Daniel Alonso-Alemany1, Aurélien Barré, Stefano Beretta, Paola Bonizzoni, Macha Nikolski, Gabriel Valiente. 1. 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.
Abstract
MOTIVATION: 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. 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. AVAILABILITY: 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/.
MOTIVATION: 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. 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. AVAILABILITY: 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/.
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