Agent skill

bio-longread-medaka

Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions. Use when improving ONT-only assemblies or calling variants from Nanopore data without short-read polishing.

View SKILL.md on GitHub Repository
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Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-longread-medaka

SKILL.md

Version Compatibility

Reference examples tested with: bcftools 1.19+, minimap2 2.26+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

  • CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Medaka Polishing and Variant Calling

"Polish my ONT assembly with medaka" → Use neural networks trained on specific basecaller models to correct assembly errors and call variants from Nanopore data.

  • CLI: medaka_polisher -i reads.fq -d draft.fa -o polished.fa -m r1041_e82_400bps_sup_v5.0.0

Basic Consensus Polishing

bash
# Polish assembly with medaka
medaka_consensus -i reads.fastq.gz \
    -d draft_assembly.fa \
    -o medaka_output \
    -t 4 \
    -m r1041_e82_400bps_sup_v5.0.0

Variant Calling (Haploid)

bash
# Call variants against reference
medaka_variant \
    -i reads.fastq.gz \
    -r reference.fa \
    -o output_dir \
    -m r1041_e82_400bps_sup_v5.0.0

Note: Diploid variant calling has been deprecated in medaka v2.0. For diploid samples, use Clair3 instead.

Step-by-Step Workflow

Goal: Polish an ONT assembly or call variants using medaka's neural network models with explicit control over each step.

Approach: Align reads with minimap2, run medaka neural network inference on the alignment, then generate either a polished consensus or variant calls from the probability output.

bash
# 1. Align reads to reference/draft
minimap2 -ax map-ont reference.fa reads.fastq.gz | \
    samtools sort -o aligned.bam
samtools index aligned.bam

# 2. Run neural network inference
medaka inference aligned.bam consensus.hdf \
    --model r1041_e82_400bps_sup_v5.0.0 \
    --threads 2                          # >2 threads has poor scaling

# 3. Create consensus sequence from probabilities
medaka sequence consensus.hdf reference.fa polished.fa

# 4. Call variants from probabilities
medaka vcf reference.fa consensus.hdf variants.vcf

List Available Models

bash
# See all available models
medaka tools list_models

# Models are named:
# r{pore}_{chemistry}_{speed}bps_{accuracy}_{version}
# e.g., r1041_e82_400bps_sup_v5.0.0

Common Models

Model Description
r1041_e82_400bps_sup_v5.0.0 R10.4.1, E8.2, SUP basecalling
r1041_e82_400bps_hac_v5.0.0 R10.4.1, E8.2, HAC basecalling
r941_min_sup_g507 R9.4.1, MinION, SUP
r941_min_hac_g507 R9.4.1, MinION, HAC

Choose Model Based on Basecaller

bash
# Check which basecaller was used in your data
# Then select matching model

# For Guppy/Dorado SUP basecalling on R10.4.1
medaka_consensus -m r1041_e82_400bps_sup_v5.0.0 ...

# For HAC basecalling
medaka_consensus -m r1041_e82_400bps_hac_v5.0.0 ...

Polish Region Only

bash
# Polish specific region
medaka inference aligned.bam consensus.hdf \
    --model r1041_e82_400bps_sup_v5.0.0 \
    --region chr1:1000000-2000000

Multiple Rounds of Polishing

bash
# First round
medaka_consensus -i reads.fastq.gz -d draft.fa -o round1 -m model

# Second round (diminishing returns, usually not needed)
medaka_consensus -i reads.fastq.gz -d round1/consensus.fasta -o round2 -m model

Call Variants from Existing BAM

bash
# If you already have aligned BAM
medaka inference aligned.bam consensus.hdf --model r1041_e82_400bps_sup_v5.0.0
medaka vcf reference.fa consensus.hdf variants.vcf

Filter VCF Output

bash
# Filter by quality
bcftools filter -i 'QUAL>20' variants.vcf > variants.filtered.vcf

# Get high-confidence calls
bcftools view -i 'FILTER="PASS"' variants.vcf > variants.pass.vcf

Output Files

File Description
consensus.fasta Polished sequence
consensus.hdf Neural network outputs
variants.vcf Variant calls
calls_to_draft.bam Alignments used

Key Parameters

Parameter Description
-i Input reads (FASTQ)
-d Draft assembly/reference
-o Output directory
-m Model name
-t Threads
-b Batch size (GPU memory)
--region Specific region to process

GPU Acceleration

bash
# Enable GPU (if available)
medaka_consensus -i reads.fastq.gz -d draft.fa -o output \
    -m r1041_e82_400bps_sup_v5.0.0 \
    -b 100 \                       # Increase batch size for GPU
    -t 4

Related Skills

  • long-read-alignment - Generate input alignments
  • structural-variants - Find SVs from polished assembly
  • variant-calling/variant-calling - Short-read variant calling comparison

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