Aug 22, 2025
- Jannes Spangenberg1,2,
- Christian Höner zu Siederdissen1,2,
- Manja Marz1,2,3,4
- 1Friedrich Schiller University Jena;
- 2RNA Bioinformatics & High-Throughput Analysis;
- 3European Virus Bioinformatics Center 2;
- 4FLI Leibniz Institute for Age Research
- Dynamont
Protocol Citation: Jannes Spangenberg, Christian Höner zu Siederdissen, Manja Marz 2025. Dynamont. protocols.io https://dx.doi.org/10.17504/protocols.io.x54v9528pl3e/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: August 22, 2025
Last Modified: August 22, 2025
Protocol Integer ID: 225246
Keywords: dynamont this protocol, dynamont, dynamont manuscript, zenodo, dataset
Funders Acknowledgements:
TMWWDG
Grant ID: FKZ5575/10-9
DFG EXC 2051
Grant ID: Project‐ID 390713860
SFB1076
Grant ID: 3.A06
BMBF
Grant ID: 01GR2305B.TP7
European Research Council
Grant ID: CoG‐101088027
Abstract
This protocol shows the commands used in the Dynamont manuscript to compare the datasets published in
Dynamont is available on
Troubleshooting
Benchmark Preparation Commands
# Extract 10.000 Random Reads
pod5 view <dataset.pod5> --ids --no-header -o all_ids.txt
sort --random-sort all_ids.txt | head --lines 10000 > 10k_ids.txt
pod5 filter <dataset.pod5> -o <dataset_r10k.pod5> --ids 10k_ids.txt
# Convert To Other Data Formats
blue-crab p2s <dataset_r10k.pod5> -o <dataset_r10k.blow5>
pod5 convert to_fast5 <dataset_r10k.pod5> -o fast5/
multi_to_single_fast5 -i fast5/ -s single_fast5/
# Basecalling
## explicitely using rna002_70bps_hac\@v3 for RNA002 data
dorado basecaller sup -x cuda:0 <dataset_r10k.pod5> > <dataset_r10k.bam>
samtools bam2fq <dataset_r10k.bam> > <dataset_r10k.fastq>
dorado summary <dataset_r10k.bam> > sequencing_summary.txt
# converting sequencing summary to tombo format (single fast5)
awk -F'\t' 'NR == 1 {print; next} {$1 = $2 ".fast5"; print}' OFS='\t' sequencing_summary.txt > tombo_sequencing_summary.txt
# Mapping
## preset = splice if RNA and h_sapiens, s_cerevisiae, e_coli, sarscov2
## preset = lr:hq for DNA R10.4.1
## preset = map-ont else
minimap2 -x -a | samtools view -hbF4 | samtools sort >
samtools index
Dynamont Segmentation Commands
# model can be added explicitely, otherwise default pore model is chosen
python segment.py --raw <path/to/pod5/dataset_r10k/> --basecalls <dataset_r10k.bam> --mode basic -o <dynamont.csv> --pore <pore>
Dorado Segmentation Commands
# Basecalling with emit moves
## explicitely using rna002_70bps_hac\@v3 for RNA002 data
dorado basecaller sup -x cuda:0 --emit-moves <dataset_r10k.pod5> > <dataset_r10k_moves.bam>
# Extracting moves as segmentation borders
python extractDoradoMoves.py <dataset_r10k_moves.bam> -o <dataset_r10k_moves.tsv>
f5c Segmentation Commands
# Indexing files
f5c index --slow5 <dataset_r10k.blow5> <dataset_r10k.fastq>
# Eventalign
## added --rna in case of RNA
f5c Eventalign -b <dataset_r10k_mapping.bam> -g <ref.fa> -r <dataset_r10k.fastq> --slow5 <dataset_r10k.blow5> --signal-index --collapse-events --pore <pore> --min-mapq 0 --summary <dataset_r10k_event.sum> > <dataset_r10k_event.tsv>
# Resquiggle
## added --rna in case of RNA
f5c Resquiggle --pore <pore> <dataset_r10k.fastq> <dataset_r10k.blow5> > <dataset_r10k_resqu.tsv>
Tombo Segmentation Commands
# Indexing files
tombo preprocess annotate_raw_with_fastqs --fast5-basedir single_fast5/ --fastq-filenames <dataset_r10k.fastq> --sequencing-summary-filenames sequencing_summary.txt
# only executed on RNA002
tombo Resquiggle --q-score 0 --rna single_fast5/ <ref.fa>
Uncalled4 Segmentation Commands
# preset = splice if RNA and h_sapiens, s_cerevisiae, e_coli, sarscov2
# preset = lr:hq for DNA R10.4.1
# preset = map-ont else
dorado basecaller sup -x cuda:0 --reference <ref.fa> --mm2-opts "-x <preset> --secondary=no" --emit-moves <dataset_r10k.pod5> > <mapped_basecalls.bam>
samtools view -hbF 2304 <mapped_basecalls.bam> > <primary_mapped_basecalls.bam>
uncalled4 align --ref <ref.fa> --reads <dataset_r10k.pod5> --bam-in <primary_mapped_basecalls.bam> --tsv-out <uncalled4_segmentation.tsv> --tsv-cols aln.read_id,dtw --min-aln-length 1