PopGLen

PopGLen performs population genomic analyses using genotype likelihood-based methods, integrating raw sequence processing with quality control and population genomic analysis within a Snakemake workflow.

Key Features:

• Genotype-likelihood framework: Performs population genomic analyses using genotype likelihood-based methods rather than called genotypes.
• Sequence processing: Processes raw sequence data from both historical and modern DNA sources.
• Quality control: Integrates quality control steps into the analytical workflow.
• Multiple filtering schemes: Implements multiple filtering schemes for data processing.
• Linkage disequilibrium estimation: Estimates linkage disequilibrium as part of population genomic analyses.
• Kinship estimation: Estimates kinship among individuals.
• Genetic diversity estimation: Estimates genetic diversity within populations.
• Genetic differentiation estimation: Estimates genetic differentiation between populations.
• Population structure analysis: Analyzes population structure from genomic data.
• Inbreeding estimation: Estimates inbreeding coefficients.
• Allele frequency estimation: Estimates allele frequencies from sequence data.
• Scalable workflow: Uses Snakemake for workflow automation and scalability across computing environments.
• Automated reporting: Compiles results into an HTML report automatically.

Scientific Applications:

• Population genomics: Analyzes population genomic datasets across a wide variety of organisms.
• Historical DNA analysis: Processes and analyzes historical DNA samples alongside modern samples.
• Conservation genomics: Supports genetic diversity, inbreeding, and kinship analyses relevant to conservation.
• Population structure inference: Infers population structure and genetic differentiation for demographic and evolutionary studies.

Methodology:

PopGLen is implemented as a Snakemake workflow that integrates raw sequence processing, quality control, multiple filtering schemes, and population genomic analysis using genotype likelihood-based methods.