메뉴
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.
Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation.
Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
Prepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning.
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
| name | bio-basecalling |
| description | 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. |
| tool_type | cli |
| primary_tool | dorado |
Reference examples tested with: samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Basecall my Nanopore data" → Convert raw electrical signal (FAST5/POD5) into nucleotide sequences with quality scores, optionally detecting modified bases.
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 is ONT's current production basecaller, replacing Guppy. It offers better accuracy and speed.
dorado basecaller sup pod5_dir/ > calls.bam
dorado basecaller fast pod5_dir/ > calls.bam
dorado basecaller hac pod5_dir/ > calls.bam
dorado basecaller sup pod5_dir/ > calls.bam
| Model | Speed | Accuracy | Use Case |
|---|---|---|---|
| fast | Fastest | Lower | Quick preview |
| hac | Medium | High | General use |
| sup | Slowest | Highest | Publication quality |
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
dorado download --list
dorado basecaller sup pod5_dir/ --emit-fastq > calls.fastq
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
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
dorado basecaller sup pod5_dir/ --batchsize 64 > calls.bam
dorado duplex sup pod5_dir/ > duplex.bam
dorado basecaller sup pod5_dir/ --kit-name SQK-NBD114-24 > calls.bam
dorado demux calls.bam --output-dir demuxed/ --kit-name SQK-NBD114-24
dorado basecaller sup pod5_dir/ --trim adapters > calls.bam
dorado basecaller sup pod5_dir/ --no-trim > calls_untrimmed.bam
dorado basecaller sup pod5_dir/ --resume-from calls.bam > calls_complete.bam
Guppy is deprecated and no longer receiving updates. Use Dorado for all new analyses. Guppy examples below are only for maintaining legacy pipelines.
guppy_basecaller \
-i fast5_dir/ \
-s output_dir/ \
-c dna_r10.4.1_e8.2_400bps_sup.cfg \
--device cuda:0
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
guppy_basecaller \
-i fast5_dir/ \
-s output_dir/ \
-c dna_r10.4.1_e8.2_400bps_hac.cfg \
--device cuda:0
guppy_basecaller \
-i fast5_dir/ \
-s output_dir/ \
-c dna_r10.4.1_e8.2_400bps_sup.cfg \
--device cuda:0
guppy_basecaller --print_workflows
ls /opt/ont/guppy/data/*.cfg
guppy_basecaller \
-i fast5_dir/ \
-s output_dir/ \
-c dna_r10.4.1_e8.2_400bps_modbases_5mc_cg_sup.cfg \
--device cuda:0
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
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 is the new format replacing FAST5.
pod5 convert fast5 fast5_dir/*.fast5 --output pod5_dir/
pod5 merge pod5_dir/*.pod5 --output merged.pod5
pod5 inspect reads input.pod5
pod5 inspect summary input.pod5
pod5 subset input.pod5 --output subset.pod5 --read-id-file read_ids.txt
gunzip -c calls.fastq.gz | chopper -q 10 -l 500 | gzip > filtered.fastq.gz
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
gunzip -c calls.fastq.gz | NanoFilt -q 10 -l 500 | gzip > filtered.fastq.gz
NanoPlot --fastq calls.fastq.gz -o qc_report/ --plots hex dot
NanoPlot --bam calls.bam -o qc_report/
pycoQC -f sequencing_summary.txt -o pycoqc_report.html
seqkit stats calls.fastq.gz
awk 'NR%4==2 {sum+=length($0); count++} END {print "Reads:", count, "Mean length:", sum/count}' calls.fastq
| 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 |
| 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 |
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.
#!/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!"
| 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 |
dorado basecaller sup pod5_dir/ --batchsize 32 > calls.bam
dorado basecaller fast pod5_dir/ --device cpu > calls.bam
nvidia-smi -l 1
watch -n 1 nvidia-smi