LIgO is a tool for simulation of adaptive immune receptors and repertoires, internally powered by immuneML. The README includes quick installation instructions and information on how to run a quickstart. For more detailed documentation, see https://uio-bmi.github.io/ligo/.
Requirements: Python 3.11 or later.
To install from PyPI (recommended), run the following command in your virtual environment:
pip install ligo
To install LIgO from the repository, run the following:
pip install git+https://github.com/uio-bmi/ligo.git
To be able to use Stitcher to export full-length sequences, download the database after installing LIgO:
stitchrdl -s human
To run LIgO simulation, it is necessary to define the YAML file describing the simulation. Here is
an example YAML specification, that will create 300 T-cell receptors. The first 100
receptors will contain signal1 (which means all of these 100 receptors will have TRBV7 gene and AS
somewhere in the receptor sequence), the next 100 receptors will contain signal2 (sequences will contain G/G
with the gap denoted by '' sign and the gap size between 1 and 2 inclusive), and the final 100 receptors
will not contain any of these signals.
definitions:
motifs:
motif1:
seed: AS
motif2:
seed: G/G
max_gap: 2
min_gap: 1
signals:
signal1:
v_call: TRBV7
motifs:
- motif1
signal2:
motifs:
- motif2
simulations:
sim1:
is_repertoire: false
paired: false
sequence_type: amino_acid
simulation_strategy: RejectionSampling
remove_seqs_with_signals: true
sim_items:
sim_item1: # group of AIRs with the same parameters
generative_model:
chain: beta
default_model_name: humanTRB
model_path: null
type: OLGA
number_of_examples: 100
signals:
signal1: 1
sim_item2:
generative_model:
chain: beta
default_model_name: humanTRB
model_path: null
type: OLGA
number_of_examples: 100
signals:
signal2: 1
sim_item3:
generative_model:
chain: beta
default_model_name: humanTRB
model_path: null
type: OLGA
number_of_examples: 100
signals: {} # no signal
instructions:
my_sim_inst:
export_p_gens: false
max_iterations: 100
number_of_processes: 4
sequence_batch_size: 1000
simulation: sim1
type: LigoSimTo run this simulation, save the YAML file above as specs.yaml and run the following:
ligo specs.yaml output_folder
Note that output_folder (user-defined name) should not exist before the run.
If you are using LIgO in any published work, please cite:
Chernigovskaya, M.; Pavlović, M.; Kanduri, C.; Gielis, S.; Robert, P. A.; Scheffer, L.; Slabodkin, A.; Haff, I. H.; Meysman, P.; Yaari, G.; Sandve, G. K.; Greiff, V “Simulation of Adaptive Immune Receptors and Repertoires with Complex Immune Information to Guide the Development and Benchmarking of AIRR Machine Learning” bioRxiv, 2023, 2023.10.20.562936. https://doi.org/10.1101/2023.10.20.562936.
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