Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
| Autor: | Aviv Regev, Orr Ashenberg, Marcin Tabaka, Yanay Rosen, Joshua Gould, Nir Hacohen, Bo Li, Yiming Yang, Siranush Sarkizova, Michal Slyper, Alexandra-Chloé Villani, Monika S. Kowalczyk, Orit Rozenblatt-Rosen, Timothy L. Tickle |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Test data generation genetic processes Genomics RNA-Seq Cloud computing computer.software_genre Biochemistry Article 03 medical and health sciences Software Molecular Biology Massively parallel 030304 developmental biology 0303 health sciences business.industry Sequence Analysis RNA RNA Computational Biology High-Throughput Nucleotide Sequencing Cell Biology Cloud Computing Scalability Data mining Single-Cell Analysis business computer Biotechnology |
| Zdroj: | Nature methods |
| ISSN: | 1548-7105 1548-7091 |
| Popis: | 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 we 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. We 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 is a cloud-based framework enabling large-scale single-cell and single-nucleus RNA sequencing data analysis. |
| Databáze: | OpenAIRE |
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