skills/bioskills/bio-epidemiological-genomics-variant-surveillance/scripts/variant_surveillance.py

• run_pangolin · function · L23-L30 — def run_pangolin(seq_fasta, out_csv, version_log, analysis_mode=PANGOLIN_DEFAULT_MODE)
• get_nextclade_dataset · function · L33-L40 — def get_nextclade_dataset(name, out_dir)
• run_nextclade · function · L43-L51 — def run_nextclade(seq_fasta, dataset_dir, out_tsv, metadata_log)
• reconcile_pangolin_nextclade · function · L54-L68 — def reconcile_pangolin_nextclade(pango_df, nc_df, key_pango='taxon', key_nc='seqName')
• status · function · L57-L66 — def status(row)
• validate_freyja_barcode_date · function · L71-L81 — def validate_freyja_barcode_date(barcode_date, sample_collection_date)
• freyja_variants_and_demix · function · L84-L91 — def freyja_variants_and_demix(bam, reference, out_prefix)
• _parse_freyja_demix · function · L94-L106 — def _parse_freyja_demix(demix_tsv)
• check_artic_dropouts · function · L109-L135 — def check_artic_dropouts(bam, primer_bed, min_coverage=20, scheme_version='V4.1')
• cojac_cooccurrence_scan · function · L138-L142 — def cojac_cooccurrence_scan(bam, primer_bed, variants_yaml, out_tsv)
• lineage_frequencies_over_time · function · L145-L154 — def lineage_frequencies_over_time(pango_results_df, date_col='collection_date', lineage_col='lineage', resample='W')
• multinomial_growth_advantage · function · L157-L172 — def multinomial_growth_advantage(frequencies_df)