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                        "user": "Grau",
                        "timestamp": "2019-02-23T22:58:01Z",
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                                "*": "__NOTOC__\n== A <font color=FireBrick>J</font>ava framework for <font color=FireBrick>st</font>atistical <font color=FireBrick>a</font>nalysis and <font color=FireBrick>c</font>lassification of biological <font color=FireBrick>s</font>equences ==\n\nSequence analysis is one of the major subjects of\n[http://en.wikipedia.org/wiki/Bioinformatics bioinformatics].\nSeveral existing libraries combine the representation of biological sequences with exact and approximate pattern matching as well as\nalignment algorithms.\nWe present Jstacs, an [http://en.wikipedia.org/wiki/Open_source open source] Java library, which focuses on the statistical analysis of biological sequences instead. Jstacs comprises an\nefficient representation of sequence data and provides implementations of many statistical models with generative and discriminative approaches\nfor parameter learning. Using Jstacs, classifiers can be assessed and\ncompared on test datasets or by cross-validation experiments evaluating several performance measures. Due to its strictly object-oriented\ndesign Jstacs is easy to use and readily extensible.\n\nJstacs is a joint project of the groups [http://www.informatik.uni-halle.de/arbeitsgruppen/bioinformatik/ Bioinformatics] and [http://www.informatik.uni-halle.de/arbeitsgruppen/mustererkennung/ Pattern Recognition and Bioinformatics] at the [http://www.informatik.uni-halle.de/ Institute of Computer Science] of [http://www.uni-halle.de/ Martin Luther University Halle-Wittenberg] and the Bioinformatics group of the [http://www.jki.bund.de/en/startseite/home.html Julius Kuehn Institute]. Initially the projects has also been developed at the [http://www.ipk-gatersleben.de Leibniz Institute of Plant Genetics and Crop Plant Research].\n\nJstacs is listed in the [http://mloss.org/software/ machine learning open-source software (mloss)] repository.\n\n== Licensing Information ==\nJstacs is free software: you can redistribute it and/or modify under the terms of the [http://www.gnu.org/licenses/gpl-3.0.html GNU General Public License version 3] or (at your option) any later version as published by the [http://www.fsf.org/ Free Software Foundation].\n\n== Current release ==\nYou can download Jstacs version 2.3 [[Downloads | here]].<br />\n''You find an overview of the new features in the [[Version history]].''<br />\nWe also provide an [http://www.jstacs.de/api/index.html API documentation], a [[Cookbook]], and a [http://www.jstacs.de/downloads/refcard.pdf Reference card] for this release.\n\nThe current Jstacs code, including changes made since the last release, is available from [https://github.com/Jstacs github].\n\n== Getting started & Cookbook==\nFor set-up instructions, a list of basic requirements, and suggestions for your first steps with Jstacs, please see [[Getting started]].\n\nSince version 2.0, we offer a [[Cookbook]] for Jstacs in addition to the [http://www.jstacs.de/api/index.html API documentation].\nThis cookbook comprises a general description of the structure of Jstacs including data handling, statistical models, classifiers, and assessments.\nThe cookbook is accompanied by a number of [[Recipes]] or [[Code examples]] that can serve as a starting point of your own applications.\n\nFor a quick reference, we also provide a [http://www.jstacs.de/downloads/refcard.pdf Reference card].\n\n== Publication ==\nThe [http://jmlr.csail.mit.edu/papers/v13/grau12a.html paper about Jstacs] has been published in the Journal of Machine Learning Research.\nIf you use Jstacs in your research, please cite\n\nJ.\u00a0Grau, J.\u00a0Keilwagen, A.\u00a0Gohr, B.\u00a0Haldemann, S.\u00a0Posch, and I.\u00a0Grosse. ''Jstacs: A java framework for statistical analysis and classification of biological sequences''. Journal of Machine Learning Research, '''13'''(Jun):1967\u20131971, 2012.