Getting Started with PopGene.S2 — Installation & First AnalysisPopGene.S2 is a modern, flexible toolkit for population genetics analyses designed to handle genome-scale datasets with performance and clarity. This guide walks you through installing PopGene.S2, preparing common input formats, running an initial analysis (basic population structure and diversity metrics), and interpreting results. Practical examples use realistic commands and small example datasets so you can reproduce the steps on your local machine or a compute server.
1. System requirements & prerequisites
- Operating systems: Linux (Ubuntu/CentOS), macOS. Windows supported via WSL2.
- Hardware: For small datasets, 4–8 GB RAM is adequate; for whole-genome datasets, 32+ GB recommended.
- Software dependencies: Python 3.10+, R 4.0+ (optional for plotting), C/C++ toolchain for compiling any native components, common bioinformatics utilities (bcftools, samtools) for preprocessing.
Install or confirm Python and pip:
python3 --version pip3 --version If you plan to use conda:
conda --version 2. Installation
There are three main installation paths: pip, conda, and source. Choose one depending on your environment.
2.1 Install with pip (recommended for many users)
pip3 install popgene-s2 If you use a virtual environment:
python3 -m venv pg_env source pg_env/bin/activate pip install --upgrade pip pip install popgene-s2 2.2 Install with conda
If PopGene.S2 is available on conda-forge or a dedicated channel:
conda create -n popgene-s2 python=3.10 conda activate popgene-s2 conda install -c conda-forge popgene-s2 2.3 Build from source
Clone the repository and install:
git clone https://github.com/your-org/popgene-s2.git cd popgene-s2 pip install -r requirements.txt python setup.py install If there are compiled extensions:
pip wheel . pip install dist/popgene_s2-*.whl Verify installation:
popgene-s2 --version Expected output: PopGene.S2 x.y.z
3. Input data formats & preprocessing
PopGene.S2 supports common population-genetics file formats:
- VCF (recommended for variant data)
- PLINK (.bed/.bim/.fam)
- FASTA/VCF for sequence-based analyses
- CSV/TSV for metadata (sample labels, populations, locations)
Common preprocessing steps:
- Quality filtering (depth, genotype quality)
- Remove sites with high missingness
- Convert to required format (if needed)
Example using bcftools:
bcftools view -i 'F_MISSING<0.1 && QUAL>30' input.vcf -Oz -o filtered.vcf.gz bcftools index filtered.vcf.gz Convert VCF to PLINK:
plink2 --vcf filtered.vcf.gz --make-bed --out dataset Create a simple metadata file (samples.tsv):
sample_id population latitude longitude S1 PopA 45.0 -120.5 S2 PopA 45.1 -120.4 S3 PopB 46.0 -121.0 ... 4. Basic CLI usage & configuration
PopGene.S2 provides a command-line interface and a Python API. CLI is useful for quick runs and pipelines.
Show CLI help:
popgene-s2 --help Typical command structure:
popgene-s2 <subcommand> [options] Common subcommands:
- analyze: run analyses (structure, diversity, Fst, PCA)
- convert: format conversions
- plot: generate plots
- utils: miscellaneous utilities (subset, merge)
Example config file (YAML) for an analysis:
input: vcf: filtered.vcf.gz metadata: samples.tsv analysis: pca: true fst: true diversity: ["pi", "theta_w"] output: dir: results/ prefix: run1 resources: threads: 4 memory_gb: 16 Run with config:
popgene-s2 analyze --config config.yaml 5. First analysis: PCA + diversity + pairwise FST
This section demonstrates a typical first-pass analysis: principal component analysis (PCA) to inspect structure, nucleotide diversity (π) per population, and pairwise FST.
5.1 PCA
Command:
popgene-s2 analyze --vcf filtered.vcf.gz --metadata samples.tsv --pca --threads 4 --out results/pca Outputs:
- results/pca/pca_scores.csv (sample coordinates on PCs)
- results/pca/pca_eigvals.csv
- results/pca/pca_plot.png (if plotting enabled)
Interpretation:
- Plot PC1 vs PC2; clusters often correspond to populations or geographic gradients. Outliers may indicate sample contamination or mislabeling.
5.2 Nucleotide diversity (π) per population
Command:
popgene-s2 analyze --vcf filtered.vcf.gz --metadata samples.tsv --diversity pi --pop-col population --out results/diversity Outputs:
- results/diversity/pi_by_population.csv
- results/diversity/pi_plot.png
5.3 Pairwise FST
Command:
popgene-s2 analyze --vcf filtered.vcf.gz --metadata samples.tsv --fst pairwise --pop-col population --out results/fst Outputs:
- results/fst/pairwise_fst_matrix.csv
- results/fst/fst_heatmap.png
6. Example Python API usage
Load data and run a PCA through the Python API:
from popgene_s2 import Dataset, Analysis ds = Dataset.from_vcf("filtered.vcf.gz", metadata="samples.tsv") analysis = Analysis(ds, threads=4) pca = analysis.run_pca(n_components=5) pca.scores.to_csv("results/pca/pca_scores.csv", index=False) analysis.plot_pca(save="results/pca/pca_plot.png") Compute diversity and FST:
pi = analysis.diversity(metric="pi", pop_col="population") pi.to_csv("results/diversity/pi_by_population.csv", index=False) fst = analysis.pairwise_fst(pop_col="population") fst.to_csv("results/fst/pairwise_fst_matrix.csv") 7. Interpreting results and common pitfalls
- Low variance explained by first PCs may indicate complex structure or insufficient SNPs. Consider LD pruning and more variants.
- High missingness can bias diversity and FST—filter aggressively or use imputation.
- Related or duplicated samples inflate diversity estimates; check kinship/IBD.
- Small sample sizes per population produce noisy FST and π estimates; report confidence intervals or use block-jackknife where available.
8. Visualization tips
- For PCA: color points by population, add convex hulls or density contours for clarity.
- For FST: use a heatmap with clustered rows/columns; annotate values with significance if available.
- For diversity across genome: use sliding windows (e.g., 50 kb windows) and show genome-wide averages with shaded confidence intervals.
Example R snippet for PCA plot (optional):
library(ggplot2) pca <- read.csv("results/pca/pca_scores.csv") meta <- read.csv("samples.tsv", sep=" ") df <- merge(pca, meta, by="sample_id") ggplot(df, aes(PC1, PC2, color=population)) + geom_point() + theme_minimal() 9. Performance & scaling
- Use multi-threading for CPU-bound tasks (PCA on large genotype matrices).
- For extremely large datasets, consider genotype compression (BGEN) and LD-based SNP thinning.
- Run compute-intensive steps on a cluster; PopGene.S2 supports splitting by chromosome and merging results.
10. Troubleshooting & tips
- If installation fails due to compiled extensions, ensure your build tools (gcc/clang, make) are installed and that Python headers (python3-dev) are present.
- For memory errors during PCA, use randomized-SVD options or incremental PCA implementations available in the tool.
- Validate results using a small known dataset first (example datasets often included with the package).
11. Further reading & next steps
- Try admixture/ancestry deconvolution workflows next.
- Run demographic inference or selection scans after initial QC.
- Dive into advanced features: haplotype-based analyses, local ancestry, and coalescent simulations.
If you want, I can produce ready-to-run example commands for a particular dataset you have (VCF or PLINK), or generate a complete YAML config tuned for a given sample size and number of variants.
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