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Bio Basecalling

OrganizationPopular
FreedomIntelligence
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.

Overview

PublisherFreedomIntelligence
RepositoryOpenClaw-Medical-Skills
Skill namebio-basecalling
Stars
3K
Forks
410
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by FreedomIntelligence on GitHub. Read the source before you install it.

Installation

Install the Bio Basecalling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.git /tmp/OpenClaw-Medical-Skills
mkdir -p .claude/skills
cp -r /tmp/OpenClaw-Medical-Skills/skills/bio-basecalling .claude/skills/bio-basecalling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bio Basecalling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bio Basecalling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bio Basecalling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

ModelSpeedAccuracyUse Case
fastFastestLowerQuick preview
hacMediumHighGeneral use
supSlowestHighestPublication 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)

ModelUse
dna_r10.4.1_e8.2_400bps_fastQuick analysis
dna_r10.4.1_e8.2_400bps_hacRoutine work
dna_r10.4.1_e8.2_400bps_supHigh accuracy

R9.4.1 Chemistry (Legacy)

ModelUse
dna_r9.4.1_450bps_fastQuick analysis
dna_r9.4.1_450bps_hacRoutine work
dna_r9.4.1_450bps_supHigh 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

ModelVRAM RequiredSpeed (R10.4.1)
fast4 GB~450 bases/s
hac8 GB~200 bases/s
sup12 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bio Basecalling AI skill do?

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.

Why use Bio Basecalling on TypingMind?

Because you install it once and use it with any model. Bio Basecalling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bio Basecalling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-basecalling. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bio Basecalling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bio Basecalling?

As many as you like. As long as a model supports skills, you can use Bio Basecalling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bio Basecalling AI skill free?

It is published on GitHub by FreedomIntelligence. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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