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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 Bioinformatics group of the Julius Kuehn Institute. Initially the projects has also been developed 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 2.1 here.
You find an overview of the new features in the Recent changes.
We also provide an API documentation, a Cookbook, and a Reference card for this release.

Getting started & Cookbook

For set-up instructions, a list of basic requirements, and suggestions for your first steps with Jstacs, please see Getting started.

Since version 2.0, we offer a Cookbook for Jstacs in addition to the API documentation. This cookbook comprises a general description of the structure of Jstacs including data handling, statistical models, classifiers, and assessments. The cookbook is accompanied by a number of Recipes or Code examples that can serve as a starting point of your own applications.

For a quick reference, we also provide a Reference card.

Publication

The paper about Jstacs has been published in the Journal of Machine Learning Research. If you use Jstacs in your research, please cite

J. Grau, J. Keilwagen, A. Gohr, B. Haldemann, S. Posch, and I. Grosse. Jstacs: A java framework for statistical analysis and classification of biological sequences. Journal of Machine Learning Research, 13(Jun):1967–1971, 2012.

BibTeX entry

Applications

Applications currently using Jstacs:

• MotifAdjuster
• Dispom
• TALgetter
• TALENoffer
• Dimont
• AnnoTALE

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 Papers

The paper Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data has been published in BMC Bioinformatics.

The paper  Varying levels of complexity in transcription factor binding motifs has been published in Nucleic Acids Research.

The paper Area under Precision-Recall Curves for Weighted and Unweighted Data has been published in PLOS ONE.

The paper A general approach for discriminative de-novo motif discovery from high-throughput data has been published in Nucleic Acids Research.

The paper TALENoffer: genome-wide TALEN off-target prediction has been published in Bioinformatics.

The paper Computational predictions provide insights into the biology of TAL effector target sites has been published in PLOS Computational Biology.

The paper Evaluation of methods for modeling transcription factor sequence specificity has been published in Nature Biotechnology.

The paper Critical assessment of automated flow cytometry data analysis techniques has been published in Nature Methods.

Further papers and projects can be found under Projects.