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name: bio-longread-medaka description: 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. tool_type: cli primary_tool: medaka measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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Medaka Polishing and Variant Calling

Basic Consensus Polishing

# 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)

# Call variants against reference (medaka v2.0+)
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 (medaka v2.0+)

# 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

# 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

# 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

# Polish specific region (medaka v2.0+)
medaka inference aligned.bam consensus.hdf \
    --model r1041_e82_400bps_sup_v5.0.0 \
    --region chr1:1000000-2000000

Multiple Rounds of Polishing

# 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

# If you already have aligned BAM (medaka v2.0+)
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

# 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

# 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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