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## Uses of Class
de.jstacs.data.Sequence

Packages that use Sequence
de.jstacs.algorithms | Provides classes for algorithms on graphs.
de.jstacs.classifier | This package provides the framework for any classifier.
de.jstacs.classifier.modelBased | Provides the classes for `Classifier`s that are based on `Model`s
de.jstacs.classifier.scoringFunctionBased | Provides the classes for `Classifier`s that are based on `ScoringFunction`s.
de.jstacs.data | Provides classes for the representation of data.
de.jstacs.data.sequences | Provides classes for representing sequences.
de.jstacs.data.sequences.annotation | Provides the facilities to annotate `Sequence`s using a number of pre-defined annotation types, or additional implementations of the `SequenceAnnotation` class
de.jstacs.models | Provides the interface `Model` and its abstract implementation `AbstractModel`, which is the super class of all other models.
de.jstacs.models.discrete |
de.jstacs.models.discrete.homogeneous |
de.jstacs.models.discrete.inhomogeneous | This package contains various inhomogeneous models.
de.jstacs.models.mixture | This package is the super package for any mixture model.
de.jstacs.models.mixture.motif |
de.jstacs.models.utils |
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.scoringFunctions | Provides `ScoringFunction`s that can be used in a `ScoreClassifier`.
de.jstacs.scoringFunctions.directedGraphicalModels | Provides `ScoringFunction`s that are equivalent to directed graphical models.
de.jstacs.scoringFunctions.homogeneous | Provides `ScoringFunction`s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence
de.jstacs.scoringFunctions.mix | Provides `ScoringFunction`s that are mixtures of other `ScoringFunction`s.
de.jstacs.scoringFunctions.mix.motifSearch |

Uses of Sequence in de.jstacs.algorithms

Methods in de.jstacs.algorithms with parameters of type Sequence
`double` | `Alignment.SimpleCosts.getCostFor(Sequence s1, Sequence s2, int i, int j, Alignment.Costs.Direction from)`
`double` | `Alignment.SimpleCosts.getCostFor(Sequence s1, Sequence s2, int i, int j, Alignment.Costs.Direction from)`
`double` | `Alignment.Costs.getCostFor(Sequence s1, Sequence s2, int i, int j, Alignment.Costs.Direction from)` Returns the costs for the alignment if `s1(i)` and `s2(j)` coming from `from`.
`double` | `Alignment.Costs.getCostFor(Sequence s1, Sequence s2, int i, int j, Alignment.Costs.Direction from)` Returns the costs for the alignment if `s1(i)` and `s2(j)` coming from `from`.

Constructors in de.jstacs.algorithms with parameters of type Sequence
`Alignment(Sequence s1, Sequence s2, Alignment.Costs costs)` Creates a new `Alignment.StringAlignment` instance that aligns the sequences `s1` and `s2` using the costs defined in `costs`.
`Alignment(Sequence s1, Sequence s2, Alignment.Costs costs)` Creates a new `Alignment.StringAlignment` instance that aligns the sequences `s1` and `s2` using the costs defined in `costs`.

Uses of Sequence in de.jstacs.classifier

Methods in de.jstacs.classifier with parameters of type Sequence
`protected void` | `AbstractScoreBasedClassifier.check(Sequence seq)` This method checks if the given `Sequence` can be used.
`byte` | `AbstractScoreBasedClassifier.classify(Sequence seq)`
`abstract byte` | `AbstractClassifier.classify(Sequence seq)` This method classifies a sequence and returns the index `i` of the class to which the sequence is assigned with `0 < i < getNumberOfClasses()`.
`protected byte` | `AbstractScoreBasedClassifier.classify(Sequence seq, boolean check)` This method classifies a `Sequence`.
`double` | `AbstractScoreBasedClassifier.getPValue(Sequence candidate, Sample bg)` Returns the p-value for a `Sequence` `candidate` with respect to a given background `Sample`.
`double` | `AbstractScoreBasedClassifier.getScore(Sequence seq, int i)` This method returns the score for a given `Sequence` and a given class.
`protected double` | `MappingClassifier.getScore(Sequence seq, int i, boolean check)`
`protected abstract double` | `AbstractScoreBasedClassifier.getScore(Sequence seq, int i, boolean check)` This method returns the score for a given `Sequence` and a given class.

