Description:

Sequence analysis is one of the major subjects of bioinformatics. Several existing libraries combine the representation of biological sequences with exact and approximate pattern matching as well as alignment algorithms. We present Jstacs, an open source Java library, which focuses on the statistical analysis of biological sequences instead. Jstacs comprises an efficient representation of sequence data and provides implementations of many statistical models with generative and discriminative approaches for parameter learning. Using Jstacs, classifiers can be assessed and compared on test datasets or by cross-validation experiments evaluating several performance measures. Due to its strictly object-oriented design Jstacs is easy to use and readily extensible.

Changes to previous version:

February 2, 2012: Jstacs 2.0 released

Jstacs 2.0 changes many names and the structure of several packages. It is not code-compatible with Jstacs 1.5 and earlier

RESTRUCTURING and RENAMING:

former ScoringFunction, NormalizableScoringFunction, Model

• new base-interface SequenceScore
• new sub-interface StatisticalModel of SequenceScore for all statistical models with sub-iterfaces DifferentiableStatisticalModel and TrainableStatisticalModel
• new interface DifferentiableSequenceScore replaces ScoringFunction
• new interface DifferentiableStatisticalModel replaces NormalizableScoringFunction
• new interface TrainableStatisticalModel replaces Model
• new abstract class AbstractDifferentiableSequenceScore
• new abstract class AbstractDifferentiableStatisticalModel replaces AbstractNormalizableScoringFunction
• new abstract class AbstractTrainableStatisticalModel replaces AbstractModel
• former Models renamed to TrainSM
• former ScoringFunction renamed to DiffSS or DiffSM
• getProbFor removed from TrainableStatisticalModel (former Model) and conceptually replaced by getLogProbFor
• getLogScore(Sequence,int,int) with changed meaning of arguments: getLogScore(Sequence,start,end) instead of getLogScore(Sequence,start,length)
• isTrained() replaced by common method isInitialized()

Parameters and Results

• new super-class of Parameters and Results: AnnotatedEntity
• common list-type for Parameters and Results: AnnotatedEntityList
• Renaming: CollectionParameter -> SelectionParameter, MultiSelectionCollectionParameter -> MultiSelectionParameter, new super-class AbstractSelectionParameter
• major refactoring due to common hierarchy and code-cleanup
• lazy evaluation of Parameter/ParameterSet hierarchies moved from ParameterSet (loadParameters()) to ParameterSetContainer (constructor on class)
• SubclassFinder adapted to lazy evaluation

performance measures

• new abstract super-class AbstractPerformanceMeasure of all performance measures
• new interface NumericalPerformanceMeasure for all performance measures that return a single number (as opposed, e.g., to curves)
• new class PerformanceMeasureParameterSet for a collection of general performance measures
• new class NumericalPerformanceMeasureParameterSet for a collection of NumericalPerformanceMeasures
• used in evaluate-method of AbstractClassifier and in ClassifierAssessments

further changes

• Sample renamed to DataSet
• evaluate and evaluateAll in AbstractClassifier joined
• new class IndependentProductDiffSS as super-class of IndepedentProductDiffSM (former IndependentProductScoringFunction)
• new class UniformDiffSS as super-class of UniformDiffSM (former UniformScoringFunction)

NEW FUNCTIONALITY:

• multi-threaded implementation of Baum-Welch and Viterbi training of hidden Markov models
• new Interface Singleton that can be used for singleton instances to save memory, current examples: DNAAlphabet, DNAAlphabetContainer, ProteinAlphabet
• added ProteinAlphabet
• added possibility to use NaN-values with ContinuousAlphabets
• added ArbitraryFloatSequence including static methods for DataSet creation for cases where double-precision is not needed
• new performance measure MaximumFMeasure
• access to Parameters in ParameterSets and Results in ResultSets by name
• emitDataSet in BayesianNetworkDiffSM
• new static method Time.getTimeInstance that returns UserTime or RealTime depending on availability of shared lib
• SubclassFinder allows for adding own base packages
• new method overlaps() in LocatedSequenceAnnotationWithLength
• AbstractTerminationCondition used in ScoreClassifier and sub-classes
• public method propagateESS in HMMFactory
• new method generateLog in DirichletMRG for drawing log-values
• added DifferentiableStatisticalModelFactory

BUGFIXES/IMPROVEMENTS:

• bugfix in propagation of equivalent sample size in HMMFactory
• bugfix in random initialization of BasicHigherOrderTransition
• improved Alignment implementation
• SafeOutputStream with new static factory method getSafeOutputStream, write methods now work on Objects

DOCUMENTATION:

• improved Javadocs in many classes and packages
• new Cookbook with extensive documentation and explanation

MISC:

• output of NonParsableException more verbose
• Exceptions in multi-threaded code now lead to exit of program instead of only stopping the thread
• update of RServe/RClient

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

URL: Project Homepage

JMLR MLOSS PaperURL: JMLR-MLOSS Paper Homepage

Supported Operating Systems: Cygwin, Linux, Macosx, Windows, Unix, Agnostic, Solaris, Freebsd, Platform Independent

Data Formats: Plain Ascii, Fasta

Tags: Bioinformatics, R, Classification, Machine Learning, Bayesian Networks, Markov Random Fields, Supervised Learning, Em, Mixture Models, Java, Learning Principles, Probabilistic Models, Motif Discovery

