Claude Code Plugins

Community-maintained marketplace

Feedback

Oxford Nanopore alignment with minimap2/dorado, reference genome management, BAM QC, and Levenshtein edit distance computation using edlib.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name ont-align
description Oxford Nanopore alignment with minimap2/dorado, reference genome management, BAM QC, and Levenshtein edit distance computation using edlib.

ONT Align - Alignment & Edit Distance

Alignment toolkit for Oxford Nanopore data with reference management, QC, and sequence comparison.

Quick Start

# Alignment
ont_align.py align reads.bam --reference GRCh38 --output aligned.bam

# Reference management
ont_align.py refs init
ont_align.py refs add GRCh38 /path/to/GRCh38.fa

# BAM QC
ont_align.py qc aligned.bam --json stats.json

# Edit distance
ont_align.py editdist "ACGTACGT" "ACGTTCGT" --cigar --normalize

Commands

Alignment

ont_align.py align <input> --reference <ref> --output <bam> [options]

Options:
  --reference REF    Reference name from registry or path to FASTA
  --output FILE      Output BAM file
  --preset PRESET    map-ont (default), lr:hq, splice, asm5, asm20
  --threads N        Number of threads (default: 8)
  --json FILE        Output alignment statistics JSON

Reference Management

ont_align.py refs init                    # Initialize registry
ont_align.py refs add <name> <fasta>      # Add reference
ont_align.py refs list                    # List references
ont_align.py refs info <name>             # Show details
ont_align.py refs import grch38|t2t       # Import standard reference

BAM QC

ont_align.py qc <bam> --json stats.json --plot coverage.png

Edit Distance (Levenshtein)

Uses edlib for fast edit distance computation.

# Direct comparison
ont_align.py editdist "ACGTACGT" "ACGTTCGT"

# With options
ont_align.py editdist "seq1" "seq2" --mode NW --cigar --normalize

# File-based batch
ont_align.py editdist --query variants.fa --target reference.fa --output distances.tsv

# All-vs-all matrix
ont_align.py editdist --query seqs.fa --self --output pairwise.tsv --threads 8

Modes:

  • NW: Global alignment (Needleman-Wunsch) - default
  • HW: Semi-global (query fully aligned, target has free end gaps)
  • SHW: Infix (find query as substring of target)

Integration

Run through ont-experiments for provenance tracking:

ont_experiments.py run alignment exp-abc123 --reference GRCh38 --output aligned.bam

Dependencies

pip install pysam edlib numpy
# System: minimap2, samtools