Uses of Sequence in de.jstacs.classifier.modelBased

Methods in de.jstacs.classifier.modelBased with parameters of type Sequence
`protected double` | `ModelBasedClassifier.getScore(Sequence seq, int i, boolean check)`

Uses of Sequence in de.jstacs.classifier.scoringFunctionBased

Methods in de.jstacs.classifier.scoringFunctionBased with parameters of type Sequence
`protected double` | `ScoreClassifier.getScore(Sequence seq, int i, boolean check)`

Uses of Sequence in de.jstacs.data

Subclasses of Sequence in de.jstacs.data
`protected static class` | `Sequence.CompositeSequence<T>` The class handles composite `Sequence`s.
`static class` | `Sequence.RecursiveSequence<T>` This is the main class for subsequences, composite sequences, ... .
`protected static class` | `Sequence.SubSequence<T>` This class handles subsequences.

Fields in de.jstacs.data declared as Sequence
`protected Sequence<T>` | `Sequence.RecursiveSequence.content` The internal sequence.
`protected Sequence<T>` | `Sequence.rc` The pointer to the reverse complement of the `Sequence.RecursiveSequence`.

Methods in de.jstacs.data that return Sequence
`Sequence` | `Sequence.annotate(boolean add, SequenceAnnotation... annotation)` This method allows to append annotation to a `Sequence.RecursiveSequence`.
`Sequence` | `Sequence.complement()` This method returns a new instance of `Sequence.RecursiveSequence` containing the complementary current `Sequence.RecursiveSequence`.
`Sequence` | `Sequence.complement(int start, int end)` This method returns a new instance of `Sequence.RecursiveSequence` containing a part of the complementary current `Sequence.RecursiveSequence`.
`static Sequence` | `Sequence.create(AlphabetContainer con, SequenceAnnotation[] annotation, String sequence, String delim)` Creates a `Sequence.RecursiveSequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim` and some `annotation` for the `Sequence.RecursiveSequence`.
`static Sequence` | `Sequence.create(AlphabetContainer con, String sequence)` Creates a `Sequence.RecursiveSequence` from a `String` based on the given `AlphabetContainer` using the standard delimiter for this `AlphabetContainer`.
`static Sequence` | `Sequence.create(AlphabetContainer con, String sequence, String delim)` Creates a `Sequence.RecursiveSequence` from a `String` based on the given `AlphabetContainer` using the given delimiter `delim`.
`protected abstract Sequence` | `Sequence.flatCloneWithoutAnnotation()` Works in analogy to `Object.clone()`, but does not clone the annotation.
`protected Sequence` | `Sequence.CompositeSequence.flatCloneWithoutAnnotation()`
`protected Sequence` | `Sequence.SubSequence.flatCloneWithoutAnnotation()`
`Sequence[]` | `Sample.getAllElements()` Returns an array of `Sequence`s containing all elements of this `Sample`.
`Sequence<T>` | `Sequence.getCompositeSequence(AlphabetContainer abc, int[] starts, int[] lengths)` This method should be used if one wants to create a `Sample` of `Sequence.CompositeSequence`s.
`Sequence` | `Sequence.getCompositeSequence(int[] starts, int[] lengths)` This is a very efficient way to create a `Sequence.CompositeSequence` for sequences with a simple `AlphabetContainer`.
`Sequence` | `Sample.getElementAt(int i)` This method returns the element, i.e. the `Sequence`, with index `i`.
`Sequence` | `Sample.WeightedSampleFactory.getElementAt(int index)` Returns the `Sequence` with index `index`.
`Sequence` | `DinucleotideProperty.getPropertyAsSequence(Sequence original)` Computes this dinucleotide property for all overlapping twomers in `original` and returns the result as a `Sequence` of length `original.getLength()-1`
`Sequence` | `DinucleotideProperty.getPropertyAsSequence(Sequence original, DinucleotideProperty.Smoothing smoothing)` Computes this dinucleotide property for all overlapping twomers in `original`, smoothes the result using `smoothing`, and returns the smoothed property as a `Sequence`.
`Sequence` | `Sequence.getSubSequence(AlphabetContainer abc, int start)` This method should be used if one wants to create a `Sample` of subsequences of defined length.
`Sequence` | `Sequence.getSubSequence(AlphabetContainer abc, int start, int length)` This method should be used if one wants to create a `Sample` of subsequences of defined length.
`Sequence` | `Sequence.getSubSequence(int start)` This is a very efficient way to create a subsequence/suffix for `Sequence.RecursiveSequence`s with a simple `AlphabetContainer`.
`Sequence` | `Sequence.getSubSequence(int start, int length)` This is a very efficient way to create a subsequence of defined length for `Sequence.RecursiveSequence`s with a simple `AlphabetContainer`.
`Sequence` | `Sample.ElementEnumerator.next()`
`Sequence` | `SequenceEnumeration.nextElement()`
`Sequence` | `SampleKMerEnumerator.nextElement()`
`Sequence` | `Sample.ElementEnumerator.nextElement()`
`Sequence<int[]>` | `DiscreteSequenceEnumerator.nextElement()`
`Sequence` | `Sequence.reverse()` This method returns a new instance of `Sequence.RecursiveSequence` containing the reverse current `Sequence.RecursiveSequence`.
`Sequence` | `Sequence.reverse(int start, int end)` This method returns a new instance of `Sequence.RecursiveSequence` containing a part of the reverse current `Sequence.RecursiveSequence`.
`Sequence` | `Sequence.reverseComplement()` This method returns a new instance of `Sequence.RecursiveSequence` containing the reverse complementary current `Sequence.RecursiveSequence`.
`Sequence` | `Sequence.reverseComplement(int start, int end)` This method returns a new instance of `Sequence.RecursiveSequence` containing a reverse part of the complementary current `Sequence.RecursiveSequence`.
`Sequence` | `Sequence.SubSequence.reverseComplement(int start, int end)`

