biopython v1.71.0 Bio.NeuralNetwork.Gene.Schema.SchemaFactory

Generate Schema from inputs of Motifs or Signatures.

Link to this section Summary

Functions

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.

Arguments:

  • ambiguity_symbol — The symbol to use when specifying that a position is arbitrary.
Link to this function _get_num_motifs()

Return the number of motif counts for the list of motifs.

Link to this function _get_unique_schema()

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.

Link to this function _schema_from_motif()

Create a schema from a given starting motif.

Arguments:

  • 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.

Returns:

  • 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.

Arguments:

  • 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.
Link to this function from_signatures()

Initialize from a signature repository (not implemented yet).