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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/.\n\n## Installation\n\nRequirements: Python 3.11 or later.\n\nTo install from PyPI (recommended), run the following command in your virtual environment:\n```\npip install ligo\n```\nTo install LIgO from the repository, run the following:\n```\npip install git+https://github.com/uio-bmi/ligo.git\n```\nTo be able to use Stitcher to export full-length sequences, download the database after installing LIgO:\n```\nstitchrdl -s human\n```\n\n## Usage\n\nTo run LIgO simulation, it is necessary to define the YAML file describing the simulation. Here is\nan example YAML specification, that will create 300 T-cell receptors. The first 100\nreceptors will contain signal1 (which means all of these 100 receptors will have TRBV7 gene and `AS` \nsomewhere in the receptor sequence), the next 100 receptors will contain signal2 (sequences will contain `G/G`\nwith the gap denoted by '\\' sign and the gap size between 1 and 2 inclusive), and the final 100 receptors\nwill not contain any of these signals.\n\n```yaml\n\n  definitions:\n    motifs:\n      motif1:\n        seed: AS\n      motif2:\n        seed: G/G\n        max_gap: 2\n        min_gap: 1\n    signals:\n      signal1:\n        v_call: TRBV7\n        motifs:\n          - motif1\n      signal2:\n        motifs:\n          - motif2\n    simulations:\n      sim1:\n        is_repertoire: false\n        paired: false\n        sequence_type: amino_acid\n        simulation_strategy: RejectionSampling\n        remove_seqs_with_signals: true \n        sim_items:\n          sim_item1: # group of AIRs with the same parameters\n            generative_model:\n              chain: beta\n              default_model_name: humanTRB\n              model_path: null\n              type: OLGA\n            number_of_examples: 100\n            signals:\n              signal1: 1\n          sim_item2:\n            generative_model:\n              chain: beta\n              default_model_name: humanTRB\n              model_path: null\n              type: OLGA\n            number_of_examples: 100\n            signals:\n              signal2: 1\n          sim_item3:\n            generative_model:\n              chain: beta\n              default_model_name: humanTRB\n              model_path: null\n              type: OLGA\n            number_of_examples: 100\n            signals: {} # no signal\n  instructions:\n    my_sim_inst:\n      export_p_gens: false\n      max_iterations: 100\n      number_of_processes: 4\n      sequence_batch_size: 1000\n      simulation: sim1\n      type: LigoSim\n```\n\nTo run this simulation, save the YAML file above as specs.yaml and run the following:\n\n```commandline\nligo specs.yaml output_folder\n```\n\nNote that `output_folder` (user-defined name) should not exist before the run.\n\n\n## Citing LIgO\n\nIf you are using LIgO in any published work, please cite:\n\nChernigovskaya, 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\n“Simulation of Adaptive Immune Receptors and Repertoires with Complex Immune Information to Guide the Development and Benchmarking of AIRR Machine Learning” \nbioRxiv, 2023, 2023.10.20.562936. https://doi.org/10.1101/2023.10.20.562936.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuio-bmi%2Fligo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fuio-bmi%2Fligo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuio-bmi%2Fligo/lists"}