scalepopgen: Bioinformatic Workflow Resources Implemented in Nextflow for Comprehensive Population Genomic Analyses

Area of Science:

• Population genomics
• Bioinformatics
• Computational biology

Background:

• Population genomic analyses often require numerous tools, leading to complex installation and data preprocessing challenges.
• Container technologies simplify tool and dependency management, but multistep analyses still benefit from workflow systems.
• Workflow management systems like Nextflow and Snakemake facilitate complex population genomic analyses.

Purpose of the Study:

• To present scalepopgen, a collection of automated workflows for population genomic analyses.
• To facilitate widely used population genomic analyses on biallelic single nucleotide polymorphism (SNP) data.
• To provide a streamlined approach for population structure inference and selection signature identification.

Main Methods:

• Developed in Nextflow, scalepopgen automates analyses on SNP data in VCF or PLINK binary formats.
• The workflow includes individual/genotype filtering, Principal Component Analysis (PCA), admixture analysis, TreeMix, and selection signature identification.
• Scalable Population Genomics (scalepopgen) runs locally or on HPC systems using Conda, Singularity, or Docker.

Main Results:

• Scalable Population Genomics (scalepopgen) automates key population genomic analyses, including population structure and selection signatures.
• The pipeline integrates various open-source tools and provides Python/R scripts for result collection and visualization.
• Automated workflows simplify complex data transformations and analyses for population genomics.

Conclusions:

• Scalable Population Genomics (scalepopgen) offers a robust and automated solution for common population genomic analyses.
• The system simplifies the application of complex population genomic methods, enhancing research efficiency.
• Freely available on GitHub, scalepopgen supports local and HPC execution, promoting accessibility.