Methods in de.jstacs.data that return types with arguments of type Sequence
`Iterator<Sequence>` | `Sample.iterator()`

Methods in de.jstacs.data with parameters of type Sequence
`int` | `Sequence.compareTo(Sequence<T> s)`
`int` | `Sequence.getHammingDistance(Sequence seq)` This method returns the Hamming distance between the current `Sequence.RecursiveSequence` and `seq`.
`double[]` | `DinucleotideProperty.getProperty(Sequence original)` Computes this dinucleotide property for all overlapping twomers in `original` and returns the result as a `double` array of length `original.getLength()-1`
`double[]` | `DinucleotideProperty.getProperty(Sequence original, DinucleotideProperty.Smoothing smoothing)` Computes this dinucleotide property for all overlapping twomers in `original`, smoothes the result using `smoothing`, and returns the smoothed property as a `double` array.
`Sequence` | `DinucleotideProperty.getPropertyAsSequence(Sequence original)` Computes this dinucleotide property for all overlapping twomers in `original` and returns the result as a `Sequence` of length `original.getLength()-1`
`Sequence` | `DinucleotideProperty.getPropertyAsSequence(Sequence original, DinucleotideProperty.Smoothing smoothing)` Computes this dinucleotide property for all overlapping twomers in `original`, smoothes the result using `smoothing`, and returns the smoothed property as a `Sequence`.
`static ImageResult` | `DinucleotideProperty.getPropertyImage(Sequence original, DinucleotideProperty prop, DinucleotideProperty.Smoothing smoothing, REnvironment re, int xLeft, String pltOptions, int width, int height)`
`boolean` | `Sequence.matches(int maxHammingDistance, Sequence shortSequence)` This method allows to answer the question whether there is a similar pattern find a match with a given maximal number of mismatches.

Constructors in de.jstacs.data with parameters of type Sequence
`Sample(String annotation, Sequence... seqs)` Creates a new `Sample` from an array of `Sequence`s and a given annotation.
`Sequence.CompositeSequence(AlphabetContainer abc, Sequence<T> seq, int[] starts, int[] lengths)` This constructor should be used if one wants to create a `Sample` of `Sequence.CompositeSequence`s.
`Sequence.CompositeSequence(Sequence seq, int[] starts, int[] lengths)` This is a very efficient way to create a `Sequence.CompositeSequence` for `Sequence`s with a simple `AlphabetContainer`.
`Sequence.RecursiveSequence(AlphabetContainer alphabet, Sequence<T> seq)` Creates a new `Sequence.RecursiveSequence` on the `Sequence` `seq` with the `AlphabetContainer` `alphabet` using the annotation of the given `Sequence`.
`Sequence.RecursiveSequence(AlphabetContainer alphabet, SequenceAnnotation[] annotation, Sequence<T> seq)` Creates a new `Sequence.RecursiveSequence` on the `Sequence` `seq` with the `AlphabetContainer` `alphabet` and the annotation `annotation`.
`Sequence.SubSequence(AlphabetContainer abc, Sequence seq, int start, int length)` This constructor should be used if one wants to create a `Sample` of `Sequence.SubSequence`s of defined length.
`Sequence.SubSequence(Sequence seq, int start, int length)` This is a very efficient way to create a `Sequence.SubSequence` of defined length for `Sequence`s with a simple `AlphabetContainer`.
`SequenceEnumeration(Sequence... sequences)` This constructor creates an instance based on the user-specified `Sequence`s `sequences`.

