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Nextflow vs. plain bash: different approaches to the parallelization of SNP calling from the whole genome sequence
Marek Sztuka1, Krzysztof Kotlarz1,2, Magda Mielczarek1,2
1Wroclaw University of Environmental and Life Sciences, Department of Genetics, the Biostatistics Group Kozuchowska 7, Wroclaw PL-51631, Poland.
NAR Genomics and Bioinformatics
|April 30, 2024
Summary
The multi-process Nextflow script offers a 15-27% speed improvement for single nucleotide polymorphism (SNP) calling workflows. This approach enhances computational efficiency in genomic data analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Efficient processing of large genomic datasets is crucial for livestock breeding and research.
- Single nucleotide polymorphism (SNP) calling workflows are fundamental in genetic analysis.
- Parallelization strategies significantly impact the performance of bioinformatics pipelines.
Purpose of the Study:
- To compare the performance of different computational parallelization approaches for an SNP calling workflow.
- To evaluate execution time, RAM usage, and disk usage of various parallelization methods.
- To identify the most efficient parallelization strategy for SNP calling in bovine DNA sequencing data.
Main Methods:
- Comparison of three parallelization methods: plain Bash script, single-process Nextflow, and multi-process Nextflow.
- Utilized DNA sequencing data from five Holstein-Friesian cows processed through a standard bioinformatics pipeline (QC, alignment, SNP calling).
- Evaluated performance metrics including execution speed, Random Access Memory (RAM) consumption, and disk space utilization across different thread counts.
Main Results:
- The multi-process Nextflow script demonstrated a 15-27% increase in average execution speed compared to the plain Bash approach, particularly with 10 threads.
- RAM usage in the multi-process Nextflow setup scaled with the number of threads, unlike the other methods.
- Disk usage was highest for the multi-process Nextflow due to the generation of intermediate and log files.
Conclusions:
- Multi-process Nextflow provides a significant performance advantage for SNP calling workflows, enhancing computational efficiency.
- The choice of parallelization strategy impacts resource utilization (RAM, disk space), requiring careful consideration for large-scale genomic analyses.
- Nextflow's multi-process approach is recommended for optimizing SNP calling in bovine genomics research.
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