IsoGen

IsoGen is a toolbox for predicting isotope distributions from protein, RNA, DNA, neutral-mass, and elemental-formula inputs.

It includes absolute FFT and BRAIN calculations plus a neural network prediction.

Pretrained models are included for both peptides and RNA based on either average mass or sequence. DNA prediction uses the RNA model due to the similarity of their elemental compositions.

The FFT methods are absolute and are limited only by the accuracy of the data you put in. They are a little faster, especially on larger species.

The BRAIN method is an absolute calculation based on a polynomial recurrence. It provides an alternative to FFT for peptide, RNA, and DNA sequence or neutral-mass inputs.

The NN methods are very accurate and can be faster on smaller species. The primary advantage of these is that they can be retrained on non-standard isotope distributions.

Installation

Install a published wheel from PyPI:

python -m pip install pyisogen

IsoGen requires Python 3.13 or newer.

Precompiled native libraries are provided for 64-bit Windows and Linux. Linux requires the FFTW 3 runtime; published Linux wheels bundle it during the manylinux repair step. For other platforms, build the native library from source using CMake.

Usage

From Python:

import isogen

protein = isogen.isodist("ACDEFGHIK", type="PEPTIDE", isolen=64)
protein_brain = isogen.isodist(
    "ACDEFGHIK", type="PEPTIDE", isolen=64, method="BRAIN"
)
rna = isogen.isodist("AUGCAGUACGUA", type="RNA", isolen=64)
dna = isogen.isodist("ATGCAGTACGTA", type="DNA", isolen=64)
glucose_mass_dist = isogen.isodist("C6H12O6", type="ATOM", isolen=32)

The output is a numpy array of shape (isolen, 2) with the first column containing the monoisotopic mass and the second column containing the relative intensity. The isolen parameter controls the number of isotopic peaks returned.

IsoGen provides FFT, BRAIN, and neural-network methods for peptides and RNA. The default is the exact FFT calculation. BRAIN selects the polynomial recurrence calculation, while NN uses the neural-network model to predict the distribution from a peptide or RNA sequence or neutral mass.

The PEPTIDE model is trained on peptide sequences, while the RNA model is trained on RNA sequences. The DNA type uses the RNA model, and

The public ATOM type uses the FFT method; no neural-network formula model is available.

Custom neural-network models

Use isodist_custom to generate a distribution from a binary model file rather than one of IsoGen's bundled neural-network models:

from pathlib import Path

import isogen

model_file = Path("models/my_peptide_model_64.bin")
custom = isogen.isodist_custom(
    "ACDEFGHIK",
    model_file=model_file,
    isolen=64,
    type="PEPTIDE",
)

The function accepts peptide, RNA, and DNA sequences or numeric neutral masses. It always uses the neural-network method. The model must have the correct input size for the selected input and type, and its output size must equal isolen. Peptide sequence models have 20 inputs, RNA/DNA sequence models have 4 inputs, and neutral-mass models have 5 inputs. Invalid, unreadable, or incompatible model files raise ValueError. As with isodist, the result has shape (isolen, 2), containing neutral masses and relative intensities.

Training custom models

Install the training dependencies before importing the training modules:

python -m pip install -e ".[training]"

Training data is stored in NumPy .npz archives. Sequence models expect a seqs array and mass models expect a masses array. Every archive also needs a dists array with shape (number_of_examples, isolen). Each row of dists is the target relative-intensity distribution for its corresponding sequence or neutral mass. For example:

import numpy as np

np.savez_compressed(
    "peptide_training.npz",
    seqs=np.asarray(["ACDE", "PEPTIDE", "MARTY"]),
    dists=np.asarray(peptide_target_distributions, dtype=np.float32),
)

np.savez_compressed(
    "mass_training.npz",
    masses=np.asarray([1_000.0, 5_000.0, 10_000.0]),
    dists=np.asarray(mass_target_distributions, dtype=np.float32),
)

Use the engine matching the kind of input the model will receive. The helper below directs generated models to a separate directory instead of overwriting the models installed with IsoGen:

from pathlib import Path

from isogen.isogenmass import IsoGenMassEngine
from isogen.isogenpep import IsoGenPepEngine
from isogen.isogenrna import IsoGenRNAEngine
from isogen.isogenrna_averagine import IsoGenRNAveragineEngine


model_dir = Path("trained_models")
model_dir.mkdir(exist_ok=True)


def set_model_directory(engine):
    """Set the output directory before a model is initialized or loaded."""
    engine.model.working_dir = str(model_dir)
    for model in engine.models:
        model.working_dir = str(model_dir)


# Peptide sequences: 20-element amino-acid composition input.
pep = IsoGenPepEngine(isolen=64)
set_model_directory(pep)
pep.train("peptide_training.npz", epochs=20, forcenew=True)

# RNA sequences: 4-element A/C/G/U composition input. This model is also
# used for DNA inference after IsoGen converts thymine to uracil.
rna = IsoGenRNAEngine(isolen=64)
set_model_directory(rna)
rna.train("rna_training.npz", epochs=20, forcenew=True)