Constructor parameters in de.jstacs.data with type arguments of type Sequence
`SequenceEnumeration(Collection<Sequence> sequences)` This constructor creates an instance based on the user-specified `Collection` of `Sequence`s `sequences`.

Uses of Sequence in de.jstacs.data.sequences

Subclasses of Sequence in de.jstacs.data.sequences
`class` | `ArbitrarySequence` This class is for any continuous or hybrid sequence.
`class` | `ByteSequence` This class is for sequences with the alphabet symbols encoded as `byte`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use only few symbols.
`class` | `DiscreteSequence` This is the super class of discrete sequences.
`class` | `IntSequence` This class is for sequences with the alphabet symbols encoded as `int`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use a huge number of symbols.
`class` | `MappedDiscreteSequence` This class allows to map a discrete `Sequence` to an new `Sequence` using some `DiscreteAlphabetMapping`s.
`class` | `MultiDimensionalDiscreteSequence` This class is for multidimensional sequences that can be used, for instance, for phylogenetic footprinting.
`class` | `PermutedSequence<T>` This class is for permuted sequences.
`class` | `ShortSequence` This class is for sequences with the alphabet symbols encoded as `shorts`s and can therefore be used for discrete `AlphabetContainer`s with alphabets that use many different symbols.
`class` | `SimpleDiscreteSequence` This is the main class for any discrete sequence.
`class` | `SparseSequence` This class is an implementation for sequences on one alphabet with length 4.

Methods in de.jstacs.data.sequences that return Sequence
`protected Sequence<int[]>` | `MultiDimensionalDiscreteSequence.flatCloneWithoutAnnotation()`
`protected Sequence` | `MappedDiscreteSequence.flatCloneWithoutAnnotation()`

Constructors in de.jstacs.data.sequences with parameters of type Sequence
`PermutedSequence(Sequence<T> seq)` Creates a new `PermutedSequence` by shuffling the symbols of a given `Sequence`.

Uses of Sequence in de.jstacs.data.sequences.annotation

Methods in de.jstacs.data.sequences.annotation that return Sequence
`Sequence` | `ReferenceSequenceAnnotation.getReferenceSequence()` Returns the reference sequence.

Constructors in de.jstacs.data.sequences.annotation with parameters of type Sequence
`ReferenceSequenceAnnotation(String identifier, Sequence ref, Result... results)` Creates a new `ReferenceSequenceAnnotation` with identifier `identifier`, reference sequence `ref`, and additional annotation (that does not fit the `SequenceAnnotation` definitions) given as a `Result` `result`.

Uses of Sequence in de.jstacs.models

Methods in de.jstacs.models with parameters of type Sequence
`double` | `Model.getLogProbFor(Sequence sequence)` Returns the logarithm of the probability of the given sequence given the model.
`double` | `AbstractModel.getLogProbFor(Sequence sequence)`
`double` | `Model.getLogProbFor(Sequence sequence, int startpos)` Returns the logarithm of the probability of (a part of) the given sequence given the model.
`double` | `AbstractModel.getLogProbFor(Sequence sequence, int startpos)`
`double` | `NormalizableScoringFunctionModel.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `Model.getLogProbFor(Sequence sequence, int startpos, int endpos)` Returns the logarithm of the probability of (a part of) the given sequence given the model.
`double` | `CompositeModel.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `AbstractModel.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `Model.getProbFor(Sequence sequence)` Returns the probability of the given sequence given the model.
`double` | `AbstractModel.getProbFor(Sequence sequence)`
`double` | `Model.getProbFor(Sequence sequence, int startpos)` Returns the probability of (a part of) the given sequence given the model.
`double` | `AbstractModel.getProbFor(Sequence sequence, int startpos)`
`double` | `VariableLengthWrapperModel.getProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `UniformModel.getProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `NormalizableScoringFunctionModel.getProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `Model.getProbFor(Sequence sequence, int startpos, int endpos)` Returns the probability of (a part of) the given sequence given the model.
`double` | `CompositeModel.getProbFor(Sequence sequence, int startpos, int endpos)`

