Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow.

Autor:	Contaldo SG; Department of Computer Science, University of Turin, Turin, Italy., Alessandri L; Molecular Biotechnology Center, University of Turin, Turin, Italy. l.alessandri@unito.it., Colonnelli I; Department of Computer Science, University of Turin, Turin, Italy., Beccuti M; Department of Computer Science, University of Turin, Turin, Italy., Aldinucci M; Department of Computer Science, University of Turin, Turin, Italy.
Jazyk:	angličtina
Zdroj:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2023; Vol. 2584, pp. 337-345.
DOI:	10.1007/978-1-0716-2756-3_17
Abstrakt:	The idea behind novel single-cell RNA sequencing (scRNA-seq) pipelines is to isolate single cells through microfluidic approaches and generate sequencing libraries in which the transcripts are tagged to track their cell of origin. Modern scRNA-seq platforms are capable of analyzing up to many thousands of cells in each run. Then, combined with massive high-throughput sequencing producing billions of reads, scRNA-seq allows the assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution.In this chapter, we describe how cell subpopulation discovery algorithms, integrated into rCASC, could be efficiently executed on cloud-HPC infrastructure. To achieve this task, we focus on the StreamFlow framework which provides container-native runtime support for scientific workflows in cloud/HPC environments. (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze:	MEDLINE
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