ParslRNA-Seq – an efficient and scalable RNAseq analysis workflow for studies of differentiated gene expression

RNA sequencing has become an increasingly affordable way to profile gene expression analyses. Researchers from the LNCC, Brazil have developed a scientific workflow implementing several open-source software executed by Parsl parallel scripting language in an high-performance computing environment. The researchers have applied the workflow to a single-cardiomyocyte RNA-seq data retrieved from Gene Expression Omnibus database. The workflow allows for the analysis (alignment, QC, sort and count reads, statistics generation) of raw RNA-seq data and seamless integration of differential expression results into a configurable script code.

In this work, the researchers aim to investigate an analytical comparison of executing the workflow in Solid State Disk and Lustre as a critical decision for improving the execution efficiency and resilience in current and upcoming RNA-Seq workflows. Based on the resulting profiling of CPU and I/O data collection, they demonstrate that they can correctly identify anomalies in transcriptomics workflow performance which is an essential resource to optimize its use of high-performance computing systems. ParslRNA-Seq showed improvements in the total execution time of up to 70% against its previous sequential implementation. Finally, the researchers discuss which workflow modeling modifications lead to improved computational performance and scalability based on provenance data information.

Availability – ParslRNA-Seq is available at

Ocaña K et al. (2022). ParslRNA-Seq: An Efficient and Scalable RNAseq Analysis Workflow for Studies of Differentiated Gene Expression. In: Navaux, P., Barrios H., C.J., Osthoff, C., Guerrero, G. (eds) High Performance Computing. CARLA 2022. Communications in Computer and Information Science, vol 1660. [abstract]

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