Uses of Sequence in de.jstacs.models.discrete

Methods in de.jstacs.models.discrete with parameters of type Sequence
`void` | `Constraint.add(Sequence seq, int start, double weight)` This method determines the specific constraint that is fulfilled by the `Sequence` `seq` and adds the `weight` to the specific counter.
`protected void` | `DiscreteGraphicalModel.check(Sequence sequence, int startpos, int endpos)` Checks some conditions on a `Sequence`.
`double` | `Constraint.getFreq(Sequence seq, int start)` This method determines the specific constraint that is fulfilled by the `Sequence` `seq` beginning at position `start`.
`abstract int` | `Constraint.satisfiesSpecificConstraint(Sequence seq, int start)` This method returns the index of the specific constraint that is fulfilled by the `Sequence` `seq` beginning at position `start`.

Uses of Sequence in de.jstacs.models.discrete.homogeneous

Methods in de.jstacs.models.discrete.homogeneous that return Sequence
`protected abstract Sequence` | `HomogeneousModel.getRandomSequence(Random r, int length)` This method creates a random `Sequence` from a trained homogeneous model.
`protected Sequence` | `HomogeneousMM.getRandomSequence(Random r, int length)`

Methods in de.jstacs.models.discrete.homogeneous with parameters of type Sequence
`void` | `HomogeneousModel.HomCondProb.addAll(Sequence seq, double weight, int start, int prevIndex)` Adds the given `weight` to the counts corresponding to the `Sequence` `seq` from `start` to the end of the `Sequence`.
`protected void` | `HomogeneousModel.check(Sequence sequence, int startpos, int endpos)` Checks some constraints, these are in general conditions on the `AlphabetContainer` of a (sub)`Sequence` between `startpos` und `endpos`.
`double` | `HomogeneousModel.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `HomogeneousModel.getProbFor(Sequence sequence, int startpos, int endpos)`
`protected abstract double` | `HomogeneousModel.logProbFor(Sequence sequence, int startpos, int endpos)` This method computes the logarithm of the probability of the given `Sequence` in the given interval.
`protected double` | `HomogeneousMM.logProbFor(Sequence sequence, int startpos, int endpos)`
`protected abstract double` | `HomogeneousModel.probFor(Sequence sequence, int startpos, int endpos)` This method computes the probability of the given `Sequence` in the given interval.
`protected double` | `HomogeneousMM.probFor(Sequence sequence, int startpos, int endpos)`
`int` | `HomogeneousModel.HomCondProb.satisfiesSpecificConstraint(Sequence seq, int start)`

Uses of Sequence in de.jstacs.models.discrete.inhomogeneous

Methods in de.jstacs.models.discrete.inhomogeneous with parameters of type Sequence
`protected void` | `InhomogeneousDGM.check(Sequence sequence, int startpos, int endpos)`
`double` | `InhCondProb.getLnFreq(Sequence s, int start)` Returns the logarithm of the relative frequency (=probability) with the position in the distribution given by the index of the specific constraint that is fulfilled by the `Sequence` `s` beginning at `start`.
`double` | `DAGModel.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`double` | `DAGModel.getProbFor(Sequence sequence, int startpos, int endpos)`
`int` | `InhConstraint.satisfiesSpecificConstraint(Sequence s, int start)`

Uses of Sequence in de.jstacs.models.mixture

Methods in de.jstacs.models.mixture that return Sequence
`protected Sequence[]` | `StrandModel.emitSampleUsingCurrentParameterSet(int n, int... lengths)`
`protected Sequence[]` | `MixtureModel.emitSampleUsingCurrentParameterSet(int n, int... lengths)`
`protected abstract Sequence[]` | `AbstractMixtureModel.emitSampleUsingCurrentParameterSet(int n, int... lengths)` The method returns an array of sequences using the current parameter set.

