Simulation with custom signal functions#

In LIgO, signals are most often defined using (gapped) k-mers or positional weight matrices. However, as this way of defining signals can be limiting to a certain degree, LIgO also allows users to have more generic definition of a signal. Signals can be specified using a custom function that takes a receptor sequence as input and outputs True/False depending on whether the sequence satisfy some custom criteria from that function.

In this tutorial, we provide a simple example of what such functions might look like and how to specify the simulation using signals with custom function. We assume that LIgO is already installed.


This way of defining signals can only be used in combination with the rejection sampling simulation strategy (not with implanting).

Step 1: Defining the custom signal function#

The custom signal function is defined in a separate Python file. The function will always get the following arguments:

  • amino acid sequence (sequence_aa)

  • nucleotide sequence (sequence)

  • V gene (v_call)

  • J gene (j_call)

The output of the function should always be a single value, either True or False.

As the first step of the tutorial, save the following code to the file

def is_present(sequence_aa: str, sequence: str, v_call: str, j_call: str) -> bool:
    return any(aa in sequence_aa for aa in ['A', 'T']) and len(sequence_aa) > 12

In this case, we assume that the sequence contains signal if it contains A or T and is longer than 12 amino acids. In principle, any logic could be implemented inside this function.

Step 2: Define the YAML specification#

The YAML specification fully defines the simulation to be performed. Save the following specification to specs.yaml in the same folder as

Signal definition here (for signal1) consists of two parameters:

  • is_present_func: stating the name of the function to use from the provided python file to assess if the signal is present,

  • source_file: stating the path to the python file where the custom function is located.

The rest of the simulation is defined in the same way as when k-mers or PWMs are used.

    signal1: # signal with the custom signal function
      is_present_func: is_present
      is_repertoire: true
      paired: false
      sequence_type: amino_acid
          generative_model: # use OLGA humanTRB model to generate sequence
            default_model_name: humanTRB
            type: OLGA
          number_of_examples: 10 # generate 10 repertoires
          receptors_in_repertoire_count: 6 # each repertoire should have 6 receptor sequences
          seed: 100 # random seed for sequence generation to ensure reproducibility
          signals: # which signals should be in the simulated repertoires
            signal1: 0.5 # 50% of receptor sequences should have signal1 and the rest should have no signal
      simulation_strategy: RejectionSampling # use rejection sampling to filter out sequences based on signal presence/absence
    export_p_gens: false # do not compute p gens for generated sequences
    max_iterations: 100
    number_of_processes: 1
    sequence_batch_size: 100
    simulation: sim1
    type: LigoSim
  format: HTML

Step 3: Running the simulation#

When the two files mentioned above are saved, run the following:

ligo specs.yaml simulation_output

The simulation for this specification should only take a few seconds and all results will be stored in the simulation_output folder.