Package Bio :: Package NeuralNetwork :: Package Gene :: Module Schema
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Module Bio.NeuralNetwork.Gene.Schema

Deal with Motifs or Signatures allowing ambiguity in the sequences.

This class contains Schema which deal with Motifs and Signatures at a higher level, by introducing `don't care` (ambiguity) symbols into the sequences. For instance, you could combine the following Motifs:

'GATC', 'GATG', 'GATG', 'GATT'

as all falling under a schema like 'GAT*', where the star indicates a character can be anything. This helps us condense a whole ton of motifs or signatures.
Classes
DifferentialSchemaFitness Calculate fitness for schemas that differentiate between sequences.
GeneticAlgorithmFinder Find schemas using a genetic algorithm approach.
MostCountSchemaFitness Calculate a fitness giving weight to schemas that match many times.
RandomMotifGenerator Generate a random motif within given parameters.
Schema Deal with motifs that have ambiguity characters in it.
SchemaCoder Convert a sequence into a representation of ambiguous motifs (schemas).
SchemaDNAAlphabet Alphabet of a simple Schema for DNA sequences.
SchemaFactory Generate Schema from inputs of Motifs or Signatures.
SchemaFinder Find schema in a set of sequences using a genetic algorithm approach.
SimpleFinisher Determine when we are done evolving motifs.

Function Summary
  matches_schema(pattern, schema, ambiguity_character)
Determine whether or not the given pattern matches the schema.

Function Details

matches_schema(pattern, schema, ambiguity_character='*')

Determine whether or not the given pattern matches the schema.

Arguments:

o pattern - A string representing the pattern we want to check for matching. This pattern can contain ambiguity characters (which are assumed to be the same as those in the schema).

o schema - A string schema with ambiguity characters.

o ambiguity_character - The character used for ambiguity in the schema.

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