Pre-trained models
FiberHMM ships with pre-trained models in the models/ directory. These can be used directly with fiberhmm-apply without needing to generate emission probabilities or train from scratch.
Available models
| Model | File | Enzyme | Platform | Mode | Organism |
|---|---|---|---|---|---|
| Hia5 PacBio | hia5_pacbio.json | Hia5 (m6A) | PacBio | pacbio-fiber | Drosophila |
| Hia5 Nanopore | hia5_nanopore.json | Hia5 (m6A) | Nanopore | nanopore-fiber | Yeast |
| DddA PacBio | ddda_pacbio.json | DddA (deamination) | PacBio | daf | Drosophila |
| DddB Nanopore | dddb_nanopore.json | DddB (deamination) | Nanopore | daf | Drosophila |
Using a pre-trained model
# PacBio Hia5 fiber-seq
fiberhmm-apply -i experiment.bam -m models/hia5_pacbio.json -o output/ -c 8
# Nanopore Hia5 fiber-seq
fiberhmm-apply -i experiment.bam -m models/hia5_nanopore.json -o output/ -c 8
# DAF-seq (DddA or DddB)
fiberhmm-apply -i experiment.bam -m models/ddda_pacbio.json -o output/ -c 8 --mode daf
The mode and context size are stored in the model JSON and auto-detected at runtime, so you generally do not need to specify --mode or -k when using a pre-trained model.
When to train your own model
The pre-trained models work well as a starting point, but you may want to train a custom model if:
- You are working with a different organism where chromatin structure differs substantially
- You are using a different methyltransferase or deaminase chemistry
- You have matched accessible and inaccessible controls for your specific experiment
See the usage guide for the full training pipeline.
Inspecting a model
You can inspect any model's parameters with:
fiberhmm-utils inspect models/hia5_pacbio.json
This prints the mode, context size, start probabilities, transition matrix, and emission probability summary statistics.