RESUMO
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.
Assuntos
Computação em Nuvem/economia , Biologia Computacional/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Biologia Computacional/economia , Sequenciamento de Nucleotídeos em Larga Escala/economia , Análise de Sequência de RNA/economiaRESUMO
The Accelerating Medicines Partnership (AMP) Lupus Network was established as a partnership between the National Institutes of Health, pharmaceutical companies, nonprofit stakeholders, and lupus investigators across multiple academic centers to apply high-throughput technologies to the analysis of renal tissue, urine, and blood from patients with lupus nephritis (LN). The AMP network provides publicly accessible data to the community with the goal of generating new scientific hypotheses and improving diagnostic and therapeutic tools so as to improve disease outcomes. We present here a description of the structure of the AMP Lupus Network and a summary of the preliminary results from the phase 1 studies. The successful completion of phase 1 sets the stage for analysis of a large cohort of LN samples in phase 2 and provides a model for establishing similar discovery cohorts.