Archive: download here

Other available revisons

Version	Changelog	Date
2.0	February 2, 2012: Jstacs 2.0 released Jstacs 2.0 changes many names and the structure of several packages. It is not code-compatible with Jstacs 1.5 and earlier RESTRUCTURING and RENAMING: former ScoringFunction, NormalizableScoringFunction, Model new base-interface SequenceScore new sub-interface StatisticalModel of SequenceScore for all statistical models with sub-iterfaces DifferentiableStatisticalModel and TrainableStatisticalModel new interface DifferentiableSequenceScore replaces ScoringFunction new interface DifferentiableStatisticalModel replaces NormalizableScoringFunction new interface TrainableStatisticalModel replaces Model new abstract class AbstractDifferentiableSequenceScore new abstract class AbstractDifferentiableStatisticalModel replaces AbstractNormalizableScoringFunction new abstract class AbstractTrainableStatisticalModel replaces AbstractModel former Models renamed to TrainSM former ScoringFunction renamed to DiffSS or DiffSM getProbFor removed from TrainableStatisticalModel (former Model) and conceptually replaced by getLogProbFor getLogScore(Sequence,int,int) with changed meaning of arguments: getLogScore(Sequence,start,end) instead of getLogScore(Sequence,start,length) isTrained() replaced by common method isInitialized() Parameters and Results new super-class of Parameters and Results: AnnotatedEntity common list-type for Parameters and Results: AnnotatedEntityList Renaming: CollectionParameter -> SelectionParameter, MultiSelectionCollectionParameter -> MultiSelectionParameter, new super-class AbstractSelectionParameter major refactoring due to common hierarchy and code-cleanup lazy evaluation of Parameter/ParameterSet hierarchies moved from ParameterSet (loadParameters()) to ParameterSetContainer (constructor on class) SubclassFinder adapted to lazy evaluation performance measures new abstract super-class AbstractPerformanceMeasure of all performance measures new interface NumericalPerformanceMeasure for all performance measures that return a single number (as opposed, e.g., to curves) new class PerformanceMeasureParameterSet for a collection of general performance measures new class NumericalPerformanceMeasureParameterSet for a collection of NumericalPerformanceMeasures used in evaluate-method of AbstractClassifier and in ClassifierAssessments further changes Sample renamed to DataSet evaluate and evaluateAll in AbstractClassifier joined new class IndependentProductDiffSS as super-class of IndepedentProductDiffSM (former IndependentProductScoringFunction) new class UniformDiffSS as super-class of UniformDiffSM (former UniformScoringFunction) NEW FUNCTIONALITY: multi-threaded implementation of Baum-Welch and Viterbi training of hidden Markov models new Interface Singleton that can be used for singleton instances to save memory, current examples: DNAAlphabet, DNAAlphabetContainer, ProteinAlphabet added ProteinAlphabet added possibility to use NaN-values with ContinuousAlphabets added ArbitraryFloatSequence including static methods for DataSet creation for cases where double-precision is not needed new performance measure MaximumFMeasure access to Parameters in ParameterSets and Results in ResultSets by name emitDataSet in BayesianNetworkDiffSM new static method Time.getTimeInstance that returns UserTime or RealTime depending on availability of shared lib SubclassFinder allows for adding own base packages new method overlaps() in LocatedSequenceAnnotationWithLength AbstractTerminationCondition used in ScoreClassifier and sub-classes public method propagateESS in HMMFactory new method generateLog in DirichletMRG for drawing log-values added DifferentiableStatisticalModelFactory BUGFIXES/IMPROVEMENTS: bugfix in propagation of equivalent sample size in HMMFactory bugfix in random initialization of BasicHigherOrderTransition improved Alignment implementation SafeOutputStream with new static factory method getSafeOutputStream, write methods now work on Objects DOCUMENTATION: improved Javadocs in many classes and packages new Cookbook with extensive documentation and explanation MISC: output of NonParsableException more verbose Exceptions in multi-threaded code now lead to exit of program instead of only stopping the thread update of RServe/RClient	February 2, 2012, 17:14:02
1.5	June 1st, 2011: Jstacs 1.5 released new package de.jstacs.algorithms.alignment for sequence alignment algorithms new class de.jstacs.models.ModelFactory with static classes to construct many standard models de.jstacs.utils.galaxy.GalaxyAdaptor, an adaptor to Galaxy, which allows for creating Galaxy applications using Jstacs ParameterSets, also requires new interface GalaxyConvertible new package de.jstacs.models.hmm for a variety of hidden Markov models, which can be learned by different learning principles including generative and discriminative learning principles, maximization and sampling methods new package de.jstacs.sampling that contains general infrastructure for parameter sampling new class de.jstacs.scoringFunctions.MappingScoringFunction that allows for internal mapping of symbols from the alphabet new package de.jstacs.classifier.scoringFunctionBases.sampling containing classifiers that sample their parameters by the Metropolis-Hastings algorithm new interface de.jstacs.scoringFunctions.SamplingScoringFunction for NormalizableScoringFunctions that can be used in Metropolis-Hastings sampling of parameters bugfix in XMLParser for cases, where the tag of interest also occurrs within other, nested tags	June 6, 2011, 10:48:08
1.4	December 31, 2010: Jstacs 1.4 released added DincleotideProperty for computing properties like melting temperature, twist, or G/C content support for multidimensional sequence data more widespread use of TerminationConditions completely rewritten XMLParser extension of motif discovery to weighted data OneSampleLogGenDisMixFunction for using the same Sample with different weights for the different classes Jstacs requires Java 1.6 now	January 2, 2011, 11:07:10
1.3.1	March 2, 2010: Jstacs 1.3.1 released Partitioning of Samples including weights Release of Dispom (de-novo discovery of differentially abundant transcription factor binding sites including their positional preference) Several bugfixes	March 2, 2010, 14:15:46
1.3	Initial Announcement on mloss.org.	December 4, 2009, 11:27:55

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