Department of Software, Technical University of Catalonia, E-08034 Barcelona, Spain, Université Bordeaux, Bordeaux Bioinformatics Center (CBiB), F-33000 Bordeaux, France, Dipartimento di Informatica Sistemistica e Comunicazione, Università Degli Studi di Milano-Bicocca, I-20125 Milan, Italy and Université Bordeaux, Laboratoire Bordelais de Recherche en Informatique (CNRS/LaBRI), F-33405 Talence, France.
Bioinformatics. 2014 Jan 1;30(1):17-23. doi: 10.1093/bioinformatics/btt256. Epub 2013 May 3.
TANGO is one of the most accurate tools for the taxonomic assignment of sequence reads. However, because of the differences in the taxonomy structures, performing a taxonomic assignment on different reference taxonomies will produce divergent results.
We have improved the TANGO pipeline to be able to perform the taxonomic assignment of a metagenomic sample using alternative reference taxonomies, coming from different sources. We highlight the novel pre-processing step, necessary to accomplish this task, and describe the improvements in the assignment process. We present the new TANGO pipeline in details, and, finally, we show its performance on four real metagenomic datasets and also on synthetic datasets.
The new version of TANGO, including implementation improvements and novel developments to perform the assignment on different reference taxonomies, is freely available at http://sourceforge.net/projects/taxoassignment/.
TANGO 是用于对序列读取进行分类分配的最准确工具之一。但是，由于分类结构的差异，在不同的参考分类学上进行分类分配将产生不同的结果。
我们改进了 TANGO 管道，使其能够使用来自不同来源的替代参考分类学对宏基因组样本进行分类分配。我们强调了完成此任务所需的新颖的预处理步骤，并描述了分配过程中的改进。我们详细介绍了新的 TANGO 管道，最后，我们在四个真实的宏基因组数据集和合成数据集中展示了它的性能。
新版本的 TANGO，包括执行改进和新的发展，以在不同的参考分类学上进行分配，可在 http://sourceforge.net/projects/taxoassignment/ 免费获得。
