Abstract
Currently, several hundreds of Terabytes of COVID-19 single-cell RNA-seq (scRNA-seq) data are available in public repositories. This data refers to multiple tissues, comorbidities, and conditions. We expect this trend to continue, and it is realistic to predict amounts of COVID-19 scRNA-seq data increasing to several Petabytes in the coming years. However, thoughtful analysis of this data requires large-scale computing infrastructures, and software systems optimized for such platforms to generate biological knowledge. This paper presents CellHeap, a portable and robust workflow for scRNA-seq customizable analyses, with quality control throughout the execution steps and deployable on supercomputers. Furthermore, we present the deployment of CellHeap in the Santos Dumont supercomputer for analyzing COVID-19 scRNA-seq datasets, and discuss a case study that processed dozens of Terabytes of COVID-19 scRNA-seq raw data.
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Notes
- 1.
Cellranger is a set of analysis pipelines that process Chromium single-cell data to align reads, generate feature-barcode matrices, perform clustering and other secondary analysis, and more. CellRanger Count is executed once for each dataset, and CellRanger Aggregate is optionally executed for aggregating several different datasets/tissues. In addition, CellRanger Count and CellRanger Aggregate generate a gene-count matrix, where the results depend on the analysis performed in a simple or an aggregated way.
- 2.
Supercomputer details in https://sdumont.lncc.br.
- 3.
Slurm details in https://slurm.schedmd.com.
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Acknowledgments
The authors acknowledge the National Laboratory for Scientific Computing (LNCC/MCTI, Brazil) for providing HPC resources of the SDumont supercomputer, which have contributed to the research results reported within this paper. URL: http://sdumont.lncc.br. The authors also acknowledge the INOVA-FIOCRUZ program (grant number VPPCB-005-FIO-20-2-34-52) for funding this research. M.E.M.T.Walter thanks CNPq for the research scholarship PQ 310785/2018-9.
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Silva, V.S. et al. (2021). CellHeap: A Workflow for Optimizing COVID-19 Single-Cell RNA-Seq Data Processing in the Santos Dumont Supercomputer. In: Stadler, P.F., Walter, M.E.M.T., Hernandez-Rosales, M., Brigido, M.M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2021. Lecture Notes in Computer Science(), vol 13063. Springer, Cham. https://doi.org/10.1007/978-3-030-91814-9_4
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