Methods in de.jstacs.models.mixture with parameters of type Sequence
`int` | `AbstractMixtureModel.getIndexOfMaximalComponentFor(Sequence s)` Returns the index `i` of the component with `P(i|s)`maximal.``
`double` | `AbstractMixtureModel.getLogProbFor(int component, Sequence s)` Returns the logarithmic probability for the sequence and the given component.
`double` | `AbstractMixtureModel.getLogProbFor(Sequence sequence, int startpos, int endpos)`
`protected double` | `StrandModel.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)`
`protected double` | `MixtureModel.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)`
`protected abstract double` | `AbstractMixtureModel.getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)` Returns the logarithmic probability for the sequence and the given component using the current parameter set.
`double` | `AbstractMixtureModel.getProbFor(Sequence sequence, int startpos, int endpos)`

Uses of Sequence in de.jstacs.models.mixture.motif

Methods in de.jstacs.models.mixture.motif that return Sequence
`protected Sequence[]` | `HiddenMotifMixture.emitSampleUsingCurrentParameterSet(int n, int... lengths)` Standard implementation throwing an `OperationNotSupportedException`.

Methods in de.jstacs.models.mixture.motif with parameters of type Sequence
`protected double` | `SingleHiddenMotifMixture.getLogProbUsingCurrentParameterSetFor(int component, Sequence seq, int start, int end)`
`double[]` | `SingleHiddenMotifMixture.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)`
`double[]` | `SingleHiddenMotifMixture.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`

Uses of Sequence in de.jstacs.models.utils

Methods in de.jstacs.models.utils that return Sequence
`static Sequence` | `ModelTester.getMostProbableSequence(Model m, int length)` Returns one most probable sequence for the discrete model `m`.

Uses of Sequence in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery that return Sequence
`static Sequence[]` | `MutableMotifDiscovererToolbox.enumerate(ScoringFunction[] funs, int[] classIndex, int[] motifIndex, RecyclableSequenceEnumerator[] rse, double weight, SFBasedOptimizableFunction opt, OutputStream out)` This method allows to enumerate all possible seeds for a number of motifs in the `MutableMotifDiscoverer`s of a specific classes.
`static Sequence` | `MutableMotifDiscovererToolbox.enumerate(ScoringFunction[] funs, int classIndex, int motifIndex, RecyclableSequenceEnumerator rse, double weight, SFBasedOptimizableFunction opt, OutputStream out)` This method allows to enumerate all possible seeds for a motif in the `MutableMotifDiscoverer` of a specific class.
`static Sequence[]` | `KMereStatistic.getCommonString(Sample data, int motifLength, boolean bothStrands)` This method returns an array of strings of length `motifLength` so that each String is contained in all sequences of the sample respectively in the sample and the reverse complementary sample.

Methods in de.jstacs.motifDiscovery that return types with arguments of type Sequence
`static LinkedList<Sequence>` | `KMereStatistic.getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic, int dataSetIndex, int threshold)` This method returns a list of `Sequence`s.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.getKmereSequenceStatistic(int k, boolean bothStrands, int addIndex, Sample... data)` This method enables the user to get a statistic over all `k`-mers in the sequences.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.merge(Hashtable<Sequence,BitSet[]> statistic, int maximalMissmatch, boolean bothStrands)` This method allows to merge the statistics of k-mers by allowing mismatches.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.removeBackground(Hashtable<Sequence,BitSet[]> statistic, int fgIndex, int bgIndex, double fgWeight, double bgWeight)` This method allows to remove those entries from the statistic that have a lower weighted foreground cardinality than the weighted background cardinality.

