Agent skill

bio-basecalling

Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Guppy is deprecated; use Dorado for all new analyses.

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Install this agent skill to your Project

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

SKILL.md

Version Compatibility

Reference examples tested with: 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.

Nanopore Basecalling

"Basecall my Nanopore data" → Convert raw electrical signal (FAST5/POD5) into nucleotide sequences with quality scores, optionally detecting modified bases.

  • CLI: dorado basecaller sup pod5/ > calls.bam (recommended), dorado basecaller sup,5mCG_5hmCG pod5/ (with modifications)

Convert raw electrical signal from Nanopore sequencing into nucleotide sequences.

Dorado (Recommended)

Dorado is ONT's current production basecaller, replacing Guppy. It offers better accuracy and speed.

Basic Basecalling

bash
dorado basecaller sup pod5_dir/ > calls.bam

Choose Model

bash
dorado basecaller fast pod5_dir/ > calls.bam
dorado basecaller hac pod5_dir/ > calls.bam
dorado basecaller sup pod5_dir/ > calls.bam

Model Speed vs Accuracy

Model Speed Accuracy Use Case
fast Fastest Lower Quick preview
hac Medium High General use
sup Slowest Highest Publication quality

Specific Model Version

bash
dorado download --model dna_r10.4.1_e8.2_400bps_sup@v5.1.0
dorado basecaller dna_r10.4.1_e8.2_400bps_sup@v5.1.0 pod5_dir/ > calls.bam

List Available Models

bash
dorado download --list

Output FASTQ Instead of BAM

bash
dorado basecaller sup pod5_dir/ --emit-fastq > calls.fastq

Modified Base Detection

bash
dorado basecaller sup,5mCG_5hmCG pod5_dir/ > calls_mods.bam
dorado basecaller sup,5mCG pod5_dir/ > calls_5mc.bam
dorado basecaller sup,6mA pod5_dir/ > calls_6ma.bam

GPU Selection

bash
dorado basecaller sup pod5_dir/ --device cuda:0 > calls.bam
dorado basecaller sup pod5_dir/ --device cuda:0,1 > calls.bam
dorado basecaller sup pod5_dir/ --device cpu > calls.bam

Batch Size for Memory

bash
dorado basecaller sup pod5_dir/ --batchsize 64 > calls.bam

Duplex Calling

bash
dorado duplex sup pod5_dir/ > duplex.bam

Demultiplexing During Basecalling

bash
dorado basecaller sup pod5_dir/ --kit-name SQK-NBD114-24 > calls.bam
dorado demux calls.bam --output-dir demuxed/ --kit-name SQK-NBD114-24

Trim Adapters

bash
dorado basecaller sup pod5_dir/ --trim adapters > calls.bam
dorado basecaller sup pod5_dir/ --no-trim > calls_untrimmed.bam

Resume Interrupted Run

bash
dorado basecaller sup pod5_dir/ --resume-from calls.bam > calls_complete.bam

Guppy (Deprecated - Legacy Only)

Guppy is deprecated and no longer receiving updates. Use Dorado for all new analyses. Guppy examples below are only for maintaining legacy pipelines.

Basic Basecalling

bash
guppy_basecaller \
    -i fast5_dir/ \
    -s output_dir/ \
    -c dna_r10.4.1_e8.2_400bps_sup.cfg \
    --device cuda:0

CPU Mode

bash
guppy_basecaller \
    -i fast5_dir/ \
    -s output_dir/ \
    -c dna_r10.4.1_e8.2_400bps_fast.cfg \
    --num_callers 8 \
    --cpu_threads_per_caller 4

High Accuracy Model

bash
guppy_basecaller \
    -i fast5_dir/ \
    -s output_dir/ \
    -c dna_r10.4.1_e8.2_400bps_hac.cfg \
    --device cuda:0

Super Accuracy Model

bash
guppy_basecaller \
    -i fast5_dir/ \
    -s output_dir/ \
    -c dna_r10.4.1_e8.2_400bps_sup.cfg \
    --device cuda:0

List Available Configs

bash
guppy_basecaller --print_workflows
ls /opt/ont/guppy/data/*.cfg

Modified Base Calling

bash
guppy_basecaller \
    -i fast5_dir/ \
    -s output_dir/ \
    -c dna_r10.4.1_e8.2_400bps_modbases_5mc_cg_sup.cfg \
    --device cuda:0

Barcoding During Basecalling

bash
guppy_basecaller \
    -i fast5_dir/ \
    -s output_dir/ \
    -c dna_r10.4.1_e8.2_400bps_sup.cfg \
    --device cuda:0 \
    --barcode_kits SQK-NBD114-24

Output BAM

bash
guppy_basecaller \
    -i fast5_dir/ \
    -s output_dir/ \
    -c dna_r10.4.1_e8.2_400bps_sup.cfg \
    --device cuda:0 \
    --bam_out \
    --index

POD5 File Handling

POD5 is the new format replacing FAST5.