\n\n[http://www.jstacs.de/downloads/jstacs_citation.bib BibTeX entry]\n\n== JstacsFX ==\nJstacsFX is a library for building applications with graphical user interface based on Jstacs classes and using JavaFX. JstacsFX builds upon the [http://www.jstacs.de/api/de/jstacs/tools/JstacsTool.html JstacsTool] interface that has also been used to create [http://www.jstacs.de/api-2.3/de/jstacs/tools/ui/cli/CLI.html command line] and [http://www.jstacs.de/api-2.3/de/jstacs/tools/ui/galaxy/Galaxy.html Galaxy] versions of tools with minimal effort. In addition it makes use of the [http://www.jstacs.de/api-2.3/de/jstacs/parameters/Parameter.html Parameter], [http://www.jstacs.de/api-2.3/de/jstacs/results/Result.html Result], and [http://www.jstacs.de/api-2.3/de/jstacs/results/savers/ResultSaver.html ResultSaver] classes of Jstacs.\n\nThe current release of JstacsFX is available from [[Downloads]] and an [http://www.jstacs.de/api-fx/index.html API documentation] is available.\n\nExample applications using JstacsFX for their graphical user interface are [[InMoDe]] and [[AnnoTALE]].\n\n== Applications ==\nApplications currently using Jstacs:\n* [[MotifAdjuster]]\n* [[Dispom]]\n* [[TALgetter]]\n* [[TALENoffer]]\n* [[Dimont]]\n* [[GeMoMa]]\n* [[AnnoTALE]]\n* [[InMoDe]]\n* [[Disentangler]]\n\n== Bug reports & Feature requests ==\nYou can submit bug reports and feature requests by mail to [mailto:jstacs@informatik.uni-halle.de jstacs@informatik.uni-halle.de].<br />\n<!-- In the Jstacs trac, we also provide a [https://trac.informatik.uni-halle.de/trac/jstacs/discussion forum] for discussions about Jstacs. -->\n\n== Latest Papers ==\nThe paper '''''[[Catchitt | Accurate prediction of cell type-specific transcription factor binding]]''''' has been published in [https://doi.org/10.1186/s13059-018-1614-y Genome Biology].\n\nThe paper '''''[[PCTLearn | Algorithms for learning parsimonious context trees]]''''' has been published in [https://link.springer.com/article/10.1007/s10994-018-5770-9 Machine Learning].\n\nThe paper '''''[[Disentangler | Disentangling transcription factor binding site complexity]]''''' has been published in [https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gky683/5063190 Nucleic Acids Research].\n\nThe paper '''''[[GeMoMa | Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi]]''''' has been published in [https://link.springer.com/article/10.1186%2Fs12859-018-2203-5 BMC Bioinformatics].\n\nThe paper '''''[[InMoDe |\u00a0InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites]]''''' has been published in [https://academic.oup.com/bioinformatics/article/33/4/580/2666342/InMoDe-tools-for-learning-and-visualizing-intra Bioinformatics].\n\nThe paper '''''[[AnnoTALE | AnnoTALE: bioinformatics tools for identification, annotation, and nomenclature of TALEs from Xanthomonas genomic sequences]]''''' has been published in [http://www.nature.com/articles/srep21077 Scientific Reports].\n\nThe paper '''''[[PMMdeNovo | Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data ]]''''' has been published in [http://www.biomedcentral.com/1471-2105/16/375 BMC Bioinformatics].\n\nThe paper '''''[[Slim | Varying levels of complexity in transcription factor binding motifs]]''''' has been published in [http://nar.oxfordjournals.org/content/early/2015/06/23/nar.gkv577.abstract Nucleic Acids Research].\n\nThe paper '''''[[AUC-PR | Area under Precision-Recall Curves for Weighted and Unweighted Data]]''''' has been published in [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0092209 PLOS ONE].\n\nThe paper '''''[[Dimont | A general approach for discriminative de-novo motif discovery from high-throughput data]]''''' has been published in [http://nar.oxfordjournals.org/content/41/21/e197.abstract.html?etoc Nucleic Acids Research].\n\nFurther papers and projects can be found under [[Projects]]."
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