Methods in de.jstacs.motifDiscovery with parameters of type Sequence
`MotifAnnotation[]` | `SignificantMotifOccurrencesFinder.findSignificantMotifOccurrences(int motif, Sequence seq, int start)` This method finds the significant motif occurrences in the sequence.
`MotifAnnotation[]` | `SignificantMotifOccurrencesFinder.findSignificantMotifOccurrences(int motif, Sequence seq, int addMax, int start)` This method finds the significant motif occurrences in the sequence.
`int` | `MotifDiscoverer.getIndexOfMaximalComponentFor(Sequence sequence)` Returns the index of the component with the maximal score for the sequence `sequence`.
`double[]` | `MotifDiscoverer.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)` Returns the profile of the scores for component `component` and motif `motif` at all possible start positions of the motif in the sequence `sequence` beginning at `startpos`.
`double[][]` | `KMereStatistic.getSmoothedProfile(int window, Sequence... seq)` This method returns an array of smoothed profiles.
`double[]` | `MotifDiscoverer.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)` This method returns the probabilities of the strand orientations for a given subsequence if it is considered as site of the motif model in a specific component.
`static ImageResult` | `MotifDiscovererToolBox.plot(MotifDiscoverer motifDisc, int component, int motif, Sequence sequence, int startpos, REnvironment r, int width, int height, MotifDiscoverer.KindOfProfile kind)` This method creates a simple plot of the profile of scores for a sequence and a start position.
`static ImageResult` | `MotifDiscovererToolBox.plotAndAnnotate(MotifDiscoverer motifDisc, int component, int motif, Sequence sequence, int startpos, REnvironment r, int width, int height, double yMin, double yMax, double threshold, MotifDiscoverer.KindOfProfile kind)` This method creates a plot of the profile of scores for a sequence and a start position and annotates bindings sites in the plot that have a higher score than `threshold`.

Method parameters in de.jstacs.motifDiscovery with type arguments of type Sequence
`static LinkedList<Sequence>` | `KMereStatistic.getConservedPatterns(Hashtable<Sequence,BitSet[]> statistic, int dataSetIndex, int threshold)` This method returns a list of `Sequence`s.
`static Pair<Sequence,BitSet[]>[]` | `KMereStatistic.getKmereSequenceStatistic(boolean bothStrands, int maxMismatch, HashSet<Sequence> filter, Sample... data)` This method enables the user to get a statistic for a set of `k`-mers.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.merge(Hashtable<Sequence,BitSet[]> statistic, int maximalMissmatch, boolean bothStrands)` This method allows to merge the statistics of k-mers by allowing mismatches.
`static Hashtable<Sequence,BitSet[]>` | `KMereStatistic.removeBackground(Hashtable<Sequence,BitSet[]> statistic, int fgIndex, int bgIndex, double fgWeight, double bgWeight)` This method allows to remove those entries from the statistic that have a lower weighted foreground cardinality than the weighted background cardinality.

Uses of Sequence in de.jstacs.scoringFunctions

Methods in de.jstacs.scoringFunctions with parameters of type Sequence
`int` | `IndependentProductScoringFunction.getIndexOfMaximalComponentFor(Sequence sequence)`
`double` | `ScoringFunction.getLogScore(Sequence seq)` Returns the logarithmic score for the `Sequence` `seq`.
`double` | `AbstractNormalizableScoringFunction.getLogScore(Sequence seq)` Returns the logarithmic score for a given `Sequence`.
`double` | `UniformScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `ScoringFunction.getLogScore(Sequence seq, int start)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.
`double` | `NormalizedScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `MRFScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `IndependentProductScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `AbstractVariableLengthScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `VariableLengthScoringFunction.getLogScore(Sequence seq, int start, int length)` This method computes the logarithm of the score for a given subsequence.
`double` | `CMMScoringFunction.getLogScore(Sequence seq, int start, int length)`
`double` | `VariableLengthScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)` This method computes the logarithm of the score and the partial derivations for a given subsequence.
`double` | `CMMScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)`
`double` | `UniformScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`
`double` | `ScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double` | `NormalizedScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `MRFScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `IndependentProductScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `AbstractVariableLengthScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList dList)`
`double` | `ScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` `seq` and fills lists with the indices and the partial derivations.
`double` | `AbstractNormalizableScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)`
`double[]` | `IndependentProductScoringFunction.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist)`
`StrandedLocatedSequenceAnnotationWithLength.Strand` | `NormalizedScoringFunction.getStrand(Sequence seq, int startPos)` This method return the preferred `StrandedLocatedSequenceAnnotationWithLength.Strand` for a `Sequence` beginning at `startPos`.
`double[]` | `IndependentProductScoringFunction.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`