# Peptide-like neutral masses: 5-element mass encoding.
mass = IsoGenMassEngine(isolen=64)
set_model_directory(mass)
mass.train_multiple(
    ["mass_training.npz"],
    inputname="masses",
    epochs=20,
    forcenew=True,
)

# RNA-like neutral masses: 5-element mass encoding.
rna_mass = IsoGenRNAveragineEngine(isolen=64)
set_model_directory(rna_mass)
rna_mass.train_multiple(
    ["rna_mass_training.npz"],
    inputname="masses",
    epochs=20,
    forcenew=True,
)

IsoGenPepEngine supports output lengths 16, 64, and 128; IsoGenRNAEngine supports 64 and 128; IsoGenMassEngine models intended for isodist_custom support 8, 32, 64, and 128; and IsoGenRNAveragineEngine supports 32, 64, and 128. The output length used to construct the engine must match the width of dists and the isolen passed to isodist_custom.

After training, each engine saves a PyTorch .pth checkpoint and a raw .bin model in trained_models. The .pth file is used to resume Python training; pass the .bin file to isodist_custom. The generated filenames are isogenpep_model_<isolen>.bin, isogenrna_model_<isolen>.bin, isogenmass_model_<isolen>.bin, and isogen_rnaveragine_model<isolen>.bin, respectively:

custom = isogen.isodist_custom(
    "ACDEFGHIK",
    model_file=model_dir / "isogenpep_model_64.bin",
    isolen=64,
    type="PEPTIDE",
)

Passing forcenew=True starts from newly initialized weights. Use forcenew=False to resume from a matching .pth checkpoint in the configured model directory. IsoGenMassEngine.train(...) and IsoGenRNAveragineEngine.train(...) can also generate standard FFT targets from random masses when a custom target archive is not needed.

Peptide ions and RNA termini

For peptide fragments, pass the fragment sequence and select its neutral terminal composition with ion_type. IsoGen supports intact H2O (the default) and the peptide a, b, c, x, y, and z ion types:

b6 = isogen.isodist("PEPTID", type="PEPTIDE", ion_type="b")
y6 = isogen.isodist("EPTIDE", type="PEPTIDE", ion_type="y")

Supply the N-terminal subsequence for a/b/c ions and the C-terminal subsequence for x/y/z ions. Returned values are neutral masses, not charge-adjusted m/z.

RNA does not currently accept named RNA fragment-ion series through ion_type. For an intact or manually truncated RNA sequence, configure the supported terminal chemistry with threeend and fiveend:

rna_5_triphosphate = isogen.isodist(
    "AUGC",
    type="RNA",
    threeend="OH",
    fiveend="TP",
)

The available 5' settings are hydroxyl (OH), monophosphate (MP, default), and triphosphate (TP); the supported explicit 3' setting is hydroxyl (OH, default). These peptide-ion and RNA-terminal options adjust the mass-axis origin. The sequence-model intensity vector retains its standard terminal composition.

From the command line:

isogen dist ACDEFGHIK --type PEPTIDE --isolen 64
isogen dist C6H12O6 --type ATOM --isolen 32
isogen plot

See python -m isogen --help for all options.

The source repository also builds a native development executable named isogen_test.exe on Windows (isogen_test on Linux). It can be run from the repository's bin directory with isogen_test.exe -mass 10000, but it is not installed by the Python wheel. Use the isogen console command for installed packages.

Documentation

Read the full IsoGen documentation. The documentation sources are also available in the repository's docs directory. To preview them locally:

python -m pip install -e ".[docs]"
python -m mkdocs serve

Tests

The test suite uses Pyteomics as an independent mass reference. Pyteomics is only part of the optional test dependencies and is not installed with IsoGen:

python -m pip install -e ".[test]"
python -m pytest

Development and model-training modules have additional dependencies:

python -m pip install -e ".[training]"

License

IsoGen is released under the BSD 3-Clause License. See LICENSE for details.

PLEASE CITE THIS SOFTWARE IN ANY PUBLICATIONS THAT USE IT (publication to follow).

Contact

If you have any questions, please email mtmarty@utexas.edu or open a ticket on GitHub.

CHANGELOG

1.0.3

Added the BRAIN polynomial-recurrence isotope calculation for peptide, RNA, and DNA sequence and neutral-mass inputs.

Added method="BRAIN" to the Python API and command-line interface.

Dramatically improved BRAIN performance by about double using some computational tricks the AI found.

Added side-by-side FFT, NN, and BRAIN protein-sequence example plots.

Added runtime-dispatched AVX2/FMA neural-network acceleration on supported x86 processors, with a portable scalar fallback.

Improved native normalization and large-input regression coverage.

Added a timing test script for internal use.

1.0.2

Added support for custom models with isogen_custom function and new C bindings for custom models.

1.0.1

Small updates to README.md

1.0.0

Initial release. Rewrote significantly from UniDec build using AI tool to improve the release and add in atomic formula support.