CellHeap: A Workflow for Optimizing COVID-19 Single-Cell RNA-Seq Data Processing in the Santos Dumont Supercomputer - Silva, Vanessa S.; Costa, Maiana O. C.; Castro, Maria Clicia S.; Silva, Helena S.; Walter, Maria Emilia M. T.; Melo, Alba C. M. A.; Ocaña, Kary A. C.; dos Santos, Marcelo T.; Nicolas, Marisa F.; Carvalho, Anna Cristina C.; Henriques-Pons, Andrea; Silva, Fabrício A. B. (Anais do Simpósio Brasileiro de Bioinformática (BSB); 2021: Anais do XIV Simpósio Brasileiro de Bioinformática, 2021)
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.