ReddRadar
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.
Stars
163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/medaka-polishing
SKILL.md
Medaka Polishing and Variant Calling
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 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+)
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 v2.0+)
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 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
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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