Unbiased Taxonomic Annotation of Metagenomic Samples

Journal of computational biology: A journal of computational molecular cell biology

FrancescRosselló^2¹  GrazianoPesole^1²  BrunoFosso^1³ 

[1] Algorithms, Bioinformatics, Complexity and Formal Methods Research Group, Technical University of Catalonia, Barcelona, Spain^3;Department of Mathematics and Computer Science, Balearic Islands Health Research Institute (IdISBa), University of the Balearic Islands, Palma de Mallorca, Spain^2;Institute of Biomembranes and Bioenergetics, Consiglio Nazionale delle Ricerche, Bari, Italy^1

关键词: classification;    correlation;    metagenomics;    set cover;    taxonomic annotation.;   

DOI  :  10.1089/cmb.2017.0144

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

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 thereceiver operating characteristic (ROC) curve andF-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.

【 授权许可】

Unknown