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A Java framework for statistical analysis and classification of biological sequences

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.

Jstacs is a joint project of the groups Bioinformatics and Pattern Recognition and Bioinformatics at the Institute of Computer Science of Martin Luther University Halle-Wittenberg and the Research Group Data Inspection at the Leibniz Institute of Plant Genetics and Crop Plant Research.

Jstacs is listed in the machine learning open-source software (mloss) repository.

Licensing Information

Jstacs is free software: you can redistribute it and/or modify under the terms of the GNU General Public License version 3 or (at your option) any later version as published by the Free Software Foundation.

Current release

You can download Jstacs version 1.4 here.
You can find the API documentation for this release here.

Applications

Applications currently using Jstacs:

• MotifAdjuster
• Dispom

Bug reports & Feature requests

You can submit bug reports and feature requests via the Jstacs trac or by mail to jstacs@informatik.uni-halle.de. Before you open a new bug ticket, please check if that bug has already been submitted in the list of existing tickets.

Latest Paper

The paper De-novo discovery of differentially abundant transcription factor binding sites including their positional preference has been published in PLoS Computational Biology.

Further papers and projects can be found under Projects.