import copy
import numpy as np
from immuneML.data_model.receptor.receptor_sequence.ReceptorSequence import ReceptorSequence
from immuneML.data_model.receptor.receptor_sequence.SequenceAnnotation import SequenceAnnotation
from immuneML.environment.EnvironmentSettings import EnvironmentSettings
from immuneML.simulation.implants.ImplantAnnotation import ImplantAnnotation
from immuneML.simulation.implants.MotifInstance import MotifInstance
from immuneML.simulation.sequence_implanting.SequenceImplantingStrategy import SequenceImplantingStrategy
from immuneML.util.PositionHelper import PositionHelper
[docs]class GappedMotifImplanting(SequenceImplantingStrategy):
[docs] def implant(self, sequence: ReceptorSequence, signal: dict, sequence_position_weights=None) -> ReceptorSequence:
motif_instance = signal["motif_instance"]
imgt_positions = self._build_imgt_positions(sequence, motif_instance)
limit = len(motif_instance.instance) - motif_instance.instance.count("/") + motif_instance.gap - 1
position_weights = PositionHelper.build_position_weights(sequence_position_weights, imgt_positions, limit)
implant_position = self._choose_implant_position(imgt_positions, position_weights)
new_sequence = self._build_new_sequence(sequence, implant_position, signal)
return new_sequence
def _build_imgt_positions(self, sequence: ReceptorSequence, motif_instance: MotifInstance):
assert len(sequence.get_sequence()) >= motif_instance.gap + len(motif_instance.instance) - 1, \
"The motif instance is longer than receptor_sequence length. Remove the receptor_sequence from the repertoire or reduce max gap length " \
"to be able to proceed. "
length = len(sequence.get_sequence())
return PositionHelper.gen_imgt_positions_from_length(length)
def _choose_implant_position(self, imgt_positions, position_weights):
imgt_implant_position = np.random.choice(list(position_weights.keys()), size=1,
p=list(position_weights.values()))
position = np.where(imgt_positions == imgt_implant_position)[0][0]
return position
def _build_new_sequence(self, sequence: ReceptorSequence, position, signal: dict) -> ReceptorSequence:
gap_length = signal["motif_instance"].gap
if "/" in signal["motif_instance"].instance:
motif_left, motif_right = signal["motif_instance"].instance.split("/")
else:
motif_left = signal["motif_instance"].instance
motif_right = ""
gap_start = position+len(motif_left)
gap_end = gap_start+gap_length
part1 = sequence.get_sequence()[:position]
part2 = sequence.get_sequence()[gap_start:gap_end]
part3 = sequence.get_sequence()[gap_end+len(motif_right):]
new_sequence_string = part1 + motif_left + part2 + motif_right + part3
annotation = SequenceAnnotation()
implant = ImplantAnnotation(signal_id=signal["signal_id"],
motif_id=signal["motif_id"],
motif_instance=signal["motif_instance"],
position=position)
annotation.add_implant(implant)
new_sequence = ReceptorSequence()
new_sequence.set_annotation(annotation)
new_sequence.set_metadata(copy.deepcopy(sequence.metadata))
new_sequence.set_sequence(new_sequence_string, EnvironmentSettings.get_sequence_type())
return new_sequence