biopython v1.71.0 Bio.NeuralNetwork.Gene.Schema.SchemaFactory
Generate Schema from inputs of Motifs or Signatures.
Link to this section Summary
Initialize the SchemaFactory
Return the number of motif counts for the list of motifs
Retrieve a unique schema from a motif
Create a schema from a given starting motif
Generate schema from a list of motifs
Initialize from a signature repository (not implemented yet)
Link to this section Functions
Initialize the SchemaFactory.
- ambiguity_symbol — The symbol to use when specifying that a position is arbitrary.
Return the number of motif counts for the list of motifs.
Retrieve a unique schema from a motif.
We don’t want to end up with schema that match the same thing, since this could lead to ambiguous results, and be messy. This tries to create schema, and checks that they do not match any currently existing schema.
Create a schema from a given starting motif.
- motif - A motif with the pattern we will start from.
- motif_list - The total motifs we have.to match to.
- num_ambiguous - The number of ambiguous characters that should be present in the schema.
- A string representing the newly generated schema.
- A list of all of the motifs in motif_list that match the schema.
Generate schema from a list of motifs.
- motif_repository - A MotifRepository class that has all of the motifs we want to convert to Schema.
- motif_percent - The percentage of motifs in the motif bank which should be matches. We’ll try to create schema that match this percentage of motifs.
- num_ambiguous - The number of ambiguous characters to include in each schema. The positions of these ambiguous characters will be randomly selected.
Initialize from a signature repository (not implemented yet).