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00925njm 22002777a 4500 dc Fosso, Bruno author Pesole, Graziano author Rossello, Francesc author Valiente, Gabriel author 2018-03 The classification of reads from a metagenomic sample using a reference taxonomy is usually based on first mapping the reads to the reference sequences and then classifying each read at a node under the lowest common ancestor of the candidate sequences in the reference taxonomy with the least classification error. However, this taxonomic annotation can be biased by an imbalanced taxonomy and also by the presence of multiple nodes in the taxonomy with the least classification error for a given read. In this article, we show that the Rand index is a better indicator of classification error than the often used area under the receiver operating characteristic (ROC) curve and F-measure for both balanced and imbalanced reference taxonomies, and we also address the second source of bias by reducing the taxonomic annotation problem for a whole metagenomic sample to a set cover problem, for which a logarithmic approximation can be obtained in linear time and an exact solution can be obtained by integer linear programming. Experimental results with a proof-of-concept implementation of the set cover approach to taxonomic annotation in a next release of the TANGO software show that the set cover approach further reduces ambiguity in the taxonomic annotation obtained with TANGO without distorting the relative abundance profile of the metagenomic sample. Fosso Bruno, Pesole Graziano,Rossello Francesc, Valiente Gabriel. Unbiased Taxonomic Annotation of Metagenomic Samples. J Comput Biol. 2018 Mar;25(3):348-360. Epub 2017 Oct 13. 10.1089/cmb.2017.0144 1557-8666 1066-5277 Journal of Computational Biology http://hdl.handle.net/20.500.13003/9391 29028181 L627115432 2-s2.0-85045469782 https://hdl.handle.net/20.500.12105/22643 429742800009 Classification Correlation Metagenomics Set cover Taxonomic annotation Unbiased Taxonomic Annotation of Metagenomic Samples