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High-performance genomic data processing using polars-bio and Polars: streaming VCF/FASTA/ BED handling, interval joins, variant annotation, and Parquet convers
Polars-Bio provides patterns and examples for large-scale genomic data processing using polars-bio and Polars. It focuses on streaming and lazy evaluation to handle files larger than memory, covering VCF parsing and conversion to Parquet, high-performance interval/overlap joins for region queries, FASTA sequence analysis (GC content, lengths), and variant annotation via point-joins to external datasets.
Use this skill when you need to process genomic variant data (VCF), perform interval-based joins with BED-like regions, convert genomic data into columnar Parquet for downstream analytics, or perform sequence-level metrics on FASTA files. It's suited for bioinformatics pipelines that must remain memory-efficient.
Best for agents able to run Python code or orchestrate Python workers (Jupyter, Copilot, Claude Code, local Python runners).
This skill has not been reviewed by our automated audit pipeline yet.
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