as cellxgene, UCSC Cell Browser and scSVA. They can also interactively explore them on Terra Jupyter notebooks using Pegasus and deposit their data into the Single Cell Portal. NK, natural killer.

Availability – Cumulus code consists of four components: the Pegasus and scPlot python packages; the Cumulus WDL workflows and Docker files; the Cumulus docker images; and the Cirrocumulus app. Pegasus source code is available at https://github.com/klarman-cell-observatory/pegasus. Pegasus documentation is available at https://pegasus.readthedocs.io. scPlot source code is available at https://github.com/klarman-cell-observatory/scPlot.