Convert FAST5 to POD5

bash
pod5 convert fast5 fast5_dir/*.fast5 --output pod5_dir/

Merge POD5 Files

bash
pod5 merge pod5_dir/*.pod5 --output merged.pod5

Inspect POD5

bash
pod5 inspect reads input.pod5
pod5 inspect summary input.pod5

Subset POD5

bash
pod5 subset input.pod5 --output subset.pod5 --read-id-file read_ids.txt

Quality Filtering

Filter with Chopper (After Basecalling)

bash
gunzip -c calls.fastq.gz | chopper -q 10 -l 500 | gzip > filtered.fastq.gz

Filter by Quality Score

bash
gunzip -c calls.fastq.gz | \
    awk 'BEGIN{OFS="\n"} {h=$0; getline seq; getline plus; getline qual;
         split(h, a, " "); split(a[4], q, "=");
         if(q[2] >= 10) print h, seq, plus, qual}' | \
    gzip > q10_filtered.fastq.gz

NanoFilt (Alternative)

bash
gunzip -c calls.fastq.gz | NanoFilt -q 10 -l 500 | gzip > filtered.fastq.gz

Basecalling QC

NanoPlot

bash
NanoPlot --fastq calls.fastq.gz -o qc_report/ --plots hex dot
NanoPlot --bam calls.bam -o qc_report/

pycoQC (From Sequencing Summary)

bash
pycoQC -f sequencing_summary.txt -o pycoqc_report.html

Basic Stats

bash
seqkit stats calls.fastq.gz

awk 'NR%4==2 {sum+=length($0); count++} END {print "Reads:", count, "Mean length:", sum/count}' calls.fastq

Model Selection Guide

R10.4.1 Chemistry (Current)

Model Use
dna_r10.4.1_e8.2_400bps_fast Quick analysis
dna_r10.4.1_e8.2_400bps_hac Routine work
dna_r10.4.1_e8.2_400bps_sup High accuracy

R9.4.1 Chemistry (Legacy)

Model Use
dna_r9.4.1_450bps_fast Quick analysis
dna_r9.4.1_450bps_hac Routine work
dna_r9.4.1_450bps_sup High accuracy

Complete Pipeline

Goal: Run the full Nanopore basecalling pipeline from raw signal data through quality-filtered reads with a QC report.

Approach: Convert FAST5 to POD5 if needed, basecall with Dorado, convert to FASTQ, filter with chopper, and generate NanoPlot QC.

bash
#!/bin/bash
INPUT=$1
OUTPUT=$2
MODEL=${3:-sup}

mkdir -p $OUTPUT

if [ -d "$INPUT/fast5" ]; then
    echo "Converting FAST5 to POD5..."
    pod5 convert fast5 $INPUT/fast5/*.fast5 --output $OUTPUT/pod5/
    INPUT_DIR="$OUTPUT/pod5"
else
    INPUT_DIR="$INPUT"
fi

echo "Basecalling with $MODEL model..."
dorado basecaller $MODEL $INPUT_DIR > $OUTPUT/calls.bam

echo "Converting to FASTQ..."
samtools fastq $OUTPUT/calls.bam | gzip > $OUTPUT/calls.fastq.gz

echo "Filtering..."
gunzip -c $OUTPUT/calls.fastq.gz | chopper -q 10 -l 500 | gzip > $OUTPUT/filtered.fastq.gz

echo "QC report..."
NanoPlot --fastq $OUTPUT/filtered.fastq.gz -o $OUTPUT/qc/

echo "Done!"

GPU Requirements

Model VRAM Required Speed (R10.4.1)
fast 4 GB ~450 bases/s
hac 8 GB ~200 bases/s
sup 12 GB ~50 bases/s

Troubleshooting

Out of Memory

bash
dorado basecaller sup pod5_dir/ --batchsize 32 > calls.bam

Slow CPU Basecalling

bash
dorado basecaller fast pod5_dir/ --device cpu > calls.bam

Check GPU Usage

bash
nvidia-smi -l 1
watch -n 1 nvidia-smi

Related Skills

  • long-read-alignment - Align basecalled reads
  • long-read-qc - QC after basecalling
  • medaka-polishing - Polish using basecalled reads
  • structural-variants - SV detection from long reads

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