ligo 1.0.11
LIgO is a tool for simulation of adaptive immune receptors and repertoires.
Requires Python: >=3.11

# LIgO


![Python application](https://github.com/uio-bmi/ligo/actions/workflows/python-app.yml/badge.svg?branch=main)
![Docker](https://github.com/uio-bmi/ligo/actions/workflows/docker-publish.yml/badge.svg?branch=main)
![PyPI](https://github.com/uio-bmi/ligo/actions/workflows/publish-to-pypi.yml/badge.svg?branch=master)

LIgO is a tool for simulation of adaptive immune receptors and repertoires, 
internally powered by [immuneML](https://immuneml.uio.no/). 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/.

## Installation

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
```

## Usage

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.

```yaml

  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: LigoSim
```

To run this simulation, save the YAML file above as specs.yaml and run the following:

```commandline
ligo specs.yaml output_folder
```

Note that `output_folder` (user-defined name) should not exist before the run.


## Citing LIgO

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.