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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).
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