Uses of Sequence in de.jstacs.scoringFunctions.directedGraphicalModels

Methods in de.jstacs.scoringFunctions.directedGraphicalModels with parameters of type Sequence
`void` | `ParameterTree.addCount(Sequence seq, int start, double count)` Adds `count` to the parameter as returned by `ParameterTree.getParameterFor(Sequence, int)`.
`double` | `Parameter.doesApplyFor(Sequence seq)` Indicates if the `Sequence` `seq` fulfills all requirements defined in the `Parameter.context`.
`double` | `BayesianNetworkScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `BayesianNetworkScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`Parameter` | `ParameterTree.getParameterFor(Sequence seq, int start)` Returns the `Parameter` that is responsible for the suffix of sequence `seq` starting at position `start`.
`double[]` | `BayesianNetworkScoringFunction.getPositionDependentKMerProb(Sequence kmer)` Returns the probability of `kmer` for all possible positions in this `BayesianNetworkScoringFunction` starting at position `kmer.getLength()-1`.``
`double` | `ParameterTree.getProbFor(Sequence sequence)` Returns the probability of `Sequence` `sequence` in this `ParameterTree`.

Uses of Sequence in de.jstacs.scoringFunctions.homogeneous

Methods in de.jstacs.scoringFunctions.homogeneous with parameters of type Sequence
`double` | `UniformHomogeneousScoringFunction.getLogScore(Sequence seq, int start, int length)`
`double` | `HMMScoringFunction.getLogScore(Sequence seq, int start, int length)`
`double` | `HMM0ScoringFunction.getLogScore(Sequence seq, int start, int length)`
`double` | `UniformHomogeneousScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)`
`double` | `HMMScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)`
`double` | `HMM0ScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList dList)`

Uses of Sequence in de.jstacs.scoringFunctions.mix

Methods in de.jstacs.scoringFunctions.mix with parameters of type Sequence
`protected void` | `StrandScoringFunction.fillComponentScores(Sequence seq, int start)`
`protected void` | `MixtureScoringFunction.fillComponentScores(Sequence seq, int start)`
`protected abstract void` | `AbstractMixtureScoringFunction.fillComponentScores(Sequence seq, int start)` Fills the internal array `AbstractMixtureScoringFunction.componentScore` with the logarithmic scores of the components given a `Sequence`.
`int` | `MixtureScoringFunction.getIndexOfMaximalComponentFor(Sequence sequence)`
`int` | `AbstractMixtureScoringFunction.getIndexOfMaximalComponentFor(Sequence seq, int start)` Returns the index of the component that has the greatest impact on the complete score for a `Sequence`.
`double` | `AbstractMixtureScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `VariableLengthMixtureScoringFunction.getLogScore(Sequence seq, int start, int length)`
`double` | `VariableLengthMixtureScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, int length, IntList indices, DoubleList partialDer)`
`double` | `StrandScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `MixtureScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double[]` | `AbstractMixtureScoringFunction.getProbsForComponent(Sequence seq)` Returns the probabilities for each component given a `Sequence`.
`double[]` | `MixtureScoringFunction.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile kind)`
`StrandedLocatedSequenceAnnotationWithLength.Strand` | `StrandScoringFunction.getStrand(Sequence seq, int startPos)` This method returns the preferred `StrandedLocatedSequenceAnnotationWithLength.Strand` for a given subsequence.
`double[]` | `MixtureScoringFunction.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`

Uses of Sequence in de.jstacs.scoringFunctions.mix.motifSearch

Methods in de.jstacs.scoringFunctions.mix.motifSearch with parameters of type Sequence
`protected int` | `HiddenMotifsMixture.fillComponentScoreOf(int i, Sequence seq, int start)` This method fills an internal array with the partial scores.
`protected void` | `HiddenMotifsMixture.fillComponentScores(Sequence seq, int start)`
`int` | `HiddenMotifsMixture.getIndexOfMaximalComponentFor(Sequence sequence)`
`double` | `PositionScoringFunction.getLogScore(Sequence seq, int start)`
`double` | `HiddenMotifsMixture.getLogScore(Sequence seq, int start)`
`double` | `PositionScoringFunction.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double` | `HiddenMotifsMixture.getLogScoreAndPartialDerivation(Sequence seq, int start, IntList indices, DoubleList partialDer)`
`double[]` | `HiddenMotifsMixture.getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist)`
`double[]` | `HiddenMotifsMixture.getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)`
`protected int[]` | `PositionScoringFunction.getValuesFromSequence(Sequence seq, int start)` This method extracts the values form a sequence.

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